content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2 values | repo_name large_stringlengths 5 125 | language large_stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.46M | extension large_stringclasses 75 values | text stringlengths 0 6.46M |
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apigateway_operations.R
\name{apigateway_update_method_response}
\alias{apigateway_update_method_response}
\title{Updates an existing MethodResponse resource}
\usage{
apigateway_update_method_response(restApiId, resourceId, httpMethod,
statusCode, patchOperations)
}
\arguments{
\item{restApiId}{[required] [Required] The string identifier of the associated RestApi.}
\item{resourceId}{[required] [Required] The Resource identifier for the MethodResponse resource.}
\item{httpMethod}{[required] [Required] The HTTP verb of the Method resource.}
\item{statusCode}{[required] [Required] The status code for the MethodResponse resource.}
\item{patchOperations}{A list of update operations to be applied to the specified resource and
in the order specified in this list.}
}
\value{
A list with the following syntax:\preformatted{list(
statusCode = "string",
responseParameters = list(
TRUE|FALSE
),
responseModels = list(
"string"
)
)
}
}
\description{
Updates an existing MethodResponse resource.
}
\section{Request syntax}{
\preformatted{svc$update_method_response(
restApiId = "string",
resourceId = "string",
httpMethod = "string",
statusCode = "string",
patchOperations = list(
list(
op = "add"|"remove"|"replace"|"move"|"copy"|"test",
path = "string",
value = "string",
from = "string"
)
)
)
}
}
\keyword{internal}
| /cran/paws.networking/man/apigateway_update_method_response.Rd | permissive | TWarczak/paws | R | false | true | 1,464 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apigateway_operations.R
\name{apigateway_update_method_response}
\alias{apigateway_update_method_response}
\title{Updates an existing MethodResponse resource}
\usage{
apigateway_update_method_response(restApiId, resourceId, httpMethod,
statusCode, patchOperations)
}
\arguments{
\item{restApiId}{[required] [Required] The string identifier of the associated RestApi.}
\item{resourceId}{[required] [Required] The Resource identifier for the MethodResponse resource.}
\item{httpMethod}{[required] [Required] The HTTP verb of the Method resource.}
\item{statusCode}{[required] [Required] The status code for the MethodResponse resource.}
\item{patchOperations}{A list of update operations to be applied to the specified resource and
in the order specified in this list.}
}
\value{
A list with the following syntax:\preformatted{list(
statusCode = "string",
responseParameters = list(
TRUE|FALSE
),
responseModels = list(
"string"
)
)
}
}
\description{
Updates an existing MethodResponse resource.
}
\section{Request syntax}{
\preformatted{svc$update_method_response(
restApiId = "string",
resourceId = "string",
httpMethod = "string",
statusCode = "string",
patchOperations = list(
list(
op = "add"|"remove"|"replace"|"move"|"copy"|"test",
path = "string",
value = "string",
from = "string"
)
)
)
}
}
\keyword{internal}
|
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
datFile <- "outcome-of-care-measures.csv"
dat <- read.table(datFile, header = TRUE, colClasses = "character", sep = ",",
check.names = FALSE)
## Clean up column names (strip extraneous spaces and convert
## to upper for search purposes); convert columns 11,17,23 (30 day mortality data)
## to numeric columns
nameList <- names(dat)
tNameList <- gsub(" ", " ", nameList)
tNameList <- toupper(tNameList)
colnames(dat) <- tNameList
suppressWarnings(dat[,11] <- as.numeric(dat[,11]))
suppressWarnings(dat[,17] <- as.numeric(dat[,17]))
suppressWarnings(dat[,23] <- as.numeric(dat[,23]))
## Check that state and outcome are valid
## Validate State
foundState <- dat[dat[, 7] == state, ]
if (nrow(foundState) == 0) {
stop("invalid state")
}
## Validate outcome
foundOutcome <- grep(outcome, tNameList, ignore.case = TRUE)
if (length(foundOutcome) == 0) {
stop("invalid outcome")
}
## Subset data to selected state
stateDat <- dat[(dat[ , 7] == toupper(state)), ]
## Return hospital name in that state with lowest 30-day death rate
## bestHospital <- min(dat[ochdr], na.rm = TRUE)
ochdr <- paste("Hospital 30-Day Death (Mortality) Rates from", outcome, sep = " ")
ucochdr <- toupper(ochdr)
## Get the column index for the appropriate outcome
colidx <- which(colnames(stateDat) == ucochdr)
## Rank the data
rankDat <- stateDat[order(stateDat[,colidx], stateDat[,2]),c(2,colidx) ]
## Append the rank order
numReturned <- nrow(rankDat)
rankOrderVector <- 1:numReturned
rankDat <- cbind(rankDat,rankOrderVector)
## Add column names
colnames(rankDat) <- c("Hospital.Name", "Rate", "Rank")
## Return the appropriate ranked hospital
if (is.numeric(num) && num > numReturned) {
return("NA")
} else if (num == "best") {
idx <- 1
} else if (num == "worst") {
worstVal <- max(rankDat[, 2], na.rm = TRUE)
worstDat <- rankDat[which(rankDat["Rate"] == worstVal), ]
idx <- max(worstDat[,"Rank"], na.rm = TRUE)
} else {
idx <- num
}
##rankDat <- stateDat[with(stateDat, order(stateDat[,colidx], stateDat[,2])), ]
return(rankDat[idx, 1])
} | /Assignments/Assignment3/rankhospital.R | no_license | kboulas/RProgramming | R | false | false | 2,728 | r | rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
datFile <- "outcome-of-care-measures.csv"
dat <- read.table(datFile, header = TRUE, colClasses = "character", sep = ",",
check.names = FALSE)
## Clean up column names (strip extraneous spaces and convert
## to upper for search purposes); convert columns 11,17,23 (30 day mortality data)
## to numeric columns
nameList <- names(dat)
tNameList <- gsub(" ", " ", nameList)
tNameList <- toupper(tNameList)
colnames(dat) <- tNameList
suppressWarnings(dat[,11] <- as.numeric(dat[,11]))
suppressWarnings(dat[,17] <- as.numeric(dat[,17]))
suppressWarnings(dat[,23] <- as.numeric(dat[,23]))
## Check that state and outcome are valid
## Validate State
foundState <- dat[dat[, 7] == state, ]
if (nrow(foundState) == 0) {
stop("invalid state")
}
## Validate outcome
foundOutcome <- grep(outcome, tNameList, ignore.case = TRUE)
if (length(foundOutcome) == 0) {
stop("invalid outcome")
}
## Subset data to selected state
stateDat <- dat[(dat[ , 7] == toupper(state)), ]
## Return hospital name in that state with lowest 30-day death rate
## bestHospital <- min(dat[ochdr], na.rm = TRUE)
ochdr <- paste("Hospital 30-Day Death (Mortality) Rates from", outcome, sep = " ")
ucochdr <- toupper(ochdr)
## Get the column index for the appropriate outcome
colidx <- which(colnames(stateDat) == ucochdr)
## Rank the data
rankDat <- stateDat[order(stateDat[,colidx], stateDat[,2]),c(2,colidx) ]
## Append the rank order
numReturned <- nrow(rankDat)
rankOrderVector <- 1:numReturned
rankDat <- cbind(rankDat,rankOrderVector)
## Add column names
colnames(rankDat) <- c("Hospital.Name", "Rate", "Rank")
## Return the appropriate ranked hospital
if (is.numeric(num) && num > numReturned) {
return("NA")
} else if (num == "best") {
idx <- 1
} else if (num == "worst") {
worstVal <- max(rankDat[, 2], na.rm = TRUE)
worstDat <- rankDat[which(rankDat["Rate"] == worstVal), ]
idx <- max(worstDat[,"Rank"], na.rm = TRUE)
} else {
idx <- num
}
##rankDat <- stateDat[with(stateDat, order(stateDat[,colidx], stateDat[,2])), ]
return(rankDat[idx, 1])
} |
library(mvtnorm)
source("FP_sup.R")
#load data
dat <- data.load(pheno="pheno.csv",marker="genotype.csv",time=1:31)
#Null hypothesis
H0 <- mle_curve(pheno=dat$phenotype,times=dat$time)
#Alternative hypothesis
ret <- mle_H1(dat,times=dat$time)
head(ret)
#P-value of all markers
P <- ret[,2]
P
| /example.R | no_license | FFP-FM/Version1 | R | false | false | 328 | r |
library(mvtnorm)
source("FP_sup.R")
#load data
dat <- data.load(pheno="pheno.csv",marker="genotype.csv",time=1:31)
#Null hypothesis
H0 <- mle_curve(pheno=dat$phenotype,times=dat$time)
#Alternative hypothesis
ret <- mle_H1(dat,times=dat$time)
head(ret)
#P-value of all markers
P <- ret[,2]
P
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/converter.R
\name{db_type_converter}
\alias{db_type_converter}
\title{db_type_converter}
\usage{
db_type_converter(data, dbname)
}
\arguments{
\item{data}{The actual data.frame to convert.}
\item{dbname}{The name of the database. Used to distinguish data.}
}
\value{
The modified data.frame
}
\description{
This function converts the type to align with the requirement. See requirement online.
}
\examples{
# data is the output from any get_from_db function
data <- get_from_db_usr("SELECT loadid FROM hedonics_new.sd_hedonics_new")
# convert the type of the columns
data <- db_type_converter(data)
# ... or you can specify the database
data <- db_type_converter(data, dbname = "zillow_2017_nov")
}
| /BDEEPZillow/man/db_type_converter.Rd | no_license | uiuc-bdeep/Zillow_Housing_Database | R | false | true | 778 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/converter.R
\name{db_type_converter}
\alias{db_type_converter}
\title{db_type_converter}
\usage{
db_type_converter(data, dbname)
}
\arguments{
\item{data}{The actual data.frame to convert.}
\item{dbname}{The name of the database. Used to distinguish data.}
}
\value{
The modified data.frame
}
\description{
This function converts the type to align with the requirement. See requirement online.
}
\examples{
# data is the output from any get_from_db function
data <- get_from_db_usr("SELECT loadid FROM hedonics_new.sd_hedonics_new")
# convert the type of the columns
data <- db_type_converter(data)
# ... or you can specify the database
data <- db_type_converter(data, dbname = "zillow_2017_nov")
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc_streak.R
\name{calc_streak}
\alias{calc_streak}
\title{Calculate hit streaks.}
\usage{
calc_streak(x)
}
\arguments{
\item{x}{A data frame or character vector of hits (\code{"H"}) and misses (\code{"M"}).}
}
\value{
A data frame with one column, \code{length}, containing the length of each hit streak.
}
\description{
Calculate hit streaks.
}
\examples{
data(kobe_basket)
calc_streak(kobe_basket$shot)
}
| /man/calc_streak.Rd | no_license | aaronbaggett/labs4316 | R | false | true | 489 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc_streak.R
\name{calc_streak}
\alias{calc_streak}
\title{Calculate hit streaks.}
\usage{
calc_streak(x)
}
\arguments{
\item{x}{A data frame or character vector of hits (\code{"H"}) and misses (\code{"M"}).}
}
\value{
A data frame with one column, \code{length}, containing the length of each hit streak.
}
\description{
Calculate hit streaks.
}
\examples{
data(kobe_basket)
calc_streak(kobe_basket$shot)
}
|
testlist <- list(data = structure(c(5.61333727981723e+112, 5.61333710690645e+112, 2.84809454419421e-306, 6.95335622242639e-310, 4.73673289555467e-299, 2.84809454946091e-306, 3.48604089790333e+30, 4.94065645841247e-324, 4.94065645841247e-324, 2.65249474364725e-315, 2.64227521380929e-308, 2.58981145570564e-307, 2.41766164638173e+35, 2.13916038880747e+30, 4.94065645841247e-324, 2.41737052174616e+35, 1.6259749693639e-260, 3.52953696534134e+30, 2.67356514185607e+29, 3.52953696534134e+30, 3.49284780194396e+30, 2.4173705217461e+35, 1.66880624265276e-307, 7.18522588728097e-304, 2.84809453888986e-306), .Dim = c(5L, 5L )), q = 1.58457842668591e+29)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610555092-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 724 | r | testlist <- list(data = structure(c(5.61333727981723e+112, 5.61333710690645e+112, 2.84809454419421e-306, 6.95335622242639e-310, 4.73673289555467e-299, 2.84809454946091e-306, 3.48604089790333e+30, 4.94065645841247e-324, 4.94065645841247e-324, 2.65249474364725e-315, 2.64227521380929e-308, 2.58981145570564e-307, 2.41766164638173e+35, 2.13916038880747e+30, 4.94065645841247e-324, 2.41737052174616e+35, 1.6259749693639e-260, 3.52953696534134e+30, 2.67356514185607e+29, 3.52953696534134e+30, 3.49284780194396e+30, 2.4173705217461e+35, 1.66880624265276e-307, 7.18522588728097e-304, 2.84809453888986e-306), .Dim = c(5L, 5L )), q = 1.58457842668591e+29)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) |
pkgname <- "DivE"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
options(pager = "console")
base::assign(".ExTimings", "DivE-Ex.timings", pos = 'CheckExEnv')
base::cat("name\tuser\tsystem\telapsed\n", file=base::get(".ExTimings", pos = 'CheckExEnv'))
base::assign(".format_ptime",
function(x) {
if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
options(OutDec = '.')
format(x[1L:3L], digits = 7L)
},
pos = 'CheckExEnv')
### * </HEADER>
library('DivE')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("Bact1")
### * Bact1
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: Bact1
### Title: Count of Medically Important Bacteria Species in a Sample
### Aliases: Bact1 Bact2
### Keywords: datasets
### ** Examples
data(Bact1)
hist(Bact1[,2], breaks=20, main="Bacterial diversity of a sample",
xlab="Number of bacteria of a given species", ylab="Number of bacterial species")
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("Bact1", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("CombDM")
### * CombDM
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: CombDM
### Title: CombDM
### Aliases: CombDM
### Keywords: diversity
### ** Examples
# See DiveMaster documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("CombDM", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("Curvature")
### * Curvature
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: Curvature
### Title: Curvature
### Aliases: Curvature
### Keywords: diversity
### ** Examples
# See DivSubsamples documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("Curvature", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DivSampleNum")
### * DivSampleNum
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DivSampleNum
### Title: DivSampleNum
### Aliases: DivSampleNum
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
DivSampleNum(Bact1, 3)
DivSampleNum(Bact1, 6)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DivSampleNum", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DivSubsamples")
### * DivSubsamples
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DivSubsamples
### Title: DivSubsamples
### Aliases: DivSubsamples print.DivSubsamples summary.DivSubsamples
### print.summary.DivSubsamples
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=100,
NResamples=10)
dss_2 <- DivSubsamples(Bact1, nrf=20, minrarefac=1, maxrarefac=100,
NResamples=10)
# Default NResamples=1000; low value of NResamples=10 is a set for quick evaluation
dss_1
dss_2
summary(dss_1)
dss_1$div_sd
dss_1$NResamples
Curvature(dss_1)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DivSubsamples", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DiveMaster")
### * DiveMaster
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DiveMaster
### Title: DiveMaster
### Aliases: DiveMaster print.DiveMaster summary.DiveMaster
### print.summary.DiveMaster
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
data(ModelSet)
data(ParamSeeds)
data(ParamRanges)
testmodels <- list()
testmeta <- list()
paramranges <- list()
# Choose a single model
testmodels <- c(testmodels, ModelSet[1])
#testmeta[[1]] <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
testmeta[[1]] <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
paramranges[[1]] <- ParamRanges[[1]]
# Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
dss <- list(dss_2, dss_1)
# Implement the function (NB: For quick illustration only -- not default parameters)
out <- DiveMaster(models=testmodels, init.params=testmeta, param.ranges=paramranges,
main.samp=Bact1, subsizes=c(65, 40), NResamples=5, fitloops=1,
dssamp=dss, numit=2, varleft=10)
# DiveMaster Outputs
out
out$estimate
out$fmm$logistic
out$fmm$logistic$global
out$ssm
summary(out)
## Combining two DiveMaster objects (assuming a second object 'out2'):
# out3 <- CombDM(list(out, out2))
## To calculate the diversity for a different population size
# PopDiversity(dm=out, popsize=10^5, TopX=1)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DiveMaster", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("FitSingleMod")
### * FitSingleMod
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: FitSingleMod
### Title: FitSingleMod
### Aliases: FitSingleMod print.FitSingleMod summary.FitSingleMod
### print.summary.FitSingleMod plot.FitSingleMod
### Keywords: diversity
### ** Examples
# See documentation of \code{ScoreSingleMod} for examples
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("FitSingleMod", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ModelSet")
### * ModelSet
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ModelSet
### Title: List of 58 candidate models to fit to data
### Aliases: ModelSet
### Keywords: datasets
### ** Examples
data(ModelSet)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ModelSet", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ParamRanges")
### * ParamRanges
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ParamRanges
### Title: List of 58 sets of upper and lower bounds for models evaluated
### by DivE
### Aliases: ParamRanges
### Keywords: datasets
### ** Examples
data(ParamRanges)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ParamRanges", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ParamSeeds")
### * ParamSeeds
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ParamSeeds
### Title: List of 58 matrices of model seeding parameters.
### Aliases: ParamSeeds
### Keywords: datasets
### ** Examples
data(ParamSeeds)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ParamSeeds", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("PopDiversity")
### * PopDiversity
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: PopDiversity
### Title: PopDiversity
### Aliases: PopDiversity
### Keywords: diversity
### ** Examples
# See DiveMaster documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("PopDiversity", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ScoreSingleMod")
### * ScoreSingleMod
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ScoreSingleMod
### Title: ScoreSingleMod
### Aliases: ScoreSingleMod print.ScoreSingleMod summary.ScoreSingleMod
### print.summary.ScoreSingleMod
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
data(ModelSet)
data(ParamSeeds)
data(ParamRanges)
testmodels <- list()
testmeta <- list()
paramranges <- list()
# Choose a single model
testmodels <- c(testmodels, ModelSet[1])
# testmeta <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
testmeta <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
paramranges <- ParamRanges[[1]]
# Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
dss <- list(dss_2, dss_1)
# Fit the model (NB: For quick illustration only -- not default parameters)
fsm <- FitSingleMod(model.list=testmodels, init.param=testmeta, param.range=paramranges,
main.samp=Bact1, dssamps=dss, fitloops=1, data.default=FALSE,
subsizes=c(65, 40),
numit=2) # numit chosen to be extremely small to speed up example
# Score the model
ssm <- ScoreSingleMod(fsm)
ssm
summary(ssm)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ScoreSingleMod", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
| /DivE.Rcheck/DivE-Ex.R | no_license | dlaydon/DivE | R | false | false | 11,362 | r | pkgname <- "DivE"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
options(pager = "console")
base::assign(".ExTimings", "DivE-Ex.timings", pos = 'CheckExEnv')
base::cat("name\tuser\tsystem\telapsed\n", file=base::get(".ExTimings", pos = 'CheckExEnv'))
base::assign(".format_ptime",
function(x) {
if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
options(OutDec = '.')
format(x[1L:3L], digits = 7L)
},
pos = 'CheckExEnv')
### * </HEADER>
library('DivE')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("Bact1")
### * Bact1
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: Bact1
### Title: Count of Medically Important Bacteria Species in a Sample
### Aliases: Bact1 Bact2
### Keywords: datasets
### ** Examples
data(Bact1)
hist(Bact1[,2], breaks=20, main="Bacterial diversity of a sample",
xlab="Number of bacteria of a given species", ylab="Number of bacterial species")
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("Bact1", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("CombDM")
### * CombDM
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: CombDM
### Title: CombDM
### Aliases: CombDM
### Keywords: diversity
### ** Examples
# See DiveMaster documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("CombDM", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("Curvature")
### * Curvature
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: Curvature
### Title: Curvature
### Aliases: Curvature
### Keywords: diversity
### ** Examples
# See DivSubsamples documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("Curvature", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DivSampleNum")
### * DivSampleNum
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DivSampleNum
### Title: DivSampleNum
### Aliases: DivSampleNum
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
DivSampleNum(Bact1, 3)
DivSampleNum(Bact1, 6)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DivSampleNum", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DivSubsamples")
### * DivSubsamples
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DivSubsamples
### Title: DivSubsamples
### Aliases: DivSubsamples print.DivSubsamples summary.DivSubsamples
### print.summary.DivSubsamples
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=100,
NResamples=10)
dss_2 <- DivSubsamples(Bact1, nrf=20, minrarefac=1, maxrarefac=100,
NResamples=10)
# Default NResamples=1000; low value of NResamples=10 is a set for quick evaluation
dss_1
dss_2
summary(dss_1)
dss_1$div_sd
dss_1$NResamples
Curvature(dss_1)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DivSubsamples", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("DiveMaster")
### * DiveMaster
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: DiveMaster
### Title: DiveMaster
### Aliases: DiveMaster print.DiveMaster summary.DiveMaster
### print.summary.DiveMaster
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
data(ModelSet)
data(ParamSeeds)
data(ParamRanges)
testmodels <- list()
testmeta <- list()
paramranges <- list()
# Choose a single model
testmodels <- c(testmodels, ModelSet[1])
#testmeta[[1]] <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
testmeta[[1]] <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
paramranges[[1]] <- ParamRanges[[1]]
# Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
dss <- list(dss_2, dss_1)
# Implement the function (NB: For quick illustration only -- not default parameters)
out <- DiveMaster(models=testmodels, init.params=testmeta, param.ranges=paramranges,
main.samp=Bact1, subsizes=c(65, 40), NResamples=5, fitloops=1,
dssamp=dss, numit=2, varleft=10)
# DiveMaster Outputs
out
out$estimate
out$fmm$logistic
out$fmm$logistic$global
out$ssm
summary(out)
## Combining two DiveMaster objects (assuming a second object 'out2'):
# out3 <- CombDM(list(out, out2))
## To calculate the diversity for a different population size
# PopDiversity(dm=out, popsize=10^5, TopX=1)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("DiveMaster", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("FitSingleMod")
### * FitSingleMod
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: FitSingleMod
### Title: FitSingleMod
### Aliases: FitSingleMod print.FitSingleMod summary.FitSingleMod
### print.summary.FitSingleMod plot.FitSingleMod
### Keywords: diversity
### ** Examples
# See documentation of \code{ScoreSingleMod} for examples
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("FitSingleMod", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ModelSet")
### * ModelSet
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ModelSet
### Title: List of 58 candidate models to fit to data
### Aliases: ModelSet
### Keywords: datasets
### ** Examples
data(ModelSet)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ModelSet", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ParamRanges")
### * ParamRanges
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ParamRanges
### Title: List of 58 sets of upper and lower bounds for models evaluated
### by DivE
### Aliases: ParamRanges
### Keywords: datasets
### ** Examples
data(ParamRanges)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ParamRanges", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ParamSeeds")
### * ParamSeeds
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ParamSeeds
### Title: List of 58 matrices of model seeding parameters.
### Aliases: ParamSeeds
### Keywords: datasets
### ** Examples
data(ParamSeeds)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ParamSeeds", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("PopDiversity")
### * PopDiversity
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: PopDiversity
### Title: PopDiversity
### Aliases: PopDiversity
### Keywords: diversity
### ** Examples
# See DiveMaster documentation for examples.
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("PopDiversity", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
cleanEx()
nameEx("ScoreSingleMod")
### * ScoreSingleMod
flush(stderr()); flush(stdout())
base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ScoreSingleMod
### Title: ScoreSingleMod
### Aliases: ScoreSingleMod print.ScoreSingleMod summary.ScoreSingleMod
### print.summary.ScoreSingleMod
### Keywords: diversity
### ** Examples
require(DivE)
data(Bact1)
data(ModelSet)
data(ParamSeeds)
data(ParamRanges)
testmodels <- list()
testmeta <- list()
paramranges <- list()
# Choose a single model
testmodels <- c(testmodels, ModelSet[1])
# testmeta <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
testmeta <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
paramranges <- ParamRanges[[1]]
# Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
dss <- list(dss_2, dss_1)
# Fit the model (NB: For quick illustration only -- not default parameters)
fsm <- FitSingleMod(model.list=testmodels, init.param=testmeta, param.range=paramranges,
main.samp=Bact1, dssamps=dss, fitloops=1, data.default=FALSE,
subsizes=c(65, 40),
numit=2) # numit chosen to be extremely small to speed up example
# Score the model
ssm <- ScoreSingleMod(fsm)
ssm
summary(ssm)
base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ScoreSingleMod", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
|
# Read data for 2007-02-01 and 2007-02-02
consumptionInitial <- read.csv("household_power_consumption.txt",sep=";",na.strings="?",nrows=70000) ## 70000 lines is enough to include the required range
consumptionInitial$Date <- as.Date(consumptionInitial$Date,"%d/%m/%Y")
consumption <- subset(consumptionInitial,consumptionInitial$Date=="2007-02-01" | consumptionInitial$Date=="2007-02-02")
rm(consumptionInitial) ## no longer needed
# Convert Date/Time formats
consumption$datetime <- paste(consumption$Date,consumption$Time,sep=" ") ## create combined "datetime" variable
consumption$datetime <- strptime(consumption$datetime, "%Y-%m-%d %H:%M:%S")
# Create Global Active Power histogram
hist(consumption$Global_active_power,col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)", ylab="Frequency")
dev.copy(png, file="plot1.png", width=480, height=480) ## generate PNG file output
dev.off()
| /plot1.R | no_license | gmastro71/ExData_Plotting1 | R | false | false | 920 | r | # Read data for 2007-02-01 and 2007-02-02
consumptionInitial <- read.csv("household_power_consumption.txt",sep=";",na.strings="?",nrows=70000) ## 70000 lines is enough to include the required range
consumptionInitial$Date <- as.Date(consumptionInitial$Date,"%d/%m/%Y")
consumption <- subset(consumptionInitial,consumptionInitial$Date=="2007-02-01" | consumptionInitial$Date=="2007-02-02")
rm(consumptionInitial) ## no longer needed
# Convert Date/Time formats
consumption$datetime <- paste(consumption$Date,consumption$Time,sep=" ") ## create combined "datetime" variable
consumption$datetime <- strptime(consumption$datetime, "%Y-%m-%d %H:%M:%S")
# Create Global Active Power histogram
hist(consumption$Global_active_power,col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)", ylab="Frequency")
dev.copy(png, file="plot1.png", width=480, height=480) ## generate PNG file output
dev.off()
|
# Script to analyze PRS-pheWAS
# Depends
library(data.table)
library(ggplot2)
# Load PRS
prs <- fread(file='prs_processed.csv')
# Load LV mass
seg <- fread(file='lvmi_seg_adjusted.tsv')
# Load processed LV mass
seg_lvm <- fread(file='seg_lvm.csv')
seg_lvm[c(!is.na(sex) & !is.na(lvmi_seg_adjusted)),
':='(lvh_ukbb = ifelse(c((sex=='Female') & (lvmi_seg_adjusted > 55)),1,
ifelse(lvmi_seg_adjusted > 72,1,0)))]
# Join
setkey(prs,sample_id); setkey(seg,IID); setkey(seg_lvm,sample_id)
seg[prs,':='(prs_std = i.prs_std)]
seg[seg_lvm,lvh_ukbb := i.lvh_ukbb]
# Remove non-white/no PRS
seg <- seg[!is.na(prs_std)]
# Check exposure ~ PRS
mod <- lm(lvmi_seg_adjusted ~ prs_std,data=seg)
mod_adj <- lm(lvmi_seg_adjusted ~ prs_std + male + age_at_mri + PC1 + PC2 + PC3 + PC4 + PC5,data=seg)
# Corr
corr <- cor.test(seg$prs_std,y=seg$lvmi_seg_adjusted)
# Plot
pdf(file='prs_lvmi_corr_ukbb.pdf',height=3,width=3,pointsize=5)
par(mar=c(3,3,1,1),oma=c(2,2,1,1))
plot(x=seg$prs_std,y=seg$lvmi_seg_adjusted,bty='n',xlab='',ylab='',xaxt='n',yaxt='n',pch=19,col='#2171b58C',
xlim=c(-5,5),ylim=c(0,150))
axis(1,cex.axis=1,at=c(-4:4))
axis(2,cex.axis=1,at=seq(0,150,25),las=2)
mtext("Standardized PRS",1,line=3,cex=1.5)
mtext(expression(paste("Indexed LV mass (g/m"^2,")")),2,line=3,cex=1.5)
text(-2.5,145,labels="r=0.29, p<0.01")
text(-2.5,138,labels="95% CI 0.28-0.30")
dev.off()
# Plot distribution stratified by LVH
lvh <- seg[lvh_ukbb==1]
no_lvh <- seg[lvh_ukbb==0]
x <- list(v1=lvh$prs_std,v2=no_lvh$prs_std)
data <- melt(x)
ggplot() + geom_density(data=data,aes(x=value,fill=L1),alpha=0.55) +
scale_x_continuous(breaks=seq(-3.5,3.5,0.5),expand=c(0.01,0),limits=c(-3.5,3.5)) +
scale_y_continuous(breaks=seq(0,0.5,0.1),expand=c(0,0),limits=c(0,0.5)) +
scale_fill_manual(values=c("#2b8cbe","#f03b20"),name='',labels=c('LVH','No LVH')) +
theme(panel.background=element_blank(),axis.line=element_line(color='black'),legend.position=c(0.20,0.90),
axis.text=element_text(size=20,color='black'),plot.margin=unit(c(0.5,0.6,0.5,0.5),'cm'),
axis.title.y = element_text(size=20,margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.x = element_text(size=20),legend.text=element_text(size=20)) +
labs(x=expression(paste("LVMI Standardized PRS")),y='Density')
ggsave(filename='prs_std_density_lvh_strat_eur.pdf',height=1.8,width=2.53,
scale=4,device='pdf')
| /misc/test_prs.R | permissive | shaankhurshid/lvmass_gwas | R | false | false | 2,428 | r | # Script to analyze PRS-pheWAS
# Depends
library(data.table)
library(ggplot2)
# Load PRS
prs <- fread(file='prs_processed.csv')
# Load LV mass
seg <- fread(file='lvmi_seg_adjusted.tsv')
# Load processed LV mass
seg_lvm <- fread(file='seg_lvm.csv')
seg_lvm[c(!is.na(sex) & !is.na(lvmi_seg_adjusted)),
':='(lvh_ukbb = ifelse(c((sex=='Female') & (lvmi_seg_adjusted > 55)),1,
ifelse(lvmi_seg_adjusted > 72,1,0)))]
# Join
setkey(prs,sample_id); setkey(seg,IID); setkey(seg_lvm,sample_id)
seg[prs,':='(prs_std = i.prs_std)]
seg[seg_lvm,lvh_ukbb := i.lvh_ukbb]
# Remove non-white/no PRS
seg <- seg[!is.na(prs_std)]
# Check exposure ~ PRS
mod <- lm(lvmi_seg_adjusted ~ prs_std,data=seg)
mod_adj <- lm(lvmi_seg_adjusted ~ prs_std + male + age_at_mri + PC1 + PC2 + PC3 + PC4 + PC5,data=seg)
# Corr
corr <- cor.test(seg$prs_std,y=seg$lvmi_seg_adjusted)
# Plot
pdf(file='prs_lvmi_corr_ukbb.pdf',height=3,width=3,pointsize=5)
par(mar=c(3,3,1,1),oma=c(2,2,1,1))
plot(x=seg$prs_std,y=seg$lvmi_seg_adjusted,bty='n',xlab='',ylab='',xaxt='n',yaxt='n',pch=19,col='#2171b58C',
xlim=c(-5,5),ylim=c(0,150))
axis(1,cex.axis=1,at=c(-4:4))
axis(2,cex.axis=1,at=seq(0,150,25),las=2)
mtext("Standardized PRS",1,line=3,cex=1.5)
mtext(expression(paste("Indexed LV mass (g/m"^2,")")),2,line=3,cex=1.5)
text(-2.5,145,labels="r=0.29, p<0.01")
text(-2.5,138,labels="95% CI 0.28-0.30")
dev.off()
# Plot distribution stratified by LVH
lvh <- seg[lvh_ukbb==1]
no_lvh <- seg[lvh_ukbb==0]
x <- list(v1=lvh$prs_std,v2=no_lvh$prs_std)
data <- melt(x)
ggplot() + geom_density(data=data,aes(x=value,fill=L1),alpha=0.55) +
scale_x_continuous(breaks=seq(-3.5,3.5,0.5),expand=c(0.01,0),limits=c(-3.5,3.5)) +
scale_y_continuous(breaks=seq(0,0.5,0.1),expand=c(0,0),limits=c(0,0.5)) +
scale_fill_manual(values=c("#2b8cbe","#f03b20"),name='',labels=c('LVH','No LVH')) +
theme(panel.background=element_blank(),axis.line=element_line(color='black'),legend.position=c(0.20,0.90),
axis.text=element_text(size=20,color='black'),plot.margin=unit(c(0.5,0.6,0.5,0.5),'cm'),
axis.title.y = element_text(size=20,margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.x = element_text(size=20),legend.text=element_text(size=20)) +
labs(x=expression(paste("LVMI Standardized PRS")),y='Density')
ggsave(filename='prs_std_density_lvh_strat_eur.pdf',height=1.8,width=2.53,
scale=4,device='pdf')
|
context("emission")
test_that("final emission works", {
expect_equal(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = T),
"FISH",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F),
RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "RJ",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
verbose = F),
cat(paste("FISH","not found in total !\n")))
# expect_equal(drop_units(emission(totalEmission(vehicles(example = TRUE,verbose = F),
# emissionFactor(example = TRUE,verbose = F),
# pol = c("CO"),verbose = T),
# "CO",
# list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "SP",verbose = F),
# RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "RJ",verbose = F)),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
# mm=1,
# verbose = T,
# aerosol = F))/28,
# drop_units(emission(totalEmission(vehicles(example = TRUE,verbose = F),
# emissionFactor(example = TRUE,verbose = F),
# pol = c("CO"),verbose = T),
# "CO",
# list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "SP",verbose = F),
# RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "RJ",verbose = F)),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
# mm=28,
# verbose = T,
# aerosol = F)))
expect_equal(sum(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = T),
"CO",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F),
RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "RJ",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
mm=28,
verbose = T,
aerosol = T,
plot = T)
),
units::as_units(362.58086806097963972206343896687030792236328125, "ug*m^-2*s^-1"))
expect_equal(sum(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = F),
"CO",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
mm=28,
verbose = T,
aerosol = F,
plot = T)
),
units::as_units(306.59299639647224466898478567600250244140625, "ug*m^-2*s^-1"))
expect_equal(nrow(emission(inventory = read("edgar_co_test.nc"),pol = "FISH",
grid = gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),
verbose = F),
mm=1,plot = T,verbose = T)
),
nrow(emission(inventory = read("edgar_co_test.nc"),pol = "FISH",
grid = gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),
verbose = F),
mm=1,plot = T, aerosol = T)
))
})
| /tests/testthat/test-emission.R | permissive | ctfysh/EmissV | R | false | false | 7,524 | r | context("emission")
test_that("final emission works", {
expect_equal(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = T),
"FISH",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F),
RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "RJ",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
verbose = F),
cat(paste("FISH","not found in total !\n")))
# expect_equal(drop_units(emission(totalEmission(vehicles(example = TRUE,verbose = F),
# emissionFactor(example = TRUE,verbose = F),
# pol = c("CO"),verbose = T),
# "CO",
# list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "SP",verbose = F),
# RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "RJ",verbose = F)),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
# mm=1,
# verbose = T,
# aerosol = F))/28,
# drop_units(emission(totalEmission(vehicles(example = TRUE,verbose = F),
# emissionFactor(example = TRUE,verbose = F),
# pol = c("CO"),verbose = T),
# "CO",
# list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "SP",verbose = F),
# RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
# raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
# name = "RJ",verbose = F)),
# gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
# mm=28,
# verbose = T,
# aerosol = F)))
expect_equal(sum(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = T),
"CO",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F),
RJ = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[17,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "RJ",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
mm=28,
verbose = T,
aerosol = T,
plot = T)
),
units::as_units(362.58086806097963972206343896687030792236328125, "ug*m^-2*s^-1"))
expect_equal(sum(emission(totalEmission(vehicles(example = TRUE,verbose = F),
emissionFactor(example = TRUE,verbose = F),
pol = c("CO"),verbose = F),
"CO",
list(SP = areaSource(raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))[22,1],
raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff")),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01")),
name = "SP",verbose = F)),
gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),verbose = F),
mm=28,
verbose = T,
aerosol = F,
plot = T)
),
units::as_units(306.59299639647224466898478567600250244140625, "ug*m^-2*s^-1"))
expect_equal(nrow(emission(inventory = read("edgar_co_test.nc"),pol = "FISH",
grid = gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),
verbose = F),
mm=1,plot = T,verbose = T)
),
nrow(emission(inventory = read("edgar_co_test.nc"),pol = "FISH",
grid = gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"),
verbose = F),
mm=1,plot = T, aerosol = T)
))
})
|
#' Split a Character String Across 2 Lines
#'
#' @description This function splits a character string across two lines, keeping lines as even as possible.
#' Replaces the middlemost " " (determined by string length) with "\\n"; and does not perform replacement if
#' lines would have words with 3 or fewer characters.
#'
#' @param text A vector of character strings.
#'
#' @export
#'
#' @note To use with ggplot2 add \code{scale_x_discrete(labels = split_line)} to your ggobject.
#'
#' @examples
#' split_line(c('Hello World!', 'Goodbye Cruel World', 'To Myself'))
#' # Returns: 'Hello\nWorld!' 'Goodbye\nCruel World' 'To Myself'
split_line <- function(text){
sapply(text, function(i){
n <- nchar(i)
spaces <- as.numeric(gregexpr(' ', i)[[1]])
spaces <- spaces[spaces > 4 & (n - spaces) > 3]
if(length(spaces) > 0){
place <- spaces[which.min(abs(spaces - n/2))]
substr(i, place, place) <- '\n'
}
i
}, USE.NAMES = F)
}
| /R/split_line.r | permissive | joshua-ruf/fidelis | R | false | false | 980 | r |
#' Split a Character String Across 2 Lines
#'
#' @description This function splits a character string across two lines, keeping lines as even as possible.
#' Replaces the middlemost " " (determined by string length) with "\\n"; and does not perform replacement if
#' lines would have words with 3 or fewer characters.
#'
#' @param text A vector of character strings.
#'
#' @export
#'
#' @note To use with ggplot2 add \code{scale_x_discrete(labels = split_line)} to your ggobject.
#'
#' @examples
#' split_line(c('Hello World!', 'Goodbye Cruel World', 'To Myself'))
#' # Returns: 'Hello\nWorld!' 'Goodbye\nCruel World' 'To Myself'
split_line <- function(text){
sapply(text, function(i){
n <- nchar(i)
spaces <- as.numeric(gregexpr(' ', i)[[1]])
spaces <- spaces[spaces > 4 & (n - spaces) > 3]
if(length(spaces) > 0){
place <- spaces[which.min(abs(spaces - n/2))]
substr(i, place, place) <- '\n'
}
i
}, USE.NAMES = F)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checkMechanismValidity.R
\name{checkMechanismValidity}
\alias{checkMechanismValidity}
\title{checkMechanismPeriodValidity(data,organisationUnit)}
\usage{
checkMechanismValidity(data, organisationUnit = NA,
return_violations = TRUE)
}
\arguments{
\item{data}{A data frame which has been parsed by either d2Parser or sims2Parser}
\item{organisationUnit}{UID of the operating unit.}
\item{return_violations}{Should the function return a list of violations?}
}
\value{
Returns a data frame of invalid period-mechanism combinations. Returns TRUE if there are no violations.
}
\description{
This function will return an object of invalid mechanisms and periods. All data which is reported
must have a period within the valid start and end dates of the attribute option combination to which it is assigned.
The mechanism must also be associated with the operating unit. If either of these two conditions
are not met, the data will be flagged as being invalid.
}
| /man/checkMechanismValidity.Rd | permissive | oitigeorge/datim-validation | R | false | true | 1,040 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checkMechanismValidity.R
\name{checkMechanismValidity}
\alias{checkMechanismValidity}
\title{checkMechanismPeriodValidity(data,organisationUnit)}
\usage{
checkMechanismValidity(data, organisationUnit = NA,
return_violations = TRUE)
}
\arguments{
\item{data}{A data frame which has been parsed by either d2Parser or sims2Parser}
\item{organisationUnit}{UID of the operating unit.}
\item{return_violations}{Should the function return a list of violations?}
}
\value{
Returns a data frame of invalid period-mechanism combinations. Returns TRUE if there are no violations.
}
\description{
This function will return an object of invalid mechanisms and periods. All data which is reported
must have a period within the valid start and end dates of the attribute option combination to which it is assigned.
The mechanism must also be associated with the operating unit. If either of these two conditions
are not met, the data will be flagged as being invalid.
}
|
\name{getFriendlyName}
\alias{getFriendlyName}
\title{Change a camelCase name to a friendlier version}
\description{
Takes a string like "fooBarBaz" and returns "Foo Bar Baz".
}
\usage{
getFriendlyName(camelName)
}
\arguments{
\item{camelName}{
The "camelCased" name to make friendly.
}
}
\value{
The friendly version of the camel-cased name.
}
\seealso{
\code{\link{galaxy}}, \code{\link{GalaxyParam}},
\code{\link{GalaxyConfig}}, \code{\link{GalaxyOutput}}
}
\details{
Used by \code{galaxy()} to create default labels based on function
and parameter names.
}
\examples{
getFriendlyName("fooBarBaz")
}
| /man/getFriendlyName.Rd | no_license | dtenenba/RGalaxy-vignette-changes | R | false | false | 641 | rd | \name{getFriendlyName}
\alias{getFriendlyName}
\title{Change a camelCase name to a friendlier version}
\description{
Takes a string like "fooBarBaz" and returns "Foo Bar Baz".
}
\usage{
getFriendlyName(camelName)
}
\arguments{
\item{camelName}{
The "camelCased" name to make friendly.
}
}
\value{
The friendly version of the camel-cased name.
}
\seealso{
\code{\link{galaxy}}, \code{\link{GalaxyParam}},
\code{\link{GalaxyConfig}}, \code{\link{GalaxyOutput}}
}
\details{
Used by \code{galaxy()} to create default labels based on function
and parameter names.
}
\examples{
getFriendlyName("fooBarBaz")
}
|
\name{SCsim-package}
\alias{SCsim-package}
\alias{SCsim}
\docType{package}
\title{
What the package does (short line)
~~ package title ~~
}
\description{
More about what it does (maybe more than one line)
~~ A concise (1-5 lines) description of the package ~~
}
\details{
\tabular{ll}{
Package: \tab SCsim\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab 2013-08-05\cr
License: \tab What license is it under?\cr
}
~~ An overview of how to use the package, including the most important ~~
~~ functions ~~
}
\author{
Who wrote it
Maintainer: Who to complain to <yourfault@somewhere.net>
~~ The author and/or maintainer of the package ~~
}
\references{
~~ Literature or other references for background information ~~
}
~~ Optionally other standard keywords, one per line, from file KEYWORDS in ~~
~~ the R documentation directory ~~
\keyword{ package }
\seealso{
~~ Optional links to other man pages, e.g. ~~
~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
}
\examples{
~~ simple examples of the most important functions ~~
}
| /man/SCsim-package.Rd | no_license | RobertWSmith/SCsim | R | false | false | 1,031 | rd | \name{SCsim-package}
\alias{SCsim-package}
\alias{SCsim}
\docType{package}
\title{
What the package does (short line)
~~ package title ~~
}
\description{
More about what it does (maybe more than one line)
~~ A concise (1-5 lines) description of the package ~~
}
\details{
\tabular{ll}{
Package: \tab SCsim\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab 2013-08-05\cr
License: \tab What license is it under?\cr
}
~~ An overview of how to use the package, including the most important ~~
~~ functions ~~
}
\author{
Who wrote it
Maintainer: Who to complain to <yourfault@somewhere.net>
~~ The author and/or maintainer of the package ~~
}
\references{
~~ Literature or other references for background information ~~
}
~~ Optionally other standard keywords, one per line, from file KEYWORDS in ~~
~~ the R documentation directory ~~
\keyword{ package }
\seealso{
~~ Optional links to other man pages, e.g. ~~
~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
}
\examples{
~~ simple examples of the most important functions ~~
}
|
x010b_fit <- function(list.in, item.noise.mag = 0.0, image.noise.mag = 0.0, fix.seed=TRUE) {
# Workflow: run C#/NUnit; copy to clipboard; nnna_raw<-get.cs(); nnna_list<-fit_nnna(nnna_raw).
# Model: InstMag ~ CI (~centered) + airmass(~centered)
# parms: list.in is list obtained from get.ForR(),
# noise.mag = added Gaussian noise per data point (sigma in magnitudes).
# image.noise.mag = added Gaussian noise per image (sigma in magnitudes).
# fix.seed = TRUE if random-number seed fixed (for same results per run;
# this should be FALSE for bootstrapping, repeated calls, e.g.)
# N.B.: if fit() is called repeatedly by another fn, that fn should first set.seed().
# This fn appends to input these list items:
# summary of model output (itself a list), and
# text lines representing this function (fit.R)
# This fn return this expanded list.
# ---------------------------------------------------------
df <- list.in[["df.cs"]] # double bracket to un-list item (to create a data.frame)
df <- df[df$CI.VI90 < 1.6,] # eliminate very red stars.
Mag <- df$InstMag - df$starMagV90 # correct Mag for inherent star mag
if (item.noise.mag > 0) {
if (fix.seed == TRUE)
set.seed(234) # arbitrary but reproducible seed.
Mag <- Mag + rnorm(nrow(df),0,item.noise.mag) # add noise to all stars.
} # if.
if (image.noise.mag > 0) {
if (fix.seed == TRUE)
set.seed(135) # arbitrary but reproducible seed.
imageList <- unique(df$imageID)
for (image in imageList) {
rows = (df$imageID == image) # select all rows belonging to this image.
fluctuation.this.image = rnorm(1,0,image.noise.mag)
Mag[rows] <- Mag[rows] + fluctuation.this.image # add same error to all stars in this image.
} # for
} # if.
Centering.CI <- 0.6 # to approximately center CI values.
CI <- df$CI.VI90 - Centering.CI # NB: we are now using color index V-I, not B-V.
Centering.SecantZA <- 2.0 # to approx. center Airmass values.
Airmass <- df$SecantZA - Centering.SecantZA
model <- lm(formula = Mag ~ CI + Airmass + CI:Airmass) # THE MODEL.
list.out <- list.in
# Capture and append to list the R Code generating raw data (this .R file)
connection <- file("010b_fit.R") # *this* code file as text
list.out$code.fit <- readLines(connection) # record this code file as next list item.
close(connection)
# Capture and append to list the fit's results.
list.out$summary <- summary(model,correlation=TRUE) # append model results as next list item.
print (list.out$summary) # print model results to screen.
list.out$model <- model
save(list.out, file="010b_list") # saves updated list as a file.
return (list.out) # returns as a list.
}
| /Statistics/010b_fit.R | permissive | edose/Spectral | R | false | false | 2,830 | r | x010b_fit <- function(list.in, item.noise.mag = 0.0, image.noise.mag = 0.0, fix.seed=TRUE) {
# Workflow: run C#/NUnit; copy to clipboard; nnna_raw<-get.cs(); nnna_list<-fit_nnna(nnna_raw).
# Model: InstMag ~ CI (~centered) + airmass(~centered)
# parms: list.in is list obtained from get.ForR(),
# noise.mag = added Gaussian noise per data point (sigma in magnitudes).
# image.noise.mag = added Gaussian noise per image (sigma in magnitudes).
# fix.seed = TRUE if random-number seed fixed (for same results per run;
# this should be FALSE for bootstrapping, repeated calls, e.g.)
# N.B.: if fit() is called repeatedly by another fn, that fn should first set.seed().
# This fn appends to input these list items:
# summary of model output (itself a list), and
# text lines representing this function (fit.R)
# This fn return this expanded list.
# ---------------------------------------------------------
df <- list.in[["df.cs"]] # double bracket to un-list item (to create a data.frame)
df <- df[df$CI.VI90 < 1.6,] # eliminate very red stars.
Mag <- df$InstMag - df$starMagV90 # correct Mag for inherent star mag
if (item.noise.mag > 0) {
if (fix.seed == TRUE)
set.seed(234) # arbitrary but reproducible seed.
Mag <- Mag + rnorm(nrow(df),0,item.noise.mag) # add noise to all stars.
} # if.
if (image.noise.mag > 0) {
if (fix.seed == TRUE)
set.seed(135) # arbitrary but reproducible seed.
imageList <- unique(df$imageID)
for (image in imageList) {
rows = (df$imageID == image) # select all rows belonging to this image.
fluctuation.this.image = rnorm(1,0,image.noise.mag)
Mag[rows] <- Mag[rows] + fluctuation.this.image # add same error to all stars in this image.
} # for
} # if.
Centering.CI <- 0.6 # to approximately center CI values.
CI <- df$CI.VI90 - Centering.CI # NB: we are now using color index V-I, not B-V.
Centering.SecantZA <- 2.0 # to approx. center Airmass values.
Airmass <- df$SecantZA - Centering.SecantZA
model <- lm(formula = Mag ~ CI + Airmass + CI:Airmass) # THE MODEL.
list.out <- list.in
# Capture and append to list the R Code generating raw data (this .R file)
connection <- file("010b_fit.R") # *this* code file as text
list.out$code.fit <- readLines(connection) # record this code file as next list item.
close(connection)
# Capture and append to list the fit's results.
list.out$summary <- summary(model,correlation=TRUE) # append model results as next list item.
print (list.out$summary) # print model results to screen.
list.out$model <- model
save(list.out, file="010b_list") # saves updated list as a file.
return (list.out) # returns as a list.
}
|
################################################################################################
# Prepared for the textbook:
# Data Analysis for Business, Economics, and Policy
# by Gabor BEKES and Gabor KEZDI
# Cambridge University Press 2021
#
# License: Free to share, modify and use for educational purposes. Not to be used for business purposes.
#
###############################################################################################x
# CHAPTER 03
# CH03B Comparing hotel prices in Europe: Vienna vs. London
# hotels-europe dataset
# version 0.9 2020-08-28
# ------------------------------------------------------------------------------------------------------
#### SET UP
# It is advised to start a new session for every case study
# CLEAR MEMORY
rm(list=ls())
# Import libraries
library(tidyverse)
#install.packages("arm")
library(arm)
#install.packages("pastecs")
library(pastecs)
#install.packages("DataCombine")
library(DataCombine)
library(janitor)
# set working directory
# option A: open material as project
# option B: set working directory for da_case_studies
# example: setwd("C:/Users/bekes.gabor/Documents/github/da_case_studies/")
# set data dir, data used
source("set-data-directory.R") # data_dir must be first defined
# alternative: give full path here,
# example data_dir="C:/Users/bekes.gabor/Dropbox (MTA KRTK)/bekes_kezdi_textbook/da_data_repo"
# load theme and functions
source("ch00-tech-prep/theme_bg.R")
source("ch00-tech-prep/da_helper_functions.R")
data_in <- paste(data_dir,"sp500","clean/", sep = "/")
use_case_dir <- "ch05-stock-market-loss-generalize/"
data_out <- use_case_dir
output <- paste0(use_case_dir,"output/")
create_output_if_doesnt_exist(output)
#-----------------------------------------------------------------------------------------
# LOAD DATA
sp500 <- read_csv(paste0(data_in,"SP500_2006_16_data.csv"),na = c("", "#N/A"))
# From web
# sp500 <- read_csv("https://osf.io/h64z2/download" , na = c("", "#N/A") )
sp500 <- subset(sp500, VALUE != "NA")
# CREATE PERCENT RETURN
sp500<- sp500 %>%
mutate(pct_return = (VALUE - lag(VALUE)) / lag(VALUE) * 100)
# CREATE DATE VARIABLE
sp500$year <- format(sp500$DATE, "%Y")
sp500$month <- format(sp500$DATE, "%m")
sp500$year <- as.numeric(sp500$year)
sp500$month <- as.numeric(sp500$month)
sp500$yearmonth <- sp500$year*100 + sp500$month
# Distribution
# Figure 5.1
returns_histogram <-ggplot(sp500,aes(pct_return))+
geom_histogram_da(binwidth = 0.25, type="frequency")+
geom_vline(xintercept = -5, size = 0.7, color=color[2])+
labs(x = "Daily return (percent)", y = "Frequency") +
coord_cartesian(xlim = c(-10, 10), ylim = c(0, 400)) +
scale_y_continuous(expand = c(0, 0)) +
geom_segment(aes(x = -6, y = 220, xend = -5, yend = 220), arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = -8, y = 220, label = "5% loss", size=2.5)+
theme_bg()
returns_histogram
#save_fig("returns_histogram_R", output, "small")
save_fig("ch05-figure-1-returns-histogram", output, "small")
# Figure 5.2 prep
# Create 10 000 samples, with 500 and 1000 observations in each sample, taken from sp500$pct_return
# in every sample: for each observation, check if it is a loss of 5% or more. Then calculate the percentage of observations out of 500 or 1000
# where the loss exceeds 5%. E.g. for the 233rd sample, 9 out of 1000 obs had larger than 5% losses.
# remove first row as it has NA in pct_return
pct_return <- sp500 %>% filter(!is.na(pct_return)) %>% pull(pct_return)
# function for a specified number of samples: draws a specified number of observations from a vector, calculates the percentage of obs with greater than 5% losses
# 3 inputs: 'vector' is a vector of the source data, in this case pct_return. 'n_samples' is the number of samples we want to use.
# 'n_obs' is the number of observations in each sample
# output is a vector
create_samples <- function(vector, n_samples, n_obs) {
samples_pcloss <- c()
for (i in 1:n_samples){
single_sample <- sample(vector,n_obs, replace = FALSE)
samples_pcloss[i] <- sum(single_sample < -5)/n_obs*100
}
samples_pcloss
}
set.seed(123)
# Figure 5.2, 5.3, 5.4 input
nobs_1000 <- create_samples(pct_return, 10000, 1000)
nobs_500 <- create_samples(pct_return, 10000, 500)
#nobs_df <- as.data.frame(cbind(nobs_500, nobs_1000))
nobs_df <- tibble(nobs_500,nobs_1000)
error <- qnorm(0.975)*sd(nobs_df$nobs_1000)/sqrt(length(nobs_df$nobs_1000))
left <- mean(nobs_df$nobs_1000)-error
right <- mean(nobs_df$nobs_1000)+error
# Figure 5.2
options(digits = 2)
resample1000<-ggplot(nobs_df,aes(nobs_1000)) +
geom_histogram(binwidth = 0.1, color = color.outline, fill = color[1], alpha = 0.8, boundary=0, closed='left') +
labs(x = "Percent of days with losses of 5% or more", y = "Frequency") +
geom_vline(aes(xintercept = mean(nobs_500)), color =color[2], size = 0.7) +
coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 2500)) +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.5), breaks = seq(0, 1.5, by = 0.25)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,2500),breaks = seq(0, 2500, by = 500)) +
geom_segment(aes(x = 0.8, y = 2000, xend = 0.53, yend = 2000), arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 0.85, y = 2200, label = "Mean", size=2.5)+
theme_bg()
resample1000
save_fig("ch05-figure-2-resample1000", output, "small")
# Figure 5.3
ggplot(nobs_df,aes(nobs_1000))+
stat_density(geom="line", aes(color = 'n1000'), bw = 0.45,size = 1,kernel = "epanechnikov")+
stat_density(geom="line",aes(nobs_500, color = "n500"), bw=0.45,linetype="twodash", size = 1,kernel = "epanechnikov")+
labs(x="Percent of days with losses over 5%", y="Density")+
geom_vline(xintercept = 0.5,colour=color[3], size = 0.7, linetype="dashed")+
geom_segment(aes(x = 0.9, y = 0.72, xend = 0.65, yend = 0.72), size = 0.5, arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 1.1, y = 0.72, label = "Larger sample", size=2)+
geom_segment(aes(x = 0.9, y = 0.68, xend = 0.65, yend = 0.68), size = 0.5, arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 1.1, y = 0.68, label = "Smaller sample", size=2) +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.5), breaks = seq(0, 1.5, by = 0.25)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,0.8),breaks = seq(0, 0.8, by = 0.2)) +
scale_color_manual(name = "", values = c(n1000= color[1], n500 = color[2]))+
theme_bg()+
theme(legend.position = "none")
#save_fig("ch05-figure-3-resample-densities", output, "small")
gh<-ggplot(data=nobs_df)+
geom_histogram( aes(x=nobs_500, y = (..count..)/sum(..count..)*100, color = "n500", fill = "n500"), binwidth = 0.2, boundary=0, closed='left', alpha=0.7) +
geom_histogram( aes(x=nobs_1000, y = (..count..)/sum(..count..)*100, color = "n1000", fill = "n1000"), binwidth = 0.2, boundary=0, closed='left', alpha=0.1, size=0.7) +
ylab("Percent") +
xlab("Percent of days with losses over 5%") +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.6), breaks = seq(0, 1.6, by = 0.2)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,50)) +
scale_color_manual(name = "", values = c(color[2], color[1])) +
scale_fill_manual(name = "", values = c(color[2], color[1])) +
theme_bg() +
theme(legend.position = c(0.7,0.9),
legend.key.size = unit(x = 0.4, units = "cm"),
legend.direction = "horizontal")
gh
save_fig("ch05-figure-3-resample-hist", output, "small")
options(digits = 1)
#table
janitor::tabyl(nobs_df$nobs_500, sort = TRUE)
#summarytools::freq(nobs_df$nobs_500, order = "freq")
janitor::tabyl(nobs_df$nobs_1000, sort = TRUE)
####################################
#BOOTSRTAP SAMPLES
set.seed(573164)
M <- 10000
Results <- matrix(rep(0,(M*10)),nrow=M,ncol=10)
for (i in 1:M){
bsample <- sample(sp500$pct_return,size=dim(sp500)[1], replace = TRUE)
for (j in 1:10){
loss <- as.numeric(bsample<(-j))*100
Results[i,j] <- mean(loss, na.rm=T)
}
}
Results <- as_tibble(Results)
names(Results) <- c("loss1","loss2","loss3","loss4","loss5","loss6",
"loss7","loss8","loss9","loss10")
# Figure 5.5
bootstrap<- ggplot(Results,aes(loss5))+
geom_histogram_da(type="frequency", binwidth = 0.04, boundary=0, closed='left')+
scale_y_continuous(expand=c(0,0),limits = c(0,1200), breaks = seq(0,1200,200)) +
scale_x_continuous(expand=c(0.01,0.01),limits=c(0,1.2), breaks = seq(0,1.2,0.1)) +
labs(x = "Percent of days with losses of 5% or more", y = "Frequency")+
theme_bg()
bootstrap
save_fig("ch05-figure-5-bootstrap", output, "small")
| /ch05-stock-market-loss-generalize/ch05-stock-market-loss-generalize.R | no_license | AlmaAlbrecht/da_case_studies | R | false | false | 8,701 | r | ################################################################################################
# Prepared for the textbook:
# Data Analysis for Business, Economics, and Policy
# by Gabor BEKES and Gabor KEZDI
# Cambridge University Press 2021
#
# License: Free to share, modify and use for educational purposes. Not to be used for business purposes.
#
###############################################################################################x
# CHAPTER 03
# CH03B Comparing hotel prices in Europe: Vienna vs. London
# hotels-europe dataset
# version 0.9 2020-08-28
# ------------------------------------------------------------------------------------------------------
#### SET UP
# It is advised to start a new session for every case study
# CLEAR MEMORY
rm(list=ls())
# Import libraries
library(tidyverse)
#install.packages("arm")
library(arm)
#install.packages("pastecs")
library(pastecs)
#install.packages("DataCombine")
library(DataCombine)
library(janitor)
# set working directory
# option A: open material as project
# option B: set working directory for da_case_studies
# example: setwd("C:/Users/bekes.gabor/Documents/github/da_case_studies/")
# set data dir, data used
source("set-data-directory.R") # data_dir must be first defined
# alternative: give full path here,
# example data_dir="C:/Users/bekes.gabor/Dropbox (MTA KRTK)/bekes_kezdi_textbook/da_data_repo"
# load theme and functions
source("ch00-tech-prep/theme_bg.R")
source("ch00-tech-prep/da_helper_functions.R")
data_in <- paste(data_dir,"sp500","clean/", sep = "/")
use_case_dir <- "ch05-stock-market-loss-generalize/"
data_out <- use_case_dir
output <- paste0(use_case_dir,"output/")
create_output_if_doesnt_exist(output)
#-----------------------------------------------------------------------------------------
# LOAD DATA
sp500 <- read_csv(paste0(data_in,"SP500_2006_16_data.csv"),na = c("", "#N/A"))
# From web
# sp500 <- read_csv("https://osf.io/h64z2/download" , na = c("", "#N/A") )
sp500 <- subset(sp500, VALUE != "NA")
# CREATE PERCENT RETURN
sp500<- sp500 %>%
mutate(pct_return = (VALUE - lag(VALUE)) / lag(VALUE) * 100)
# CREATE DATE VARIABLE
sp500$year <- format(sp500$DATE, "%Y")
sp500$month <- format(sp500$DATE, "%m")
sp500$year <- as.numeric(sp500$year)
sp500$month <- as.numeric(sp500$month)
sp500$yearmonth <- sp500$year*100 + sp500$month
# Distribution
# Figure 5.1
returns_histogram <-ggplot(sp500,aes(pct_return))+
geom_histogram_da(binwidth = 0.25, type="frequency")+
geom_vline(xintercept = -5, size = 0.7, color=color[2])+
labs(x = "Daily return (percent)", y = "Frequency") +
coord_cartesian(xlim = c(-10, 10), ylim = c(0, 400)) +
scale_y_continuous(expand = c(0, 0)) +
geom_segment(aes(x = -6, y = 220, xend = -5, yend = 220), arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = -8, y = 220, label = "5% loss", size=2.5)+
theme_bg()
returns_histogram
#save_fig("returns_histogram_R", output, "small")
save_fig("ch05-figure-1-returns-histogram", output, "small")
# Figure 5.2 prep
# Create 10 000 samples, with 500 and 1000 observations in each sample, taken from sp500$pct_return
# in every sample: for each observation, check if it is a loss of 5% or more. Then calculate the percentage of observations out of 500 or 1000
# where the loss exceeds 5%. E.g. for the 233rd sample, 9 out of 1000 obs had larger than 5% losses.
# remove first row as it has NA in pct_return
pct_return <- sp500 %>% filter(!is.na(pct_return)) %>% pull(pct_return)
# function for a specified number of samples: draws a specified number of observations from a vector, calculates the percentage of obs with greater than 5% losses
# 3 inputs: 'vector' is a vector of the source data, in this case pct_return. 'n_samples' is the number of samples we want to use.
# 'n_obs' is the number of observations in each sample
# output is a vector
create_samples <- function(vector, n_samples, n_obs) {
samples_pcloss <- c()
for (i in 1:n_samples){
single_sample <- sample(vector,n_obs, replace = FALSE)
samples_pcloss[i] <- sum(single_sample < -5)/n_obs*100
}
samples_pcloss
}
set.seed(123)
# Figure 5.2, 5.3, 5.4 input
nobs_1000 <- create_samples(pct_return, 10000, 1000)
nobs_500 <- create_samples(pct_return, 10000, 500)
#nobs_df <- as.data.frame(cbind(nobs_500, nobs_1000))
nobs_df <- tibble(nobs_500,nobs_1000)
error <- qnorm(0.975)*sd(nobs_df$nobs_1000)/sqrt(length(nobs_df$nobs_1000))
left <- mean(nobs_df$nobs_1000)-error
right <- mean(nobs_df$nobs_1000)+error
# Figure 5.2
options(digits = 2)
resample1000<-ggplot(nobs_df,aes(nobs_1000)) +
geom_histogram(binwidth = 0.1, color = color.outline, fill = color[1], alpha = 0.8, boundary=0, closed='left') +
labs(x = "Percent of days with losses of 5% or more", y = "Frequency") +
geom_vline(aes(xintercept = mean(nobs_500)), color =color[2], size = 0.7) +
coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 2500)) +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.5), breaks = seq(0, 1.5, by = 0.25)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,2500),breaks = seq(0, 2500, by = 500)) +
geom_segment(aes(x = 0.8, y = 2000, xend = 0.53, yend = 2000), arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 0.85, y = 2200, label = "Mean", size=2.5)+
theme_bg()
resample1000
save_fig("ch05-figure-2-resample1000", output, "small")
# Figure 5.3
ggplot(nobs_df,aes(nobs_1000))+
stat_density(geom="line", aes(color = 'n1000'), bw = 0.45,size = 1,kernel = "epanechnikov")+
stat_density(geom="line",aes(nobs_500, color = "n500"), bw=0.45,linetype="twodash", size = 1,kernel = "epanechnikov")+
labs(x="Percent of days with losses over 5%", y="Density")+
geom_vline(xintercept = 0.5,colour=color[3], size = 0.7, linetype="dashed")+
geom_segment(aes(x = 0.9, y = 0.72, xend = 0.65, yend = 0.72), size = 0.5, arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 1.1, y = 0.72, label = "Larger sample", size=2)+
geom_segment(aes(x = 0.9, y = 0.68, xend = 0.65, yend = 0.68), size = 0.5, arrow = arrow(length = unit(0.1, "cm")))+
annotate("text", x = 1.1, y = 0.68, label = "Smaller sample", size=2) +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.5), breaks = seq(0, 1.5, by = 0.25)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,0.8),breaks = seq(0, 0.8, by = 0.2)) +
scale_color_manual(name = "", values = c(n1000= color[1], n500 = color[2]))+
theme_bg()+
theme(legend.position = "none")
#save_fig("ch05-figure-3-resample-densities", output, "small")
gh<-ggplot(data=nobs_df)+
geom_histogram( aes(x=nobs_500, y = (..count..)/sum(..count..)*100, color = "n500", fill = "n500"), binwidth = 0.2, boundary=0, closed='left', alpha=0.7) +
geom_histogram( aes(x=nobs_1000, y = (..count..)/sum(..count..)*100, color = "n1000", fill = "n1000"), binwidth = 0.2, boundary=0, closed='left', alpha=0.1, size=0.7) +
ylab("Percent") +
xlab("Percent of days with losses over 5%") +
scale_x_continuous(expand=c(0.01, 0.01), limits = c(0,1.6), breaks = seq(0, 1.6, by = 0.2)) +
scale_y_continuous(expand=c(0.00, 0.00), limits = c(0,50)) +
scale_color_manual(name = "", values = c(color[2], color[1])) +
scale_fill_manual(name = "", values = c(color[2], color[1])) +
theme_bg() +
theme(legend.position = c(0.7,0.9),
legend.key.size = unit(x = 0.4, units = "cm"),
legend.direction = "horizontal")
gh
save_fig("ch05-figure-3-resample-hist", output, "small")
options(digits = 1)
#table
janitor::tabyl(nobs_df$nobs_500, sort = TRUE)
#summarytools::freq(nobs_df$nobs_500, order = "freq")
janitor::tabyl(nobs_df$nobs_1000, sort = TRUE)
####################################
#BOOTSRTAP SAMPLES
set.seed(573164)
M <- 10000
Results <- matrix(rep(0,(M*10)),nrow=M,ncol=10)
for (i in 1:M){
bsample <- sample(sp500$pct_return,size=dim(sp500)[1], replace = TRUE)
for (j in 1:10){
loss <- as.numeric(bsample<(-j))*100
Results[i,j] <- mean(loss, na.rm=T)
}
}
Results <- as_tibble(Results)
names(Results) <- c("loss1","loss2","loss3","loss4","loss5","loss6",
"loss7","loss8","loss9","loss10")
# Figure 5.5
bootstrap<- ggplot(Results,aes(loss5))+
geom_histogram_da(type="frequency", binwidth = 0.04, boundary=0, closed='left')+
scale_y_continuous(expand=c(0,0),limits = c(0,1200), breaks = seq(0,1200,200)) +
scale_x_continuous(expand=c(0.01,0.01),limits=c(0,1.2), breaks = seq(0,1.2,0.1)) +
labs(x = "Percent of days with losses of 5% or more", y = "Frequency")+
theme_bg()
bootstrap
save_fig("ch05-figure-5-bootstrap", output, "small")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulation.R
\name{ComputePropCnst}
\alias{ComputePropCnst}
\title{Compute Proportionality Constant}
\usage{
ComputePropCnst(n, traj, lim)
}
\arguments{
\item{n}{Number of samples.}
\item{traj}{A function of time (intensity).}
\item{lim}{A numeric tuple of start and end time.}
}
\value{
A numeric scalar as proportionality constant.
}
\description{
Compute proportionality constant to produce a correct expected number of
samples
}
| /man/ComputePropCnst.Rd | permissive | Mamie/PILAF | R | false | true | 513 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulation.R
\name{ComputePropCnst}
\alias{ComputePropCnst}
\title{Compute Proportionality Constant}
\usage{
ComputePropCnst(n, traj, lim)
}
\arguments{
\item{n}{Number of samples.}
\item{traj}{A function of time (intensity).}
\item{lim}{A numeric tuple of start and end time.}
}
\value{
A numeric scalar as proportionality constant.
}
\description{
Compute proportionality constant to produce a correct expected number of
samples
}
|
\name{heartrate}
\alias{heartrate}
\docType{data}
\title{Heart rate baroreflexes for rabbits}
\description{
The dataset contains measurements of mean arterial pressure (mmHG) and heart rate (b/min) for a baroreflex curve.
}
\usage{data(heartrate)}
\format{
A data frame with 18 observations on the following 2 variables.
\describe{
\item{\code{pressure}}{a numeric vector containing measurements of arterial pressure.}
\item{\code{rate}}{a numeric vector containing measurements of heart rate.}
}
}
\details{
The dataset is an example of an asymmetric dose-response curve, that is not
easily handled using the log-logistic or Weibull models.
}
\source{
Ricketts, J. H. and Head, G. A. (1999) A five-parameter logistic equation for investigating asymmetry of
curvature in baroreflex studies,
\emph{Am. J. Physiol. (Regulatory Integrative Comp. Physiol. 46)}, \bold{277}, 441--454.
}
\examples{
\dontrun{
library(drc)
## Fitting the baro5 model
heartrate.m1 <- drm(rate~pressure, data=heartrate, fct=baro5())
plot(heartrate.m1)
coef(heartrate.m1)
#Output:
#b1:(Intercept) b2:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept)
# 11.07984 46.67492 150.33588 351.29613 75.59392
## Inserting the estimated baro5 model function in deriv()
baro5Derivative <- deriv(~ 150.33588 + ((351.29613 - 150.33588)/
(1 + (1/(1 + exp((2 * 11.07984 * 46.67492/(11.07984 + 46.67492)) *
(log(x) - log(75.59392 ))))) * (exp(11.07984 * (log(x) - log(75.59392)))) +
(1 - (1/(1 + exp((2 * 11.07984 * 46.67492/(11.07984 + 46.67492)) *
(log(x) - log(75.59392 )))))) * (exp(46.67492 * (log(x) - log(75.59392 )))))), "x", function(x){})
## Plotting the derivative
#pressureVector <- 50:100
pressureVector <- seq(50, 100, length.out=300)
derivativeVector <- attr(baro5Derivative(pressureVector), "gradient")
plot(pressureVector, derivativeVector, type = "l")
## Finding the minimum
pressureVector[which.min(derivativeVector)]
}
}
\keyword{datasets}
| /man/heartrate.Rd | no_license | DoseResponse/drcData | R | false | false | 1,996 | rd | \name{heartrate}
\alias{heartrate}
\docType{data}
\title{Heart rate baroreflexes for rabbits}
\description{
The dataset contains measurements of mean arterial pressure (mmHG) and heart rate (b/min) for a baroreflex curve.
}
\usage{data(heartrate)}
\format{
A data frame with 18 observations on the following 2 variables.
\describe{
\item{\code{pressure}}{a numeric vector containing measurements of arterial pressure.}
\item{\code{rate}}{a numeric vector containing measurements of heart rate.}
}
}
\details{
The dataset is an example of an asymmetric dose-response curve, that is not
easily handled using the log-logistic or Weibull models.
}
\source{
Ricketts, J. H. and Head, G. A. (1999) A five-parameter logistic equation for investigating asymmetry of
curvature in baroreflex studies,
\emph{Am. J. Physiol. (Regulatory Integrative Comp. Physiol. 46)}, \bold{277}, 441--454.
}
\examples{
\dontrun{
library(drc)
## Fitting the baro5 model
heartrate.m1 <- drm(rate~pressure, data=heartrate, fct=baro5())
plot(heartrate.m1)
coef(heartrate.m1)
#Output:
#b1:(Intercept) b2:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept)
# 11.07984 46.67492 150.33588 351.29613 75.59392
## Inserting the estimated baro5 model function in deriv()
baro5Derivative <- deriv(~ 150.33588 + ((351.29613 - 150.33588)/
(1 + (1/(1 + exp((2 * 11.07984 * 46.67492/(11.07984 + 46.67492)) *
(log(x) - log(75.59392 ))))) * (exp(11.07984 * (log(x) - log(75.59392)))) +
(1 - (1/(1 + exp((2 * 11.07984 * 46.67492/(11.07984 + 46.67492)) *
(log(x) - log(75.59392 )))))) * (exp(46.67492 * (log(x) - log(75.59392 )))))), "x", function(x){})
## Plotting the derivative
#pressureVector <- 50:100
pressureVector <- seq(50, 100, length.out=300)
derivativeVector <- attr(baro5Derivative(pressureVector), "gradient")
plot(pressureVector, derivativeVector, type = "l")
## Finding the minimum
pressureVector[which.min(derivativeVector)]
}
}
\keyword{datasets}
|
## load required packages.
require(tsne)
require(pheatmap)
require(MASS)
require(cluster)
require(mclust)
require(flexmix)
require(lattice)
require(fpc)
require(amap)
require(RColorBrewer)
require(locfit)
require(vegan)
## class definition
SCseq <- setClass("SCseq", slots = c(expdata = "data.frame", ndata = "data.frame", fdata = "data.frame", distances = "matrix", tsne = "data.frame", cluster = "list", background = "list", out = "list", cpart = "vector", fcol = "vector", filterpar = "list", clusterpar = "list", outlierpar ="list" ))
setValidity("SCseq",
function(object) {
msg <- NULL
if ( ! is.data.frame(object@expdata) ){
msg <- c(msg, "input data must be data.frame")
}else if ( nrow(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one row")
}else if ( ncol(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one column")
}else if (sum( apply( is.na(object@expdata),1,sum ) ) > 0 ){
msg <- c(msg, "NAs are not allowed in input data")
}else if (sum( apply( object@expdata,1,min ) ) < 0 ){
msg <- c(msg, "negative values are not allowed in input data")
}
if (is.null(msg)) TRUE
else msg
}
)
setMethod("initialize",
signature = "SCseq",
definition = function(.Object, expdata ){
.Object@expdata <- expdata
.Object@ndata <- expdata
.Object@fdata <- expdata
validObject(.Object)
return(.Object)
}
)
setGeneric("filterdata", function(object, mintotal=3000, minexpr=5, minnumber=1, maxexpr=Inf, downsample=TRUE, dsn=1, rseed=17000) standardGeneric("filterdata"))
setMethod("filterdata",
signature = "SCseq",
definition = function(object,mintotal,minexpr,minnumber,maxexpr,downsample,dsn,rseed) {
if ( ! is.numeric(mintotal) ) stop( "mintotal has to be a positive number" ) else if ( mintotal <= 0 ) stop( "mintotal has to be a positive number" )
if ( ! is.numeric(minexpr) ) stop( "minexpr has to be a non-negative number" ) else if ( minexpr < 0 ) stop( "minexpr has to be a non-negative number" )
if ( ! is.numeric(minnumber) ) stop( "minnumber has to be a non-negative integer number" ) else if ( round(minnumber) != minnumber | minnumber < 0 ) stop( "minnumber has to be a non-negative integer number" )
if ( ! ( is.numeric(downsample) | is.logical(downsample) ) ) stop( "downsample has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(dsn) ) stop( "dsn has to be a positive integer number" ) else if ( round(dsn) != dsn | dsn <= 0 ) stop( "dsn has to be a positive integer number" )
object@filterpar <- list(mintotal=mintotal, minexpr=minexpr, minnumber=minnumber, maxexpr=maxexpr, downsample=downsample, dsn=dsn)
object@ndata <- object@expdata[,apply(object@expdata,2,sum,na.rm=TRUE) >= mintotal]
if ( downsample ){
set.seed(rseed)
object@ndata <- downsample(object@expdata,n=mintotal,dsn=dsn)
}else{
x <- object@ndata
object@ndata <- as.data.frame( t(t(x)/apply(x,2,sum))*median(apply(x,2,sum,na.rm=TRUE)) + .1 )
}
x <- object@ndata
object@fdata <- x[apply(x>=minexpr,1,sum,na.rm=TRUE) >= minnumber,]
x <- object@fdata
object@fdata <- x[apply(x,1,max,na.rm=TRUE) < maxexpr,]
return(object)
}
)
downsample <- function(x,n,dsn){
x <- round( x[,apply(x,2,sum,na.rm=TRUE) >= n], 0)
nn <- min( apply(x,2,sum) )
for ( j in 1:dsn ){
z <- data.frame(GENEID=rownames(x))
rownames(z) <- rownames(x)
initv <- rep(0,nrow(z))
for ( i in 1:dim(x)[2] ){
y <- aggregate(rep(1,nn),list(sample(rep(rownames(x),x[,i]),nn)),sum)
na <- names(x)[i]
names(y) <- c("GENEID",na)
rownames(y) <- y$GENEID
z[,na] <- initv
k <- intersect(rownames(z),y$GENEID)
z[k,na] <- y[k,na]
z[is.na(z[,na]),na] <- 0
}
rownames(z) <- as.vector(z$GENEID)
ds <- if ( j == 1 ) z[,-1] else ds + z[,-1]
}
ds <- ds/dsn + .1
return(ds)
}
dist.gen <- function(x,method="euclidean", ...) if ( method %in% c("spearman","pearson","kendall") ) as.dist( 1 - cor(t(x),method=method,...) ) else dist(x,method=method,...)
dist.gen.pairs <- function(x,y,...) dist.gen(t(cbind(x,y)),...)
binompval <- function(p,N,n){
pval <- pbinom(n,round(N,0),p,lower.tail=TRUE)
pval[!is.na(pval) & pval > 0.5] <- 1-pval[!is.na(pval) & pval > 0.5]
return(pval)
}
setGeneric("plotgap", function(object) standardGeneric("plotgap"))
setMethod("plotgap",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotgap")
if ( sum(is.na(object@cluster$gap$Tab[,3])) > 0 ) stop("run clustexp with do.gap = TRUE first")
plot(object@cluster$gap,ylim=c( min(object@cluster$gap$Tab[,3] - object@cluster$gap$Tab[,4]), max(object@cluster$gap$Tab[,3] + object@cluster$gap$Tab[,4])))
}
)
setGeneric("plotjaccard", function(object) standardGeneric("plotjaccard"))
setMethod("plotjaccard",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotjaccard")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no Jaccard's similarity plot")
barplot(object@cluster$jaccard,names.arg=1:length(object@cluster$jaccard),ylab="Jaccard's similarity")
}
)
setGeneric("plotoutlierprobs", function(object) standardGeneric("plotoutlierprobs"))
setMethod("plotoutlierprobs",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotoutlierprobs")
p <- object@cluster$kpart[ order(object@cluster$kpart,decreasing=FALSE)]
x <- object@out$cprobs[names(p)]
fcol <- object@fcol
for ( i in 1:max(p) ){
y <- -log10(x + 2.2e-16)
y[p != i] <- 0
if ( i == 1 ) b <- barplot(y,ylim=c(0,max(-log10(x + 2.2e-16))*1.1),col=fcol[i],border=fcol[i],names.arg=FALSE,ylab="-log10prob") else barplot(y,add=TRUE,col=fcol[i],border=fcol[i],names.arg=FALSE,axes=FALSE)
}
abline(-log10(object@outlierpar$probthr),0,col="black",lty=2)
d <- b[2,1] - b[1,1]
y <- 0
for ( i in 1:max(p) ) y <- append(y,b[sum(p <=i),1] + d/2)
axis(1,at=(y[1:(length(y)-1)] + y[-1])/2,lab=1:max(p))
box()
}
)
setGeneric("plotbackground", function(object) standardGeneric("plotbackground"))
setMethod("plotbackground",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotbackground")
m <- apply(object@fdata,1,mean)
v <- apply(object@fdata,1,var)
fit <- locfit(v~lp(m,nn=.7),family="gamma",maxk=500)
plot(log2(m),log2(v),pch=20,xlab="log2mean",ylab="log2var",col="grey")
lines(log2(m[order(m)]),log2(object@background$lvar(m[order(m)],object)),col="red",lwd=2)
lines(log2(m[order(m)]),log2(fitted(fit)[order(m)]),col="orange",lwd=2,lty=2)
legend("topleft",legend=substitute(paste("y = ",a,"*x^2 + ",b,"*x + ",d,sep=""),list(a=round(coef(object@background$vfit)[3],2),b=round(coef(object@background$vfit)[2],2),d=round(coef(object@background$vfit)[3],2))),bty="n")
}
)
setGeneric("plotsensitivity", function(object) standardGeneric("plotsensitivity"))
setMethod("plotsensitivity",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotsensitivity")
plot(log10(object@out$thr), object@out$stest, type="l",xlab="log10 Probability cutoff", ylab="Number of outliers")
abline(v=log10(object@outlierpar$probthr),col="red",lty=2)
}
)
setGeneric("diffgenes", function(object,cl1,cl2,mincount=5) standardGeneric("diffgenes"))
setMethod("diffgenes",
signature = "SCseq",
definition = function(object,cl1,cl2,mincount){
part <- object@cpart
cl1 <- c(cl1)
cl2 <- c(cl2)
if ( length(part) == 0 ) stop("run findoutliers before diffgenes")
if ( ! is.numeric(mincount) ) stop("mincount has to be a non-negative number") else if ( mincount < 0 ) stop("mincount has to be a non-negative number")
if ( length(intersect(cl1, part)) < length(unique(cl1)) ) stop( paste("cl1 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
if ( length(intersect(cl2, part)) < length(unique(cl2)) ) stop( paste("cl2 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
f <- apply(object@ndata[,part %in% c(cl1,cl2)],1,max) > mincount
x <- object@ndata[f,part %in% cl1]
y <- object@ndata[f,part %in% cl2]
if ( sum(part %in% cl1) == 1 ) m1 <- x else m1 <- apply(x,1,mean)
if ( sum(part %in% cl2) == 1 ) m2 <- y else m2 <- apply(y,1,mean)
if ( sum(part %in% cl1) == 1 ) s1 <- sqrt(x) else s1 <- sqrt(apply(x,1,var))
if ( sum(part %in% cl2) == 1 ) s2 <- sqrt(y) else s2 <- sqrt(apply(y,1,var))
d <- ( m1 - m2 )/ apply( cbind( s1, s2 ),1,mean )
names(d) <- rownames(object@ndata)[f]
if ( sum(part %in% cl1) == 1 ){
names(x) <- names(d)
x <- x[order(d,decreasing=TRUE)]
}else{
x <- x[order(d,decreasing=TRUE),]
}
if ( sum(part %in% cl2) == 1 ){
names(y) <- names(d)
y <- y[order(d,decreasing=TRUE)]
}else{
y <- y[order(d,decreasing=TRUE),]
}
return(list(z=d[order(d,decreasing=TRUE)],cl1=x,cl2=y,cl1n=cl1,cl2n=cl2))
}
)
plotdiffgenes <- function(z,gene=g){
if ( ! is.list(z) ) stop("first arguments needs to be output of function diffgenes")
if ( length(z) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( sum(names(z) == c("z","cl1","cl2","cl1n","cl2n")) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( length(gene) > 1 ) stop("only single value allowed for argument gene")
if ( !is.numeric(gene) & !(gene %in% names(z$z)) ) stop("argument gene needs to be within rownames of first argument or a positive integer number")
if ( is.numeric(gene) ){ if ( gene < 0 | round(gene) != gene ) stop("argument gene needs to be within rownames of first argument or a positive integer number") }
x <- if ( is.null(dim(z$cl1)) ) z$cl1[gene] else t(z$cl1[gene,])
y <- if ( is.null(dim(z$cl2)) ) z$cl2[gene] else t(z$cl2[gene,])
plot(1:length(c(x,y)),c(x,y),ylim=c(0,max(c(x,y))),xlab="",ylab="Expression",main=gene,cex=0,axes=FALSE)
axis(2)
box()
u <- 1:length(x)
rect(u - .5,0,u + .5,x,col="red")
v <- c(min(u) - .5,max(u) + .5)
axis(1,at=mean(v),lab=paste(z$cl1n,collapse=","))
lines(v,rep(mean(x),length(v)))
lines(v,rep(mean(x)-sqrt(var(x)),length(v)),lty=2)
lines(v,rep(mean(x)+sqrt(var(x)),length(v)),lty=2)
u <- ( length(x) + 1 ):length(c(x,y))
v <- c(min(u) - .5,max(u) + .5)
rect(u - .5,0,u + .5,y,col="blue")
axis(1,at=mean(v),lab=paste(z$cl2n,collapse=","))
lines(v,rep(mean(y),length(v)))
lines(v,rep(mean(y)-sqrt(var(y)),length(v)),lty=2)
lines(v,rep(mean(y)+sqrt(var(y)),length(v)),lty=2)
abline(v=length(x) + .5)
}
setGeneric("plottsne", function(object,final=TRUE) standardGeneric("plottsne"))
setMethod("plottsne",
signature = "SCseq",
definition = function(object,final){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before plottsne")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before plottsne")
part <- if ( final ) object@cpart else object@cluster$kpart
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
for ( i in 1:max(part) ){
if ( sum(part == i) > 0 ) text(object@tsne[part == i,1],object@tsne[part == i,2],i,col=object@fcol[i],cex=.75,font=4)
}
}
)
setGeneric("plotlabelstsne", function(object,labels=NULL) standardGeneric("plotlabelstsne"))
setMethod("plotlabelstsne",
signature = "SCseq",
definition = function(object,labels){
if ( is.null(labels ) ) labels <- names(object@ndata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotlabelstsne")
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
text(object@tsne[,1],object@tsne[,2],labels,cex=.5)
}
)
setGeneric("plotsymbolstsne", function(object,types=NULL) standardGeneric("plotsymbolstsne"))
setMethod("plotsymbolstsne",
signature = "SCseq",
definition = function(object,types){
if ( is.null(types) ) types <- names(object@fdata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotsymbolstsne")
if ( length(types) != ncol(object@fdata) ) stop("types argument has wrong length. Length has to equal to the column number of object@ndata")
coloc <- rainbow(length(unique(types)))
syms <- c()
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,col="grey")
for ( i in 1:length(unique(types)) ){
f <- types == sort(unique(types))[i]
syms <- append( syms, ( (i-1) %% 25 ) + 1 )
points(object@tsne[f,1],object@tsne[f,2],col=coloc[i],pch=( (i-1) %% 25 ) + 1,cex=1)
}
legend("topleft", legend=sort(unique(types)), col=coloc, pch=syms)
}
)
setGeneric("plotexptsne", function(object,g,n="",logsc=FALSE) standardGeneric("plotexptsne"))
setMethod("plotexptsne",
signature = "SCseq",
definition = function(object,g,n="",logsc=FALSE){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( length(intersect(g,rownames(object@ndata))) < length(unique(g)) ) stop("second argument does not correspond to set of rownames slot ndata of SCseq object")
if ( !is.numeric(logsc) & !is.logical(logsc) ) stop("argument logsc has to be logical (TRUE/FALSE)")
if ( n == "" ) n <- g[1]
l <- apply(object@ndata[g,] - .1,2,sum) + .1
if (logsc) {
f <- l == 0
l <- log2(l)
l[f] <- NA
}
mi <- min(l,na.rm=TRUE)
ma <- max(l,na.rm=TRUE)
ColorRamp <- colorRampPalette(rev(brewer.pal(n = 7,name = "RdYlBu")))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
v <- round((l - mi)/(ma - mi)*99 + 1,0)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
par(mar = c(3,5,2.5,2))
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",main=n,pch=20,cex=0,col="grey")
for ( k in 1:length(v) ){
points(object@tsne[k,1],object@tsne[k,2],col=ColorRamp[v[k]],pch=20,cex=1.5)
}
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
}
)
plot.err.bars.y <- function(x, y, y.err, col="black", lwd=1, lty=1, h=0.1){
arrows(x,y-y.err,x,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y-y.err,x+h,y-y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y+y.err,x+h,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
}
clusGapExt <-function (x, FUNcluster, K.max, B = 100, verbose = interactive(), method="euclidean",random=TRUE,
...)
{
stopifnot(is.function(FUNcluster), length(dim(x)) == 2, K.max >=
2, (n <- nrow(x)) >= 1, (p <- ncol(x)) >= 1)
if (B != (B. <- as.integer(B)) || (B <- B.) <= 0)
stop("'B' has to be a positive integer")
if (is.data.frame(x))
x <- as.matrix(x)
ii <- seq_len(n)
W.k <- function(X, kk) {
clus <- if (kk > 1)
FUNcluster(X, kk, ...)$cluster
else rep.int(1L, nrow(X))
0.5 * sum(vapply(split(ii, clus), function(I) {
xs <- X[I, , drop = FALSE]
sum(dist.gen(xs,method=method)/nrow(xs))
}, 0))
}
logW <- E.logW <- SE.sim <- numeric(K.max)
if (verbose)
cat("Clustering k = 1,2,..., K.max (= ", K.max, "): .. ",
sep = "")
for (k in 1:K.max) logW[k] <- log(W.k(x, k))
if (verbose)
cat("done\n")
xs <- scale(x, center = TRUE, scale = FALSE)
m.x <- rep(attr(xs, "scaled:center"), each = n)
V.sx <- svd(xs)$v
rng.x1 <- apply(xs %*% V.sx, 2, range)
logWks <- matrix(0, B, K.max)
if (random){
if (verbose)
cat("Bootstrapping, b = 1,2,..., B (= ", B, ") [one \".\" per sample]:\n",
sep = "")
for (b in 1:B) {
z1 <- apply(rng.x1, 2, function(M, nn) runif(nn, min = M[1],
max = M[2]), nn = n)
z <- tcrossprod(z1, V.sx) + m.x
##z <- apply(x,2,function(m) runif(length(m),min=min(m),max=max(m)))
##z <- apply(x,2,function(m) sample(m))
for (k in 1:K.max) {
logWks[b, k] <- log(W.k(z, k))
}
if (verbose)
cat(".", if (b%%50 == 0)
paste(b, "\n"))
}
if (verbose && (B%%50 != 0))
cat("", B, "\n")
E.logW <- colMeans(logWks)
SE.sim <- sqrt((1 + 1/B) * apply(logWks, 2, var))
}else{
E.logW <- rep(NA,K.max)
SE.sim <- rep(NA,K.max)
}
structure(class = "clusGap", list(Tab = cbind(logW, E.logW,
gap = E.logW - logW, SE.sim), n = n, B = B, FUNcluster = FUNcluster))
}
clustfun <- function(x,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids",distances=NULL,link="single")
{
if ( clustnr < 2) stop("Choose clustnr > 1")
di <- if ( FUNcluster == "kmedoids" ) t(x) else dist.gen(t(x),method=metric)
if ( nrow(di) - 1 < clustnr ) clustnr <- nrow(di) - 1
if ( do.gap | sat | cln > 0 ){
gpr <- NULL
f <- if ( cln == 0 ) TRUE else FALSE
if ( do.gap ){
set.seed(rseed)
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, method=metric)
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- hclusterCBI(x,k,link=link,scaling=FALSE); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap)
if ( f ) cln <- maxSE(gpr$Tab[,3],gpr$Tab[,4],method=SE.method,SE.factor)
}
if ( sat ){
if ( ! do.gap ){
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100, random=FALSE)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, random=FALSE, method=metric)
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- hclusterCBI(x,k,link=link,scaling=FALSE); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap, random=FALSE)
}
g <- gpr$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
if ( f ) cln <- max(min(which( y - (mm + nn) < 0 )),1)
}
if ( cln <= 1 ) {
clb <- list(result=list(partition=rep(1,dim(x)[2])),bootmean=1)
names(clb$result$partition) <- names(x)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster == "kmedoids" ) dist.gen(di,method=metric) else di))
}
if ( FUNcluster == "kmeans" ) clb <- clusterboot(di,B=bootnr,distances=FALSE,bootmethod="boot",clustermethod=kmeansCBI,krange=cln,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "kmedoids" ) clb <- clusterboot(dist.gen(di,method=metric),B=bootnr,bootmethod="boot",clustermethod=pamkCBI,k=cln,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "hclust" ) clb <- clusterboot(di,B=bootnr,distances=FALSE,bootmethod="boot",clustermethod=hclusterCBI,k=cln,link=link,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster == "kmedoids" ) dist.gen(di,method=metric) else di))
}
}
setGeneric("clustexp", function(object,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids") standardGeneric("clustexp"))
setMethod("clustexp",
signature = "SCseq",
definition = function(object,clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster) {
if ( ! is.numeric(clustnr) ) stop("clustnr has to be a positive integer") else if ( round(clustnr) != clustnr | clustnr <= 0 ) stop("clustnr has to be a positive integer")
if ( ! is.numeric(bootnr) ) stop("bootnr has to be a positive integer") else if ( round(bootnr) != bootnr | bootnr <= 0 ) stop("bootnr has to be a positive integer")
if ( ! ( metric %in% c( "spearman","pearson","kendall","euclidean","maximum","manhattan","canberra","binary","minkowski") ) ) stop("metric has to be one of the following: spearman, pearson, kendall, euclidean, maximum, manhattan, canberra, binary, minkowski")
if ( ! ( SE.method %in% c( "firstSEmax","Tibs2001SEmax","globalSEmax","firstmax","globalmax") ) ) stop("SE.method has to be one of the following: firstSEmax, Tibs2001SEmax, globalSEmax, firstmax, globalmax")
if ( ! is.numeric(SE.factor) ) stop("SE.factor has to be a non-negative integer") else if ( SE.factor < 0 ) stop("SE.factor has to be a non-negative integer")
if ( ! ( is.numeric(do.gap) | is.logical(do.gap) ) ) stop( "do.gap has to be logical (TRUE/FALSE)" )
if ( ! ( is.numeric(sat) | is.logical(sat) ) ) stop( "sat has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(B.gap) ) stop("B.gap has to be a positive integer") else if ( round(B.gap) != B.gap | B.gap <= 0 ) stop("B.gap has to be a positive integer")
if ( ! is.numeric(cln) ) stop("cln has to be a non-negative integer") else if ( round(cln) != cln | cln < 0 ) stop("cln has to be a non-negative integer")
if ( ! is.numeric(rseed) ) stop("rseed has to be numeric")
if ( !do.gap & !sat & cln == 0 ) stop("cln has to be a positive integer or either do.gap or sat has to be TRUE")
if ( ! ( FUNcluster %in% c("kmeans","hclust","kmedoids") ) ) stop("FUNcluster has to be one of the following: kmeans, hclust,kmedoids")
object@clusterpar <- list(clustnr=clustnr,bootnr=bootnr,metric=metric,do.gap=do.gap,sat=sat,SE.method=SE.method,SE.factor=SE.factor,B.gap=B.gap,cln=cln,rseed=rseed,FUNcluster=FUNcluster)
y <- clustfun(object@fdata,clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster)
object@cluster <- list(kpart=y$clb$result$partition, jaccard=y$clb$bootmean, gap=y$gpr, clb=y$clb)
object@distances <- as.matrix( y$di )
set.seed(111111)
object@fcol <- sample(rainbow(max(y$clb$result$partition)))
return(object)
}
)
setGeneric("findoutliers", function(object,outminc=5,outlg=2,probthr=1e-3,thr=2**-(1:40),outdistquant=.95) standardGeneric("findoutliers"))
setMethod("findoutliers",
signature = "SCseq",
definition = function(object,outminc,outlg,probthr,thr,outdistquant) {
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before findoutliers")
if ( ! is.numeric(outminc) ) stop("outminc has to be a non-negative integer") else if ( round(outminc) != outminc | outminc < 0 ) stop("outminc has to be a non-negative integer")
if ( ! is.numeric(outlg) ) stop("outlg has to be a non-negative integer") else if ( round(outlg) != outlg | outlg < 0 ) stop("outlg has to be a non-negative integer")
if ( ! is.numeric(probthr) ) stop("probthr has to be a number between 0 and 1") else if ( probthr < 0 | probthr > 1 ) stop("probthr has to be a number between 0 and 1")
if ( ! is.numeric(thr) ) stop("thr hast to be a vector of numbers between 0 and 1") else if ( min(thr) < 0 | max(thr) > 1 ) stop("thr hast to be a vector of numbers between 0 and 1")
if ( ! is.numeric(outdistquant) ) stop("outdistquant has to be a number between 0 and 1") else if ( outdistquant < 0 | outdistquant > 1 ) stop("outdistquant has to be a number between 0 and 1")
object@outlierpar <- list( outminc=outminc,outlg=outlg,probthr=probthr,thr=thr,outdistquant=outdistquant )
### calibrate background model
m <- log2(apply(object@fdata,1,mean))
v <- log2(apply(object@fdata,1,var))
f <- m > -Inf & v > -Inf
m <- m[f]
v <- v[f]
mm <- -8
repeat{
fit <- lm(v ~ m + I(m^2))
if( coef(fit)[3] >= 0 | mm >= 3){
break
}
mm <- mm + .5
f <- m > mm
m <- m[f]
v <- v[f]
}
object@background <- list()
object@background$vfit <- fit
object@background$lvar <- function(x,object) 2**(coef(object@background$vfit)[1] + log2(x)*coef(object@background$vfit)[2] + coef(object@background$vfit)[3] * log2(x)**2)
object@background$lsize <- function(x,object) x**2/(max(x + 1e-6,object@background$lvar(x,object)) - x)
### identify outliers
out <- c()
stest <- rep(0,length(thr))
cprobs <- c()
for ( n in 1:max(object@cluster$kpart) ){
if ( sum(object@cluster$kpart == n) == 1 ){ cprobs <- append(cprobs,.5); names(cprobs)[length(cprobs)] <- names(object@cluster$kpart)[object@cluster$kpart == n]; next }
x <- object@fdata[,object@cluster$kpart == n]
x <- x[apply(x,1,max) > outminc,]
z <- t( apply(x,1,function(x){ apply( cbind( pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) , 1 - pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) ),1, min) } ) )
cp <- apply(z,2,function(x){ y <- p.adjust(x,method="BH"); y <- y[order(y,decreasing=FALSE)]; return(y[outlg]);})
f <- cp < probthr
cprobs <- append(cprobs,cp)
if ( sum(f) > 0 ) out <- append(out,names(x)[f])
for ( j in 1:length(thr) ) stest[j] <- stest[j] + sum( cp < thr[j] )
}
object@out <-list(out=out,stest=stest,thr=thr,cprobs=cprobs)
### cluster outliers
clp2p.cl <- c()
cols <- names(object@fdata)
cpart <- object@cluster$kpart
di <- as.data.frame(object@distances)
for ( i in 1:max(cpart) ) {
tcol <- cols[cpart == i]
if ( sum(!(tcol %in% out)) > 1 ) clp2p.cl <- append(clp2p.cl,as.vector(t(di[tcol[!(tcol %in% out)],tcol[!(tcol %in% out)]])))
}
clp2p.cl <- clp2p.cl[clp2p.cl>0]
cadd <- list()
if ( length(out) > 0 ){
if (length(out) == 1){
cadd <- list(out)
}else{
n <- out
m <- as.data.frame(di[out,out])
for ( i in 1:length(out) ){
if ( length(n) > 1 ){
o <- order(apply(cbind(m,1:dim(m)[1]),1,function(x) min(x[1:(length(x)-1)][-x[length(x)]])),decreasing=FALSE)
m <- m[o,o]
n <- n[o]
f <- m[,1] < quantile(clp2p.cl,outdistquant) | m[,1] == min(clp2p.cl)
ind <- 1
if ( sum(f) > 1 ) for ( j in 2:sum(f) ) if ( apply(m[f,f][j,c(ind,j)] > quantile(clp2p.cl,outdistquant) ,1,sum) == 0 ) ind <- append(ind,j)
cadd[[i]] <- n[f][ind]
g <- ! n %in% n[f][ind]
n <- n[g]
m <- m[g,g]
if ( sum(g) == 0 ) break
}else if (length(n) == 1){
cadd[[i]] <- n
break
}
}
}
for ( i in 1:length(cadd) ){
cpart[cols %in% cadd[[i]]] <- max(cpart) + 1
}
}
### determine final clusters
for ( i in 1:max(cpart) ){
if ( sum(cpart == i) == 0 ) next
f <- cols[cpart == i]
d <- object@fdata
if ( length(f) == 1 ){
cent <- d[,f]
}else{
if ( object@clusterpar$FUNcluster == "kmedoids" ){
x <- apply(as.matrix(dist.gen(t(d[,f]),method=object@clusterpar$metric)),2,mean)
cent <- d[,f[which(x == min(x))[1]]]
}else{
cent <- apply(d[,f],1,mean)
}
}
if ( i == 1 ) dcent <- data.frame(cent) else dcent <- cbind(dcent,cent)
if ( i == 1 ) tmp <- data.frame(apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric)) else tmp <- cbind(tmp,apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric))
}
cpart <- apply(tmp,1,function(x) order(x,decreasing=FALSE)[1])
for ( i in max(cpart):1){if (sum(cpart==i)==0) cpart[cpart>i] <- cpart[cpart>i] - 1 }
object@cpart <- cpart
set.seed(111111)
object@fcol <- sample(rainbow(max(cpart)))
return(object)
}
)
setGeneric("comptsne", function(object,rseed=15555,sammonmap=FALSE,initial_cmd=TRUE,...) standardGeneric("comptsne"))
setMethod("comptsne",
signature = "SCseq",
definition = function(object,rseed,sammonmap,...){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before comptsne")
set.seed(rseed)
di <- if ( object@clusterpar$FUNcluster == "kmedoids") as.dist(object@distances) else dist.gen(as.matrix(object@distances))
if ( sammonmap ){
object@tsne <- as.data.frame(sammon(di,k=2)$points)
}else{
ts <- if ( initial_cmd ) tsne(di,initial_config=cmdscale(di,k=2),...) else tsne(di,k=2,...)
object@tsne <- as.data.frame(ts)
}
return(object)
}
)
setGeneric("clustdiffgenes", function(object,pvalue=.01) standardGeneric("clustdiffgenes"))
setMethod("clustdiffgenes",
signature = "SCseq",
definition = function(object,pvalue){
if ( length(object@cpart) == 0 ) stop("run findoutliers before clustdiffgenes")
if ( ! is.numeric(pvalue) ) stop("pvalue has to be a number between 0 and 1") else if ( pvalue < 0 | pvalue > 1 ) stop("pvalue has to be a number between 0 and 1")
cdiff <- list()
x <- object@ndata
y <- object@expdata[,names(object@ndata)]
part <- object@cpart
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
m <- if ( sum(part != i) > 1 ) apply(x[,part != i],1,mean) else x[,part != i]
n <- if ( sum(part == i) > 1 ) apply(x[,part == i],1,mean) else x[,part == i]
no <- if ( sum(part == i) > 1 ) median(apply(y[,part == i],2,sum))/median(apply(x[,part == i],2,sum)) else sum(y[,part == i])/sum(x[,part == i])
m <- m*no
n <- n*no
pv <- binompval(m/sum(m),sum(n),n)
d <- data.frame(mean.ncl=m,mean.cl=n,fc=n/m,pv=pv)[order(pv,decreasing=FALSE),]
cdiff[[paste("cl",i,sep=".")]] <- d[d$pv < pvalue,]
}
return(cdiff)
}
)
setGeneric("plotsaturation", function(object,disp=FALSE) standardGeneric("plotsaturation"))
setMethod("plotsaturation",
signature = "SCseq",
definition = function(object,disp){
if ( length(object@cluster$gap) == 0 ) stop("run clustexp before plotsaturation")
g <- object@cluster$gap$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
cln <- max(min(which( y - (mm + nn) < 0 )),1)
x <- 1:length(y)
if (disp){
x <- 1:length(g)
plot(x,g,pch=20,col="grey",xlab="k",ylab="log within cluster dispersion")
f <- x == cln
points(x[f],g[f],col="blue")
}else{
plot(x,y,pch=20,col="grey",xlab="k",ylab="Change in log within cluster dispersion")
points(x,mm,col="red",pch=20)
plot.err.bars.y(x,mm,nn,col="red")
points(x,y,col="grey",pch=20)
f <- x == cln
points(x[f],y[f],col="blue")
}
}
)
setGeneric("plotsilhouette", function(object) standardGeneric("plotsilhouette"))
setMethod("plotsilhouette",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotsilhouette")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no silhouette plot")
kpart <- object@cluster$kpart
distances <- if ( object@clusterpar$FUNcluster == "kmedoids" ) as.dist(object@distances) else dist.gen(object@distances)
si <- silhouette(kpart,distances)
plot(si)
}
)
compmedoids <- function(x,part,metric="pearson"){
m <- c()
for ( i in sort(unique(part)) ){
f <- names(x)[part == i]
if ( length(f) == 1 ){
m <- append(m,f)
}else{
y <- apply(as.matrix(dist.gen(t(x[,f]),method=metric)),2,mean)
m <- append(m,f[which(y == min(y))[1]])
}
}
m
}
setGeneric("clustheatmap", function(object,final=FALSE,hmethod="single") standardGeneric("clustheatmap"))
setMethod("clustheatmap",
signature = "SCseq",
definition = function(object,final,hmethod){
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before clustheatmap")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before clustheatmap")
x <- object@fdata
part <- if ( final ) object@cpart else object@cluster$kpart
na <- c()
j <- 0
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
j <- j + 1
na <- append(na,i)
d <- x[,part == i]
if ( sum(part == i) == 1 ) cent <- d else cent <- apply(d,1,mean)
if ( j == 1 ) tmp <- data.frame(cent) else tmp <- cbind(tmp,cent)
}
names(tmp) <- paste("cl",na,sep=".")
ld <- if ( object@clusterpar$FUNcluster == "kmedoids" ) dist.gen(t(tmp),method=object@clusterpar$metric) else dist.gen(as.matrix(dist.gen(t(tmp),method=object@clusterpar$metric)))
if ( max(part) > 1 ) cclmo <- hclust(ld,method=hmethod)$order else cclmo <- 1
q <- part
for ( i in 1:max(part) ){
q[part == na[cclmo[i]]] <- i
}
part <- q
di <- if ( object@clusterpar$FUNcluster == "kmedoids" ) object@distances else as.data.frame( as.matrix( dist.gen(t(object@distances)) ) )
pto <- part[order(part,decreasing=FALSE)]
ptn <- c()
for ( i in 1:max(pto) ){ pt <- names(pto)[pto == i]; z <- if ( length(pt) == 1 ) pt else pt[hclust(as.dist(t(di[pt,pt])),method=hmethod)$order]; ptn <- append(ptn,z) }
col <- object@fcol
mi <- min(di,na.rm=TRUE)
ma <- max(di,na.rm=TRUE)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
ColorRamp <- colorRampPalette(brewer.pal(n = 7,name = "RdYlBu"))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
if ( mi == ma ){
ColorLevels <- seq(0.99*mi, 1.01*ma, length=length(ColorRamp))
}
par(mar = c(3,5,2.5,2))
image(as.matrix(di[ptn,ptn]),col=ColorRamp,axes=FALSE)
abline(0,1)
box()
tmp <- c()
for ( u in 1:max(part) ){
ol <- (0:(length(part) - 1)/(length(part) - 1))[ptn %in% names(x)[part == u]]
points(rep(0,length(ol)),ol,col=col[cclmo[u]],pch=15,cex=.75)
points(ol,rep(0,length(ol)),col=col[cclmo[u]],pch=15,cex=.75)
tmp <- append(tmp,mean(ol))
}
axis(1,at=tmp,lab=cclmo)
axis(2,at=tmp,lab=cclmo)
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
return(cclmo)
}
)
## class definition
Ltree <- setClass("Ltree", slots = c(sc = "SCseq", ldata = "list", entropy = "vector", trproj = "list", par = "list", prback = "data.frame", prbacka = "data.frame", ltcoord = "matrix", prtree = "list", sigcell = "vector", cdata = "list" ))
setValidity("Ltree",
function(object) {
msg <- NULL
if ( class(object@sc)[1] != "SCseq" ){
msg <- c(msg, "input data must be of class SCseq")
}
if (is.null(msg)) TRUE
else msg
}
)
setMethod("initialize",
signature = "Ltree",
definition = function(.Object, sc ){
.Object@sc <- sc
validObject(.Object)
return(.Object)
}
)
setGeneric("compentropy", function(object) standardGeneric("compentropy"))
setMethod("compentropy",
signature = "Ltree",
definition = function(object){
probs <- t(t(object@sc@ndata)/apply(object@sc@ndata,2,sum))
object@entropy <- -apply(probs*log(probs)/log(nrow(object@sc@ndata)),2,sum)
return(object)
}
)
compproj <- function(pdiloc,lploc,cnloc,mloc,d=NULL){
pd <- data.frame(pdiloc)
k <- paste("X",sort(rep(1:nrow(pdiloc),length(mloc))),sep="")
pd$k <- paste("X",1:nrow(pdiloc),sep="")
pd <- merge(data.frame(k=k),pd,by="k")
if ( is.null(d) ){
cnv <- t(matrix(rep(t(cnloc),nrow(pdiloc)),nrow=ncol(pdiloc)))
pdcl <- paste("X",lploc[as.numeric(sub("X","",pd$k))],sep="")
rownames(cnloc) <- paste("X",mloc,sep="")
pdcn <- cnloc[pdcl,]
v <- cnv - pdcn
}else{
v <- d$v
pdcn <- d$pdcn
}
w <- pd[,names(pd) != "k"] - pdcn
h <- apply(cbind(v,w),1,function(x){
x1 <- x[1:(length(x)/2)];
x2 <- x[(length(x)/2 + 1):length(x)];
x1s <- sqrt(sum(x1**2)); x2s <- sqrt(sum(x2**2)); y <- sum(x1*x2)/x1s/x2s; return( if (x1s == 0 | x2s == 0 ) NA else y ) })
rma <- as.data.frame(matrix(h,ncol=nrow(pdiloc)))
names(rma) <- unique(pd$k)
pdclu <- lploc[as.numeric(sub("X","",names(rma)))]
pdclp <- apply(t(rma),1,function(x) if (sum(!is.na(x)) == 0 ) NA else mloc[which(abs(x) == max(abs(x),na.rm=TRUE))[1]])
pdclh <- apply(t(rma),1,function(x) if (sum(!is.na(x)) == 0 ) NA else x[which(abs(x) == max(abs(x),na.rm=TRUE))[1]])
pdcln <- names(lploc)[as.numeric(sub("X","",names(rma)))]
names(rma) <- pdcln
rownames(rma) <- paste("X",mloc,sep="")
res <- data.frame(o=pdclu,l=pdclp,h=pdclh)
rownames(res) <- pdcln
return(list(res=res[names(lploc),],rma=as.data.frame(t(rma[,names(lploc)])),d=list(v=v,pdcn=pdcn)))
}
pdishuffle <- function(pdi,lp,cn,m,all=FALSE){
if ( all ){
d <- as.data.frame(pdi)
y <- t(apply(pdi,1,function(x) runif(length(x),min=min(x),max=max(x))))
names(y) <- names(d)
rownames(y) <- rownames(d)
return(y)
}else{
fl <- TRUE
for ( i in unique(lp)){
if ( sum(lp == i) > 1 ){
y <-data.frame( t(apply(as.data.frame(pdi[,lp == i]),1,sample)) )
}else{
y <- as.data.frame(pdi[,lp == i])
}
names(y) <- names(lp)[lp == i]
rownames(y) <- names(lp)
z <- if (fl) y else cbind(z,y)
fl <- FALSE
}
z <- t(z[,names(lp)])
return(z)
}
}
setGeneric("projcells", function(object,cthr=0,nmode=FALSE) standardGeneric("projcells"))
setMethod("projcells",
signature = "Ltree",
definition = function(object,cthr,nmode){
if ( ! is.numeric(cthr) ) stop( "cthr has to be a non-negative number" ) else if ( cthr < 0 ) stop( "cthr has to be a non-negative number" )
if ( ! length(object@sc@cpart == 0) ) stop( "please run findoutliers on the SCseq input object before initializing Ltree" )
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
object@par$cthr <- cthr
object@par$nmode <- nmode
lp <- object@sc@cpart
ld <- object@sc@distances
n <- aggregate(rep(1,length(lp)),list(lp),sum)
n <- as.vector(n[order(n[,1],decreasing=FALSE),-1])
m <- (1:length(n))[n>cthr]
f <- lp %in% m
lp <- lp[f]
ld <- ld[f,f]
pdil <- sc@tsne[f,]
cnl <- aggregate(pdil,by=list(lp),median)
cnl <- cnl[order(cnl[,1],decreasing=FALSE),-1]
pdi <- suppressWarnings( cmdscale(as.matrix(ld),k=ncol(ld)-1) )
cn <- as.data.frame(pdi[compmedoids(sc@fdata[,names(lp)],lp),])
rownames(cn) <- 1:nrow(cn)
x <- compproj(pdi,lp,cn,m)
res <- x$res
if ( nmode ){
rma <- x$rma
z <- paste("X",t(as.vector(apply(cbind(lp,ld),1,function(x){ f <- lp != x[1]; lp[f][which(x[-1][f] == min(x[-1][f]))[1]] }))),sep="")
k <- apply(cbind(z,rma),1,function(x) (x[-1])[names(rma) == x[1]])
rn <- res
rn$l <- as.numeric(sub("X","",z))
rn$h <- as.numeric(k)
res <- rn
x$res <- res
}
object@ldata <- list(lp=lp,ld=ld,m=m,pdi=pdi,pdil=pdil,cn=cn,cnl=cnl)
object@trproj <- x
return(object)
}
)
setGeneric("projback", function(object,pdishuf=2000,nmode=FALSE,rseed=17000) standardGeneric("projback"))
setMethod("projback",
signature = "Ltree",
definition = function(object,pdishuf,nmode,rseed){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( ! is.numeric(pdishuf) ) stop( "pdishuf has to be a non-negative integer number" ) else if ( round(pdishuf) != pdishuf | pdishuf < 0 ) stop( "pdishuf has to be a non-negative integer number" )
if ( length(object@trproj) == 0 ) stop("run projcells before projback")
object@par$pdishuf <- pdishuf
object@par$rseed <- rseed
if ( ! nmode ){
set.seed(rseed)
for ( i in 1:pdishuf ){
cat("pdishuffle:",i,"\n")
x <- compproj(pdishuffle(object@ldata$pdi,object@ldata$lp,object@ldata$cn,object@ldata$m,all=TRUE),object@ldata$lp,object@ldata$cn,object@ldata$m,d=object@trproj$d)$res
y <- if ( i == 1 ) t(x) else cbind(y,t(x))
}
##important
object@prback <- as.data.frame(t(y))
x <- object@prback
x$n <- as.vector(t(matrix(rep(1:(nrow(x)/nrow(object@ldata$pdi)),nrow(object@ldata$pdi)),ncol=nrow(object@ldata$pdi))))
object@prbacka <- aggregate(data.frame(count=rep(1,nrow(x))),by=list(n=x$n,o=x$o,l=x$l),sum)
}
return( object )
}
)
setGeneric("lineagetree", function(object,pthr=0.01,nmode=FALSE) standardGeneric("lineagetree"))
setMethod("lineagetree",
signature = "Ltree",
definition = function(object,pthr,nmode){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( length(object@trproj) == 0 ) stop("run projcells before lineagetree")
if ( max(dim(object@prback)) == 0 & ! nmode ) stop("run projback before lineagetree")
if ( ! is.numeric(pthr) ) stop( "pthr has to be a non-negative number" ) else if ( pthr < 0 ) stop( "pthr has to be a non-negative number" )
object@par$pthr <- pthr
cnl <- object@ldata$cnl
pdil <- object@ldata$pdil
cn <- object@ldata$cn
pdi <- object@ldata$pdi
m <- object@ldata$m
lp <- object@ldata$lp
res <- object@trproj$res
rma <- object@trproj$rma
prback <- object@prback
cm <- as.matrix(dist(cnl))*0
linl <- list()
linn <- list()
for ( i in 1:length(m) ){
for ( j in i:length(m) ){
linl[[paste(m[i],m[j],sep=".")]] <- c()
linn[[paste(m[i],m[j],sep=".")]] <- c()
}
}
sigcell <- c()
for ( i in 1:nrow(res) ){
a <- which( m == res$o[i])
if ( sum( lp == m[a] ) == 1 ){
k <- t(cnl)[,a]
k <- NA
sigcell <- append(sigcell, FALSE)
}else{
b <- which(m == res$l[i] )
h <- res$h[i]
if ( nmode ){
sigcell <- append(sigcell, FALSE)
}else{
f <- prback$o == m[a] & prback$l == m[b]
if ( sum(f) > 0 ){
ql <- quantile(prback[f,"h"],pthr,na.rm=TRUE)
qh <- quantile(prback[f,"h"],1 - pthr,na.rm=TRUE)
}else{
ql <- 0
qh <- 0
}
sigcell <- if (is.na(h) ) append(sigcell, NA) else if ( h > qh | h < min(0,ql) ) append(sigcell, TRUE) else append(sigcell, FALSE)
}
if ( !is.na(res$h[i]) ){
w <- t(pdil)[,i] - t(cnl)[,a]
v <- t(cnl)[,b] - t(cnl)[,a]
wo <- t(pdi)[,i] - t(cn)[,a]
vo <- t(cn)[,b] - t(cn)[,a]
df <-( h*sqrt(sum(wo*wo)) )/sqrt(sum(vo*vo))*v
k <- df + t(cnl)[,a]
cm[a,b] <- cm[a,b] + 1
so <- m[sort(c(a,b))]
dfl <- sign(h)*sqrt( sum( df*df ) )/sqrt(sum(v*v))
if ( a > b ) dfl <- 1 - dfl
linn[[paste(so[1],so[2],sep=".")]] <- append( linn[[paste(so[1],so[2],sep=".")]], rownames(pdi)[i] )
linl[[paste(so[1],so[2],sep=".")]] <- append( linl[[paste(so[1],so[2],sep=".")]], dfl )
}else{
k <- t(cnl)[,a]
for ( j in unique(lp[lp != m[a]]) ){
b <- which(j == m)
so <- m[sort(c(a,b))]
dfl <- 0
if ( a > b ) dfl <- 1 - dfl
linn[[paste(so[1],so[2],sep=".")]] <- append( linn[[paste(so[1],so[2],sep=".")]], rownames(pdi)[i] )
linl[[paste(so[1],so[2],sep=".")]] <- append( linl[[paste(so[1],so[2],sep=".")]], dfl )
}
}
}
lt <- if ( i == 1 ) data.frame(k) else cbind(lt,k)
}
lt <- t(lt)
cm <- as.data.frame(cm)
names(cm) <- paste("cl",m,sep=".")
rownames(cm) <- paste("cl",m,sep=".")
lt <- as.data.frame(lt)
rownames(lt) <- rownames(res)
object@ltcoord <- as.matrix(lt)
object@prtree <- list(n=linn,l=linl)
object@cdata$counts <- cm
names(sigcell) <- rownames(res)
object@sigcell <- sigcell
return( object )
}
)
setGeneric("comppvalue", function(object,pethr=0.01,nmode=FALSE) standardGeneric("comppvalue"))
setMethod("comppvalue",
signature = "Ltree",
definition = function(object,pethr,nmode){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( length(object@prtree) == 0 ) stop("run lineagetree before comppvalue")
if ( ! is.numeric(pethr) ) stop( "pethr has to be a non-negative number" ) else if ( pethr < 0 ) stop( "pethr has to be a non-negative number" )
object@par$pethr <- pethr
cm <- object@cdata$counts
cmpv <- cm*NA
cmpvd <- cm*NA
cmbr <- cm*NA
cmpvn <- cm*NA
cmpvnd <- cm*NA
cmfr <- cm/apply(cm,1,sum)
if ( nmode ){
N <- apply(cm,1,sum) + 1
N0 <- sum(N) - N
n0 <- t(matrix(rep(N,length(N)),ncol=length(N)))
p <- n0/N0
n <- cm
k <- cbind(N,p,n)
cmpv <- apply(k,1,function(x){N <- x[1]; p <- x[2:( ncol(cm) + 1 )]; n <- x[( ncol(cm) + 2 ):( 2*ncol(cm) + 1)]; apply(cbind(n,p),1,function(x,N) binom.test(x[1],N,min(1,x[2]),alternative="g")$p.value,N=N)})
cmpvd <- apply(k,1,function(x){N <- x[1]; p <- x[2:( ncol(cm) + 1 )]; n <- x[( ncol(cm) + 2 ):( 2*ncol(cm) + 1)]; apply(cbind(n,p),1,function(x,N) binom.test(x[1],N,min(1,x[2]),alternative="l")$p.value,N=N)})
cmpvn <- cmpv
cmpvnd <- cmpvd
cmbr <- as.data.frame(n0/N0*N)
names(cmbr) <- names(cm)
rownames(cmbr) <- rownames(cm)
}else{
for ( i in 1:nrow(cm) ){
for ( j in 1:ncol(cm) ){
f <- object@prbacka$o == object@ldata$m[i] & object@prbacka$l == object@ldata$m[j]
x <- object@prbacka$count[f]
if ( sum(f) < object@par$pdishuf ) x <- append(x,rep(0, object@par$pdishuf - sum(f)))
cmbr[i,j] <- if ( sum(f) > 0 ) mean(x) else 0
cmpv[i,j] <- if ( quantile(x,1 - pethr) < cm[i,j] ) 0 else 0.5
cmpvd[i,j] <- if ( quantile(x,pethr) > cm[i,j] ) 0 else 0.5
cmpvn[i,j] <- sum( x >= cm[i,j])/length(x)
cmpvnd[i,j] <- sum( x <= cm[i,j])/length(x)
}
}
}
diag(cmpv) <- .5
diag(cmpvd) <- .5
diag(cmpvn) <- NA
diag(cmpvnd) <- NA
object@cdata$counts.br <- cmbr
object@cdata$pv.e <- cmpv
object@cdata$pv.d <- cmpvd
object@cdata$pvn.e <- cmpvn
object@cdata$pvn.d <- cmpvnd
m <- object@ldata$m
linl <- object@prtree$l
ls <- as.data.frame(matrix(rep(NA,length(m)**2),ncol=length(m)))
names(ls) <- rownames(ls) <- paste("cl",m,sep=".")
for ( i in 1:( length(m) - 1 )){
for ( j in (i + 1):length(m) ){
na <- paste(m[i],m[j],sep=".")
if ( na %in% names(linl) & min(cmpv[i,j],cmpv[j,i],na.rm=TRUE) < pethr ){
y <- sort(linl[[na]])
nn <- ( 1 - max(y[-1] - y[-length(y)]) )
}else{
nn <- 0
}
ls[i,j] <- nn
}
}
object@cdata$linkscore <- ls
return(object)
}
)
setGeneric("plotlinkpv", function(object) standardGeneric("plotlinkpv"))
setMethod("plotlinkpv",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotlinkpv")
pheatmap(-log2(object@cdata$pvn.e + 1/object@par$pdishuf/2))
}
)
setGeneric("plotlinkscore", function(object) standardGeneric("plotlinkscore"))
setMethod("plotlinkscore",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotlinkscore")
pheatmap(object@cdata$linkscore,cluster_rows=FALSE,cluster_cols=FALSE)
}
)
setGeneric("plotmapprojections", function(object) standardGeneric("plotmapprojections"))
setMethod("plotmapprojections",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmapprojections")
cent <- object@sc@fdata[,compmedoids(object@sc@fdata,object@sc@cpart)]
dc <- as.data.frame(1 - cor(cent))
names(dc) <- sort(unique(object@sc@cpart))
rownames(dc) <- sort(unique(object@sc@cpart))
trl <- spantree(dc[object@ldata$m,object@ldata$m])
u <- object@ltcoord[,1]
v <- object@ltcoord[,2]
cnl <- object@ldata$cnl
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
for ( i in unique(object@ldata$lp) ){ f <- object@ldata$lp == i; text(u[f],v[f],i,cex=.75,font=4,col=object@sc@fcol[i]) }
points(cnl[,1],cnl[,2])
text(cnl[,1],cnl[,2],object@ldata$m,cex=2)
for ( i in 1:length(trl$kid) ){
lines(c(cnl[i+1,1],cnl[trl$kid[i],1]),c(cnl[i+1,2],cnl[trl$kid[i],2]),col="black")
}
}
)
setGeneric("plotmap", function(object) standardGeneric("plotmap"))
setMethod("plotmap",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
cent <- object@sc@fdata[,compmedoids(object@sc@fdata,object@sc@cpart)]
dc <- as.data.frame(1 - cor(cent))
names(dc) <- sort(unique(object@sc@cpart))
rownames(dc) <- sort(unique(object@sc@cpart))
trl <- spantree(dc[object@ldata$m,object@ldata$m])
u <- object@ldata$pdil[,1]
v <- object@ldata$pdil[,2]
cnl <- object@ldata$cnl
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
for ( i in unique(object@ldata$lp) ){ f <- object@ldata$lp == i; text(u[f],v[f],i,cex=.75,font=4,col=object@sc@fcol[i]) }
points(cnl[,1],cnl[,2])
text(cnl[,1],cnl[,2],object@ldata$m,cex=2)
for ( i in 1:length(trl$kid) ){
lines(c(cnl[i+1,1],cnl[trl$kid[i],1]),c(cnl[i+1,2],cnl[trl$kid[i],2]),col="black")
}
}
)
setGeneric("plottree", function(object,showCells=TRUE,nmode=FALSE,scthr=0) standardGeneric("plottree"))
setMethod("plottree",
signature = "Ltree",
definition = function(object,showCells,nmode,scthr){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( !is.numeric(showCells) & !is.logical(showCells) ) stop("argument showCells has to be logical (TRUE/FALSE)")
if ( ! is.numeric(scthr) ) stop( "scthr has to be a non-negative number" ) else if ( scthr < 0 | scthr > 1 ) stop( "scthr has to be a number between 0 and 1" )
ramp <- colorRamp(c("darkgreen", "yellow", "brown"))
mcol <- rgb( ramp(seq(0, 1, length = 101)), max = 255)
co <- object@cdata
fc <- (co$counts/( co$counts.br + .5))*(co$pv.e < object@par$pethr)
fc <- fc*(fc > t(fc)) + t(fc)*(t(fc) >= fc)
fc <- log2(fc + (fc == 0))
k <- -log10(sort(unique(as.vector(t(co$pvn.e))[as.vector(t(co$pv.e))<object@par$pethr])) + 1/object@par$pdishuf)
if (length(k) == 1) k <- c(k - k/100,k)
mlpv <- -log10(co$pvn.e + 1/object@par$pdishuf)
diag(mlpv) <- min(mlpv,na.rm=TRUE)
dcc <- t(apply(round(100*(mlpv - min(k))/(max(k) - min(k)),0) + 1,1,function(x){y <- c(); for ( n in x ) y <- append(y,if ( n < 1 ) NA else mcol[n]); y }))
cx <- c()
cy <- c()
va <- c()
m <- object@ldata$m
for ( i in 1:( length(m) - 1 ) ){
for ( j in ( i + 1 ):length(m) ){
if ( min(co$pv.e[i,j],co$pv.e[j,i],na.rm=TRUE) < object@par$pethr ){
if ( mlpv[i,j] > mlpv[j,i] ){
va <- append(va,dcc[i,j])
}else{
va <- append(va,dcc[j,i])
}
cx <- append(cx,i)
cy <- append(cy,j)
}
}
}
cnl <- object@ldata$cnl
u <- object@ltcoord[,1]
v <- object@ltcoord[,2]
layout( cbind(c(1, 1), c(2, 3)),widths=c(5,1,1),height=c(5,5,1))
par(mar = c(12,5,1,1))
if ( showCells ){
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
if ( !nmode ) points(u[object@sigcell],v[object@sigcell],col="black")
}else{
plot(u,v,cex=0,xlab="Dim 1",ylab="Dim 2")
}
if ( length(va) > 0 ){
f <- order(va,decreasing=TRUE)
for ( i in 1:length(va) ){
if ( object@cdata$linkscore[cx[i],cy[i]] > scthr ){
if ( showCells ){
lines(cnl[c(cx[i],cy[i]),1],cnl[c(cx[i],cy[i]),2],col=va[i],lwd=2)
}else{
##nn <- min(10,fc[cx[i],cy[i]])
lines(cnl[c(cx[i],cy[i]),1],cnl[c(cx[i],cy[i]),2],col=va[i],lwd=5*object@cdata$linkscore[cx[i],cy[i]])
}
}
}
}
en <- aggregate(object@entropy,list(object@sc@cpart),median)
en <- en[en$Group.1 %in% m,]
mi <- min(en[,2],na.rm=TRUE)
ma <- max(en[,2],na.rm=TRUE)
w <- round((en[,2] - mi)/(ma - mi)*99 + 1,0)
ramp <- colorRamp(c("red","orange", "pink","purple", "blue"))
ColorRamp <- rgb( ramp(seq(0, 1, length = 101)), max = 255)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
if ( mi == ma ){
ColorLevels <- seq(0.99*mi, 1.01*ma, length=length(ColorRamp))
}
for ( i in m ){
f <- en[,1] == m
points(cnl[f,1],cnl[f,2],cex=5,col=ColorRamp[w[f]],pch=20)
}
text(cnl[,1],cnl[,2],m,cex=1.25,font=4,col="white")
par(mar = c(5, 4, 1, 2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
coll <- seq(min(k), max(k), length=length(mcol))
image(1, coll,
matrix(data=coll, ncol=length(mcol),nrow=1),
col=mcol,
xlab="",ylab="",
xaxt="n")
layout(1)
}
)
setGeneric("plotdistanceratio", function(object) standardGeneric("plotdistanceratio"))
setMethod("plotdistanceratio",
signature = "Ltree",
definition = function(object){
if ( length(object@ldata) <= 0 ) stop("run projcells before plotdistanceratio")
l <- as.matrix(dist(object@ldata$pdi))
z <- (l/object@ldata$ld)
hist(log2(z),breaks=100,xlab=" log2 emb. distance/distance",main="")
}
)
setGeneric("getproj", function(object,i) standardGeneric("getproj"))
setMethod("getproj",
signature = "Ltree",
definition = function(object,i){
if ( length(object@ldata) <= 0 ) stop("run projcells before plotdistanceratio")
if ( ! i %in% object@ldata$m ) stop(paste("argument i has to be one of",paste(object@ldata$m,collapse=",")))
x <- object@trproj$rma[names(object@ldata$lp)[object@ldata$lp == i],]
x <- x[,names(x) != paste("X",i,sep="")]
f <- !is.na(x[,1])
x <- x[f,]
if ( nrow(x) > 1 ){
y <- x
y <- as.data.frame(t(apply(y,1,function(x) (x - mean(x))/sqrt(var(x)))))
}
names(x) = sub("X","cl.",names(x))
names(y) = sub("X","cl.",names(y))
return(list(pr=x,prz=y))
}
)
setGeneric("projenrichment", function(object) standardGeneric("projenrichment"))
setMethod("projenrichment",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
ze <- ( object@cdata$pv.e < object@par$pethr | object@cdata$pv.d < object@par$pethr) * (object@cdata$counts + .1)/( object@cdata$counts.br + .1 )
pheatmap(log2(ze + ( ze == 0 ) ),cluster_rows=FALSE,cluster_cols=FALSE)
}
)
setGeneric("compscore", function(object,nn=1) standardGeneric("compscore"))
setMethod("compscore",
signature = "Ltree",
definition = function(object,nn){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before compscore")
if ( ! is.numeric(nn) ) stop( "nn has to be a non-negative integer number" ) else if ( round(nn) != nn | nn < 0 ) stop( "nn has to be a non-negative integer number" )
x <- object@cdata$counts*(object@cdata$pv.e < object@par$pethr)>0
y <- x | t(x)
if ( max(y) > 0 ){
z <- apply(y,1,sum)
nl <- list()
n <- list()
for ( i in 1:nn ){
if ( i == 1 ){
n[[i]] <- as.list(apply(y,1,function(x) grep(TRUE,x)))
nl <- data.frame( apply(y,1,sum) )
}
if ( i > 1 ){
v <- rep(0,nrow(nl))
n[[i]] <- list()
for ( j in 1:length(n[[i-1]]) ){
cl <- n[[i-1]][[j]]
if ( length(cl) == 0 ){
n[[i]][[paste("d",j,sep="")]] <- NA
v[j] <- 0
}else{
k <- if ( length(cl) > 1 ) apply(y[cl,],2,sum) > 0 else if ( length(cl) == 1 ) y[cl,]
n[[i]][[paste("d",j,sep="")]] <- sort(unique(c(cl,grep(TRUE,k))))
v[j] <- length(n[[i]][[paste("d",j,sep="")]])
}
}
names(n[[i]]) <- names(z)
nl <- cbind(nl,v)
}
}
x <- nl[,i]
names(x) <- rownames(nl)
}else{
x <- rep(0,length(object@ldata$m))
names(x) <- paste("cl",object@ldata$m,sep=".")
}
v <- aggregate(object@entropy,list(object@sc@cpart),median)
v <- v[v$Group.1 %in% object@ldata$m,]
w <- as.vector(v[,-1])
names(w) <- paste("cl.",v$Group.1,sep="")
w <- w - min(w)
return(list(links=x,entropy=w,StemIDscore=x*w))
}
)
setGeneric("plotscore", function(object,nn=1) standardGeneric("plotscore"))
setMethod("plotscore",
signature = "Ltree",
definition = function(object,nn){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotscore")
x <- compscore(object,nn)
layout(1:3)
barplot(x$links,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Number of links",cex.names=1)
barplot(x$entropy,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Delta-Entropy",cex.names=1)
barplot(x$StemIDscore,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Number of links * Delta-Entropy",cex.names=1)
layout(1)
}
)
setGeneric("branchcells", function(object,br) standardGeneric("branchcells"))
setMethod("branchcells",
signature = "Ltree",
definition = function(object,br){
if ( length(object@ldata) <= 0 ) stop("run projcells before branchcells")
msg <- paste("br needs to be list of length two containing two branches, where each has to be one of", paste(names(object@prtree$n),collapse=","))
if ( !is.list(br) ) stop(msg) else if ( length(br) != 2 ) stop(msg) else if ( ! br[[1]] %in% names(object@prtree$n) | ! br[[2]] %in% names(object@prtree$n) ) stop(msg)
n <- list()
scl <- object@sc
k <- c()
cl <- intersect( as.numeric(strsplit(br[[1]],"\\.")[[1]]), as.numeric(strsplit(br[[2]],"\\.")[[1]]))
if ( length(cl) == 0 ) stop("the two branches in br need to have one cluster in common.")
for ( i in 1:length(br) ){
f <- object@sc@cpart[ object@prtree$n[[br[[i]]]] ] %in% cl
if ( sum(f) > 0 ){
n[[i]] <- names(object@sc@cpart[ object@prtree$n[[br[[i]]]] ])[f]
k <- append(k, max( scl@cpart ) + 1)
scl@cpart[n[[i]]] <- max( scl@cpart ) + 1
}else{
stop(paste("no cells on branch",br[[i]],"fall into clusters",cl))
}
}
set.seed(111111)
scl@fcol <- sample(rainbow(max(scl@cpart)))
z <- diffgenes(scl,k[1],k[2])
return( list(n=n,scl=scl,k=k,diffgenes=z) )
}
)
| /RaceID2_StemID_class.R | no_license | gruenD/StemID | R | false | false | 67,517 | r | ## load required packages.
require(tsne)
require(pheatmap)
require(MASS)
require(cluster)
require(mclust)
require(flexmix)
require(lattice)
require(fpc)
require(amap)
require(RColorBrewer)
require(locfit)
require(vegan)
## class definition
SCseq <- setClass("SCseq", slots = c(expdata = "data.frame", ndata = "data.frame", fdata = "data.frame", distances = "matrix", tsne = "data.frame", cluster = "list", background = "list", out = "list", cpart = "vector", fcol = "vector", filterpar = "list", clusterpar = "list", outlierpar ="list" ))
setValidity("SCseq",
function(object) {
msg <- NULL
if ( ! is.data.frame(object@expdata) ){
msg <- c(msg, "input data must be data.frame")
}else if ( nrow(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one row")
}else if ( ncol(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one column")
}else if (sum( apply( is.na(object@expdata),1,sum ) ) > 0 ){
msg <- c(msg, "NAs are not allowed in input data")
}else if (sum( apply( object@expdata,1,min ) ) < 0 ){
msg <- c(msg, "negative values are not allowed in input data")
}
if (is.null(msg)) TRUE
else msg
}
)
setMethod("initialize",
signature = "SCseq",
definition = function(.Object, expdata ){
.Object@expdata <- expdata
.Object@ndata <- expdata
.Object@fdata <- expdata
validObject(.Object)
return(.Object)
}
)
setGeneric("filterdata", function(object, mintotal=3000, minexpr=5, minnumber=1, maxexpr=Inf, downsample=TRUE, dsn=1, rseed=17000) standardGeneric("filterdata"))
setMethod("filterdata",
signature = "SCseq",
definition = function(object,mintotal,minexpr,minnumber,maxexpr,downsample,dsn,rseed) {
if ( ! is.numeric(mintotal) ) stop( "mintotal has to be a positive number" ) else if ( mintotal <= 0 ) stop( "mintotal has to be a positive number" )
if ( ! is.numeric(minexpr) ) stop( "minexpr has to be a non-negative number" ) else if ( minexpr < 0 ) stop( "minexpr has to be a non-negative number" )
if ( ! is.numeric(minnumber) ) stop( "minnumber has to be a non-negative integer number" ) else if ( round(minnumber) != minnumber | minnumber < 0 ) stop( "minnumber has to be a non-negative integer number" )
if ( ! ( is.numeric(downsample) | is.logical(downsample) ) ) stop( "downsample has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(dsn) ) stop( "dsn has to be a positive integer number" ) else if ( round(dsn) != dsn | dsn <= 0 ) stop( "dsn has to be a positive integer number" )
object@filterpar <- list(mintotal=mintotal, minexpr=minexpr, minnumber=minnumber, maxexpr=maxexpr, downsample=downsample, dsn=dsn)
object@ndata <- object@expdata[,apply(object@expdata,2,sum,na.rm=TRUE) >= mintotal]
if ( downsample ){
set.seed(rseed)
object@ndata <- downsample(object@expdata,n=mintotal,dsn=dsn)
}else{
x <- object@ndata
object@ndata <- as.data.frame( t(t(x)/apply(x,2,sum))*median(apply(x,2,sum,na.rm=TRUE)) + .1 )
}
x <- object@ndata
object@fdata <- x[apply(x>=minexpr,1,sum,na.rm=TRUE) >= minnumber,]
x <- object@fdata
object@fdata <- x[apply(x,1,max,na.rm=TRUE) < maxexpr,]
return(object)
}
)
downsample <- function(x,n,dsn){
x <- round( x[,apply(x,2,sum,na.rm=TRUE) >= n], 0)
nn <- min( apply(x,2,sum) )
for ( j in 1:dsn ){
z <- data.frame(GENEID=rownames(x))
rownames(z) <- rownames(x)
initv <- rep(0,nrow(z))
for ( i in 1:dim(x)[2] ){
y <- aggregate(rep(1,nn),list(sample(rep(rownames(x),x[,i]),nn)),sum)
na <- names(x)[i]
names(y) <- c("GENEID",na)
rownames(y) <- y$GENEID
z[,na] <- initv
k <- intersect(rownames(z),y$GENEID)
z[k,na] <- y[k,na]
z[is.na(z[,na]),na] <- 0
}
rownames(z) <- as.vector(z$GENEID)
ds <- if ( j == 1 ) z[,-1] else ds + z[,-1]
}
ds <- ds/dsn + .1
return(ds)
}
dist.gen <- function(x,method="euclidean", ...) if ( method %in% c("spearman","pearson","kendall") ) as.dist( 1 - cor(t(x),method=method,...) ) else dist(x,method=method,...)
dist.gen.pairs <- function(x,y,...) dist.gen(t(cbind(x,y)),...)
binompval <- function(p,N,n){
pval <- pbinom(n,round(N,0),p,lower.tail=TRUE)
pval[!is.na(pval) & pval > 0.5] <- 1-pval[!is.na(pval) & pval > 0.5]
return(pval)
}
setGeneric("plotgap", function(object) standardGeneric("plotgap"))
setMethod("plotgap",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotgap")
if ( sum(is.na(object@cluster$gap$Tab[,3])) > 0 ) stop("run clustexp with do.gap = TRUE first")
plot(object@cluster$gap,ylim=c( min(object@cluster$gap$Tab[,3] - object@cluster$gap$Tab[,4]), max(object@cluster$gap$Tab[,3] + object@cluster$gap$Tab[,4])))
}
)
setGeneric("plotjaccard", function(object) standardGeneric("plotjaccard"))
setMethod("plotjaccard",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotjaccard")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no Jaccard's similarity plot")
barplot(object@cluster$jaccard,names.arg=1:length(object@cluster$jaccard),ylab="Jaccard's similarity")
}
)
setGeneric("plotoutlierprobs", function(object) standardGeneric("plotoutlierprobs"))
setMethod("plotoutlierprobs",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotoutlierprobs")
p <- object@cluster$kpart[ order(object@cluster$kpart,decreasing=FALSE)]
x <- object@out$cprobs[names(p)]
fcol <- object@fcol
for ( i in 1:max(p) ){
y <- -log10(x + 2.2e-16)
y[p != i] <- 0
if ( i == 1 ) b <- barplot(y,ylim=c(0,max(-log10(x + 2.2e-16))*1.1),col=fcol[i],border=fcol[i],names.arg=FALSE,ylab="-log10prob") else barplot(y,add=TRUE,col=fcol[i],border=fcol[i],names.arg=FALSE,axes=FALSE)
}
abline(-log10(object@outlierpar$probthr),0,col="black",lty=2)
d <- b[2,1] - b[1,1]
y <- 0
for ( i in 1:max(p) ) y <- append(y,b[sum(p <=i),1] + d/2)
axis(1,at=(y[1:(length(y)-1)] + y[-1])/2,lab=1:max(p))
box()
}
)
setGeneric("plotbackground", function(object) standardGeneric("plotbackground"))
setMethod("plotbackground",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotbackground")
m <- apply(object@fdata,1,mean)
v <- apply(object@fdata,1,var)
fit <- locfit(v~lp(m,nn=.7),family="gamma",maxk=500)
plot(log2(m),log2(v),pch=20,xlab="log2mean",ylab="log2var",col="grey")
lines(log2(m[order(m)]),log2(object@background$lvar(m[order(m)],object)),col="red",lwd=2)
lines(log2(m[order(m)]),log2(fitted(fit)[order(m)]),col="orange",lwd=2,lty=2)
legend("topleft",legend=substitute(paste("y = ",a,"*x^2 + ",b,"*x + ",d,sep=""),list(a=round(coef(object@background$vfit)[3],2),b=round(coef(object@background$vfit)[2],2),d=round(coef(object@background$vfit)[3],2))),bty="n")
}
)
setGeneric("plotsensitivity", function(object) standardGeneric("plotsensitivity"))
setMethod("plotsensitivity",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotsensitivity")
plot(log10(object@out$thr), object@out$stest, type="l",xlab="log10 Probability cutoff", ylab="Number of outliers")
abline(v=log10(object@outlierpar$probthr),col="red",lty=2)
}
)
setGeneric("diffgenes", function(object,cl1,cl2,mincount=5) standardGeneric("diffgenes"))
setMethod("diffgenes",
signature = "SCseq",
definition = function(object,cl1,cl2,mincount){
part <- object@cpart
cl1 <- c(cl1)
cl2 <- c(cl2)
if ( length(part) == 0 ) stop("run findoutliers before diffgenes")
if ( ! is.numeric(mincount) ) stop("mincount has to be a non-negative number") else if ( mincount < 0 ) stop("mincount has to be a non-negative number")
if ( length(intersect(cl1, part)) < length(unique(cl1)) ) stop( paste("cl1 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
if ( length(intersect(cl2, part)) < length(unique(cl2)) ) stop( paste("cl2 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
f <- apply(object@ndata[,part %in% c(cl1,cl2)],1,max) > mincount
x <- object@ndata[f,part %in% cl1]
y <- object@ndata[f,part %in% cl2]
if ( sum(part %in% cl1) == 1 ) m1 <- x else m1 <- apply(x,1,mean)
if ( sum(part %in% cl2) == 1 ) m2 <- y else m2 <- apply(y,1,mean)
if ( sum(part %in% cl1) == 1 ) s1 <- sqrt(x) else s1 <- sqrt(apply(x,1,var))
if ( sum(part %in% cl2) == 1 ) s2 <- sqrt(y) else s2 <- sqrt(apply(y,1,var))
d <- ( m1 - m2 )/ apply( cbind( s1, s2 ),1,mean )
names(d) <- rownames(object@ndata)[f]
if ( sum(part %in% cl1) == 1 ){
names(x) <- names(d)
x <- x[order(d,decreasing=TRUE)]
}else{
x <- x[order(d,decreasing=TRUE),]
}
if ( sum(part %in% cl2) == 1 ){
names(y) <- names(d)
y <- y[order(d,decreasing=TRUE)]
}else{
y <- y[order(d,decreasing=TRUE),]
}
return(list(z=d[order(d,decreasing=TRUE)],cl1=x,cl2=y,cl1n=cl1,cl2n=cl2))
}
)
plotdiffgenes <- function(z,gene=g){
if ( ! is.list(z) ) stop("first arguments needs to be output of function diffgenes")
if ( length(z) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( sum(names(z) == c("z","cl1","cl2","cl1n","cl2n")) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( length(gene) > 1 ) stop("only single value allowed for argument gene")
if ( !is.numeric(gene) & !(gene %in% names(z$z)) ) stop("argument gene needs to be within rownames of first argument or a positive integer number")
if ( is.numeric(gene) ){ if ( gene < 0 | round(gene) != gene ) stop("argument gene needs to be within rownames of first argument or a positive integer number") }
x <- if ( is.null(dim(z$cl1)) ) z$cl1[gene] else t(z$cl1[gene,])
y <- if ( is.null(dim(z$cl2)) ) z$cl2[gene] else t(z$cl2[gene,])
plot(1:length(c(x,y)),c(x,y),ylim=c(0,max(c(x,y))),xlab="",ylab="Expression",main=gene,cex=0,axes=FALSE)
axis(2)
box()
u <- 1:length(x)
rect(u - .5,0,u + .5,x,col="red")
v <- c(min(u) - .5,max(u) + .5)
axis(1,at=mean(v),lab=paste(z$cl1n,collapse=","))
lines(v,rep(mean(x),length(v)))
lines(v,rep(mean(x)-sqrt(var(x)),length(v)),lty=2)
lines(v,rep(mean(x)+sqrt(var(x)),length(v)),lty=2)
u <- ( length(x) + 1 ):length(c(x,y))
v <- c(min(u) - .5,max(u) + .5)
rect(u - .5,0,u + .5,y,col="blue")
axis(1,at=mean(v),lab=paste(z$cl2n,collapse=","))
lines(v,rep(mean(y),length(v)))
lines(v,rep(mean(y)-sqrt(var(y)),length(v)),lty=2)
lines(v,rep(mean(y)+sqrt(var(y)),length(v)),lty=2)
abline(v=length(x) + .5)
}
setGeneric("plottsne", function(object,final=TRUE) standardGeneric("plottsne"))
setMethod("plottsne",
signature = "SCseq",
definition = function(object,final){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before plottsne")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before plottsne")
part <- if ( final ) object@cpart else object@cluster$kpart
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
for ( i in 1:max(part) ){
if ( sum(part == i) > 0 ) text(object@tsne[part == i,1],object@tsne[part == i,2],i,col=object@fcol[i],cex=.75,font=4)
}
}
)
setGeneric("plotlabelstsne", function(object,labels=NULL) standardGeneric("plotlabelstsne"))
setMethod("plotlabelstsne",
signature = "SCseq",
definition = function(object,labels){
if ( is.null(labels ) ) labels <- names(object@ndata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotlabelstsne")
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
text(object@tsne[,1],object@tsne[,2],labels,cex=.5)
}
)
setGeneric("plotsymbolstsne", function(object,types=NULL) standardGeneric("plotsymbolstsne"))
setMethod("plotsymbolstsne",
signature = "SCseq",
definition = function(object,types){
if ( is.null(types) ) types <- names(object@fdata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotsymbolstsne")
if ( length(types) != ncol(object@fdata) ) stop("types argument has wrong length. Length has to equal to the column number of object@ndata")
coloc <- rainbow(length(unique(types)))
syms <- c()
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,col="grey")
for ( i in 1:length(unique(types)) ){
f <- types == sort(unique(types))[i]
syms <- append( syms, ( (i-1) %% 25 ) + 1 )
points(object@tsne[f,1],object@tsne[f,2],col=coloc[i],pch=( (i-1) %% 25 ) + 1,cex=1)
}
legend("topleft", legend=sort(unique(types)), col=coloc, pch=syms)
}
)
setGeneric("plotexptsne", function(object,g,n="",logsc=FALSE) standardGeneric("plotexptsne"))
setMethod("plotexptsne",
signature = "SCseq",
definition = function(object,g,n="",logsc=FALSE){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( length(intersect(g,rownames(object@ndata))) < length(unique(g)) ) stop("second argument does not correspond to set of rownames slot ndata of SCseq object")
if ( !is.numeric(logsc) & !is.logical(logsc) ) stop("argument logsc has to be logical (TRUE/FALSE)")
if ( n == "" ) n <- g[1]
l <- apply(object@ndata[g,] - .1,2,sum) + .1
if (logsc) {
f <- l == 0
l <- log2(l)
l[f] <- NA
}
mi <- min(l,na.rm=TRUE)
ma <- max(l,na.rm=TRUE)
ColorRamp <- colorRampPalette(rev(brewer.pal(n = 7,name = "RdYlBu")))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
v <- round((l - mi)/(ma - mi)*99 + 1,0)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
par(mar = c(3,5,2.5,2))
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",main=n,pch=20,cex=0,col="grey")
for ( k in 1:length(v) ){
points(object@tsne[k,1],object@tsne[k,2],col=ColorRamp[v[k]],pch=20,cex=1.5)
}
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
}
)
plot.err.bars.y <- function(x, y, y.err, col="black", lwd=1, lty=1, h=0.1){
arrows(x,y-y.err,x,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y-y.err,x+h,y-y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y+y.err,x+h,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
}
clusGapExt <-function (x, FUNcluster, K.max, B = 100, verbose = interactive(), method="euclidean",random=TRUE,
...)
{
stopifnot(is.function(FUNcluster), length(dim(x)) == 2, K.max >=
2, (n <- nrow(x)) >= 1, (p <- ncol(x)) >= 1)
if (B != (B. <- as.integer(B)) || (B <- B.) <= 0)
stop("'B' has to be a positive integer")
if (is.data.frame(x))
x <- as.matrix(x)
ii <- seq_len(n)
W.k <- function(X, kk) {
clus <- if (kk > 1)
FUNcluster(X, kk, ...)$cluster
else rep.int(1L, nrow(X))
0.5 * sum(vapply(split(ii, clus), function(I) {
xs <- X[I, , drop = FALSE]
sum(dist.gen(xs,method=method)/nrow(xs))
}, 0))
}
logW <- E.logW <- SE.sim <- numeric(K.max)
if (verbose)
cat("Clustering k = 1,2,..., K.max (= ", K.max, "): .. ",
sep = "")
for (k in 1:K.max) logW[k] <- log(W.k(x, k))
if (verbose)
cat("done\n")
xs <- scale(x, center = TRUE, scale = FALSE)
m.x <- rep(attr(xs, "scaled:center"), each = n)
V.sx <- svd(xs)$v
rng.x1 <- apply(xs %*% V.sx, 2, range)
logWks <- matrix(0, B, K.max)
if (random){
if (verbose)
cat("Bootstrapping, b = 1,2,..., B (= ", B, ") [one \".\" per sample]:\n",
sep = "")
for (b in 1:B) {
z1 <- apply(rng.x1, 2, function(M, nn) runif(nn, min = M[1],
max = M[2]), nn = n)
z <- tcrossprod(z1, V.sx) + m.x
##z <- apply(x,2,function(m) runif(length(m),min=min(m),max=max(m)))
##z <- apply(x,2,function(m) sample(m))
for (k in 1:K.max) {
logWks[b, k] <- log(W.k(z, k))
}
if (verbose)
cat(".", if (b%%50 == 0)
paste(b, "\n"))
}
if (verbose && (B%%50 != 0))
cat("", B, "\n")
E.logW <- colMeans(logWks)
SE.sim <- sqrt((1 + 1/B) * apply(logWks, 2, var))
}else{
E.logW <- rep(NA,K.max)
SE.sim <- rep(NA,K.max)
}
structure(class = "clusGap", list(Tab = cbind(logW, E.logW,
gap = E.logW - logW, SE.sim), n = n, B = B, FUNcluster = FUNcluster))
}
clustfun <- function(x,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids",distances=NULL,link="single")
{
if ( clustnr < 2) stop("Choose clustnr > 1")
di <- if ( FUNcluster == "kmedoids" ) t(x) else dist.gen(t(x),method=metric)
if ( nrow(di) - 1 < clustnr ) clustnr <- nrow(di) - 1
if ( do.gap | sat | cln > 0 ){
gpr <- NULL
f <- if ( cln == 0 ) TRUE else FALSE
if ( do.gap ){
set.seed(rseed)
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, method=metric)
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- hclusterCBI(x,k,link=link,scaling=FALSE); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap)
if ( f ) cln <- maxSE(gpr$Tab[,3],gpr$Tab[,4],method=SE.method,SE.factor)
}
if ( sat ){
if ( ! do.gap ){
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100, random=FALSE)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, random=FALSE, method=metric)
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- hclusterCBI(x,k,link=link,scaling=FALSE); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap, random=FALSE)
}
g <- gpr$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
if ( f ) cln <- max(min(which( y - (mm + nn) < 0 )),1)
}
if ( cln <= 1 ) {
clb <- list(result=list(partition=rep(1,dim(x)[2])),bootmean=1)
names(clb$result$partition) <- names(x)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster == "kmedoids" ) dist.gen(di,method=metric) else di))
}
if ( FUNcluster == "kmeans" ) clb <- clusterboot(di,B=bootnr,distances=FALSE,bootmethod="boot",clustermethod=kmeansCBI,krange=cln,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "kmedoids" ) clb <- clusterboot(dist.gen(di,method=metric),B=bootnr,bootmethod="boot",clustermethod=pamkCBI,k=cln,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "hclust" ) clb <- clusterboot(di,B=bootnr,distances=FALSE,bootmethod="boot",clustermethod=hclusterCBI,k=cln,link=link,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster == "kmedoids" ) dist.gen(di,method=metric) else di))
}
}
setGeneric("clustexp", function(object,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids") standardGeneric("clustexp"))
setMethod("clustexp",
signature = "SCseq",
definition = function(object,clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster) {
if ( ! is.numeric(clustnr) ) stop("clustnr has to be a positive integer") else if ( round(clustnr) != clustnr | clustnr <= 0 ) stop("clustnr has to be a positive integer")
if ( ! is.numeric(bootnr) ) stop("bootnr has to be a positive integer") else if ( round(bootnr) != bootnr | bootnr <= 0 ) stop("bootnr has to be a positive integer")
if ( ! ( metric %in% c( "spearman","pearson","kendall","euclidean","maximum","manhattan","canberra","binary","minkowski") ) ) stop("metric has to be one of the following: spearman, pearson, kendall, euclidean, maximum, manhattan, canberra, binary, minkowski")
if ( ! ( SE.method %in% c( "firstSEmax","Tibs2001SEmax","globalSEmax","firstmax","globalmax") ) ) stop("SE.method has to be one of the following: firstSEmax, Tibs2001SEmax, globalSEmax, firstmax, globalmax")
if ( ! is.numeric(SE.factor) ) stop("SE.factor has to be a non-negative integer") else if ( SE.factor < 0 ) stop("SE.factor has to be a non-negative integer")
if ( ! ( is.numeric(do.gap) | is.logical(do.gap) ) ) stop( "do.gap has to be logical (TRUE/FALSE)" )
if ( ! ( is.numeric(sat) | is.logical(sat) ) ) stop( "sat has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(B.gap) ) stop("B.gap has to be a positive integer") else if ( round(B.gap) != B.gap | B.gap <= 0 ) stop("B.gap has to be a positive integer")
if ( ! is.numeric(cln) ) stop("cln has to be a non-negative integer") else if ( round(cln) != cln | cln < 0 ) stop("cln has to be a non-negative integer")
if ( ! is.numeric(rseed) ) stop("rseed has to be numeric")
if ( !do.gap & !sat & cln == 0 ) stop("cln has to be a positive integer or either do.gap or sat has to be TRUE")
if ( ! ( FUNcluster %in% c("kmeans","hclust","kmedoids") ) ) stop("FUNcluster has to be one of the following: kmeans, hclust,kmedoids")
object@clusterpar <- list(clustnr=clustnr,bootnr=bootnr,metric=metric,do.gap=do.gap,sat=sat,SE.method=SE.method,SE.factor=SE.factor,B.gap=B.gap,cln=cln,rseed=rseed,FUNcluster=FUNcluster)
y <- clustfun(object@fdata,clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster)
object@cluster <- list(kpart=y$clb$result$partition, jaccard=y$clb$bootmean, gap=y$gpr, clb=y$clb)
object@distances <- as.matrix( y$di )
set.seed(111111)
object@fcol <- sample(rainbow(max(y$clb$result$partition)))
return(object)
}
)
setGeneric("findoutliers", function(object,outminc=5,outlg=2,probthr=1e-3,thr=2**-(1:40),outdistquant=.95) standardGeneric("findoutliers"))
setMethod("findoutliers",
signature = "SCseq",
definition = function(object,outminc,outlg,probthr,thr,outdistquant) {
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before findoutliers")
if ( ! is.numeric(outminc) ) stop("outminc has to be a non-negative integer") else if ( round(outminc) != outminc | outminc < 0 ) stop("outminc has to be a non-negative integer")
if ( ! is.numeric(outlg) ) stop("outlg has to be a non-negative integer") else if ( round(outlg) != outlg | outlg < 0 ) stop("outlg has to be a non-negative integer")
if ( ! is.numeric(probthr) ) stop("probthr has to be a number between 0 and 1") else if ( probthr < 0 | probthr > 1 ) stop("probthr has to be a number between 0 and 1")
if ( ! is.numeric(thr) ) stop("thr hast to be a vector of numbers between 0 and 1") else if ( min(thr) < 0 | max(thr) > 1 ) stop("thr hast to be a vector of numbers between 0 and 1")
if ( ! is.numeric(outdistquant) ) stop("outdistquant has to be a number between 0 and 1") else if ( outdistquant < 0 | outdistquant > 1 ) stop("outdistquant has to be a number between 0 and 1")
object@outlierpar <- list( outminc=outminc,outlg=outlg,probthr=probthr,thr=thr,outdistquant=outdistquant )
### calibrate background model
m <- log2(apply(object@fdata,1,mean))
v <- log2(apply(object@fdata,1,var))
f <- m > -Inf & v > -Inf
m <- m[f]
v <- v[f]
mm <- -8
repeat{
fit <- lm(v ~ m + I(m^2))
if( coef(fit)[3] >= 0 | mm >= 3){
break
}
mm <- mm + .5
f <- m > mm
m <- m[f]
v <- v[f]
}
object@background <- list()
object@background$vfit <- fit
object@background$lvar <- function(x,object) 2**(coef(object@background$vfit)[1] + log2(x)*coef(object@background$vfit)[2] + coef(object@background$vfit)[3] * log2(x)**2)
object@background$lsize <- function(x,object) x**2/(max(x + 1e-6,object@background$lvar(x,object)) - x)
### identify outliers
out <- c()
stest <- rep(0,length(thr))
cprobs <- c()
for ( n in 1:max(object@cluster$kpart) ){
if ( sum(object@cluster$kpart == n) == 1 ){ cprobs <- append(cprobs,.5); names(cprobs)[length(cprobs)] <- names(object@cluster$kpart)[object@cluster$kpart == n]; next }
x <- object@fdata[,object@cluster$kpart == n]
x <- x[apply(x,1,max) > outminc,]
z <- t( apply(x,1,function(x){ apply( cbind( pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) , 1 - pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) ),1, min) } ) )
cp <- apply(z,2,function(x){ y <- p.adjust(x,method="BH"); y <- y[order(y,decreasing=FALSE)]; return(y[outlg]);})
f <- cp < probthr
cprobs <- append(cprobs,cp)
if ( sum(f) > 0 ) out <- append(out,names(x)[f])
for ( j in 1:length(thr) ) stest[j] <- stest[j] + sum( cp < thr[j] )
}
object@out <-list(out=out,stest=stest,thr=thr,cprobs=cprobs)
### cluster outliers
clp2p.cl <- c()
cols <- names(object@fdata)
cpart <- object@cluster$kpart
di <- as.data.frame(object@distances)
for ( i in 1:max(cpart) ) {
tcol <- cols[cpart == i]
if ( sum(!(tcol %in% out)) > 1 ) clp2p.cl <- append(clp2p.cl,as.vector(t(di[tcol[!(tcol %in% out)],tcol[!(tcol %in% out)]])))
}
clp2p.cl <- clp2p.cl[clp2p.cl>0]
cadd <- list()
if ( length(out) > 0 ){
if (length(out) == 1){
cadd <- list(out)
}else{
n <- out
m <- as.data.frame(di[out,out])
for ( i in 1:length(out) ){
if ( length(n) > 1 ){
o <- order(apply(cbind(m,1:dim(m)[1]),1,function(x) min(x[1:(length(x)-1)][-x[length(x)]])),decreasing=FALSE)
m <- m[o,o]
n <- n[o]
f <- m[,1] < quantile(clp2p.cl,outdistquant) | m[,1] == min(clp2p.cl)
ind <- 1
if ( sum(f) > 1 ) for ( j in 2:sum(f) ) if ( apply(m[f,f][j,c(ind,j)] > quantile(clp2p.cl,outdistquant) ,1,sum) == 0 ) ind <- append(ind,j)
cadd[[i]] <- n[f][ind]
g <- ! n %in% n[f][ind]
n <- n[g]
m <- m[g,g]
if ( sum(g) == 0 ) break
}else if (length(n) == 1){
cadd[[i]] <- n
break
}
}
}
for ( i in 1:length(cadd) ){
cpart[cols %in% cadd[[i]]] <- max(cpart) + 1
}
}
### determine final clusters
for ( i in 1:max(cpart) ){
if ( sum(cpart == i) == 0 ) next
f <- cols[cpart == i]
d <- object@fdata
if ( length(f) == 1 ){
cent <- d[,f]
}else{
if ( object@clusterpar$FUNcluster == "kmedoids" ){
x <- apply(as.matrix(dist.gen(t(d[,f]),method=object@clusterpar$metric)),2,mean)
cent <- d[,f[which(x == min(x))[1]]]
}else{
cent <- apply(d[,f],1,mean)
}
}
if ( i == 1 ) dcent <- data.frame(cent) else dcent <- cbind(dcent,cent)
if ( i == 1 ) tmp <- data.frame(apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric)) else tmp <- cbind(tmp,apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric))
}
cpart <- apply(tmp,1,function(x) order(x,decreasing=FALSE)[1])
for ( i in max(cpart):1){if (sum(cpart==i)==0) cpart[cpart>i] <- cpart[cpart>i] - 1 }
object@cpart <- cpart
set.seed(111111)
object@fcol <- sample(rainbow(max(cpart)))
return(object)
}
)
setGeneric("comptsne", function(object,rseed=15555,sammonmap=FALSE,initial_cmd=TRUE,...) standardGeneric("comptsne"))
setMethod("comptsne",
signature = "SCseq",
definition = function(object,rseed,sammonmap,...){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before comptsne")
set.seed(rseed)
di <- if ( object@clusterpar$FUNcluster == "kmedoids") as.dist(object@distances) else dist.gen(as.matrix(object@distances))
if ( sammonmap ){
object@tsne <- as.data.frame(sammon(di,k=2)$points)
}else{
ts <- if ( initial_cmd ) tsne(di,initial_config=cmdscale(di,k=2),...) else tsne(di,k=2,...)
object@tsne <- as.data.frame(ts)
}
return(object)
}
)
setGeneric("clustdiffgenes", function(object,pvalue=.01) standardGeneric("clustdiffgenes"))
setMethod("clustdiffgenes",
signature = "SCseq",
definition = function(object,pvalue){
if ( length(object@cpart) == 0 ) stop("run findoutliers before clustdiffgenes")
if ( ! is.numeric(pvalue) ) stop("pvalue has to be a number between 0 and 1") else if ( pvalue < 0 | pvalue > 1 ) stop("pvalue has to be a number between 0 and 1")
cdiff <- list()
x <- object@ndata
y <- object@expdata[,names(object@ndata)]
part <- object@cpart
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
m <- if ( sum(part != i) > 1 ) apply(x[,part != i],1,mean) else x[,part != i]
n <- if ( sum(part == i) > 1 ) apply(x[,part == i],1,mean) else x[,part == i]
no <- if ( sum(part == i) > 1 ) median(apply(y[,part == i],2,sum))/median(apply(x[,part == i],2,sum)) else sum(y[,part == i])/sum(x[,part == i])
m <- m*no
n <- n*no
pv <- binompval(m/sum(m),sum(n),n)
d <- data.frame(mean.ncl=m,mean.cl=n,fc=n/m,pv=pv)[order(pv,decreasing=FALSE),]
cdiff[[paste("cl",i,sep=".")]] <- d[d$pv < pvalue,]
}
return(cdiff)
}
)
setGeneric("plotsaturation", function(object,disp=FALSE) standardGeneric("plotsaturation"))
setMethod("plotsaturation",
signature = "SCseq",
definition = function(object,disp){
if ( length(object@cluster$gap) == 0 ) stop("run clustexp before plotsaturation")
g <- object@cluster$gap$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
cln <- max(min(which( y - (mm + nn) < 0 )),1)
x <- 1:length(y)
if (disp){
x <- 1:length(g)
plot(x,g,pch=20,col="grey",xlab="k",ylab="log within cluster dispersion")
f <- x == cln
points(x[f],g[f],col="blue")
}else{
plot(x,y,pch=20,col="grey",xlab="k",ylab="Change in log within cluster dispersion")
points(x,mm,col="red",pch=20)
plot.err.bars.y(x,mm,nn,col="red")
points(x,y,col="grey",pch=20)
f <- x == cln
points(x[f],y[f],col="blue")
}
}
)
setGeneric("plotsilhouette", function(object) standardGeneric("plotsilhouette"))
setMethod("plotsilhouette",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotsilhouette")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no silhouette plot")
kpart <- object@cluster$kpart
distances <- if ( object@clusterpar$FUNcluster == "kmedoids" ) as.dist(object@distances) else dist.gen(object@distances)
si <- silhouette(kpart,distances)
plot(si)
}
)
compmedoids <- function(x,part,metric="pearson"){
m <- c()
for ( i in sort(unique(part)) ){
f <- names(x)[part == i]
if ( length(f) == 1 ){
m <- append(m,f)
}else{
y <- apply(as.matrix(dist.gen(t(x[,f]),method=metric)),2,mean)
m <- append(m,f[which(y == min(y))[1]])
}
}
m
}
setGeneric("clustheatmap", function(object,final=FALSE,hmethod="single") standardGeneric("clustheatmap"))
setMethod("clustheatmap",
signature = "SCseq",
definition = function(object,final,hmethod){
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before clustheatmap")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before clustheatmap")
x <- object@fdata
part <- if ( final ) object@cpart else object@cluster$kpart
na <- c()
j <- 0
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
j <- j + 1
na <- append(na,i)
d <- x[,part == i]
if ( sum(part == i) == 1 ) cent <- d else cent <- apply(d,1,mean)
if ( j == 1 ) tmp <- data.frame(cent) else tmp <- cbind(tmp,cent)
}
names(tmp) <- paste("cl",na,sep=".")
ld <- if ( object@clusterpar$FUNcluster == "kmedoids" ) dist.gen(t(tmp),method=object@clusterpar$metric) else dist.gen(as.matrix(dist.gen(t(tmp),method=object@clusterpar$metric)))
if ( max(part) > 1 ) cclmo <- hclust(ld,method=hmethod)$order else cclmo <- 1
q <- part
for ( i in 1:max(part) ){
q[part == na[cclmo[i]]] <- i
}
part <- q
di <- if ( object@clusterpar$FUNcluster == "kmedoids" ) object@distances else as.data.frame( as.matrix( dist.gen(t(object@distances)) ) )
pto <- part[order(part,decreasing=FALSE)]
ptn <- c()
for ( i in 1:max(pto) ){ pt <- names(pto)[pto == i]; z <- if ( length(pt) == 1 ) pt else pt[hclust(as.dist(t(di[pt,pt])),method=hmethod)$order]; ptn <- append(ptn,z) }
col <- object@fcol
mi <- min(di,na.rm=TRUE)
ma <- max(di,na.rm=TRUE)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
ColorRamp <- colorRampPalette(brewer.pal(n = 7,name = "RdYlBu"))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
if ( mi == ma ){
ColorLevels <- seq(0.99*mi, 1.01*ma, length=length(ColorRamp))
}
par(mar = c(3,5,2.5,2))
image(as.matrix(di[ptn,ptn]),col=ColorRamp,axes=FALSE)
abline(0,1)
box()
tmp <- c()
for ( u in 1:max(part) ){
ol <- (0:(length(part) - 1)/(length(part) - 1))[ptn %in% names(x)[part == u]]
points(rep(0,length(ol)),ol,col=col[cclmo[u]],pch=15,cex=.75)
points(ol,rep(0,length(ol)),col=col[cclmo[u]],pch=15,cex=.75)
tmp <- append(tmp,mean(ol))
}
axis(1,at=tmp,lab=cclmo)
axis(2,at=tmp,lab=cclmo)
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
return(cclmo)
}
)
## class definition
Ltree <- setClass("Ltree", slots = c(sc = "SCseq", ldata = "list", entropy = "vector", trproj = "list", par = "list", prback = "data.frame", prbacka = "data.frame", ltcoord = "matrix", prtree = "list", sigcell = "vector", cdata = "list" ))
setValidity("Ltree",
function(object) {
msg <- NULL
if ( class(object@sc)[1] != "SCseq" ){
msg <- c(msg, "input data must be of class SCseq")
}
if (is.null(msg)) TRUE
else msg
}
)
setMethod("initialize",
signature = "Ltree",
definition = function(.Object, sc ){
.Object@sc <- sc
validObject(.Object)
return(.Object)
}
)
setGeneric("compentropy", function(object) standardGeneric("compentropy"))
setMethod("compentropy",
signature = "Ltree",
definition = function(object){
probs <- t(t(object@sc@ndata)/apply(object@sc@ndata,2,sum))
object@entropy <- -apply(probs*log(probs)/log(nrow(object@sc@ndata)),2,sum)
return(object)
}
)
compproj <- function(pdiloc,lploc,cnloc,mloc,d=NULL){
pd <- data.frame(pdiloc)
k <- paste("X",sort(rep(1:nrow(pdiloc),length(mloc))),sep="")
pd$k <- paste("X",1:nrow(pdiloc),sep="")
pd <- merge(data.frame(k=k),pd,by="k")
if ( is.null(d) ){
cnv <- t(matrix(rep(t(cnloc),nrow(pdiloc)),nrow=ncol(pdiloc)))
pdcl <- paste("X",lploc[as.numeric(sub("X","",pd$k))],sep="")
rownames(cnloc) <- paste("X",mloc,sep="")
pdcn <- cnloc[pdcl,]
v <- cnv - pdcn
}else{
v <- d$v
pdcn <- d$pdcn
}
w <- pd[,names(pd) != "k"] - pdcn
h <- apply(cbind(v,w),1,function(x){
x1 <- x[1:(length(x)/2)];
x2 <- x[(length(x)/2 + 1):length(x)];
x1s <- sqrt(sum(x1**2)); x2s <- sqrt(sum(x2**2)); y <- sum(x1*x2)/x1s/x2s; return( if (x1s == 0 | x2s == 0 ) NA else y ) })
rma <- as.data.frame(matrix(h,ncol=nrow(pdiloc)))
names(rma) <- unique(pd$k)
pdclu <- lploc[as.numeric(sub("X","",names(rma)))]
pdclp <- apply(t(rma),1,function(x) if (sum(!is.na(x)) == 0 ) NA else mloc[which(abs(x) == max(abs(x),na.rm=TRUE))[1]])
pdclh <- apply(t(rma),1,function(x) if (sum(!is.na(x)) == 0 ) NA else x[which(abs(x) == max(abs(x),na.rm=TRUE))[1]])
pdcln <- names(lploc)[as.numeric(sub("X","",names(rma)))]
names(rma) <- pdcln
rownames(rma) <- paste("X",mloc,sep="")
res <- data.frame(o=pdclu,l=pdclp,h=pdclh)
rownames(res) <- pdcln
return(list(res=res[names(lploc),],rma=as.data.frame(t(rma[,names(lploc)])),d=list(v=v,pdcn=pdcn)))
}
pdishuffle <- function(pdi,lp,cn,m,all=FALSE){
if ( all ){
d <- as.data.frame(pdi)
y <- t(apply(pdi,1,function(x) runif(length(x),min=min(x),max=max(x))))
names(y) <- names(d)
rownames(y) <- rownames(d)
return(y)
}else{
fl <- TRUE
for ( i in unique(lp)){
if ( sum(lp == i) > 1 ){
y <-data.frame( t(apply(as.data.frame(pdi[,lp == i]),1,sample)) )
}else{
y <- as.data.frame(pdi[,lp == i])
}
names(y) <- names(lp)[lp == i]
rownames(y) <- names(lp)
z <- if (fl) y else cbind(z,y)
fl <- FALSE
}
z <- t(z[,names(lp)])
return(z)
}
}
setGeneric("projcells", function(object,cthr=0,nmode=FALSE) standardGeneric("projcells"))
setMethod("projcells",
signature = "Ltree",
definition = function(object,cthr,nmode){
if ( ! is.numeric(cthr) ) stop( "cthr has to be a non-negative number" ) else if ( cthr < 0 ) stop( "cthr has to be a non-negative number" )
if ( ! length(object@sc@cpart == 0) ) stop( "please run findoutliers on the SCseq input object before initializing Ltree" )
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
object@par$cthr <- cthr
object@par$nmode <- nmode
lp <- object@sc@cpart
ld <- object@sc@distances
n <- aggregate(rep(1,length(lp)),list(lp),sum)
n <- as.vector(n[order(n[,1],decreasing=FALSE),-1])
m <- (1:length(n))[n>cthr]
f <- lp %in% m
lp <- lp[f]
ld <- ld[f,f]
pdil <- sc@tsne[f,]
cnl <- aggregate(pdil,by=list(lp),median)
cnl <- cnl[order(cnl[,1],decreasing=FALSE),-1]
pdi <- suppressWarnings( cmdscale(as.matrix(ld),k=ncol(ld)-1) )
cn <- as.data.frame(pdi[compmedoids(sc@fdata[,names(lp)],lp),])
rownames(cn) <- 1:nrow(cn)
x <- compproj(pdi,lp,cn,m)
res <- x$res
if ( nmode ){
rma <- x$rma
z <- paste("X",t(as.vector(apply(cbind(lp,ld),1,function(x){ f <- lp != x[1]; lp[f][which(x[-1][f] == min(x[-1][f]))[1]] }))),sep="")
k <- apply(cbind(z,rma),1,function(x) (x[-1])[names(rma) == x[1]])
rn <- res
rn$l <- as.numeric(sub("X","",z))
rn$h <- as.numeric(k)
res <- rn
x$res <- res
}
object@ldata <- list(lp=lp,ld=ld,m=m,pdi=pdi,pdil=pdil,cn=cn,cnl=cnl)
object@trproj <- x
return(object)
}
)
setGeneric("projback", function(object,pdishuf=2000,nmode=FALSE,rseed=17000) standardGeneric("projback"))
setMethod("projback",
signature = "Ltree",
definition = function(object,pdishuf,nmode,rseed){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( ! is.numeric(pdishuf) ) stop( "pdishuf has to be a non-negative integer number" ) else if ( round(pdishuf) != pdishuf | pdishuf < 0 ) stop( "pdishuf has to be a non-negative integer number" )
if ( length(object@trproj) == 0 ) stop("run projcells before projback")
object@par$pdishuf <- pdishuf
object@par$rseed <- rseed
if ( ! nmode ){
set.seed(rseed)
for ( i in 1:pdishuf ){
cat("pdishuffle:",i,"\n")
x <- compproj(pdishuffle(object@ldata$pdi,object@ldata$lp,object@ldata$cn,object@ldata$m,all=TRUE),object@ldata$lp,object@ldata$cn,object@ldata$m,d=object@trproj$d)$res
y <- if ( i == 1 ) t(x) else cbind(y,t(x))
}
##important
object@prback <- as.data.frame(t(y))
x <- object@prback
x$n <- as.vector(t(matrix(rep(1:(nrow(x)/nrow(object@ldata$pdi)),nrow(object@ldata$pdi)),ncol=nrow(object@ldata$pdi))))
object@prbacka <- aggregate(data.frame(count=rep(1,nrow(x))),by=list(n=x$n,o=x$o,l=x$l),sum)
}
return( object )
}
)
setGeneric("lineagetree", function(object,pthr=0.01,nmode=FALSE) standardGeneric("lineagetree"))
setMethod("lineagetree",
signature = "Ltree",
definition = function(object,pthr,nmode){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( length(object@trproj) == 0 ) stop("run projcells before lineagetree")
if ( max(dim(object@prback)) == 0 & ! nmode ) stop("run projback before lineagetree")
if ( ! is.numeric(pthr) ) stop( "pthr has to be a non-negative number" ) else if ( pthr < 0 ) stop( "pthr has to be a non-negative number" )
object@par$pthr <- pthr
cnl <- object@ldata$cnl
pdil <- object@ldata$pdil
cn <- object@ldata$cn
pdi <- object@ldata$pdi
m <- object@ldata$m
lp <- object@ldata$lp
res <- object@trproj$res
rma <- object@trproj$rma
prback <- object@prback
cm <- as.matrix(dist(cnl))*0
linl <- list()
linn <- list()
for ( i in 1:length(m) ){
for ( j in i:length(m) ){
linl[[paste(m[i],m[j],sep=".")]] <- c()
linn[[paste(m[i],m[j],sep=".")]] <- c()
}
}
sigcell <- c()
for ( i in 1:nrow(res) ){
a <- which( m == res$o[i])
if ( sum( lp == m[a] ) == 1 ){
k <- t(cnl)[,a]
k <- NA
sigcell <- append(sigcell, FALSE)
}else{
b <- which(m == res$l[i] )
h <- res$h[i]
if ( nmode ){
sigcell <- append(sigcell, FALSE)
}else{
f <- prback$o == m[a] & prback$l == m[b]
if ( sum(f) > 0 ){
ql <- quantile(prback[f,"h"],pthr,na.rm=TRUE)
qh <- quantile(prback[f,"h"],1 - pthr,na.rm=TRUE)
}else{
ql <- 0
qh <- 0
}
sigcell <- if (is.na(h) ) append(sigcell, NA) else if ( h > qh | h < min(0,ql) ) append(sigcell, TRUE) else append(sigcell, FALSE)
}
if ( !is.na(res$h[i]) ){
w <- t(pdil)[,i] - t(cnl)[,a]
v <- t(cnl)[,b] - t(cnl)[,a]
wo <- t(pdi)[,i] - t(cn)[,a]
vo <- t(cn)[,b] - t(cn)[,a]
df <-( h*sqrt(sum(wo*wo)) )/sqrt(sum(vo*vo))*v
k <- df + t(cnl)[,a]
cm[a,b] <- cm[a,b] + 1
so <- m[sort(c(a,b))]
dfl <- sign(h)*sqrt( sum( df*df ) )/sqrt(sum(v*v))
if ( a > b ) dfl <- 1 - dfl
linn[[paste(so[1],so[2],sep=".")]] <- append( linn[[paste(so[1],so[2],sep=".")]], rownames(pdi)[i] )
linl[[paste(so[1],so[2],sep=".")]] <- append( linl[[paste(so[1],so[2],sep=".")]], dfl )
}else{
k <- t(cnl)[,a]
for ( j in unique(lp[lp != m[a]]) ){
b <- which(j == m)
so <- m[sort(c(a,b))]
dfl <- 0
if ( a > b ) dfl <- 1 - dfl
linn[[paste(so[1],so[2],sep=".")]] <- append( linn[[paste(so[1],so[2],sep=".")]], rownames(pdi)[i] )
linl[[paste(so[1],so[2],sep=".")]] <- append( linl[[paste(so[1],so[2],sep=".")]], dfl )
}
}
}
lt <- if ( i == 1 ) data.frame(k) else cbind(lt,k)
}
lt <- t(lt)
cm <- as.data.frame(cm)
names(cm) <- paste("cl",m,sep=".")
rownames(cm) <- paste("cl",m,sep=".")
lt <- as.data.frame(lt)
rownames(lt) <- rownames(res)
object@ltcoord <- as.matrix(lt)
object@prtree <- list(n=linn,l=linl)
object@cdata$counts <- cm
names(sigcell) <- rownames(res)
object@sigcell <- sigcell
return( object )
}
)
setGeneric("comppvalue", function(object,pethr=0.01,nmode=FALSE) standardGeneric("comppvalue"))
setMethod("comppvalue",
signature = "Ltree",
definition = function(object,pethr,nmode){
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( length(object@prtree) == 0 ) stop("run lineagetree before comppvalue")
if ( ! is.numeric(pethr) ) stop( "pethr has to be a non-negative number" ) else if ( pethr < 0 ) stop( "pethr has to be a non-negative number" )
object@par$pethr <- pethr
cm <- object@cdata$counts
cmpv <- cm*NA
cmpvd <- cm*NA
cmbr <- cm*NA
cmpvn <- cm*NA
cmpvnd <- cm*NA
cmfr <- cm/apply(cm,1,sum)
if ( nmode ){
N <- apply(cm,1,sum) + 1
N0 <- sum(N) - N
n0 <- t(matrix(rep(N,length(N)),ncol=length(N)))
p <- n0/N0
n <- cm
k <- cbind(N,p,n)
cmpv <- apply(k,1,function(x){N <- x[1]; p <- x[2:( ncol(cm) + 1 )]; n <- x[( ncol(cm) + 2 ):( 2*ncol(cm) + 1)]; apply(cbind(n,p),1,function(x,N) binom.test(x[1],N,min(1,x[2]),alternative="g")$p.value,N=N)})
cmpvd <- apply(k,1,function(x){N <- x[1]; p <- x[2:( ncol(cm) + 1 )]; n <- x[( ncol(cm) + 2 ):( 2*ncol(cm) + 1)]; apply(cbind(n,p),1,function(x,N) binom.test(x[1],N,min(1,x[2]),alternative="l")$p.value,N=N)})
cmpvn <- cmpv
cmpvnd <- cmpvd
cmbr <- as.data.frame(n0/N0*N)
names(cmbr) <- names(cm)
rownames(cmbr) <- rownames(cm)
}else{
for ( i in 1:nrow(cm) ){
for ( j in 1:ncol(cm) ){
f <- object@prbacka$o == object@ldata$m[i] & object@prbacka$l == object@ldata$m[j]
x <- object@prbacka$count[f]
if ( sum(f) < object@par$pdishuf ) x <- append(x,rep(0, object@par$pdishuf - sum(f)))
cmbr[i,j] <- if ( sum(f) > 0 ) mean(x) else 0
cmpv[i,j] <- if ( quantile(x,1 - pethr) < cm[i,j] ) 0 else 0.5
cmpvd[i,j] <- if ( quantile(x,pethr) > cm[i,j] ) 0 else 0.5
cmpvn[i,j] <- sum( x >= cm[i,j])/length(x)
cmpvnd[i,j] <- sum( x <= cm[i,j])/length(x)
}
}
}
diag(cmpv) <- .5
diag(cmpvd) <- .5
diag(cmpvn) <- NA
diag(cmpvnd) <- NA
object@cdata$counts.br <- cmbr
object@cdata$pv.e <- cmpv
object@cdata$pv.d <- cmpvd
object@cdata$pvn.e <- cmpvn
object@cdata$pvn.d <- cmpvnd
m <- object@ldata$m
linl <- object@prtree$l
ls <- as.data.frame(matrix(rep(NA,length(m)**2),ncol=length(m)))
names(ls) <- rownames(ls) <- paste("cl",m,sep=".")
for ( i in 1:( length(m) - 1 )){
for ( j in (i + 1):length(m) ){
na <- paste(m[i],m[j],sep=".")
if ( na %in% names(linl) & min(cmpv[i,j],cmpv[j,i],na.rm=TRUE) < pethr ){
y <- sort(linl[[na]])
nn <- ( 1 - max(y[-1] - y[-length(y)]) )
}else{
nn <- 0
}
ls[i,j] <- nn
}
}
object@cdata$linkscore <- ls
return(object)
}
)
setGeneric("plotlinkpv", function(object) standardGeneric("plotlinkpv"))
setMethod("plotlinkpv",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotlinkpv")
pheatmap(-log2(object@cdata$pvn.e + 1/object@par$pdishuf/2))
}
)
setGeneric("plotlinkscore", function(object) standardGeneric("plotlinkscore"))
setMethod("plotlinkscore",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotlinkscore")
pheatmap(object@cdata$linkscore,cluster_rows=FALSE,cluster_cols=FALSE)
}
)
setGeneric("plotmapprojections", function(object) standardGeneric("plotmapprojections"))
setMethod("plotmapprojections",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmapprojections")
cent <- object@sc@fdata[,compmedoids(object@sc@fdata,object@sc@cpart)]
dc <- as.data.frame(1 - cor(cent))
names(dc) <- sort(unique(object@sc@cpart))
rownames(dc) <- sort(unique(object@sc@cpart))
trl <- spantree(dc[object@ldata$m,object@ldata$m])
u <- object@ltcoord[,1]
v <- object@ltcoord[,2]
cnl <- object@ldata$cnl
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
for ( i in unique(object@ldata$lp) ){ f <- object@ldata$lp == i; text(u[f],v[f],i,cex=.75,font=4,col=object@sc@fcol[i]) }
points(cnl[,1],cnl[,2])
text(cnl[,1],cnl[,2],object@ldata$m,cex=2)
for ( i in 1:length(trl$kid) ){
lines(c(cnl[i+1,1],cnl[trl$kid[i],1]),c(cnl[i+1,2],cnl[trl$kid[i],2]),col="black")
}
}
)
setGeneric("plotmap", function(object) standardGeneric("plotmap"))
setMethod("plotmap",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
cent <- object@sc@fdata[,compmedoids(object@sc@fdata,object@sc@cpart)]
dc <- as.data.frame(1 - cor(cent))
names(dc) <- sort(unique(object@sc@cpart))
rownames(dc) <- sort(unique(object@sc@cpart))
trl <- spantree(dc[object@ldata$m,object@ldata$m])
u <- object@ldata$pdil[,1]
v <- object@ldata$pdil[,2]
cnl <- object@ldata$cnl
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
for ( i in unique(object@ldata$lp) ){ f <- object@ldata$lp == i; text(u[f],v[f],i,cex=.75,font=4,col=object@sc@fcol[i]) }
points(cnl[,1],cnl[,2])
text(cnl[,1],cnl[,2],object@ldata$m,cex=2)
for ( i in 1:length(trl$kid) ){
lines(c(cnl[i+1,1],cnl[trl$kid[i],1]),c(cnl[i+1,2],cnl[trl$kid[i],2]),col="black")
}
}
)
setGeneric("plottree", function(object,showCells=TRUE,nmode=FALSE,scthr=0) standardGeneric("plottree"))
setMethod("plottree",
signature = "Ltree",
definition = function(object,showCells,nmode,scthr){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
if ( !is.numeric(nmode) & !is.logical(nmode) ) stop("argument nmode has to be logical (TRUE/FALSE)")
if ( !is.numeric(showCells) & !is.logical(showCells) ) stop("argument showCells has to be logical (TRUE/FALSE)")
if ( ! is.numeric(scthr) ) stop( "scthr has to be a non-negative number" ) else if ( scthr < 0 | scthr > 1 ) stop( "scthr has to be a number between 0 and 1" )
ramp <- colorRamp(c("darkgreen", "yellow", "brown"))
mcol <- rgb( ramp(seq(0, 1, length = 101)), max = 255)
co <- object@cdata
fc <- (co$counts/( co$counts.br + .5))*(co$pv.e < object@par$pethr)
fc <- fc*(fc > t(fc)) + t(fc)*(t(fc) >= fc)
fc <- log2(fc + (fc == 0))
k <- -log10(sort(unique(as.vector(t(co$pvn.e))[as.vector(t(co$pv.e))<object@par$pethr])) + 1/object@par$pdishuf)
if (length(k) == 1) k <- c(k - k/100,k)
mlpv <- -log10(co$pvn.e + 1/object@par$pdishuf)
diag(mlpv) <- min(mlpv,na.rm=TRUE)
dcc <- t(apply(round(100*(mlpv - min(k))/(max(k) - min(k)),0) + 1,1,function(x){y <- c(); for ( n in x ) y <- append(y,if ( n < 1 ) NA else mcol[n]); y }))
cx <- c()
cy <- c()
va <- c()
m <- object@ldata$m
for ( i in 1:( length(m) - 1 ) ){
for ( j in ( i + 1 ):length(m) ){
if ( min(co$pv.e[i,j],co$pv.e[j,i],na.rm=TRUE) < object@par$pethr ){
if ( mlpv[i,j] > mlpv[j,i] ){
va <- append(va,dcc[i,j])
}else{
va <- append(va,dcc[j,i])
}
cx <- append(cx,i)
cy <- append(cy,j)
}
}
}
cnl <- object@ldata$cnl
u <- object@ltcoord[,1]
v <- object@ltcoord[,2]
layout( cbind(c(1, 1), c(2, 3)),widths=c(5,1,1),height=c(5,5,1))
par(mar = c(12,5,1,1))
if ( showCells ){
plot(u,v,cex=1.5,col="grey",pch=20,xlab="Dim 1",ylab="Dim 2")
if ( !nmode ) points(u[object@sigcell],v[object@sigcell],col="black")
}else{
plot(u,v,cex=0,xlab="Dim 1",ylab="Dim 2")
}
if ( length(va) > 0 ){
f <- order(va,decreasing=TRUE)
for ( i in 1:length(va) ){
if ( object@cdata$linkscore[cx[i],cy[i]] > scthr ){
if ( showCells ){
lines(cnl[c(cx[i],cy[i]),1],cnl[c(cx[i],cy[i]),2],col=va[i],lwd=2)
}else{
##nn <- min(10,fc[cx[i],cy[i]])
lines(cnl[c(cx[i],cy[i]),1],cnl[c(cx[i],cy[i]),2],col=va[i],lwd=5*object@cdata$linkscore[cx[i],cy[i]])
}
}
}
}
en <- aggregate(object@entropy,list(object@sc@cpart),median)
en <- en[en$Group.1 %in% m,]
mi <- min(en[,2],na.rm=TRUE)
ma <- max(en[,2],na.rm=TRUE)
w <- round((en[,2] - mi)/(ma - mi)*99 + 1,0)
ramp <- colorRamp(c("red","orange", "pink","purple", "blue"))
ColorRamp <- rgb( ramp(seq(0, 1, length = 101)), max = 255)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
if ( mi == ma ){
ColorLevels <- seq(0.99*mi, 1.01*ma, length=length(ColorRamp))
}
for ( i in m ){
f <- en[,1] == m
points(cnl[f,1],cnl[f,2],cex=5,col=ColorRamp[w[f]],pch=20)
}
text(cnl[,1],cnl[,2],m,cex=1.25,font=4,col="white")
par(mar = c(5, 4, 1, 2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
coll <- seq(min(k), max(k), length=length(mcol))
image(1, coll,
matrix(data=coll, ncol=length(mcol),nrow=1),
col=mcol,
xlab="",ylab="",
xaxt="n")
layout(1)
}
)
setGeneric("plotdistanceratio", function(object) standardGeneric("plotdistanceratio"))
setMethod("plotdistanceratio",
signature = "Ltree",
definition = function(object){
if ( length(object@ldata) <= 0 ) stop("run projcells before plotdistanceratio")
l <- as.matrix(dist(object@ldata$pdi))
z <- (l/object@ldata$ld)
hist(log2(z),breaks=100,xlab=" log2 emb. distance/distance",main="")
}
)
setGeneric("getproj", function(object,i) standardGeneric("getproj"))
setMethod("getproj",
signature = "Ltree",
definition = function(object,i){
if ( length(object@ldata) <= 0 ) stop("run projcells before plotdistanceratio")
if ( ! i %in% object@ldata$m ) stop(paste("argument i has to be one of",paste(object@ldata$m,collapse=",")))
x <- object@trproj$rma[names(object@ldata$lp)[object@ldata$lp == i],]
x <- x[,names(x) != paste("X",i,sep="")]
f <- !is.na(x[,1])
x <- x[f,]
if ( nrow(x) > 1 ){
y <- x
y <- as.data.frame(t(apply(y,1,function(x) (x - mean(x))/sqrt(var(x)))))
}
names(x) = sub("X","cl.",names(x))
names(y) = sub("X","cl.",names(y))
return(list(pr=x,prz=y))
}
)
setGeneric("projenrichment", function(object) standardGeneric("projenrichment"))
setMethod("projenrichment",
signature = "Ltree",
definition = function(object){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotmap")
ze <- ( object@cdata$pv.e < object@par$pethr | object@cdata$pv.d < object@par$pethr) * (object@cdata$counts + .1)/( object@cdata$counts.br + .1 )
pheatmap(log2(ze + ( ze == 0 ) ),cluster_rows=FALSE,cluster_cols=FALSE)
}
)
setGeneric("compscore", function(object,nn=1) standardGeneric("compscore"))
setMethod("compscore",
signature = "Ltree",
definition = function(object,nn){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before compscore")
if ( ! is.numeric(nn) ) stop( "nn has to be a non-negative integer number" ) else if ( round(nn) != nn | nn < 0 ) stop( "nn has to be a non-negative integer number" )
x <- object@cdata$counts*(object@cdata$pv.e < object@par$pethr)>0
y <- x | t(x)
if ( max(y) > 0 ){
z <- apply(y,1,sum)
nl <- list()
n <- list()
for ( i in 1:nn ){
if ( i == 1 ){
n[[i]] <- as.list(apply(y,1,function(x) grep(TRUE,x)))
nl <- data.frame( apply(y,1,sum) )
}
if ( i > 1 ){
v <- rep(0,nrow(nl))
n[[i]] <- list()
for ( j in 1:length(n[[i-1]]) ){
cl <- n[[i-1]][[j]]
if ( length(cl) == 0 ){
n[[i]][[paste("d",j,sep="")]] <- NA
v[j] <- 0
}else{
k <- if ( length(cl) > 1 ) apply(y[cl,],2,sum) > 0 else if ( length(cl) == 1 ) y[cl,]
n[[i]][[paste("d",j,sep="")]] <- sort(unique(c(cl,grep(TRUE,k))))
v[j] <- length(n[[i]][[paste("d",j,sep="")]])
}
}
names(n[[i]]) <- names(z)
nl <- cbind(nl,v)
}
}
x <- nl[,i]
names(x) <- rownames(nl)
}else{
x <- rep(0,length(object@ldata$m))
names(x) <- paste("cl",object@ldata$m,sep=".")
}
v <- aggregate(object@entropy,list(object@sc@cpart),median)
v <- v[v$Group.1 %in% object@ldata$m,]
w <- as.vector(v[,-1])
names(w) <- paste("cl.",v$Group.1,sep="")
w <- w - min(w)
return(list(links=x,entropy=w,StemIDscore=x*w))
}
)
setGeneric("plotscore", function(object,nn=1) standardGeneric("plotscore"))
setMethod("plotscore",
signature = "Ltree",
definition = function(object,nn){
if ( length(object@cdata) <= 0 ) stop("run comppvalue before plotscore")
x <- compscore(object,nn)
layout(1:3)
barplot(x$links,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Number of links",cex.names=1)
barplot(x$entropy,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Delta-Entropy",cex.names=1)
barplot(x$StemIDscore,names.arg=sub("cl\\.","",object@ldata$m),xlab="Cluster",ylab="Number of links * Delta-Entropy",cex.names=1)
layout(1)
}
)
setGeneric("branchcells", function(object,br) standardGeneric("branchcells"))
setMethod("branchcells",
signature = "Ltree",
definition = function(object,br){
if ( length(object@ldata) <= 0 ) stop("run projcells before branchcells")
msg <- paste("br needs to be list of length two containing two branches, where each has to be one of", paste(names(object@prtree$n),collapse=","))
if ( !is.list(br) ) stop(msg) else if ( length(br) != 2 ) stop(msg) else if ( ! br[[1]] %in% names(object@prtree$n) | ! br[[2]] %in% names(object@prtree$n) ) stop(msg)
n <- list()
scl <- object@sc
k <- c()
cl <- intersect( as.numeric(strsplit(br[[1]],"\\.")[[1]]), as.numeric(strsplit(br[[2]],"\\.")[[1]]))
if ( length(cl) == 0 ) stop("the two branches in br need to have one cluster in common.")
for ( i in 1:length(br) ){
f <- object@sc@cpart[ object@prtree$n[[br[[i]]]] ] %in% cl
if ( sum(f) > 0 ){
n[[i]] <- names(object@sc@cpart[ object@prtree$n[[br[[i]]]] ])[f]
k <- append(k, max( scl@cpart ) + 1)
scl@cpart[n[[i]]] <- max( scl@cpart ) + 1
}else{
stop(paste("no cells on branch",br[[i]],"fall into clusters",cl))
}
}
set.seed(111111)
scl@fcol <- sample(rainbow(max(scl@cpart)))
z <- diffgenes(scl,k[1],k[2])
return( list(n=n,scl=scl,k=k,diffgenes=z) )
}
)
|
set.seed(123)
fair_50rolls <- roll(fair_die, times = 50)
fair50_sum <- summary(fair_50rolls)
fair50_sum
#' @export
plot.roll <-function(x){
s<-summary(x)
barplot((s$freqs)[,3],
main = 'Relative Frequencies in a series of 50 rolls',
xlab = 'sides of device',
ylab = 'relative frequencies',
names.arg = c("1", "2", "3","4","5","6")
)
}
#plot(fair_50rolls)
plot.roll(fair_50rolls)
| /hw-stat133/roller/R/plot.rolls.R | no_license | zhenjiasun/demo-repo | R | false | false | 430 | r | set.seed(123)
fair_50rolls <- roll(fair_die, times = 50)
fair50_sum <- summary(fair_50rolls)
fair50_sum
#' @export
plot.roll <-function(x){
s<-summary(x)
barplot((s$freqs)[,3],
main = 'Relative Frequencies in a series of 50 rolls',
xlab = 'sides of device',
ylab = 'relative frequencies',
names.arg = c("1", "2", "3","4","5","6")
)
}
#plot(fair_50rolls)
plot.roll(fair_50rolls)
|
doorbusters.names.in.csv <- c("id", "door buster", "in stock", "is online", "launch date", "has image",
"status code", "status", "Has Price", "Price")
doorbusters.names <- c("id", "door.buster", "in.stock", "is.online", "launch.date", "has.image",
"status.code", "status", "Has.Price", "Price")
test.read.doorbuster.csv_read.proper.format <- function()
{
#############################################
############## GIVEN ####################
#############################################
test.csv <- "temp.csv"
#create the test data to write to a csv#
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
test.data <- as.data.frame(rbind(row.1, row.2))
names(test.data) <- doorbusters.names.in.csv
write.csv(test.data, file = test.csv, row.names = FALSE)
#############################################
############### WHEN ####################
#############################################
#sut = system under test#
sut <- read.doorbuster.csv(csv.name = test.csv)
#############################################
############### THEN ####################
#############################################
checkEquals(2, nrow(sut)) # verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
#cleanup#
file.remove(test.csv)
rm(test.data)
rm(sut)
}
# test that the read.all.doorbuster.files() function reads both csv files#
test.read.all.doorbuster.files_exactly.two <- function()
{
#############################################
############## GIVEN ####################
#############################################
doorbusters1.csv <- "./tests/test_data/doorbuster1.csv"
doorbusters2.csv <- "./tests/test_data/doorbuster2.csv"
# CREATE THE TEST DATA #
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.3 <- c("6", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.4 <- c("7", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
file1.data <- as.data.frame(rbind(row.1, row.2))
file2.data <- as.data.frame(rbind(row.3, row.4))
names(file1.data) <- doorbusters.names.in.csv
names(file2.data) <- doorbusters.names.in.csv
write.csv(file1.data, file=doorbusters1.csv, row.names=FALSE)
write.csv(file2.data, file=doorbusters2.csv, row.names=FALSE)
#############################################
############### WHEN ####################
#############################################
sut <- read.all.doorbuster.files("./tests/test_data/")
#############################################
############### THEN ####################
#############################################
checkEquals(4, nrow(sut)) #verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(row.3, as.character(sut[3,]))) # verify row 3
checkTrue(setequal(row.4, as.character(sut[4,]))) # verify row 4
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
# CLEANUP #
file.remove(doorbusters1.csv)
file.remove(doorbusters2.csv)
rm(file1.data)
rm(file2.data)
rm(sut)
}
# test that the read.all.doorbuster.files() function reads ONLY the two csv files#
test.read.all.doorbuster.files_three.files <- function()
{
#############################################
############## GIVEN ####################
#############################################
doorbusters1.csv <- "./tests/test_data/doorbuster1.csv"
doorbusters2.csv <- "./tests/test_data/doorbuster2.csv"
doorbusters3.csv <- "./tests/test_data/doorbuster3.csv"
# CREATE THE TEST DATA #
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.3 <- c("6", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.4 <- c("7", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.5 <- c("8", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.6 <- c("9", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
file1.data <- as.data.frame(rbind(row.1, row.2))
file2.data <- as.data.frame(rbind(row.3, row.4))
file3.data <- as.data.frame(rbind(row.5, row.6))
names(file1.data) <- doorbusters.names.in.csv
names(file2.data) <- doorbusters.names.in.csv
names(file3.data) <- doorbusters.names.in.csv
write.csv(file1.data, file=doorbusters1.csv, row.names=FALSE)
write.csv(file2.data, file=doorbusters2.csv, row.names=FALSE)
write.csv(file3.data, file=doorbusters3.csv, row.names=FALSE)
#############################################
############### WHEN ####################
#############################################
sut <- read.all.doorbuster.files("./tests/test_data/")
#############################################
############### THEN ####################
#############################################
checkEquals(4, dim(sut)[1]) #verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(row.3, as.character(sut[3,]))) # verify row 3
checkTrue(setequal(row.4, as.character(sut[4,]))) # verify row 4
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
# CLEANUP #
file.remove(doorbusters1.csv)
file.remove(doorbusters2.csv)
file.remove(doorbusters3.csv)
rm(file1.data)
rm(file2.data)
rm(file3.data)
rm(sut)
} | /tests/doorbusters_tests_data_acquisition_readcsv.R | no_license | donaldsawyer/test-driven-data-wrangling-r | R | false | false | 6,038 | r | doorbusters.names.in.csv <- c("id", "door buster", "in stock", "is online", "launch date", "has image",
"status code", "status", "Has Price", "Price")
doorbusters.names <- c("id", "door.buster", "in.stock", "is.online", "launch.date", "has.image",
"status.code", "status", "Has.Price", "Price")
test.read.doorbuster.csv_read.proper.format <- function()
{
#############################################
############## GIVEN ####################
#############################################
test.csv <- "temp.csv"
#create the test data to write to a csv#
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
test.data <- as.data.frame(rbind(row.1, row.2))
names(test.data) <- doorbusters.names.in.csv
write.csv(test.data, file = test.csv, row.names = FALSE)
#############################################
############### WHEN ####################
#############################################
#sut = system under test#
sut <- read.doorbuster.csv(csv.name = test.csv)
#############################################
############### THEN ####################
#############################################
checkEquals(2, nrow(sut)) # verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
#cleanup#
file.remove(test.csv)
rm(test.data)
rm(sut)
}
# test that the read.all.doorbuster.files() function reads both csv files#
test.read.all.doorbuster.files_exactly.two <- function()
{
#############################################
############## GIVEN ####################
#############################################
doorbusters1.csv <- "./tests/test_data/doorbuster1.csv"
doorbusters2.csv <- "./tests/test_data/doorbuster2.csv"
# CREATE THE TEST DATA #
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.3 <- c("6", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.4 <- c("7", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
file1.data <- as.data.frame(rbind(row.1, row.2))
file2.data <- as.data.frame(rbind(row.3, row.4))
names(file1.data) <- doorbusters.names.in.csv
names(file2.data) <- doorbusters.names.in.csv
write.csv(file1.data, file=doorbusters1.csv, row.names=FALSE)
write.csv(file2.data, file=doorbusters2.csv, row.names=FALSE)
#############################################
############### WHEN ####################
#############################################
sut <- read.all.doorbuster.files("./tests/test_data/")
#############################################
############### THEN ####################
#############################################
checkEquals(4, nrow(sut)) #verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(row.3, as.character(sut[3,]))) # verify row 3
checkTrue(setequal(row.4, as.character(sut[4,]))) # verify row 4
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
# CLEANUP #
file.remove(doorbusters1.csv)
file.remove(doorbusters2.csv)
rm(file1.data)
rm(file2.data)
rm(sut)
}
# test that the read.all.doorbuster.files() function reads ONLY the two csv files#
test.read.all.doorbuster.files_three.files <- function()
{
#############################################
############## GIVEN ####################
#############################################
doorbusters1.csv <- "./tests/test_data/doorbuster1.csv"
doorbusters2.csv <- "./tests/test_data/doorbuster2.csv"
doorbusters3.csv <- "./tests/test_data/doorbuster3.csv"
# CREATE THE TEST DATA #
row.1 <- c("4", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.2 <- c("5", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.3 <- c("6", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.4 <- c("7", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.5 <- c("8", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
row.6 <- c("9", "1", "1", "1", "1/1/2015", "1", "2", "In Progress", "1", "10.00")
file1.data <- as.data.frame(rbind(row.1, row.2))
file2.data <- as.data.frame(rbind(row.3, row.4))
file3.data <- as.data.frame(rbind(row.5, row.6))
names(file1.data) <- doorbusters.names.in.csv
names(file2.data) <- doorbusters.names.in.csv
names(file3.data) <- doorbusters.names.in.csv
write.csv(file1.data, file=doorbusters1.csv, row.names=FALSE)
write.csv(file2.data, file=doorbusters2.csv, row.names=FALSE)
write.csv(file3.data, file=doorbusters3.csv, row.names=FALSE)
#############################################
############### WHEN ####################
#############################################
sut <- read.all.doorbuster.files("./tests/test_data/")
#############################################
############### THEN ####################
#############################################
checkEquals(4, dim(sut)[1]) #verify the number of rows
checkTrue(setequal(row.1, as.character(sut[1,]))) # verify row 1
checkTrue(setequal(row.2, as.character(sut[2,]))) # verify row 2
checkTrue(setequal(row.3, as.character(sut[3,]))) # verify row 3
checkTrue(setequal(row.4, as.character(sut[4,]))) # verify row 4
checkTrue(setequal(doorbusters.names, names(sut))) # verify the column names
# CLEANUP #
file.remove(doorbusters1.csv)
file.remove(doorbusters2.csv)
file.remove(doorbusters3.csv)
rm(file1.data)
rm(file2.data)
rm(file3.data)
rm(sut)
} |
getMSS <- function(hgnc, entrezgene, path, src, reconVersion, step) {
# entrezgene=gene
# path=outdir
# reconVersion=2.0
# library("XLConnect")
reconVersion=2.0
path="./results"
src="./src"
if (reconVersion==2.2){
load(paste(src, "Recon_2.2_biomodels.RData", sep="/"))
load(paste(src, "recon2chebi_MODEL1603150001.RData", sep="/"))
rownames(recon2chebi)=recon2chebi[,1]
recon2chebi=recon2chebi[model@met_id,]
} else if (reconVersion==2.0){
load(paste(src, "Recon2.RData", sep="/"))
model=recon2$modelR204[,,1]
recon2chebi=NULL
}
files = list.files("./results/metabolite_sets_step_0,1,2,3_1.0_filter_1.1/mss_WG_step_0")
for (i in 1:length(files)){
load(paste("./results//metabolite_sets_step_0,1,2,3_1.0_filter_1.1/mss_WG_step_0", files[i], sep="/"))
if (!is.null(retVal)){
hgnc = unlist(strsplit(files[i], split = ".", fixed = TRUE))[1]
rval = retVal
retVal= NULL
outdir=paste(path, "mss_0", sep="/")
step = 0
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)){
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
outdir=paste(path, "mss_1", sep="/")
step = 1
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)) {
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
} else if (!is.null(retVal)){
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
outdir=paste(path, "mss_2", sep="/")
step = 2
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)) {
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
} else if (!is.null(retVal)){
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
}
}
# save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
###############
# mss = list.files(path = "./results/mss_P14_primary")
# for (j in 1:length(mss)){
# load(paste("./results/mss_P14_primary", mss[j], sep="/"))
# hgnc=strsplit(mss[j], split=".", fixed =TRUE)[[1]][1]
# ###############
# retVal$mets=retVal$mets[c(1:14),]
###########################################################################################
# if (!is.null(retVal$mets)){
#
# metsExtend=NULL
#
# if (is.null(dim(retVal$mets))){
# retVal$mets=t(as.matrix(retVal$mets))
# rownames(retVal$mets)=retVal$mets[,"rxn_id"]
# }
#
# if (dim(retVal$mets)[1]>0){
# extend CoA2CarnitineGlycine
# #############################################################################################
# for (i in 1:length(retVal$mets[,"met_long"])){
# message(retVal$mets[i,"met_long"])
#
# if (gregexpr(pattern ="coa", retVal$mets[i,"met_short"])[[1]][1]>1) {
# #
# consumed = rbind(retVal$mets[i,],retVal$mets[i,])
# colnames(consumed)[9]="CheBI_id"
# colnames(consumed)[8]="KEGG_id"
# colnames(consumed)[10]="PubChem_id"
# index_consumed = getMets2ReconID(consumed, model)
#
# tmp = getPreviousNext(model, index_consumed, produced=TRUE)
#
# #metsExtend=rbind(metsExtend,tmp$mets[which(tmp$mets[,"left_right"]=="right"),])
# metsExtend=rbind(metsExtend,tmp$mets)
#
# tmp = getPreviousNext(model, index_consumed, produced=FALSE)
#
# #metsExtend=rbind(metsExtend,tmp$mets[which(tmp$mets[,"left_right"]=="right"),])
# metsExtend=rbind(metsExtend,tmp$mets)
# }
# }
#
# # only keep carnitine compounds
# metsExtend = rbind(metsExtend[grep(metsExtend[,"met_long"], pattern = "carnitine", fixed = TRUE),,drop=FALSE],
# metsExtend[grep(metsExtend[,"met_long"], pattern = "glycine", fixed = TRUE),,drop=FALSE])
#
# metsExtend = rbind(retVal$mets, removeMetsFromSet(metsExtend, model)$result_mets)
#
# tmp = metsExtend[,"met_long"]
# unic = !duplicated(tmp) ## logical vector of unique values
# index = seq_along(tmp)[unic] ## indices
# retVal$mets = metsExtend[index,]
# #############################################################################################
# extend transcription factor
# #################################################################################################################
# controlledGenes = findControlsExpressionOf(hgnc)
# controlledGenes = unique(controlledGenes)
#
# if (length(controlledGenes)>0){
# message(paste("Bingo transcription factor found!!!",hgnc))
#
# ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
# gene_map_table2 = getBM(attributes=c('hgnc_symbol', 'entrezgene', 'ensembl_gene_id'),
# filters = 'hgnc_symbol', values = controlledGenes, mart = ensembl)
#
# for (k in 1:length(controlledGenes)){
#
# if (is.null(retVal$mets) || (dim(retVal$mets)[1]==0)){
# retVal = findMetabolicEnvironment(gene_map_table2$entrezgene[k], model, gene_map_table2$hgnc_symbol[k])
# } else {
# tmp = findMetabolicEnvironment(gene_map_table2$entrezgene[k], model, gene_map_table2$hgnc_symbol[k])
# if (!is.null(tmp$mets)){
# retVal$mets = rbind(retVal$mets, tmp$mets)
# }
# }
# }
# }
# #################################################################################################################
# }
# }
# if (is.null(retVal$mets)) {
# message(paste("Empty set for", hgnc))
# } else {
# if (is.null(dim(retVal$mets))) {
# retVal$mets=t(as.data.frame(retVal$mets))
# }
# if (dim(retVal$mets)[1]==0) {
# message(paste("Empty set for", hgnc))
# } else {
# save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
# # genExcelFileShort(retVal$mets, paste(outdir, paste(hgnc, "xls", sep="."),sep="/"))
# }
# }
# #########
# }
# #########
} | /src_Metabolite_Set_Creation/Supportive/old : unnecessary/getMSS.R | permissive | UMCUGenetics/Crossomics | R | false | false | 6,563 | r | getMSS <- function(hgnc, entrezgene, path, src, reconVersion, step) {
# entrezgene=gene
# path=outdir
# reconVersion=2.0
# library("XLConnect")
reconVersion=2.0
path="./results"
src="./src"
if (reconVersion==2.2){
load(paste(src, "Recon_2.2_biomodels.RData", sep="/"))
load(paste(src, "recon2chebi_MODEL1603150001.RData", sep="/"))
rownames(recon2chebi)=recon2chebi[,1]
recon2chebi=recon2chebi[model@met_id,]
} else if (reconVersion==2.0){
load(paste(src, "Recon2.RData", sep="/"))
model=recon2$modelR204[,,1]
recon2chebi=NULL
}
files = list.files("./results/metabolite_sets_step_0,1,2,3_1.0_filter_1.1/mss_WG_step_0")
for (i in 1:length(files)){
load(paste("./results//metabolite_sets_step_0,1,2,3_1.0_filter_1.1/mss_WG_step_0", files[i], sep="/"))
if (!is.null(retVal)){
hgnc = unlist(strsplit(files[i], split = ".", fixed = TRUE))[1]
rval = retVal
retVal= NULL
outdir=paste(path, "mss_0", sep="/")
step = 0
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)){
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
outdir=paste(path, "mss_1", sep="/")
step = 1
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)) {
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
} else if (!is.null(retVal)){
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
outdir=paste(path, "mss_2", sep="/")
step = 2
tmp = findMetabolicEnvironment(hgnc, model, recon2chebi, hgnc, step, reconVersion, src, rval)
if (!is.null(tmp)) {
retVal=tmp
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
} else if (!is.null(retVal)){
save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
}
}
}
# save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
###############
# mss = list.files(path = "./results/mss_P14_primary")
# for (j in 1:length(mss)){
# load(paste("./results/mss_P14_primary", mss[j], sep="/"))
# hgnc=strsplit(mss[j], split=".", fixed =TRUE)[[1]][1]
# ###############
# retVal$mets=retVal$mets[c(1:14),]
###########################################################################################
# if (!is.null(retVal$mets)){
#
# metsExtend=NULL
#
# if (is.null(dim(retVal$mets))){
# retVal$mets=t(as.matrix(retVal$mets))
# rownames(retVal$mets)=retVal$mets[,"rxn_id"]
# }
#
# if (dim(retVal$mets)[1]>0){
# extend CoA2CarnitineGlycine
# #############################################################################################
# for (i in 1:length(retVal$mets[,"met_long"])){
# message(retVal$mets[i,"met_long"])
#
# if (gregexpr(pattern ="coa", retVal$mets[i,"met_short"])[[1]][1]>1) {
# #
# consumed = rbind(retVal$mets[i,],retVal$mets[i,])
# colnames(consumed)[9]="CheBI_id"
# colnames(consumed)[8]="KEGG_id"
# colnames(consumed)[10]="PubChem_id"
# index_consumed = getMets2ReconID(consumed, model)
#
# tmp = getPreviousNext(model, index_consumed, produced=TRUE)
#
# #metsExtend=rbind(metsExtend,tmp$mets[which(tmp$mets[,"left_right"]=="right"),])
# metsExtend=rbind(metsExtend,tmp$mets)
#
# tmp = getPreviousNext(model, index_consumed, produced=FALSE)
#
# #metsExtend=rbind(metsExtend,tmp$mets[which(tmp$mets[,"left_right"]=="right"),])
# metsExtend=rbind(metsExtend,tmp$mets)
# }
# }
#
# # only keep carnitine compounds
# metsExtend = rbind(metsExtend[grep(metsExtend[,"met_long"], pattern = "carnitine", fixed = TRUE),,drop=FALSE],
# metsExtend[grep(metsExtend[,"met_long"], pattern = "glycine", fixed = TRUE),,drop=FALSE])
#
# metsExtend = rbind(retVal$mets, removeMetsFromSet(metsExtend, model)$result_mets)
#
# tmp = metsExtend[,"met_long"]
# unic = !duplicated(tmp) ## logical vector of unique values
# index = seq_along(tmp)[unic] ## indices
# retVal$mets = metsExtend[index,]
# #############################################################################################
# extend transcription factor
# #################################################################################################################
# controlledGenes = findControlsExpressionOf(hgnc)
# controlledGenes = unique(controlledGenes)
#
# if (length(controlledGenes)>0){
# message(paste("Bingo transcription factor found!!!",hgnc))
#
# ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
# gene_map_table2 = getBM(attributes=c('hgnc_symbol', 'entrezgene', 'ensembl_gene_id'),
# filters = 'hgnc_symbol', values = controlledGenes, mart = ensembl)
#
# for (k in 1:length(controlledGenes)){
#
# if (is.null(retVal$mets) || (dim(retVal$mets)[1]==0)){
# retVal = findMetabolicEnvironment(gene_map_table2$entrezgene[k], model, gene_map_table2$hgnc_symbol[k])
# } else {
# tmp = findMetabolicEnvironment(gene_map_table2$entrezgene[k], model, gene_map_table2$hgnc_symbol[k])
# if (!is.null(tmp$mets)){
# retVal$mets = rbind(retVal$mets, tmp$mets)
# }
# }
# }
# }
# #################################################################################################################
# }
# }
# if (is.null(retVal$mets)) {
# message(paste("Empty set for", hgnc))
# } else {
# if (is.null(dim(retVal$mets))) {
# retVal$mets=t(as.data.frame(retVal$mets))
# }
# if (dim(retVal$mets)[1]==0) {
# message(paste("Empty set for", hgnc))
# } else {
# save(retVal, file=paste(outdir, paste(hgnc, "RData", sep="."), sep="/"))
# # genExcelFileShort(retVal$mets, paste(outdir, paste(hgnc, "xls", sep="."),sep="/"))
# }
# }
# #########
# }
# #########
} |
#' Creat an htmlwidget that shows differences between files or directories
#'
#' This function can be used for viewing differences between current test
#' results and the expected results
#'
#' @param old,new Names of the old and new directories to compare.
#' Alternatively, they can be a character vectors of specific files to
#' compare.
#' @param pattern A filter to apply to the old and new directories.
#' @param width Width of the htmlwidget.
#' @param height Height of the htmlwidget
#'
#' @export
diffviewer_widget <- function(old, new, width = NULL, height = NULL,
pattern = NULL)
{
if (xor(assertthat::is.dir(old), assertthat::is.dir(new))) {
stop("`old` and `new` must both be directories, or character vectors of filenames.")
}
# If `old` or `new` are directories, get a list of filenames from both directories
if (assertthat::is.dir(old)) {
all_filenames <- sort(unique(c(
dir(old, recursive = TRUE, pattern = pattern),
dir(new, recursive = TRUE, pattern = pattern)
)))
}
# TODO: Make sure old and new are the same length. Needed if someone passes
# in files directly.
#
# Also, make it work with file lists in general.
get_file_contents <- function(filename) {
if (!file.exists(filename)) {
return(NULL)
}
bin_data <- read_raw(filename)
# Assume .json and .download files are text
if (grepl("\\.json$", filename) || grepl("\\.download$", filename)) {
raw_to_utf8(bin_data)
} else if (grepl("\\.png$", filename)) {
paste0("data:image/png;base64,", jsonlite::base64_enc(bin_data))
} else {
""
}
}
get_both_file_contents <- function(filename) {
list(
filename = filename,
old = get_file_contents(file.path(old, filename)),
new = get_file_contents(file.path(new, filename))
)
}
diff_data <- lapply(all_filenames, get_both_file_contents)
htmlwidgets::createWidget(
name = "diffviewer",
list(
diff_data = diff_data
),
sizingPolicy = htmlwidgets::sizingPolicy(
defaultWidth = "100%",
defaultHeight = "100%",
browser.padding = 10,
viewer.fill = FALSE
),
package = "shinytest"
)
}
#' Interactive viewer widget for changes in test results
#'
#' @param appDir Directory of the Shiny application that was tested.
#' @param testname Name of test to compare.
#'
#' @export
viewTestDiffWidget <- function(appDir = ".", testname = NULL) {
expected <- file.path(appDir, "tests", paste0(testname, "-expected"))
current <- file.path(appDir, "tests", paste0(testname, "-current"))
diffviewer_widget(expected, current)
}
#' Interactive viewer for changes in test results
#'
#' @inheritParams viewTestDiffWidget
#' @import shiny
#' @export
viewTestDiff <- function(appDir = ".", testname = NULL) {
valid_testnames <- dir(file.path(appDir, "tests"), pattern = "-(expected|current)$")
valid_testnames <- sub("-(expected|current)$", "", valid_testnames)
valid_testnames <- unique(valid_testnames)
if (is.null(testname) || !(testname %in% valid_testnames)) {
stop('"', testname, '" ',
'is not a valid testname for the app. Valid names are: "',
paste(valid_testnames, collapse = '", "'), '".'
)
}
withr::with_options(
list(
shinytest.app.dir = normalizePath(appDir, mustWork = TRUE),
shinytest.test.name = testname
),
invisible(
shiny::runApp(system.file("diffviewerapp", package = "shinytest"))
)
)
}
| /R/view-diff.R | permissive | fxcebx/shinytest | R | false | false | 3,480 | r | #' Creat an htmlwidget that shows differences between files or directories
#'
#' This function can be used for viewing differences between current test
#' results and the expected results
#'
#' @param old,new Names of the old and new directories to compare.
#' Alternatively, they can be a character vectors of specific files to
#' compare.
#' @param pattern A filter to apply to the old and new directories.
#' @param width Width of the htmlwidget.
#' @param height Height of the htmlwidget
#'
#' @export
diffviewer_widget <- function(old, new, width = NULL, height = NULL,
pattern = NULL)
{
if (xor(assertthat::is.dir(old), assertthat::is.dir(new))) {
stop("`old` and `new` must both be directories, or character vectors of filenames.")
}
# If `old` or `new` are directories, get a list of filenames from both directories
if (assertthat::is.dir(old)) {
all_filenames <- sort(unique(c(
dir(old, recursive = TRUE, pattern = pattern),
dir(new, recursive = TRUE, pattern = pattern)
)))
}
# TODO: Make sure old and new are the same length. Needed if someone passes
# in files directly.
#
# Also, make it work with file lists in general.
get_file_contents <- function(filename) {
if (!file.exists(filename)) {
return(NULL)
}
bin_data <- read_raw(filename)
# Assume .json and .download files are text
if (grepl("\\.json$", filename) || grepl("\\.download$", filename)) {
raw_to_utf8(bin_data)
} else if (grepl("\\.png$", filename)) {
paste0("data:image/png;base64,", jsonlite::base64_enc(bin_data))
} else {
""
}
}
get_both_file_contents <- function(filename) {
list(
filename = filename,
old = get_file_contents(file.path(old, filename)),
new = get_file_contents(file.path(new, filename))
)
}
diff_data <- lapply(all_filenames, get_both_file_contents)
htmlwidgets::createWidget(
name = "diffviewer",
list(
diff_data = diff_data
),
sizingPolicy = htmlwidgets::sizingPolicy(
defaultWidth = "100%",
defaultHeight = "100%",
browser.padding = 10,
viewer.fill = FALSE
),
package = "shinytest"
)
}
#' Interactive viewer widget for changes in test results
#'
#' @param appDir Directory of the Shiny application that was tested.
#' @param testname Name of test to compare.
#'
#' @export
viewTestDiffWidget <- function(appDir = ".", testname = NULL) {
expected <- file.path(appDir, "tests", paste0(testname, "-expected"))
current <- file.path(appDir, "tests", paste0(testname, "-current"))
diffviewer_widget(expected, current)
}
#' Interactive viewer for changes in test results
#'
#' @inheritParams viewTestDiffWidget
#' @import shiny
#' @export
viewTestDiff <- function(appDir = ".", testname = NULL) {
valid_testnames <- dir(file.path(appDir, "tests"), pattern = "-(expected|current)$")
valid_testnames <- sub("-(expected|current)$", "", valid_testnames)
valid_testnames <- unique(valid_testnames)
if (is.null(testname) || !(testname %in% valid_testnames)) {
stop('"', testname, '" ',
'is not a valid testname for the app. Valid names are: "',
paste(valid_testnames, collapse = '", "'), '".'
)
}
withr::with_options(
list(
shinytest.app.dir = normalizePath(appDir, mustWork = TRUE),
shinytest.test.name = testname
),
invisible(
shiny::runApp(system.file("diffviewerapp", package = "shinytest"))
)
)
}
|
\name{do_admb}
\alias{do_admb}
\title{Compile and/or run an ADMB model, collect output}
\usage{
do_admb(fn, data, params, bounds = NULL, phase = NULL,
re = NULL, data_type = NULL, safe = TRUE,
profile = FALSE, profpars = NULL, mcmc = FALSE,
mcmc.opts = mcmc.control(), impsamp = FALSE,
verbose = FALSE, run.opts = run.control(),
objfunname = "f", workdir = getwd(),
admb_errors = c("stop", "warn", "ignore"), extra.args)
}
\arguments{
\item{fn}{(character) base name of a TPL function,
located in the working directory}
\item{data}{a list of input data variables (order must
match TPL file)}
\item{params}{a list of starting parameter values (order
must match TPL file)}
\item{bounds}{named list of 2-element vectors of lower
and upper bounds for specified parameters}
\item{phase}{named numeric vector of phases (not
implemented yet)}
\item{re}{a named list of the identities and dimensions
of any random effects vectors or matrices used in the TPL
file}
\item{data_type}{a named vector specifying (optional)
data types for parameters, in parname="storage mode"
format (e.g. \code{c(x="integer",y="numeric")})}
\item{safe}{(logical) compile in safe mode?}
\item{profile}{(logical) generate likelihood profiles?
(untested!)}
\item{profpars}{(character) vector of names of parameters
to profile}
\item{mcmc}{(logical) run MCMC around best fit?}
\item{mcmc.opts}{options for MCMC (see
\code{\link{mcmc.control}} for details)}
\item{impsamp}{(logical) run importance sampling?}
\item{verbose}{(logical) print details}
\item{run.opts}{options for ADMB run (see
\code{\link{run.control}} for details)}
\item{objfunname}{(character) name for objective function
in TPL file (only relevant if \code{checkparam} is set to
"write")}
\item{workdir}{temporary working directory (dat/pin/tpl
files will be copied)}
\item{admb_errors}{how to treat ADMB errors (in either
compilation or run): use at your own risk!}
\item{extra.args}{(character) extra argument string to
pass to admb}
}
\value{
An object of class \code{admb}.
}
\description{
Compile an ADMB model, run it, collect output
}
\details{
\code{do_admb} will attempt to do everything required to
start from the model definition (TPL file) specified by
\code{fn}, the data list, and the list of input
parameters, compile and run (i.e. minimize the objective
function of) the model in AD Model Builder, and read the
results back into an object of class \code{admb} in R. If
\code{checkparam} or \code{checkdata} are set to "write",
it will attempt to construct a DATA section, and
construct or (augment an existing) PARAMETER section
(which may contain definitions of non-input parameters to
be used in the model). It copies the input TPL file to a
backup (.bak); on finishing, it restores the original TPL
file and leaves the auto-generated TPL file in a file
called [fn]_gen.tpl.
}
\note{
1. Mixed-case file names are ignored by ADMB; this
function makes a temporary copy with the file name
translated to lower case. 2. Parameter names containing
periods/full stops will not work, because this violates C
syntax (currently not checked). 3. There are many, many,
implicit restrictions and assumptions: for example, all
vectors and matrices are assumed to be indexed starting
from 1.
}
\examples{
\dontrun{
setup_admb()
file.copy(system.file("tplfiles","ReedfrogSizepred0.tpl",package="R2admb"),"tadpole.tpl")
tadpoledat <-
data.frame(TBL = rep(c(9,12,21,25,37),each=3),
Kill = c(0,2,1,3,4,5,0,0,0,0,1,0,0,0,0L),
nexposed=rep(10,15))
m1 <- do_admb("tadpole",
data=c(list(nobs=15),tadpoledat),
params=list(c=0.45,d=13,g=1),
bounds=list(c=c(0,1),d=c(0,50),g=c(-1,25)),
run.opts=run.control(checkparam="write",
checkdata="write",clean="all"))
unlink("tadpole.tpl")
}
}
\author{
Ben Bolker
}
\keyword{misc}
| /R2admb/man/do_admb.Rd | no_license | jlaake/R2admb | R | false | false | 4,132 | rd | \name{do_admb}
\alias{do_admb}
\title{Compile and/or run an ADMB model, collect output}
\usage{
do_admb(fn, data, params, bounds = NULL, phase = NULL,
re = NULL, data_type = NULL, safe = TRUE,
profile = FALSE, profpars = NULL, mcmc = FALSE,
mcmc.opts = mcmc.control(), impsamp = FALSE,
verbose = FALSE, run.opts = run.control(),
objfunname = "f", workdir = getwd(),
admb_errors = c("stop", "warn", "ignore"), extra.args)
}
\arguments{
\item{fn}{(character) base name of a TPL function,
located in the working directory}
\item{data}{a list of input data variables (order must
match TPL file)}
\item{params}{a list of starting parameter values (order
must match TPL file)}
\item{bounds}{named list of 2-element vectors of lower
and upper bounds for specified parameters}
\item{phase}{named numeric vector of phases (not
implemented yet)}
\item{re}{a named list of the identities and dimensions
of any random effects vectors or matrices used in the TPL
file}
\item{data_type}{a named vector specifying (optional)
data types for parameters, in parname="storage mode"
format (e.g. \code{c(x="integer",y="numeric")})}
\item{safe}{(logical) compile in safe mode?}
\item{profile}{(logical) generate likelihood profiles?
(untested!)}
\item{profpars}{(character) vector of names of parameters
to profile}
\item{mcmc}{(logical) run MCMC around best fit?}
\item{mcmc.opts}{options for MCMC (see
\code{\link{mcmc.control}} for details)}
\item{impsamp}{(logical) run importance sampling?}
\item{verbose}{(logical) print details}
\item{run.opts}{options for ADMB run (see
\code{\link{run.control}} for details)}
\item{objfunname}{(character) name for objective function
in TPL file (only relevant if \code{checkparam} is set to
"write")}
\item{workdir}{temporary working directory (dat/pin/tpl
files will be copied)}
\item{admb_errors}{how to treat ADMB errors (in either
compilation or run): use at your own risk!}
\item{extra.args}{(character) extra argument string to
pass to admb}
}
\value{
An object of class \code{admb}.
}
\description{
Compile an ADMB model, run it, collect output
}
\details{
\code{do_admb} will attempt to do everything required to
start from the model definition (TPL file) specified by
\code{fn}, the data list, and the list of input
parameters, compile and run (i.e. minimize the objective
function of) the model in AD Model Builder, and read the
results back into an object of class \code{admb} in R. If
\code{checkparam} or \code{checkdata} are set to "write",
it will attempt to construct a DATA section, and
construct or (augment an existing) PARAMETER section
(which may contain definitions of non-input parameters to
be used in the model). It copies the input TPL file to a
backup (.bak); on finishing, it restores the original TPL
file and leaves the auto-generated TPL file in a file
called [fn]_gen.tpl.
}
\note{
1. Mixed-case file names are ignored by ADMB; this
function makes a temporary copy with the file name
translated to lower case. 2. Parameter names containing
periods/full stops will not work, because this violates C
syntax (currently not checked). 3. There are many, many,
implicit restrictions and assumptions: for example, all
vectors and matrices are assumed to be indexed starting
from 1.
}
\examples{
\dontrun{
setup_admb()
file.copy(system.file("tplfiles","ReedfrogSizepred0.tpl",package="R2admb"),"tadpole.tpl")
tadpoledat <-
data.frame(TBL = rep(c(9,12,21,25,37),each=3),
Kill = c(0,2,1,3,4,5,0,0,0,0,1,0,0,0,0L),
nexposed=rep(10,15))
m1 <- do_admb("tadpole",
data=c(list(nobs=15),tadpoledat),
params=list(c=0.45,d=13,g=1),
bounds=list(c=c(0,1),d=c(0,50),g=c(-1,25)),
run.opts=run.control(checkparam="write",
checkdata="write",clean="all"))
unlink("tadpole.tpl")
}
}
\author{
Ben Bolker
}
\keyword{misc}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ols-breusch-pagan-test.R
\name{ols_test_breusch_pagan}
\alias{ols_test_breusch_pagan}
\alias{ols_bp_test}
\title{Breusch pagan test}
\usage{
ols_test_breusch_pagan(
model,
fitted.values = TRUE,
rhs = FALSE,
multiple = FALSE,
p.adj = c("none", "bonferroni", "sidak", "holm"),
vars = NA
)
}
\arguments{
\item{model}{An object of class \code{lm}.}
\item{fitted.values}{Logical; if TRUE, use fitted values of regression model.}
\item{rhs}{Logical; if TRUE, specifies that tests for heteroskedasticity be
performed for the right-hand-side (explanatory) variables of the fitted
regression model.}
\item{multiple}{Logical; if TRUE, specifies that multiple testing be performed.}
\item{p.adj}{Adjustment for p value, the following options are available:
bonferroni, holm, sidak and none.}
\item{vars}{Variables to be used for heteroskedasticity test.}
}
\value{
\code{ols_test_breusch_pagan} returns an object of class \code{"ols_test_breusch_pagan"}.
An object of class \code{"ols_test_breusch_pagan"} is a list containing the
following components:
\item{bp}{breusch pagan statistic}
\item{p}{p-value of \code{bp}}
\item{fv}{fitted values of the regression model}
\item{rhs}{names of explanatory variables of fitted regression model}
\item{multiple}{logical value indicating if multiple tests should be performed}
\item{padj}{adjusted p values}
\item{vars}{variables to be used for heteroskedasticity test}
\item{resp}{response variable}
\item{preds}{predictors}
}
\description{
Test for constant variance. It assumes that the error terms are normally
distributed.
}
\details{
Breusch Pagan Test was introduced by Trevor Breusch and Adrian Pagan in 1979.
It is used to test for heteroskedasticity in a linear regression model.
It test whether variance of errors from a regression is dependent on the
values of a independent variable.
\itemize{
\item Null Hypothesis: Equal/constant variances
\item Alternative Hypothesis: Unequal/non-constant variances
}
Computation
\itemize{
\item Fit a regression model
\item Regress the squared residuals from the above model on the independent variables
\item Compute \eqn{nR^2}. It follows a chi square distribution with p -1 degrees of
freedom, where p is the number of independent variables, n is the sample size and
\eqn{R^2} is the coefficient of determination from the regression in step 2.
}
}
\section{Deprecated Function}{
\code{ols_bp_test()} has been deprecated. Instead use \code{ols_test_breusch_pagan()}.
}
\examples{
# model
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
# use fitted values of the model
ols_test_breusch_pagan(model)
# use independent variables of the model
ols_test_breusch_pagan(model, rhs = TRUE)
# use independent variables of the model and perform multiple tests
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)
# bonferroni p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'bonferroni')
# sidak p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'sidak')
# holm's p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'holm')
}
\references{
T.S. Breusch & A.R. Pagan (1979), A Simple Test for Heteroscedasticity and
Random Coefficient Variation. Econometrica 47, 1287–1294
Cook, R. D.; Weisberg, S. (1983). "Diagnostics for Heteroskedasticity in Regression". Biometrika. 70 (1): 1–10.
}
\seealso{
Other heteroskedasticity tests:
\code{\link{ols_test_bartlett}()},
\code{\link{ols_test_f}()},
\code{\link{ols_test_score}()}
}
\concept{heteroskedasticity tests}
| /man/ols_test_breusch_pagan.Rd | no_license | kaushikmanikonda/olsrr | R | false | true | 3,686 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ols-breusch-pagan-test.R
\name{ols_test_breusch_pagan}
\alias{ols_test_breusch_pagan}
\alias{ols_bp_test}
\title{Breusch pagan test}
\usage{
ols_test_breusch_pagan(
model,
fitted.values = TRUE,
rhs = FALSE,
multiple = FALSE,
p.adj = c("none", "bonferroni", "sidak", "holm"),
vars = NA
)
}
\arguments{
\item{model}{An object of class \code{lm}.}
\item{fitted.values}{Logical; if TRUE, use fitted values of regression model.}
\item{rhs}{Logical; if TRUE, specifies that tests for heteroskedasticity be
performed for the right-hand-side (explanatory) variables of the fitted
regression model.}
\item{multiple}{Logical; if TRUE, specifies that multiple testing be performed.}
\item{p.adj}{Adjustment for p value, the following options are available:
bonferroni, holm, sidak and none.}
\item{vars}{Variables to be used for heteroskedasticity test.}
}
\value{
\code{ols_test_breusch_pagan} returns an object of class \code{"ols_test_breusch_pagan"}.
An object of class \code{"ols_test_breusch_pagan"} is a list containing the
following components:
\item{bp}{breusch pagan statistic}
\item{p}{p-value of \code{bp}}
\item{fv}{fitted values of the regression model}
\item{rhs}{names of explanatory variables of fitted regression model}
\item{multiple}{logical value indicating if multiple tests should be performed}
\item{padj}{adjusted p values}
\item{vars}{variables to be used for heteroskedasticity test}
\item{resp}{response variable}
\item{preds}{predictors}
}
\description{
Test for constant variance. It assumes that the error terms are normally
distributed.
}
\details{
Breusch Pagan Test was introduced by Trevor Breusch and Adrian Pagan in 1979.
It is used to test for heteroskedasticity in a linear regression model.
It test whether variance of errors from a regression is dependent on the
values of a independent variable.
\itemize{
\item Null Hypothesis: Equal/constant variances
\item Alternative Hypothesis: Unequal/non-constant variances
}
Computation
\itemize{
\item Fit a regression model
\item Regress the squared residuals from the above model on the independent variables
\item Compute \eqn{nR^2}. It follows a chi square distribution with p -1 degrees of
freedom, where p is the number of independent variables, n is the sample size and
\eqn{R^2} is the coefficient of determination from the regression in step 2.
}
}
\section{Deprecated Function}{
\code{ols_bp_test()} has been deprecated. Instead use \code{ols_test_breusch_pagan()}.
}
\examples{
# model
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
# use fitted values of the model
ols_test_breusch_pagan(model)
# use independent variables of the model
ols_test_breusch_pagan(model, rhs = TRUE)
# use independent variables of the model and perform multiple tests
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)
# bonferroni p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'bonferroni')
# sidak p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'sidak')
# holm's p value adjustment
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'holm')
}
\references{
T.S. Breusch & A.R. Pagan (1979), A Simple Test for Heteroscedasticity and
Random Coefficient Variation. Econometrica 47, 1287–1294
Cook, R. D.; Weisberg, S. (1983). "Diagnostics for Heteroskedasticity in Regression". Biometrika. 70 (1): 1–10.
}
\seealso{
Other heteroskedasticity tests:
\code{\link{ols_test_bartlett}()},
\code{\link{ols_test_f}()},
\code{\link{ols_test_score}()}
}
\concept{heteroskedasticity tests}
|
# Children receiving dental care
FYC <- FYC %>%
mutate(
child_2to17 = (1 < AGELAST & AGELAST < 18),
child_dental = ((DVTOT.yy. > 0) & (child_2to17==1))*1,
child_dental = recode_factor(
child_dental, .default = "Missing", .missing = "Missing",
"1" = "One or more dental visits",
"0" = "No dental visits in past year"))
| /build_hc_tables/code/r/grps/child_dental.R | permissive | RandomCriticalAnalysis/MEPS-summary-tables | R | false | false | 367 | r | # Children receiving dental care
FYC <- FYC %>%
mutate(
child_2to17 = (1 < AGELAST & AGELAST < 18),
child_dental = ((DVTOT.yy. > 0) & (child_2to17==1))*1,
child_dental = recode_factor(
child_dental, .default = "Missing", .missing = "Missing",
"1" = "One or more dental visits",
"0" = "No dental visits in past year"))
|
\alias{gtkIconInfoGetAttachPoints}
\name{gtkIconInfoGetAttachPoints}
\title{gtkIconInfoGetAttachPoints}
\description{Fetches the set of attach points for an icon. An attach point
is a location in the icon that can be used as anchor points for attaching
emblems or overlays to the icon.}
\usage{gtkIconInfoGetAttachPoints(object)}
\arguments{\item{\verb{object}}{a \code{\link{GtkIconInfo}}}}
\details{Since 2.4}
\value{
A list containing the following elements:
\item{retval}{[logical] \code{TRUE} if there are any attach points for the icon.}
\item{\verb{points}}{(array length=n_points) (out): location to store pointer to a list of points, or \code{NULL}
free the list of points with \code{gFree()}. \emph{[ \acronym{allow-none} ]}}
\item{\verb{n.points}}{location to store the number of points in \code{points}, or \code{NULL}. \emph{[ \acronym{allow-none} ]}}
}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
| /RGtk2/man/gtkIconInfoGetAttachPoints.Rd | no_license | lawremi/RGtk2 | R | false | false | 942 | rd | \alias{gtkIconInfoGetAttachPoints}
\name{gtkIconInfoGetAttachPoints}
\title{gtkIconInfoGetAttachPoints}
\description{Fetches the set of attach points for an icon. An attach point
is a location in the icon that can be used as anchor points for attaching
emblems or overlays to the icon.}
\usage{gtkIconInfoGetAttachPoints(object)}
\arguments{\item{\verb{object}}{a \code{\link{GtkIconInfo}}}}
\details{Since 2.4}
\value{
A list containing the following elements:
\item{retval}{[logical] \code{TRUE} if there are any attach points for the icon.}
\item{\verb{points}}{(array length=n_points) (out): location to store pointer to a list of points, or \code{NULL}
free the list of points with \code{gFree()}. \emph{[ \acronym{allow-none} ]}}
\item{\verb{n.points}}{location to store the number of points in \code{points}, or \code{NULL}. \emph{[ \acronym{allow-none} ]}}
}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
rankall <- function(outcome, num = "best") {
## Read outcome data from csv
## Check that state and outcome are valid
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
## If an invalid state value function should throw message “invalid state”.
## If an invalid outcome value is passed throw message “invalid outcome”.
## Variables to use:
## [2] "Hospital.Name"
## [11] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [17] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [23] "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
##Read data
df <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check valid outcome
if (!validate_outcome(outcome))
stop("invalid outcome")
## Check valid num
if (num != "worst" && num!="best" && is.character(num))
stop("invalid rank")
## Check valid num count
## returns NA in case num is lager than hospitals count in a state
if (num != "worst" && num!="best")
if (!is.numeric(num))
return(NA)
## Select nth hospital
## Call best hostpital using the apropiate column number)
outcome_name<-c("ha","hf","pn")
#outcome_measure<-c(11,17,23)
outcome_measure<-c(
"Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack",
"Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure",
"Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia")
names(outcome_measure)<-outcome_name
if (outcome=="heart attack")
out=nth_hospitals(df,outcome_measure["ha"],num)
if (outcome=="heart failure")
out=nth_hospitals(df,outcome_measure["hf"],num)
if (outcome=="pneumonia")
out=nth_hospitals(df,outcome_measure["pn"],num)
out
}
nth_hospitals<-function(df,measure,num) {
## recieves dataframe,
## the column number( measure) to work with
## position to return (num)
## returns hospital name (variable 2) having the nth measure value
## and state abbreviation
##Selects only the state,hospital name and the measure columns
df<-df[,c("State","Hospital.Name",measure)]
## adds a column to set up the hospital position
df$pos=0
## dicards NA values
sel=!is.na(as.numeric(df[[measure]]))
df<-df[sel,]
##Sorts based state, measure and hospital name
df<-df[ order(df["State"],as.numeric(df[[measure]]), df["Hospital.Name"]),]
## ranks using State and measure
lst<-tapply(as.numeric(df[[measure]]),df$State,function (x) rank(x,ties.method="first"))
##converts list to vector and Copy calculated Rank to DF$pos
##df$pos=data.frame(matrix(unlist(lst),nrow=nrow(df),byrow=T),stringsAsFactors=FALSE)
df$pos=as.vector(matrix(unlist(lst),nrow=nrow(df),byrow=T))
## Gets the max value for each state to handle worst case scenarios
lst2<-tapply(as.numeric(df$pos),df$State,function (x) max(x) )
df$Max<-lst2[df[,"State"]]
if (num=="best")
num<-1
if (num=="worst")
## If worst case, then set pos to Max to retreive worst
sel2<-df$pos==df$Max
else {
## Selects Hopsitals having less than the pos selected
sel3<-df$Max<num
## Set Name to NA
df[sel3,"Hospital.Name"]=NA
## For those NA Hospitals, Set pos=num for the max value
df[df$pos==df$Max & is.na(df$Hospital.Name),"pos"]=num
##Selects the position of each state
sel2<-df$pos==num
}
#Copy Selected rows to df
df<-df[sel2,c("Hospital.Name","State")]
## Set names as asked in excersice
names(df)[1]<-"hospital"
names(df)[2]<-"state"
return(df)
}
validate_outcome<-function(outcome) {
## takes an outcome name and compare it to the valid list
## if exists then returns 1 otherwise returns 0
valid_outcome<-c("heart attack","heart failure","pneumonia")
sum(outcome==valid_outcome)
}
| /rankall.R | no_license | DataScienceGB/test | R | false | false | 3,875 | r | rankall <- function(outcome, num = "best") {
## Read outcome data from csv
## Check that state and outcome are valid
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
## If an invalid state value function should throw message “invalid state”.
## If an invalid outcome value is passed throw message “invalid outcome”.
## Variables to use:
## [2] "Hospital.Name"
## [11] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [17] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [23] "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
##Read data
df <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check valid outcome
if (!validate_outcome(outcome))
stop("invalid outcome")
## Check valid num
if (num != "worst" && num!="best" && is.character(num))
stop("invalid rank")
## Check valid num count
## returns NA in case num is lager than hospitals count in a state
if (num != "worst" && num!="best")
if (!is.numeric(num))
return(NA)
## Select nth hospital
## Call best hostpital using the apropiate column number)
outcome_name<-c("ha","hf","pn")
#outcome_measure<-c(11,17,23)
outcome_measure<-c(
"Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack",
"Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure",
"Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia")
names(outcome_measure)<-outcome_name
if (outcome=="heart attack")
out=nth_hospitals(df,outcome_measure["ha"],num)
if (outcome=="heart failure")
out=nth_hospitals(df,outcome_measure["hf"],num)
if (outcome=="pneumonia")
out=nth_hospitals(df,outcome_measure["pn"],num)
out
}
nth_hospitals<-function(df,measure,num) {
## recieves dataframe,
## the column number( measure) to work with
## position to return (num)
## returns hospital name (variable 2) having the nth measure value
## and state abbreviation
##Selects only the state,hospital name and the measure columns
df<-df[,c("State","Hospital.Name",measure)]
## adds a column to set up the hospital position
df$pos=0
## dicards NA values
sel=!is.na(as.numeric(df[[measure]]))
df<-df[sel,]
##Sorts based state, measure and hospital name
df<-df[ order(df["State"],as.numeric(df[[measure]]), df["Hospital.Name"]),]
## ranks using State and measure
lst<-tapply(as.numeric(df[[measure]]),df$State,function (x) rank(x,ties.method="first"))
##converts list to vector and Copy calculated Rank to DF$pos
##df$pos=data.frame(matrix(unlist(lst),nrow=nrow(df),byrow=T),stringsAsFactors=FALSE)
df$pos=as.vector(matrix(unlist(lst),nrow=nrow(df),byrow=T))
## Gets the max value for each state to handle worst case scenarios
lst2<-tapply(as.numeric(df$pos),df$State,function (x) max(x) )
df$Max<-lst2[df[,"State"]]
if (num=="best")
num<-1
if (num=="worst")
## If worst case, then set pos to Max to retreive worst
sel2<-df$pos==df$Max
else {
## Selects Hopsitals having less than the pos selected
sel3<-df$Max<num
## Set Name to NA
df[sel3,"Hospital.Name"]=NA
## For those NA Hospitals, Set pos=num for the max value
df[df$pos==df$Max & is.na(df$Hospital.Name),"pos"]=num
##Selects the position of each state
sel2<-df$pos==num
}
#Copy Selected rows to df
df<-df[sel2,c("Hospital.Name","State")]
## Set names as asked in excersice
names(df)[1]<-"hospital"
names(df)[2]<-"state"
return(df)
}
validate_outcome<-function(outcome) {
## takes an outcome name and compare it to the valid list
## if exists then returns 1 otherwise returns 0
valid_outcome<-c("heart attack","heart failure","pneumonia")
sum(outcome==valid_outcome)
}
|
\name{plot3d.ca}
\alias{plot3d.ca}
\title{Plotting 3D maps in correspondence analysis}
\description{Graphical display of correspondence analysis in three dimensions}
\usage{\method{plot3d}{ca}(x, dim = c(1, 2, 3), map = "symmetric", what = c("all", "all"),
contrib = c("none", "none"), col = c("#6666FF","#FF6666"),
labcol = c("#0000FF", "#FF0000"), pch = c(16, 1, 18, 9),
labels = c(2, 2), sf = 0.00001, arrows = c(FALSE, FALSE),
axiscol = "#333333", axislcol = "#333333",
laboffset = list(x = 0, y = 0.075, z = 0.05), ...) }
\arguments{
\item{x}{Simple correspondence analysis object returned by ca}
\item{dim}{Numerical vector of length 2 indicating the dimensions to plot}
\item{map}{Character string specifying the map type. Allowed options include \cr
\kbd{"symmetric"} (default) \cr
\kbd{"rowprincipal"} \cr
\kbd{"colprincipal"} \cr
\kbd{"symbiplot"} \cr
\kbd{"rowgab"} \cr
\kbd{"colgab"} \cr
\kbd{"rowgreen"} \cr
\kbd{"colgreen"}
}
\item{what}{Vector of two character strings specifying the contents of the plot. First entry sets the rows and the second entry the columns. Allowed values are \cr
\kbd{"none"} (no points are displayed) \cr
\kbd{"active"} (only active points are displayed, default) \cr
\kbd{"supplementary"} (only supplementary points are displayed) \cr
\kbd{"all"} (all available points) \cr
The status (active or supplementary) is set in \code{\link{ca}}.}
\item{contrib}{Vector of two character strings specifying if contributions (relative or absolute) should be indicated by different colour intensities. Available options are\cr
\kbd{"none"} (contributions are not indicated in the plot).\cr
\kbd{"absolute"} (absolute contributions are indicated by colour intensities).\cr
\kbd{"relative"} (relative conrributions are indicated by colour intensities).\cr
If set to \kbd{"absolute"} or \kbd{"relative"}, points with zero contribution are displayed in white. The higher the contribution of a point, the closer the corresponding colour to the one specified by the \code{col} option.}
\item{col}{Vector of length 2 specifying the colours of row and column profiles. Colours can be entered in hexadecimal (e.g. \kbd{"\#FF0000"}), rgb (e.g. \kbd{rgb(1,0,0)}) values or by R-name (e.g. \kbd{"red"}). }
\item{labcol}{Vector of length 2 specifying the colours of row and column labels. }
\item{pch}{Vector of length 2 giving the type of points to be used for rows and columns.}
\item{labels}{Vector of length two specifying if the plot should contain symbols only (\kbd{0}), labels only (\kbd{1}) or both symbols and labels (\kbd{2}). Setting \code{labels} to \kbd{2} results in the symbols being plotted at the coordinates and the labels with an offset.}
\item{sf}{A scaling factor for the volume of the 3d primitives.}
\item{arrows}{Vector of two logicals specifying if the plot should contain points (FALSE, default) or arrows (TRUE). First value sets the rows and the second value sets the columns.}
\item{axiscol}{Colour of the axis line.}
\item{axislcol}{Colour of the axis labels.}
\item{laboffset}{List with 3 slots specifying the label offset in x, y, and z direction.}
\item{...}{Further arguments passed to the rgl functions.}
}
\seealso{\code{\link{ca}}}
| /man/plot3d.ca.rd | no_license | cran/ca | R | false | false | 3,601 | rd | \name{plot3d.ca}
\alias{plot3d.ca}
\title{Plotting 3D maps in correspondence analysis}
\description{Graphical display of correspondence analysis in three dimensions}
\usage{\method{plot3d}{ca}(x, dim = c(1, 2, 3), map = "symmetric", what = c("all", "all"),
contrib = c("none", "none"), col = c("#6666FF","#FF6666"),
labcol = c("#0000FF", "#FF0000"), pch = c(16, 1, 18, 9),
labels = c(2, 2), sf = 0.00001, arrows = c(FALSE, FALSE),
axiscol = "#333333", axislcol = "#333333",
laboffset = list(x = 0, y = 0.075, z = 0.05), ...) }
\arguments{
\item{x}{Simple correspondence analysis object returned by ca}
\item{dim}{Numerical vector of length 2 indicating the dimensions to plot}
\item{map}{Character string specifying the map type. Allowed options include \cr
\kbd{"symmetric"} (default) \cr
\kbd{"rowprincipal"} \cr
\kbd{"colprincipal"} \cr
\kbd{"symbiplot"} \cr
\kbd{"rowgab"} \cr
\kbd{"colgab"} \cr
\kbd{"rowgreen"} \cr
\kbd{"colgreen"}
}
\item{what}{Vector of two character strings specifying the contents of the plot. First entry sets the rows and the second entry the columns. Allowed values are \cr
\kbd{"none"} (no points are displayed) \cr
\kbd{"active"} (only active points are displayed, default) \cr
\kbd{"supplementary"} (only supplementary points are displayed) \cr
\kbd{"all"} (all available points) \cr
The status (active or supplementary) is set in \code{\link{ca}}.}
\item{contrib}{Vector of two character strings specifying if contributions (relative or absolute) should be indicated by different colour intensities. Available options are\cr
\kbd{"none"} (contributions are not indicated in the plot).\cr
\kbd{"absolute"} (absolute contributions are indicated by colour intensities).\cr
\kbd{"relative"} (relative conrributions are indicated by colour intensities).\cr
If set to \kbd{"absolute"} or \kbd{"relative"}, points with zero contribution are displayed in white. The higher the contribution of a point, the closer the corresponding colour to the one specified by the \code{col} option.}
\item{col}{Vector of length 2 specifying the colours of row and column profiles. Colours can be entered in hexadecimal (e.g. \kbd{"\#FF0000"}), rgb (e.g. \kbd{rgb(1,0,0)}) values or by R-name (e.g. \kbd{"red"}). }
\item{labcol}{Vector of length 2 specifying the colours of row and column labels. }
\item{pch}{Vector of length 2 giving the type of points to be used for rows and columns.}
\item{labels}{Vector of length two specifying if the plot should contain symbols only (\kbd{0}), labels only (\kbd{1}) or both symbols and labels (\kbd{2}). Setting \code{labels} to \kbd{2} results in the symbols being plotted at the coordinates and the labels with an offset.}
\item{sf}{A scaling factor for the volume of the 3d primitives.}
\item{arrows}{Vector of two logicals specifying if the plot should contain points (FALSE, default) or arrows (TRUE). First value sets the rows and the second value sets the columns.}
\item{axiscol}{Colour of the axis line.}
\item{axislcol}{Colour of the axis labels.}
\item{laboffset}{List with 3 slots specifying the label offset in x, y, and z direction.}
\item{...}{Further arguments passed to the rgl functions.}
}
\seealso{\code{\link{ca}}}
|
## Taylor Plot for modis reflectance - Figure 6
##########
library(tidyverse)
# modisBRDF weights
# we are taking modisBRDF and the site lists - maybe left join by site and time, selecting out the kernel - arrgh, this will be a little tricky, but we should be ok.
load('mcmc-results/modeled-rSoil-results.Rda')
#modeled_rSoil %>% filter(model_id=='dead_soil') %>%
# ggplot() + geom_point(aes(x=modeled,y=measured,color=site))
#flux_data %>% ggplot() + geom_point(aes(x=soilC,y=rSoil,color=site)) + facet_grid(.~treatment)
taylor_values <- modeled_rSoil %>%
group_by(type,model,site,treatment) %>%
summarize(
sd_meas = 1,
sd_model = sd(modeled) / sd(measured),
r = cor(modeled,measured),
centered_rms = sd((measured-mean(measured))-((modeled-mean(modeled))))/sd(measured),
x_coord = sd_model*r,
y_coord = sd_model*sin(acos(r))
)
# load up the results from the empirical model
load('mcmc-results/empirical-taylor-results.Rda')
taylor_values <- rbind(taylor_values,taylor_values_empirical)
# normalize the results, see Taylor 2001
# E = E'/sigma_meas
# sigma_model = sigma_model/sigma_meas
# sigma_meas = 1
t_plot <- taylor_plot()
curr_plot <- t_plot +
geom_point(data=taylor_values,aes(x=x_coord,y=y_coord,color=model,shape=type),size=2) +
facet_grid(site~treatment) +
labs(x="",y=expression(italic("\u03C3")[model]),color="Model",shape="Estimate type") +
theme_bw() +
theme(legend.position = "bottom",
axis.text = element_text(size=14),
axis.title=element_text(size=28),
title=element_text(size=26),
legend.text=element_text(size=12),
legend.title=element_text(size=14),
strip.text.x = element_text(size=12),
strip.text.y = element_text(size=12),
strip.background = element_rect(colour="white", fill="white")) +
theme( panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
fileName <- paste0('manuscript-figures/taylor-plot.png')
ggsave(fileName,plot=curr_plot,width=6,dpi=600)
| /plot-process/taylor-plot.R | no_license | jmzobitz/SoilModeling | R | false | false | 2,057 | r | ## Taylor Plot for modis reflectance - Figure 6
##########
library(tidyverse)
# modisBRDF weights
# we are taking modisBRDF and the site lists - maybe left join by site and time, selecting out the kernel - arrgh, this will be a little tricky, but we should be ok.
load('mcmc-results/modeled-rSoil-results.Rda')
#modeled_rSoil %>% filter(model_id=='dead_soil') %>%
# ggplot() + geom_point(aes(x=modeled,y=measured,color=site))
#flux_data %>% ggplot() + geom_point(aes(x=soilC,y=rSoil,color=site)) + facet_grid(.~treatment)
taylor_values <- modeled_rSoil %>%
group_by(type,model,site,treatment) %>%
summarize(
sd_meas = 1,
sd_model = sd(modeled) / sd(measured),
r = cor(modeled,measured),
centered_rms = sd((measured-mean(measured))-((modeled-mean(modeled))))/sd(measured),
x_coord = sd_model*r,
y_coord = sd_model*sin(acos(r))
)
# load up the results from the empirical model
load('mcmc-results/empirical-taylor-results.Rda')
taylor_values <- rbind(taylor_values,taylor_values_empirical)
# normalize the results, see Taylor 2001
# E = E'/sigma_meas
# sigma_model = sigma_model/sigma_meas
# sigma_meas = 1
t_plot <- taylor_plot()
curr_plot <- t_plot +
geom_point(data=taylor_values,aes(x=x_coord,y=y_coord,color=model,shape=type),size=2) +
facet_grid(site~treatment) +
labs(x="",y=expression(italic("\u03C3")[model]),color="Model",shape="Estimate type") +
theme_bw() +
theme(legend.position = "bottom",
axis.text = element_text(size=14),
axis.title=element_text(size=28),
title=element_text(size=26),
legend.text=element_text(size=12),
legend.title=element_text(size=14),
strip.text.x = element_text(size=12),
strip.text.y = element_text(size=12),
strip.background = element_rect(colour="white", fill="white")) +
theme( panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
fileName <- paste0('manuscript-figures/taylor-plot.png')
ggsave(fileName,plot=curr_plot,width=6,dpi=600)
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#' @include arrow-datum.R
# Base class for RecordBatch and Table for S3 method dispatch only.
# Does not exist in C++ class hierarchy
ArrowTabular <- R6Class("ArrowTabular", inherit = ArrowObject,
public = list(
ToString = function() ToString_tabular(self),
Take = function(i) {
if (is.numeric(i)) {
i <- as.integer(i)
}
if (is.integer(i)) {
i <- Array$create(i)
}
assert_that(is.Array(i))
call_function("take", self, i)
},
Filter = function(i, keep_na = TRUE) {
if (is.logical(i)) {
i <- Array$create(i)
}
assert_that(is.Array(i, "bool"))
call_function("filter", self, i, options = list(keep_na = keep_na))
},
SortIndices = function(names, descending = FALSE) {
assert_that(is.character(names))
assert_that(length(names) > 0)
assert_that(!any(is.na(names)))
if (length(descending) == 1L) {
descending <- rep_len(descending, length(names))
}
assert_that(is.logical(descending))
assert_that(identical(length(names), length(descending)))
assert_that(!any(is.na(descending)))
call_function(
"sort_indices",
self,
# cpp11 does not support logical vectors so convert to integer
options = list(names = names, orders = as.integer(descending))
)
}
)
)
#' @export
as.data.frame.ArrowTabular <- function(x, row.names = NULL, optional = FALSE, ...) {
tryCatch(
df <- x$to_data_frame(),
error = handle_embedded_nul_error
)
if (!is.null(r_metadata <- x$metadata$r)) {
df <- apply_arrow_r_metadata(df, .unserialize_arrow_r_metadata(r_metadata))
}
df
}
#' @export
`names<-.ArrowTabular` <- function(x, value) x$RenameColumns(value)
#' @importFrom methods as
#' @export
`[.ArrowTabular` <- function(x, i, j, ..., drop = FALSE) {
if (nargs() == 2L) {
# List-like column extraction (x[i])
return(x[, i])
}
if (!missing(j)) {
# Selecting columns is cheaper than filtering rows, so do it first.
# That way, if we're filtering too, we have fewer arrays to filter/slice/take
if (is.character(j)) {
j_new <- match(j, names(x))
if (any(is.na(j_new))) {
stop("Column not found: ", oxford_paste(j[is.na(j_new)]), call. = FALSE)
}
j <- j_new
}
if (is_integerish(j)) {
if (any(is.na(j))) {
stop("Column indices cannot be NA", call. = FALSE)
}
if (length(j) && all(j < 0)) {
# in R, negative j means "everything but j"
j <- setdiff(seq_len(x$num_columns), -1 * j)
}
x <- x$SelectColumns(as.integer(j) - 1L)
}
if (drop && ncol(x) == 1L) {
x <- x$column(0)
}
}
if (!missing(i)) {
x <- filter_rows(x, i, ...)
}
x
}
#' @export
`[[.ArrowTabular` <- function(x, i, ...) {
if (is.character(i)) {
x$GetColumnByName(i)
} else if (is.numeric(i)) {
x$column(i - 1)
} else {
stop("'i' must be character or numeric, not ", class(i), call. = FALSE)
}
}
#' @export
`$.ArrowTabular` <- function(x, name, ...) {
assert_that(is.string(name))
if (name %in% ls(x)) {
get(name, x)
} else {
x$GetColumnByName(name)
}
}
#' @export
`[[<-.ArrowTabular` <- function(x, i, value) {
if (!is.character(i) & !is.numeric(i)) {
stop("'i' must be character or numeric, not ", class(i), call. = FALSE)
}
assert_that(length(i) == 1, !is.na(i))
if (is.null(value)) {
if (is.character(i)) {
i <- match(i, names(x))
}
x <- x$RemoveColumn(i - 1L)
} else {
if (!is.character(i)) {
# get or create a/the column name
if (i <= x$num_columns) {
i <- names(x)[i]
} else {
i <- as.character(i)
}
}
# auto-magic recycling on non-ArrowObjects
if (!inherits(value, "ArrowObject")) {
value <- vctrs::vec_recycle(value, x$num_rows)
}
# construct the field
if (inherits(x, "RecordBatch") && !inherits(value, "Array")) {
value <- Array$create(value)
} else if (inherits(x, "Table") && !inherits(value, "ChunkedArray")) {
value <- ChunkedArray$create(value)
}
new_field <- field(i, value$type)
if (i %in% names(x)) {
i <- match(i, names(x)) - 1L
x <- x$SetColumn(i, new_field, value)
} else {
i <- x$num_columns
x <- x$AddColumn(i, new_field, value)
}
}
x
}
#' @export
`$<-.ArrowTabular` <- function(x, i, value) {
assert_that(is.string(i))
# We need to check if `i` is in names in case it is an active binding (e.g.
# `metadata`, in which case we use assign to change the active binding instead
# of the column in the table)
if (i %in% ls(x)) {
assign(i, value, x)
} else {
x[[i]] <- value
}
x
}
#' @export
dim.ArrowTabular <- function(x) c(x$num_rows, x$num_columns)
#' @export
as.list.ArrowTabular <- function(x, ...) as.list(as.data.frame(x, ...))
#' @export
row.names.ArrowTabular <- function(x) as.character(seq_len(nrow(x)))
#' @export
dimnames.ArrowTabular <- function(x) list(row.names(x), names(x))
#' @export
head.ArrowTabular <- head.ArrowDatum
#' @export
tail.ArrowTabular <- tail.ArrowDatum
#' @export
na.fail.ArrowTabular <- function(object, ...){
for (col in seq_len(object$num_columns)) {
if (object$column(col - 1L)$null_count > 0) {
stop("missing values in object", call. = FALSE)
}
}
object
}
#' @export
na.omit.ArrowTabular <- function(object, ...){
not_na <- map(object$columns, ~build_array_expression("is_valid", .x))
not_na_agg <- Reduce("&", not_na)
object$Filter(eval_array_expression(not_na_agg))
}
#' @export
na.exclude.ArrowTabular <- na.omit.ArrowTabular
ToString_tabular <- function(x, ...) {
# Generic to work with both RecordBatch and Table
sch <- unlist(strsplit(x$schema$ToString(), "\n"))
sch <- sub("(.*): (.*)", "$\\1 <\\2>", sch)
dims <- sprintf("%s rows x %s columns", nrow(x), ncol(x))
paste(c(dims, sch), collapse = "\n")
}
| /r/R/arrow-tabular.R | permissive | abs-tudelft/arrow | R | false | false | 6,748 | r | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#' @include arrow-datum.R
# Base class for RecordBatch and Table for S3 method dispatch only.
# Does not exist in C++ class hierarchy
ArrowTabular <- R6Class("ArrowTabular", inherit = ArrowObject,
public = list(
ToString = function() ToString_tabular(self),
Take = function(i) {
if (is.numeric(i)) {
i <- as.integer(i)
}
if (is.integer(i)) {
i <- Array$create(i)
}
assert_that(is.Array(i))
call_function("take", self, i)
},
Filter = function(i, keep_na = TRUE) {
if (is.logical(i)) {
i <- Array$create(i)
}
assert_that(is.Array(i, "bool"))
call_function("filter", self, i, options = list(keep_na = keep_na))
},
SortIndices = function(names, descending = FALSE) {
assert_that(is.character(names))
assert_that(length(names) > 0)
assert_that(!any(is.na(names)))
if (length(descending) == 1L) {
descending <- rep_len(descending, length(names))
}
assert_that(is.logical(descending))
assert_that(identical(length(names), length(descending)))
assert_that(!any(is.na(descending)))
call_function(
"sort_indices",
self,
# cpp11 does not support logical vectors so convert to integer
options = list(names = names, orders = as.integer(descending))
)
}
)
)
#' @export
as.data.frame.ArrowTabular <- function(x, row.names = NULL, optional = FALSE, ...) {
tryCatch(
df <- x$to_data_frame(),
error = handle_embedded_nul_error
)
if (!is.null(r_metadata <- x$metadata$r)) {
df <- apply_arrow_r_metadata(df, .unserialize_arrow_r_metadata(r_metadata))
}
df
}
#' @export
`names<-.ArrowTabular` <- function(x, value) x$RenameColumns(value)
#' @importFrom methods as
#' @export
`[.ArrowTabular` <- function(x, i, j, ..., drop = FALSE) {
if (nargs() == 2L) {
# List-like column extraction (x[i])
return(x[, i])
}
if (!missing(j)) {
# Selecting columns is cheaper than filtering rows, so do it first.
# That way, if we're filtering too, we have fewer arrays to filter/slice/take
if (is.character(j)) {
j_new <- match(j, names(x))
if (any(is.na(j_new))) {
stop("Column not found: ", oxford_paste(j[is.na(j_new)]), call. = FALSE)
}
j <- j_new
}
if (is_integerish(j)) {
if (any(is.na(j))) {
stop("Column indices cannot be NA", call. = FALSE)
}
if (length(j) && all(j < 0)) {
# in R, negative j means "everything but j"
j <- setdiff(seq_len(x$num_columns), -1 * j)
}
x <- x$SelectColumns(as.integer(j) - 1L)
}
if (drop && ncol(x) == 1L) {
x <- x$column(0)
}
}
if (!missing(i)) {
x <- filter_rows(x, i, ...)
}
x
}
#' @export
`[[.ArrowTabular` <- function(x, i, ...) {
if (is.character(i)) {
x$GetColumnByName(i)
} else if (is.numeric(i)) {
x$column(i - 1)
} else {
stop("'i' must be character or numeric, not ", class(i), call. = FALSE)
}
}
#' @export
`$.ArrowTabular` <- function(x, name, ...) {
assert_that(is.string(name))
if (name %in% ls(x)) {
get(name, x)
} else {
x$GetColumnByName(name)
}
}
#' @export
`[[<-.ArrowTabular` <- function(x, i, value) {
if (!is.character(i) & !is.numeric(i)) {
stop("'i' must be character or numeric, not ", class(i), call. = FALSE)
}
assert_that(length(i) == 1, !is.na(i))
if (is.null(value)) {
if (is.character(i)) {
i <- match(i, names(x))
}
x <- x$RemoveColumn(i - 1L)
} else {
if (!is.character(i)) {
# get or create a/the column name
if (i <= x$num_columns) {
i <- names(x)[i]
} else {
i <- as.character(i)
}
}
# auto-magic recycling on non-ArrowObjects
if (!inherits(value, "ArrowObject")) {
value <- vctrs::vec_recycle(value, x$num_rows)
}
# construct the field
if (inherits(x, "RecordBatch") && !inherits(value, "Array")) {
value <- Array$create(value)
} else if (inherits(x, "Table") && !inherits(value, "ChunkedArray")) {
value <- ChunkedArray$create(value)
}
new_field <- field(i, value$type)
if (i %in% names(x)) {
i <- match(i, names(x)) - 1L
x <- x$SetColumn(i, new_field, value)
} else {
i <- x$num_columns
x <- x$AddColumn(i, new_field, value)
}
}
x
}
#' @export
`$<-.ArrowTabular` <- function(x, i, value) {
assert_that(is.string(i))
# We need to check if `i` is in names in case it is an active binding (e.g.
# `metadata`, in which case we use assign to change the active binding instead
# of the column in the table)
if (i %in% ls(x)) {
assign(i, value, x)
} else {
x[[i]] <- value
}
x
}
#' @export
dim.ArrowTabular <- function(x) c(x$num_rows, x$num_columns)
#' @export
as.list.ArrowTabular <- function(x, ...) as.list(as.data.frame(x, ...))
#' @export
row.names.ArrowTabular <- function(x) as.character(seq_len(nrow(x)))
#' @export
dimnames.ArrowTabular <- function(x) list(row.names(x), names(x))
#' @export
head.ArrowTabular <- head.ArrowDatum
#' @export
tail.ArrowTabular <- tail.ArrowDatum
#' @export
na.fail.ArrowTabular <- function(object, ...){
for (col in seq_len(object$num_columns)) {
if (object$column(col - 1L)$null_count > 0) {
stop("missing values in object", call. = FALSE)
}
}
object
}
#' @export
na.omit.ArrowTabular <- function(object, ...){
not_na <- map(object$columns, ~build_array_expression("is_valid", .x))
not_na_agg <- Reduce("&", not_na)
object$Filter(eval_array_expression(not_na_agg))
}
#' @export
na.exclude.ArrowTabular <- na.omit.ArrowTabular
ToString_tabular <- function(x, ...) {
# Generic to work with both RecordBatch and Table
sch <- unlist(strsplit(x$schema$ToString(), "\n"))
sch <- sub("(.*): (.*)", "$\\1 <\\2>", sch)
dims <- sprintf("%s rows x %s columns", nrow(x), ncol(x))
paste(c(dims, sch), collapse = "\n")
}
|
library(rioja)
View(dissimilarity)
diss=dist(dissimilarity,method='canberra')
#clust=chclust(diss,method = "coniss") #To plot the dendogram using coniss method
clust=chclust(diss,method = "conslink") #To plot the dendogram using conslink method
plot(clust,hang=-1)
#creating the hclust object to implement hierarchial clustering
hc = hclust(d = dist(dissimilarity, method = 'canberra'), method = 'ward.D')
y_hc = cutree(hc,6)
diss=as.matrix(diss) #To convert diss into a data matrix
# Visualising the clusters
library(cluster)
clusplot(diss,
y_hc,
lines = 0,
shade = FALSE,
color = TRUE,
labels= 1,
plotchar = FALSE,
span = TRUE,
main = paste('Clusters'),
)
| /HAC Clustering easy 1.R | no_license | yasheel-vyas/Constrained-Hierarchical-Agglomerative-Clustering-GSOC | R | false | false | 791 | r | library(rioja)
View(dissimilarity)
diss=dist(dissimilarity,method='canberra')
#clust=chclust(diss,method = "coniss") #To plot the dendogram using coniss method
clust=chclust(diss,method = "conslink") #To plot the dendogram using conslink method
plot(clust,hang=-1)
#creating the hclust object to implement hierarchial clustering
hc = hclust(d = dist(dissimilarity, method = 'canberra'), method = 'ward.D')
y_hc = cutree(hc,6)
diss=as.matrix(diss) #To convert diss into a data matrix
# Visualising the clusters
library(cluster)
clusplot(diss,
y_hc,
lines = 0,
shade = FALSE,
color = TRUE,
labels= 1,
plotchar = FALSE,
span = TRUE,
main = paste('Clusters'),
)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TestOneMuSmfg.R
\name{test1musm}
\alias{test1musm}
\title{Tests the hypothesis over population mean based on one sample summary statistics by Sv-plot2.}
\usage{
test1musm(n=20,xbar=3,s=2,mu0=4.5,alpha=0.05,
unkwnsigma=TRUE,sigma=NULL,xlab="x",
title="Single mean summary: Hypothesis testing by Sv-plot2",
samcol="grey5",popcol="grey45",thrcol="black",...)
}
\arguments{
\item{n}{sample size, \emph{n=20} by default.}
\item{xbar}{sample average, \emph{xbar=3} by default.}
\item{s}{sample standard deviation, \emph{s=2} by default.}
\item{mu0}{hypothesized population mean, \emph{mu0=4.5} by default.}
\item{alpha}{significance level, \emph{alpha=0.05} by default.}
\item{unkwnsigma}{population standard deviation is unknown, \emph{TRUE} by default.}
\item{sigma}{population standard deviation, \emph{NULL} by default.}
\item{xlab}{\eqn{x}-axis label, \eqn{x} by default.}
\item{title}{title of the plot, \emph{Single mean: Hypothesis testing by Sv-plot2 by default} by default.}
\item{samcol}{sample Sv-plot2 color, \emph{grey5} by default.}
\item{popcol}{sample Sv-plot2 color, \emph{grey45} by default.}
\item{thrcol}{threshold color, \emph{black}.}
\item{...}{other graphical parameters.}
}
\value{
Decision on testing hypotheses over single population mean by Sv-plot2.
}
\description{
Decision on hypothesis testing over single mean is made by graphing sample and population Sv-plot2s along with the threshold line. Intersecting Sv-plots on or above the horizontal line concludes the alternative hypothesis.
}
\examples{
## For summary data
test1musm(n=20,xbar=3,s=2,mu0=4.5,alpha=0.05, unkwnsigma=TRUE,sigma=NULL,xlab="x",
title="Single mean summary: Hypothesis testing by Sv-plot2",
samcol="grey5",popcol="grey45",thrcol="black")
}
\references{
Wijesuriya, U. A. (2020). Sv-plots for identifying characteristics of the
distribution and testing hypotheses. \emph{Communications in Statistics-Simulation and Computation}, \doi{10.1080/03610918.2020.1851716}.
}
| /man/test1musm.Rd | no_license | cran/svplots | R | false | true | 2,186 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TestOneMuSmfg.R
\name{test1musm}
\alias{test1musm}
\title{Tests the hypothesis over population mean based on one sample summary statistics by Sv-plot2.}
\usage{
test1musm(n=20,xbar=3,s=2,mu0=4.5,alpha=0.05,
unkwnsigma=TRUE,sigma=NULL,xlab="x",
title="Single mean summary: Hypothesis testing by Sv-plot2",
samcol="grey5",popcol="grey45",thrcol="black",...)
}
\arguments{
\item{n}{sample size, \emph{n=20} by default.}
\item{xbar}{sample average, \emph{xbar=3} by default.}
\item{s}{sample standard deviation, \emph{s=2} by default.}
\item{mu0}{hypothesized population mean, \emph{mu0=4.5} by default.}
\item{alpha}{significance level, \emph{alpha=0.05} by default.}
\item{unkwnsigma}{population standard deviation is unknown, \emph{TRUE} by default.}
\item{sigma}{population standard deviation, \emph{NULL} by default.}
\item{xlab}{\eqn{x}-axis label, \eqn{x} by default.}
\item{title}{title of the plot, \emph{Single mean: Hypothesis testing by Sv-plot2 by default} by default.}
\item{samcol}{sample Sv-plot2 color, \emph{grey5} by default.}
\item{popcol}{sample Sv-plot2 color, \emph{grey45} by default.}
\item{thrcol}{threshold color, \emph{black}.}
\item{...}{other graphical parameters.}
}
\value{
Decision on testing hypotheses over single population mean by Sv-plot2.
}
\description{
Decision on hypothesis testing over single mean is made by graphing sample and population Sv-plot2s along with the threshold line. Intersecting Sv-plots on or above the horizontal line concludes the alternative hypothesis.
}
\examples{
## For summary data
test1musm(n=20,xbar=3,s=2,mu0=4.5,alpha=0.05, unkwnsigma=TRUE,sigma=NULL,xlab="x",
title="Single mean summary: Hypothesis testing by Sv-plot2",
samcol="grey5",popcol="grey45",thrcol="black")
}
\references{
Wijesuriya, U. A. (2020). Sv-plots for identifying characteristics of the
distribution and testing hypotheses. \emph{Communications in Statistics-Simulation and Computation}, \doi{10.1080/03610918.2020.1851716}.
}
|
get_lncRNA_mRNA_pairs = function(originalCorrMatrix, corrThreshold){
correlation_pairs = which(originalCorrMatrix > corrThreshold, arr.ind = TRUE)
lncRNA = rownames(originalCorrMatrix)[correlation_pairs[,1]]
mRNA = colnames(originalCorrMatrix)[correlation_pairs[,2]]
dataframe = as.data.frame(cbind(correlation_pairs, lncRNA, mRNA))
colnames(dataframe) = c("lncRNA_index", "mRNA_index","lncRNA", "mRNA")
dataframe$lncRNA_index = as.numeric(as.character(dataframe$lncRNA_index))
dataframe$mRNA_index = as.numeric(as.character(dataframe$mRNA_index))
# correlation_vector = normal_lncRNA_mRNA_corr_matrix[dataframe$lncRNA_index,dataframe$mRNA_index]
dataframe = cbind(dataframe, originalCorrMatrix[which(originalCorrMatrix > corrThreshold)])
colnames(dataframe) = c("lncRNA_index", "mRNA_index","lncRNA", "mRNA", "corr")
dataframe = dataframe[order(-dataframe$corr),]
return(dataframe)
}
get_encRNA = function(matrix, miRNA_mRNA_corr, miRNA_lncRNA_corr, lncRNA_mRNA_corr, threshold){
triple = which(matrix > threshold, arr.ind = TRUE)
encRNAs = rownames(matrix)[triple[,1]]
miRNA = colnames(matrix)[triple[,2]]
dataframe = as.data.frame(cbind(triple, encRNAs, miRNA))
colnames(dataframe) = c("encRNA_pair_index", "miRNA_index","encRNA_pair", "miRNA")
dataframe$encRNA_pair_index = as.numeric(as.character(dataframe$encRNA_pair_index))
dataframe$miRNA_index = as.numeric(as.character(dataframe$miRNA_index))
dataframe = cbind(dataframe, matrix[which(matrix > threshold)])
colnames(dataframe) = c("encRNA_pair_index", "miRNA_index","encRNA_pair", "miRNA", "sensitivity")
dataframe = dataframe[order(-dataframe$sensitivity),]
# dataframe$lncRNA = substr(dataframe$encRNA_pair, start = 1, stop = 17)
# dataframe$mRNA = substr(dataframe$encRNA_pair, start = 19, stop = nchar(as.character(dataframe$encRNA_pair)))
pos = regexpr("-",as.character(dataframe$encRNA_pair))
dataframe$lncRNA = substr(as.character(dataframe$encRNA_pair), start = 1, stop = pos - 1)
dataframe$mRNA = substr(as.character(dataframe$encRNA_pair), start = pos + 1, stop = nchar(as.character(dataframe$encRNA_pair)))
dataframe$lncRNA_miRNA_corr = rep(NA, nrow(dataframe))
dataframe$mRNA_miRNA_corr = rep(NA, nrow(dataframe))
dataframe$lncRNA_miRNA_corr = sapply(1:nrow(dataframe), function(index){
miRNA_lncRNA_corr[as.character(dataframe$miRNA[index]),as.character(dataframe$lncRNA[index])]
})
dataframe$mRNA_miRNA_corr = sapply(1:nrow(dataframe), function(index){
miRNA_mRNA_corr[as.character(dataframe$miRNA[index]),as.character(dataframe$mRNA[index])]
})
dataframe$lncRNA_mRNA_corr = sapply(1:nrow(dataframe), function(index){
lncRNA_mRNA_corr[as.character(dataframe$lncRNA[index]),as.character(dataframe$mRNA[index])]
})
dataframe$encRNA_triple = paste(dataframe$encRNA_pair, dataframe$miRNA, sep = "-")
dataframe$lncRNA_miRNA_pair = paste(dataframe$lncRNA, dataframe$miRNA, sep = "-")
dataframe$mRNA_miRNA_pair = paste(dataframe$mRNA, dataframe$miRNA, sep = "-")
dataframe = dataframe[,c("lncRNA", "mRNA", "miRNA", "sensitivity",
"lncRNA_mRNA_corr", "lncRNA_miRNA_corr", "mRNA_miRNA_corr",
"encRNA_pair", "encRNA_triple",
"lncRNA_miRNA_pair" , "mRNA_miRNA_pair",
"encRNA_pair_index", "miRNA_index")]
return(dataframe)
}
# for each lncRNA ensemble ids, find potential miRNA interaction
getMiRNAs = function(miRNA_family = NULL){
if (is.null(miRNA_family)){
print("must provide miRNA_family")
break;
}
part1 = substr(miRNA_family, start = 1, stop = 3)
part1 = paste("hsa",tolower(part1),sep = "-")
# substring anything after miR till the end, divided by "/"
part2 = substr(miRNA_family,start = 5, stop = nchar(miRNA_family))
part2 = unlist(strsplit(x = part2, split = "/"))
# foreach element, remove 3p and 5p parts
part2 = gsub("-3p","",part2)
part2 = gsub("-5p","",part2)
# return individual mircRNA,
# example: 106abc will be disconstructed into 106a, 106b, 106c
part2 = sapply(part2, function(element){
if (grepl("\\D",element)){
digit_part = gsub(pattern = "\\D", replacement = "", x = element)
character_parts = gsub(pattern = "\\d", replacement = "", x = element)
character_parts = unlist(strsplit(x = character_parts,split = ""))
returned_value = paste(digit_part, character_parts,sep = "")
}else{
element
}
})
part2 = unname(unlist(part2))
return(paste(part1,part2,sep="-"))
}
get_putative_lncRNA_miRNA = function(dataframe){
require(rlist)
l = list()
#i = 1;
apply(dataframe, 1, function(r){
k = getMiRNAs(r[2])
l <<- list.append(l, k)
#print(i); i <<- i + 1;
})
names(l) = dataframe$gene_id
df = reshape2::melt(l)
colnames(df) = c("miRNA", "putative_lncRNAs")
df$lncRNA_miRNA_pair = paste(df$putative_lncRNAs, df$miRNA, sep = "-")
return(df)
}
get_putative_lncRNA_miRNA_2 = function(dataframe){
require(rlist)
l = list()
i = 1;
apply(dataframe, 1, function(r){
k = getMiRNAs(r[2])
l <<- list.append(l, k)
print(i); i <<- i + 1;
})
names(l) = dataframe$gene_id
df = data.frame(
miRNA = unlist(l),
putative_lncRNAs = rep(names(l), lapply(l, length))
)
colnames(df) = c("miRNA", "putative_lncRNAs")
df$lncRNA_miRNA_pair = paste(df$putative_lncRNAs, df$miRNA, sep = "-")
return(df)
}
get_putative_encRNA_interaction = function(df){
if (!("brca_putative_encRNA" %in% ls())){
load("data_Saved_R_Objects/miRNA_target/brca_putative_encRNA.rda"); gc()
}
brca_putative_encRNA_subset = brca_putative_encRNA[which(brca_putative_encRNA$miRNA %in% df$miRNA),]
brca_putative_encRNA_subset = brca_putative_encRNA_subset[which(brca_putative_encRNA_subset$lncRNA %in% df$lncRNA),]
brca_putative_encRNA_subset = brca_putative_encRNA_subset[which(brca_putative_encRNA_subset$mRNA %in% df$mRNA),]
brca_putative_encRNA_subset$encRNA_triple = paste(brca_putative_encRNA_subset$lncRNA,
brca_putative_encRNA_subset$mRNA,
brca_putative_encRNA_subset$miRNA,
sep = "-")
common_triplets = intersect(df$encRNA_triple, brca_putative_encRNA_subset$encRNA_triple)
return(normal_encRNA[which(df$encRNA_triple %in% common_triplets),])
}
get_matched_enRNA_sensitivity_with_putative_binding = function(encRNA_sensitivity){
load("mircode_objects.rda")
lncRNAs_overlapped = intersect(unique(mircode_lncRNA$gene_id), unique(encRNA_sensitivity$lncRNA))
# subset the encRNA_sensivivity to include only lncRNAs matches lncRNAs in miRcode
encRNA_sensitivity_subset1 = encRNA_sensitivity[which(encRNA_sensitivity$lncRNA %in% lncRNAs_overlapped),]
# similarly, subset the mircode_lncRNA to include only lncRNAs matches lncRNAs in encRNA_sensivivity
mircode_lncRNA_subset1 = mircode_lncRNA[which(mircode_lncRNA$gene_id %in% lncRNAs_overlapped),
c("gene_id","microrna")]
mircode_lncRNA_subset1 = get_putative_lncRNA_miRNA(mircode_lncRNA_subset1) # divide miRNAs familily into individual miRNAs
# now, subset encRNA_sensivivity_subset1 to include only the lncRNA-miRNA pairs which also shows up in mircode_lncRNA_subset1
# length(intersect(unique(encRNA_sensitivity_subset1$lncRNA_miRNA_pair), unique(mircode_lncRNA_subset1$lncRNA_miRNA_pair)))
intersected_lncRNA_miRNA_pairs = intersect(unique(encRNA_sensitivity_subset1$lncRNA_miRNA_pair), unique(mircode_lncRNA_subset1$lncRNA_miRNA_pair))
encRNA_sensitivity_subset2 = encRNA_sensitivity_subset1[which(encRNA_sensitivity_subset1$lncRNA_miRNA_pair %in% intersected_lncRNA_miRNA_pairs),]
# now, we have already found all lncRNA_miRNA pairs in the sensitivity matrix that are also included in miRcode, thus the duty of miRcode is done now
# next, we will be working on starbase. First, find all the intersected miRNAs between starbase and encRNA_sensitivity_subset2
starbase = process_starBase()
intersected_miRNAs = intersect(unique(starbase$miRNA), unique(encRNA_sensitivity_subset2$miRNA))
# subset starbase to include only miRNA shown up in encRNA_sensitivity_subset2;
# similarly, subset encRNA_sensitivity_subset2
starbase_subset = starbase[which(starbase$miRNA %in% intersected_miRNAs),]
encRNA_sensitivity_subset3 = encRNA_sensitivity_subset2[which(encRNA_sensitivity_subset2$miRNA %in% intersected_miRNAs),]
# now, find all intersected miRNA_mRNA pairs between encRNA_sensitivity_subset3 and starbase_subset
intersected_lncRNA_miRNA_pairs = intersect(unique(encRNA_sensitivity_subset3$mRNA_miRNA_pair), unique(starbase_subset$mRNA_miRNA_pair))
encRNA_sensitivity_subset4 = encRNA_sensitivity_subset2[which(encRNA_sensitivity_subset3$mRNA_miRNA_pair %in% intersected_lncRNA_miRNA_pairs),]
return(encRNA_sensitivity_subset4)
}
process_starBase = function(){
load("starbase_mRNA_miRNA_interactions.rda")
processed = starbase_mrna_mirna_interaction
colnames(processed)[1:2] = c("miRNA", "putative_mRNA")
#dim(processed); View(processed)
processed$miRNA = tolower(processed$miRNA)
# foreach element, remove 3p and 5p parts
processed$miRNA = gsub("-3p","",processed$miRNA)
processed$miRNA = gsub("-5p","",processed$miRNA)
processed$mRNA_miRNA_pair = paste(processed$putative_mRNA, processed$miRNA, sep = "-")
processed = processed[,c("miRNA", "putative_mRNA", "mRNA_miRNA_pair")]
processed = unique(processed)
return(processed)
}
get_scca_result = function(x, z, nperms = 100){
require(PMA)
perm_out <- CCA.permute(x = x,
z = z,
typex = "standard",
typez = "standard",
nperms = nperms)
out <- CCA(x = x,
z = z,
typex = "standard",
typez = "standard",
penaltyx = perm_out$bestpenaltyx,
penaltyz = perm_out$bestpenaltyz,
v = perm_out$v.init,
K = 1)
l = list(perm = perm_out, out = out)
return(l)
}
| /code_correlation_analysis/helper_functions.R | no_license | cwt1/encRNA | R | false | false | 10,189 | r | get_lncRNA_mRNA_pairs = function(originalCorrMatrix, corrThreshold){
correlation_pairs = which(originalCorrMatrix > corrThreshold, arr.ind = TRUE)
lncRNA = rownames(originalCorrMatrix)[correlation_pairs[,1]]
mRNA = colnames(originalCorrMatrix)[correlation_pairs[,2]]
dataframe = as.data.frame(cbind(correlation_pairs, lncRNA, mRNA))
colnames(dataframe) = c("lncRNA_index", "mRNA_index","lncRNA", "mRNA")
dataframe$lncRNA_index = as.numeric(as.character(dataframe$lncRNA_index))
dataframe$mRNA_index = as.numeric(as.character(dataframe$mRNA_index))
# correlation_vector = normal_lncRNA_mRNA_corr_matrix[dataframe$lncRNA_index,dataframe$mRNA_index]
dataframe = cbind(dataframe, originalCorrMatrix[which(originalCorrMatrix > corrThreshold)])
colnames(dataframe) = c("lncRNA_index", "mRNA_index","lncRNA", "mRNA", "corr")
dataframe = dataframe[order(-dataframe$corr),]
return(dataframe)
}
get_encRNA = function(matrix, miRNA_mRNA_corr, miRNA_lncRNA_corr, lncRNA_mRNA_corr, threshold){
triple = which(matrix > threshold, arr.ind = TRUE)
encRNAs = rownames(matrix)[triple[,1]]
miRNA = colnames(matrix)[triple[,2]]
dataframe = as.data.frame(cbind(triple, encRNAs, miRNA))
colnames(dataframe) = c("encRNA_pair_index", "miRNA_index","encRNA_pair", "miRNA")
dataframe$encRNA_pair_index = as.numeric(as.character(dataframe$encRNA_pair_index))
dataframe$miRNA_index = as.numeric(as.character(dataframe$miRNA_index))
dataframe = cbind(dataframe, matrix[which(matrix > threshold)])
colnames(dataframe) = c("encRNA_pair_index", "miRNA_index","encRNA_pair", "miRNA", "sensitivity")
dataframe = dataframe[order(-dataframe$sensitivity),]
# dataframe$lncRNA = substr(dataframe$encRNA_pair, start = 1, stop = 17)
# dataframe$mRNA = substr(dataframe$encRNA_pair, start = 19, stop = nchar(as.character(dataframe$encRNA_pair)))
pos = regexpr("-",as.character(dataframe$encRNA_pair))
dataframe$lncRNA = substr(as.character(dataframe$encRNA_pair), start = 1, stop = pos - 1)
dataframe$mRNA = substr(as.character(dataframe$encRNA_pair), start = pos + 1, stop = nchar(as.character(dataframe$encRNA_pair)))
dataframe$lncRNA_miRNA_corr = rep(NA, nrow(dataframe))
dataframe$mRNA_miRNA_corr = rep(NA, nrow(dataframe))
dataframe$lncRNA_miRNA_corr = sapply(1:nrow(dataframe), function(index){
miRNA_lncRNA_corr[as.character(dataframe$miRNA[index]),as.character(dataframe$lncRNA[index])]
})
dataframe$mRNA_miRNA_corr = sapply(1:nrow(dataframe), function(index){
miRNA_mRNA_corr[as.character(dataframe$miRNA[index]),as.character(dataframe$mRNA[index])]
})
dataframe$lncRNA_mRNA_corr = sapply(1:nrow(dataframe), function(index){
lncRNA_mRNA_corr[as.character(dataframe$lncRNA[index]),as.character(dataframe$mRNA[index])]
})
dataframe$encRNA_triple = paste(dataframe$encRNA_pair, dataframe$miRNA, sep = "-")
dataframe$lncRNA_miRNA_pair = paste(dataframe$lncRNA, dataframe$miRNA, sep = "-")
dataframe$mRNA_miRNA_pair = paste(dataframe$mRNA, dataframe$miRNA, sep = "-")
dataframe = dataframe[,c("lncRNA", "mRNA", "miRNA", "sensitivity",
"lncRNA_mRNA_corr", "lncRNA_miRNA_corr", "mRNA_miRNA_corr",
"encRNA_pair", "encRNA_triple",
"lncRNA_miRNA_pair" , "mRNA_miRNA_pair",
"encRNA_pair_index", "miRNA_index")]
return(dataframe)
}
# for each lncRNA ensemble ids, find potential miRNA interaction
getMiRNAs = function(miRNA_family = NULL){
if (is.null(miRNA_family)){
print("must provide miRNA_family")
break;
}
part1 = substr(miRNA_family, start = 1, stop = 3)
part1 = paste("hsa",tolower(part1),sep = "-")
# substring anything after miR till the end, divided by "/"
part2 = substr(miRNA_family,start = 5, stop = nchar(miRNA_family))
part2 = unlist(strsplit(x = part2, split = "/"))
# foreach element, remove 3p and 5p parts
part2 = gsub("-3p","",part2)
part2 = gsub("-5p","",part2)
# return individual mircRNA,
# example: 106abc will be disconstructed into 106a, 106b, 106c
part2 = sapply(part2, function(element){
if (grepl("\\D",element)){
digit_part = gsub(pattern = "\\D", replacement = "", x = element)
character_parts = gsub(pattern = "\\d", replacement = "", x = element)
character_parts = unlist(strsplit(x = character_parts,split = ""))
returned_value = paste(digit_part, character_parts,sep = "")
}else{
element
}
})
part2 = unname(unlist(part2))
return(paste(part1,part2,sep="-"))
}
get_putative_lncRNA_miRNA = function(dataframe){
require(rlist)
l = list()
#i = 1;
apply(dataframe, 1, function(r){
k = getMiRNAs(r[2])
l <<- list.append(l, k)
#print(i); i <<- i + 1;
})
names(l) = dataframe$gene_id
df = reshape2::melt(l)
colnames(df) = c("miRNA", "putative_lncRNAs")
df$lncRNA_miRNA_pair = paste(df$putative_lncRNAs, df$miRNA, sep = "-")
return(df)
}
get_putative_lncRNA_miRNA_2 = function(dataframe){
require(rlist)
l = list()
i = 1;
apply(dataframe, 1, function(r){
k = getMiRNAs(r[2])
l <<- list.append(l, k)
print(i); i <<- i + 1;
})
names(l) = dataframe$gene_id
df = data.frame(
miRNA = unlist(l),
putative_lncRNAs = rep(names(l), lapply(l, length))
)
colnames(df) = c("miRNA", "putative_lncRNAs")
df$lncRNA_miRNA_pair = paste(df$putative_lncRNAs, df$miRNA, sep = "-")
return(df)
}
get_putative_encRNA_interaction = function(df){
if (!("brca_putative_encRNA" %in% ls())){
load("data_Saved_R_Objects/miRNA_target/brca_putative_encRNA.rda"); gc()
}
brca_putative_encRNA_subset = brca_putative_encRNA[which(brca_putative_encRNA$miRNA %in% df$miRNA),]
brca_putative_encRNA_subset = brca_putative_encRNA_subset[which(brca_putative_encRNA_subset$lncRNA %in% df$lncRNA),]
brca_putative_encRNA_subset = brca_putative_encRNA_subset[which(brca_putative_encRNA_subset$mRNA %in% df$mRNA),]
brca_putative_encRNA_subset$encRNA_triple = paste(brca_putative_encRNA_subset$lncRNA,
brca_putative_encRNA_subset$mRNA,
brca_putative_encRNA_subset$miRNA,
sep = "-")
common_triplets = intersect(df$encRNA_triple, brca_putative_encRNA_subset$encRNA_triple)
return(normal_encRNA[which(df$encRNA_triple %in% common_triplets),])
}
get_matched_enRNA_sensitivity_with_putative_binding = function(encRNA_sensitivity){
load("mircode_objects.rda")
lncRNAs_overlapped = intersect(unique(mircode_lncRNA$gene_id), unique(encRNA_sensitivity$lncRNA))
# subset the encRNA_sensivivity to include only lncRNAs matches lncRNAs in miRcode
encRNA_sensitivity_subset1 = encRNA_sensitivity[which(encRNA_sensitivity$lncRNA %in% lncRNAs_overlapped),]
# similarly, subset the mircode_lncRNA to include only lncRNAs matches lncRNAs in encRNA_sensivivity
mircode_lncRNA_subset1 = mircode_lncRNA[which(mircode_lncRNA$gene_id %in% lncRNAs_overlapped),
c("gene_id","microrna")]
mircode_lncRNA_subset1 = get_putative_lncRNA_miRNA(mircode_lncRNA_subset1) # divide miRNAs familily into individual miRNAs
# now, subset encRNA_sensivivity_subset1 to include only the lncRNA-miRNA pairs which also shows up in mircode_lncRNA_subset1
# length(intersect(unique(encRNA_sensitivity_subset1$lncRNA_miRNA_pair), unique(mircode_lncRNA_subset1$lncRNA_miRNA_pair)))
intersected_lncRNA_miRNA_pairs = intersect(unique(encRNA_sensitivity_subset1$lncRNA_miRNA_pair), unique(mircode_lncRNA_subset1$lncRNA_miRNA_pair))
encRNA_sensitivity_subset2 = encRNA_sensitivity_subset1[which(encRNA_sensitivity_subset1$lncRNA_miRNA_pair %in% intersected_lncRNA_miRNA_pairs),]
# now, we have already found all lncRNA_miRNA pairs in the sensitivity matrix that are also included in miRcode, thus the duty of miRcode is done now
# next, we will be working on starbase. First, find all the intersected miRNAs between starbase and encRNA_sensitivity_subset2
starbase = process_starBase()
intersected_miRNAs = intersect(unique(starbase$miRNA), unique(encRNA_sensitivity_subset2$miRNA))
# subset starbase to include only miRNA shown up in encRNA_sensitivity_subset2;
# similarly, subset encRNA_sensitivity_subset2
starbase_subset = starbase[which(starbase$miRNA %in% intersected_miRNAs),]
encRNA_sensitivity_subset3 = encRNA_sensitivity_subset2[which(encRNA_sensitivity_subset2$miRNA %in% intersected_miRNAs),]
# now, find all intersected miRNA_mRNA pairs between encRNA_sensitivity_subset3 and starbase_subset
intersected_lncRNA_miRNA_pairs = intersect(unique(encRNA_sensitivity_subset3$mRNA_miRNA_pair), unique(starbase_subset$mRNA_miRNA_pair))
encRNA_sensitivity_subset4 = encRNA_sensitivity_subset2[which(encRNA_sensitivity_subset3$mRNA_miRNA_pair %in% intersected_lncRNA_miRNA_pairs),]
return(encRNA_sensitivity_subset4)
}
process_starBase = function(){
load("starbase_mRNA_miRNA_interactions.rda")
processed = starbase_mrna_mirna_interaction
colnames(processed)[1:2] = c("miRNA", "putative_mRNA")
#dim(processed); View(processed)
processed$miRNA = tolower(processed$miRNA)
# foreach element, remove 3p and 5p parts
processed$miRNA = gsub("-3p","",processed$miRNA)
processed$miRNA = gsub("-5p","",processed$miRNA)
processed$mRNA_miRNA_pair = paste(processed$putative_mRNA, processed$miRNA, sep = "-")
processed = processed[,c("miRNA", "putative_mRNA", "mRNA_miRNA_pair")]
processed = unique(processed)
return(processed)
}
get_scca_result = function(x, z, nperms = 100){
require(PMA)
perm_out <- CCA.permute(x = x,
z = z,
typex = "standard",
typez = "standard",
nperms = nperms)
out <- CCA(x = x,
z = z,
typex = "standard",
typez = "standard",
penaltyx = perm_out$bestpenaltyx,
penaltyz = perm_out$bestpenaltyz,
v = perm_out$v.init,
K = 1)
l = list(perm = perm_out, out = out)
return(l)
}
|
#' Script to extract raster data for all sampling localities
#' and find the most important features.
#'
#' This script takes as input a directory of rasters, crops them to the
#' sampling extent, finds the raster values at each sample locality,
#' and uses MAXENT and ENMeval to determine the most important raster
#' layers (i.e. features).
#'
#'
#' Function to load in and crop the raster layers to the sampling extent
#'
#' This function takes as input a directory of rasters. Only the desired
#' rasters should be included in raster.dir. You also will need a
#' comma-delimited sample.file that has four columns in a specific order:
#' (sampleIDs,populationIDs,latitude,longitude). You can choose if a header
#' line is present. The rasters will all be put into a stack and cropped to
#' the extent of the sample localities + bb.buffer.
#' @param raster.dir Directory of rasters to load and crop
#' @param sample.file CSV file with sample information (sampleID,popID,lat,lon)
#' @param header Boolean; Does sample.file have a header line?
#' @param bb.buffer Integer; Buffer around sample bounding box.
#' bb.buffer = bb.buffer * resolution (in arc-seconds)
#' @param plotDIR Directory to save plots to
#' @return List with cropped rasters and other info
#' @export
prepare_rasters <- function(raster.dir,
sample.file,
header = TRUE,
bb.buffer = 10,
plotDIR = "./plots"){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
dir.create(plotDIR, showWarnings = FALSE)
samples <-
read.csv(file = sample.file,
header = header,
stringsAsFactors = FALSE)
# Make sure values are numeric.
samples[,3] <- as.numeric(as.character(samples[,3]))
samples[,4] <- as.numeric(as.character(samples[,4]))
# Get dataframe of longitude, latitude.
coords <- data.frame(samples[,4], samples[,3])
# Make into factor for bg color assignment later.
pops <- base::as.factor(samples[,2])
# Change column names.
colnames(coords) <- c("lng", "lat")
# Get raster filenames (all in one directory)
files <- list.files(path = raster.dir, full.names = T)
writeLines("\n\nOrder of raster files: \n")
# Order alphanumerically.
files <- gtools::mixedsort(files)
for (i in 1:length(files)){
writeLines(paste0(i, ": ", files[i]))
}
# Put the rasters into a RasterStack.
envs <- raster::stack(files)
writeLines(paste0("\n\nLoaded ", raster::nlayers(envs),
" raster layers.."))
# Plot first raster in the stack, bio1.
#raster::plot(envs[[1]], main=names(envs)[1])
# Get CRS (coordinate reference system)
mycrs <- raster::crs(envs[[1]])
# Create spatialpoints object.
p <- sp::SpatialPoints(coords = coords, proj4string=mycrs)
# Get the bounding box of the points
bb <- raster::bbox(p)
# Add (bb.buffer * resolution / 2) to sample bounds for background extent.
bb.buf <- raster::extent(bb[1]-bb.buffer,
bb[3]+bb.buffer,
bb[2]-bb.buffer,
bb[4]+bb.buffer)
# Add bb.buffer * resolution (in arc-seconds) to bb for cropping raster layer.
envs.buf <- raster::extent(bb[1]-(bb.buffer/2),
bb[3]+(bb.buffer/2),
bb[2]-(bb.buffer/2),
bb[4]+(bb.buffer/2))
writeLines(paste0("\n\nCropping to samples with buffer of ",
(bb.buffer/2),
" degrees\n"))
# Crop raster extent to sample bounding box + bb.buffer
envs.cropped <- raster::crop(envs, envs.buf)
writeLines(paste0("\nCropping background layers with buffer of ",
bb.buffer,
" degrees\n"))
# Crop environmental layers to tmatch the study extent.
# Used for background data later.
envs.backg <- raster::crop(envs, bb.buf)
counter <- 1
# Save raster plots with sample points layered on top
pdf(file = file.path(plotDIR, "croppedRasterPlots.pdf"),
width = 7,
height = 7,
onefile = T)
for (i in 1:raster::nlayers(envs.backg)){
writeLines(paste0("Saving raster plot ", i, " to disk..."))
raster::plot(envs.backg[[i]])
dismo::points(coords, pch=21, bg=pops)
counter <- counter+1
}
dev.off()
envList <- list(envs.cropped, envs.backg, coords, p, pops, samples[,1])
rm(envs, p, bb, bb.buf, envs.buf, envs.cropped, envs.backg)
gc(verbose = FALSE)
return(envList)
}
#' Function to make partitions from background and foreground rasters.
#'
#' This function uses the output from prepare_raster() and makes
#' background partitions using several methods.
#' @param env.list Object output from prepare_rasters() function
#' @param number.bg.points Number of background points to generate
#' @param bg.raster Raster to use for background points
#' @param agg.factor A vector of 1 or 2 numbers for the checkerboard
#' methods
#' @param plotDIR Directory to save the plots to
#' @return Object with background points
#' @export
partition_raster_bg <- function(env.list,
number.bg.points = 10000,
bg.raster = 1,
plotDIR = "./plots",
agg.factor = 2){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
dir.create(plotDIR, showWarnings = FALSE)
bg.raster <- as.integer(as.character(bg.raster))
envs.cropped <- env.list[[1]]
envs.backg <- env.list[[2]]
coords <- env.list[[3]]
p <- env.list[[4]]
pops <- env.list[[5]]
inds <- env.list[[6]]
rm(env.list)
gc(verbose = FALSE)
writeLines(paste0("\n\nGenerating ", number.bg.points, " random points..."))
# Randomly sample 10,000 background points from one background extent raster
# (only one per cell without replacement).
# Note: If the raster has <10,000 pixels,
# you'll get a warning and all pixels will be used for background.
bg <- dismo::randomPoints(envs.backg[[bg.raster]], n=number.bg.points)
bg <- raster::as.data.frame(bg)
# Change column names.
colnames(coords) <- c("lng", "lat")
rm(pops)
gc(verbose = FALSE)
writeLines("\nSaving background partition plots...")
writeLines("\nPerforming block method...")
### Block Method
blocks <- ENMeval::get.block(coords, bg)
pdf(file = file.path(plotDIR, "bgPartitions_blocks.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(envs.backg[[bg.raster]], col="gray",
main = "Partitions into Training and Test Data",
sub="Block Method",
xlab = "Longitude",
ylab = "Latitude")
dismo::points(coords, pch=21, bg=blocks$occ.grp)
dev.off()
rm(blocks)
gc(verbose = FALSE)
writeLines("Performing checkerboard1 method...")
### Checkerboard1 Method
check1 <- ENMeval::get.checkerboard1(coords,
envs.cropped,
bg,
aggregation.factor = agg.factor)
pdf(file = file.path(plotDIR, "bgPartitions_checkerboard1.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(
envs.backg[[bg.raster]],
col="gray",
main = "Partitions into Training and Test Data (Aggregation Factor=5)",
sub="Checkerboard1 Method",
xlab = "Longitude",
ylab = "Latitude"
)
dismo::points(bg, pch=21, bg=check1$occ.grp)
dev.off()
rm(check1)
gc(verbose = FALSE)
writeLines("Performing checkerboard2 method...")
### Using the Checkerboard2 method.
check2 <-
ENMeval::get.checkerboard2(coords,
envs.cropped,
bg,
aggregation.factor = c(agg.factor,
agg.factor))
pdf(file = file.path(plotDIR, "bgPartitions_checkerboard2.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(envs.backg[[bg.raster]], col="gray",
main = "Partitions into Training and Test Data",
sub="Checkerboard2 Method",
xlab = "Longitude",
ylab = "Latitude")
dismo::points(bg, pch=21, bg=check2$bg.grp)
dismo::points(coords, pch=21,
bg=check2$occ.grp,
col="white",
cex=1.5)
dev.off()
rm(check2)
gc(verbose = FALSE)
writeLines("\nDone!")
return(bg)
}
#' Function to run ENMeval and MAXENT on the raster layers.
#'
#' This function takes as input the object output from preparte_rasters() and
#' the background partitioning method you would like to use
#' (see ENMeval documentation). You can visualize how the partitioning
#' methods will look by viewing the PDF output from partition_raster_bg.
#' See ?partition_raster_bg for more info.
#' @param envs.fg First element of envs.list returned from prepare_raster_bg()
#' @param bg Object with background points; generated from partition_raster_bg
#' @param coords Data.Frame with (lon,lat). 3rd element of envs.list
#' @param partition.method Method used for background point partitioning
#' @param parallel If TRUE, ENMeval is run parallelized with np CPU cores
#' @param np Number of parallel cores to use if parallel = TRUE
#' @param RMvalues Vector of non-negative regularization multiplier values.
#' Higher values impose a stronger penalty on model complexity
#' @param feature.classes Character vector of feature classes to be used
#' @param categoricals Vector indicating which (if any) of the input
#' environmental layers are categorical.
#' @param agg.factor Aggregation factor(s) for checkerboard
#' partitioning method.
#' @param algorithm Character vector. Defaults to dismo's maxent.jar
#' @return ENMeval object
#' @export
runENMeval <- function(envs.fg,
bg,
coords,
partition.method,
parallel = FALSE,
np = 2,
RMvalues = seq(0.5, 4, 0.5),
feature.classes = c("L",
"LQ",
"H",
"LQH",
"LQHP",
"LQHPT"),
categoricals = NULL,
agg.factor = 2,
algorithm = "maxent.jar"
){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
if (partition.method == "checkerboard2"){
agg.factor <- c(agg.factor, agg.factor)
}
if (parallel == TRUE){
eval.par <-
ENMeval::ENMevaluate(
occ = coords,
env = envs.fg,
bg.coords = bg,
method = partition.method,
RMvalues = RMvalues,
fc = feature.classes,
parallel = parallel,
algorithm = algorithm,
categoricals = categoricals,
aggregation.factor = agg.factor,
numCores = np
)
} else if (parallel == FALSE){
eval.par <-
ENMeval::ENMevaluate(
occ = coords,
env = envs.fg,
bg.coords = bg,
method = partition.method,
RMvalues = RMvalues,
fc = feature.classes,
parallel = parallel,
algorithm = algorithm,
categoricals = categoricals,
aggregation.factor = agg.factor
)
} else {
stop("Parallel must be either TRUE or FALSE")
}
return(eval.par)
}
#' Function to summarize ENMeval output
#' @param eval.par Object returned from runENMeval
#' @param minLat Minimum latitude to plot for predictions
#' @param maxLat Maximum latitude to plot for predictions
#' @param minLon Minimum longitude to plot for predictions
#' @param maxLon Maximum longitude to plot for predictions
#' @param examine.predictions Character vector of feature classes to examine
#' how complexity affects predictions
#' @param RMvalues Vector of non-negative RM values to examine how
#' complexity affects predictions
#' @param plotDIR Directory to save plots to
#' @param niche.overlap Boolean. If TRUE, calculates pairwise niche overlap
#' matrix
#' @param plot.width Integer. Specify plot widths
#' @param plot.height Integer. Specify plot heights
#' @param imp.margins Integer vector. Margins of variable importance barplot.
#' c(bottom, left, top, right)
#' @export
summarize_ENMeval <- function(eval.par,
minLat = 20,
maxLat = 50,
minLon = -110,
maxLon = -40,
examine.predictions = c("L",
"LQ",
"H",
"LQH",
"LQHP",
"LQHPT"),
RMvalues = seq(0.5, 4, 0.5),
plotDIR = "./plots",
niche.overlap = FALSE,
plot.width = 7,
plot.height = 7,
imp.margins = c(10.0, 4.1, 4.1, 2.1)){
dir.create(plotDIR, showWarnings = FALSE)
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
if (niche.overlap){
# Calculate niche overlap.
overlap <- ENMeval::calc.niche.overlap(eval.par@predictions, stat="D")
# Write niche overlap to a file.
write.csv(x = overlap, file = file.path(plotDIR,
"ENMeval_nicheOverlap.csv"))
}
# Write ENMeval results to file.
write.csv(x = eval.par@results, file = file.path(plotDIR,
"ENMeval_results.csv"))
# Get best model.
eval.par@results[which(eval.par@results$delta.AICc==0),]
eval.par@results[which(eval.par@results$delta.AICc==0),]
bestAICc <- eval.par@results[which(eval.par@results$delta.AICc==0),]
# Write to file.
write.csv(bestAICc, file = file.path(plotDIR, "ENMeval_bestAICc.csv"))
# Save it to file.
pdf(file = file.path(plotDIR,
"ENMeval_relativeOccurenceRate_bestModel.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
raster::plot(eval.par@predictions[[which(eval.par@results$delta.AICc==0)]],
main="Relative Occurrence Rate")
dev.off()
# Look at the model object for our "AICc optimal" model:
aic.opt <- eval.par@models[[which(eval.par@results$delta.AICc==0)]]
# Write it to file.
write.csv(x = aic.opt@results,
file = file.path(plotDIR,
"ENMeval_maxentModel_aicOptimal.csv"))
# Get a data.frame of two variable importance metrics:
# percent contribution and permutation importance.
varImportance <- ENMeval::var.importance(aic.opt)
# Write them to file.
write.csv(varImportance, file = file.path(plotDIR,
"ENMeval_varImportance.csv"))
# The "lambdas" slot shows which variables were included in the model.
# If the coefficient is 0, that variable was not included in the model.
lambdas <- aic.opt@lambdas
write.csv(lambdas, file = file.path(plotDIR, "ENMeval_lambdas.csv"))
pdf(file = file.path(plotDIR, "ENMeval_AUC_importancePlots.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
ENMeval::eval.plot(eval.par@results)
ENMeval::eval.plot(eval.par@results, 'avg.test.AUC',
variance ='var.test.AUC')
ENMeval::eval.plot(eval.par@results, "avg.diff.AUC",
variance="var.diff.AUC")
# Plot permutation importance.
df <- ENMeval::var.importance(aic.opt)
par(mar=imp.margins)
raster::barplot(df$permutation.importance,
names.arg = df$variable,
las = 2,
ylab = "Permutation Importance")
dev.off()
# Let's see how model complexity changes the predictions in our example
pdf(file = file.path(plotDIR, "modelPredictions.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
for (i in 1:length(examine.predictions)){
for (j in 1:length(RMvalues)){
raster::plot(eval.par@predictions[[paste(examine.predictions[i],
RMvalues[j],
sep = "_")]],
ylim=c(minLat,maxLat),
xlim=c(minLon, maxLon),
main=paste0(examine.predictions[i],
"_",
RMvalues[j],
" Prediction"),
)
}
}
dev.off()
}
#' Function to extract raster values at each sample point for raster stack
#' @param env.list List object generated from prepare_rasters()
#' @return List of data.frames with raster values at each sample locality
#' for each raster layer, and list of raster names
#' @export
extractPointValues <- function(env.list){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
envs.cropped <- env.list[[1]]
p <- env.list[[4]]
inds <- env.list[[6]]
raster.points <- list()
writeLines("\nExtracting raster values at sample points")
# Extract raster values at each sample point.
for (i in 1:raster::nlayers(envs.cropped)){
raster.points[[i]] <- raster::extract(envs.cropped[[i]], p)
}
writeLines("\nAdding sampleIDs to raster dataframe")
# Add sample IDs to point/raster value dataframe.
rp.df <- lapply(raster.points,
function(x) {
cbind.data.frame(
inds,
raster::coordinates(p),
x
)
}
)
rasterNames <- env.list[[1]]@data@names
# Use raster name as x colname
for (i in 1:length(rasterNames)){
colnames(rp.df[[i]]) <- c("inds", "lng", "lat", rasterNames[i])
}
rm(raster.points)
gc(verbose = FALSE)
# Get which samples are on NA raster values.
test4na <- lapply(rp.df, function(x) x[raster::rowSums(is.na(x)) > 0,])
# If some samples are on NA raster values: Print them and stop with error.
allEmpty <- lapply(test4na, function(x) nrow(x) == 0)
allEmpty <- unlist(allEmpty)
if(!all(allEmpty)){
writeLines("\n\nThe following samples are located on NA raster values:\n")
print(test4na)
stop("The above samples have NA raster values. Please remove the them.")
}
rm(test4na)
gc(verbose = FALSE)
return(rp.df)
}
| /R/get_important_rasters.R | no_license | tkchafin/ClinePlotR | R | false | false | 22,493 | r | #' Script to extract raster data for all sampling localities
#' and find the most important features.
#'
#' This script takes as input a directory of rasters, crops them to the
#' sampling extent, finds the raster values at each sample locality,
#' and uses MAXENT and ENMeval to determine the most important raster
#' layers (i.e. features).
#'
#'
#' Function to load in and crop the raster layers to the sampling extent
#'
#' This function takes as input a directory of rasters. Only the desired
#' rasters should be included in raster.dir. You also will need a
#' comma-delimited sample.file that has four columns in a specific order:
#' (sampleIDs,populationIDs,latitude,longitude). You can choose if a header
#' line is present. The rasters will all be put into a stack and cropped to
#' the extent of the sample localities + bb.buffer.
#' @param raster.dir Directory of rasters to load and crop
#' @param sample.file CSV file with sample information (sampleID,popID,lat,lon)
#' @param header Boolean; Does sample.file have a header line?
#' @param bb.buffer Integer; Buffer around sample bounding box.
#' bb.buffer = bb.buffer * resolution (in arc-seconds)
#' @param plotDIR Directory to save plots to
#' @return List with cropped rasters and other info
#' @export
prepare_rasters <- function(raster.dir,
sample.file,
header = TRUE,
bb.buffer = 10,
plotDIR = "./plots"){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
dir.create(plotDIR, showWarnings = FALSE)
samples <-
read.csv(file = sample.file,
header = header,
stringsAsFactors = FALSE)
# Make sure values are numeric.
samples[,3] <- as.numeric(as.character(samples[,3]))
samples[,4] <- as.numeric(as.character(samples[,4]))
# Get dataframe of longitude, latitude.
coords <- data.frame(samples[,4], samples[,3])
# Make into factor for bg color assignment later.
pops <- base::as.factor(samples[,2])
# Change column names.
colnames(coords) <- c("lng", "lat")
# Get raster filenames (all in one directory)
files <- list.files(path = raster.dir, full.names = T)
writeLines("\n\nOrder of raster files: \n")
# Order alphanumerically.
files <- gtools::mixedsort(files)
for (i in 1:length(files)){
writeLines(paste0(i, ": ", files[i]))
}
# Put the rasters into a RasterStack.
envs <- raster::stack(files)
writeLines(paste0("\n\nLoaded ", raster::nlayers(envs),
" raster layers.."))
# Plot first raster in the stack, bio1.
#raster::plot(envs[[1]], main=names(envs)[1])
# Get CRS (coordinate reference system)
mycrs <- raster::crs(envs[[1]])
# Create spatialpoints object.
p <- sp::SpatialPoints(coords = coords, proj4string=mycrs)
# Get the bounding box of the points
bb <- raster::bbox(p)
# Add (bb.buffer * resolution / 2) to sample bounds for background extent.
bb.buf <- raster::extent(bb[1]-bb.buffer,
bb[3]+bb.buffer,
bb[2]-bb.buffer,
bb[4]+bb.buffer)
# Add bb.buffer * resolution (in arc-seconds) to bb for cropping raster layer.
envs.buf <- raster::extent(bb[1]-(bb.buffer/2),
bb[3]+(bb.buffer/2),
bb[2]-(bb.buffer/2),
bb[4]+(bb.buffer/2))
writeLines(paste0("\n\nCropping to samples with buffer of ",
(bb.buffer/2),
" degrees\n"))
# Crop raster extent to sample bounding box + bb.buffer
envs.cropped <- raster::crop(envs, envs.buf)
writeLines(paste0("\nCropping background layers with buffer of ",
bb.buffer,
" degrees\n"))
# Crop environmental layers to tmatch the study extent.
# Used for background data later.
envs.backg <- raster::crop(envs, bb.buf)
counter <- 1
# Save raster plots with sample points layered on top
pdf(file = file.path(plotDIR, "croppedRasterPlots.pdf"),
width = 7,
height = 7,
onefile = T)
for (i in 1:raster::nlayers(envs.backg)){
writeLines(paste0("Saving raster plot ", i, " to disk..."))
raster::plot(envs.backg[[i]])
dismo::points(coords, pch=21, bg=pops)
counter <- counter+1
}
dev.off()
envList <- list(envs.cropped, envs.backg, coords, p, pops, samples[,1])
rm(envs, p, bb, bb.buf, envs.buf, envs.cropped, envs.backg)
gc(verbose = FALSE)
return(envList)
}
#' Function to make partitions from background and foreground rasters.
#'
#' This function uses the output from prepare_raster() and makes
#' background partitions using several methods.
#' @param env.list Object output from prepare_rasters() function
#' @param number.bg.points Number of background points to generate
#' @param bg.raster Raster to use for background points
#' @param agg.factor A vector of 1 or 2 numbers for the checkerboard
#' methods
#' @param plotDIR Directory to save the plots to
#' @return Object with background points
#' @export
partition_raster_bg <- function(env.list,
number.bg.points = 10000,
bg.raster = 1,
plotDIR = "./plots",
agg.factor = 2){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
dir.create(plotDIR, showWarnings = FALSE)
bg.raster <- as.integer(as.character(bg.raster))
envs.cropped <- env.list[[1]]
envs.backg <- env.list[[2]]
coords <- env.list[[3]]
p <- env.list[[4]]
pops <- env.list[[5]]
inds <- env.list[[6]]
rm(env.list)
gc(verbose = FALSE)
writeLines(paste0("\n\nGenerating ", number.bg.points, " random points..."))
# Randomly sample 10,000 background points from one background extent raster
# (only one per cell without replacement).
# Note: If the raster has <10,000 pixels,
# you'll get a warning and all pixels will be used for background.
bg <- dismo::randomPoints(envs.backg[[bg.raster]], n=number.bg.points)
bg <- raster::as.data.frame(bg)
# Change column names.
colnames(coords) <- c("lng", "lat")
rm(pops)
gc(verbose = FALSE)
writeLines("\nSaving background partition plots...")
writeLines("\nPerforming block method...")
### Block Method
blocks <- ENMeval::get.block(coords, bg)
pdf(file = file.path(plotDIR, "bgPartitions_blocks.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(envs.backg[[bg.raster]], col="gray",
main = "Partitions into Training and Test Data",
sub="Block Method",
xlab = "Longitude",
ylab = "Latitude")
dismo::points(coords, pch=21, bg=blocks$occ.grp)
dev.off()
rm(blocks)
gc(verbose = FALSE)
writeLines("Performing checkerboard1 method...")
### Checkerboard1 Method
check1 <- ENMeval::get.checkerboard1(coords,
envs.cropped,
bg,
aggregation.factor = agg.factor)
pdf(file = file.path(plotDIR, "bgPartitions_checkerboard1.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(
envs.backg[[bg.raster]],
col="gray",
main = "Partitions into Training and Test Data (Aggregation Factor=5)",
sub="Checkerboard1 Method",
xlab = "Longitude",
ylab = "Latitude"
)
dismo::points(bg, pch=21, bg=check1$occ.grp)
dev.off()
rm(check1)
gc(verbose = FALSE)
writeLines("Performing checkerboard2 method...")
### Using the Checkerboard2 method.
check2 <-
ENMeval::get.checkerboard2(coords,
envs.cropped,
bg,
aggregation.factor = c(agg.factor,
agg.factor))
pdf(file = file.path(plotDIR, "bgPartitions_checkerboard2.pdf"),
width = 7,
height = 7,
onefile = TRUE)
raster::plot(envs.backg[[bg.raster]], col="gray",
main = "Partitions into Training and Test Data",
sub="Checkerboard2 Method",
xlab = "Longitude",
ylab = "Latitude")
dismo::points(bg, pch=21, bg=check2$bg.grp)
dismo::points(coords, pch=21,
bg=check2$occ.grp,
col="white",
cex=1.5)
dev.off()
rm(check2)
gc(verbose = FALSE)
writeLines("\nDone!")
return(bg)
}
#' Function to run ENMeval and MAXENT on the raster layers.
#'
#' This function takes as input the object output from preparte_rasters() and
#' the background partitioning method you would like to use
#' (see ENMeval documentation). You can visualize how the partitioning
#' methods will look by viewing the PDF output from partition_raster_bg.
#' See ?partition_raster_bg for more info.
#' @param envs.fg First element of envs.list returned from prepare_raster_bg()
#' @param bg Object with background points; generated from partition_raster_bg
#' @param coords Data.Frame with (lon,lat). 3rd element of envs.list
#' @param partition.method Method used for background point partitioning
#' @param parallel If TRUE, ENMeval is run parallelized with np CPU cores
#' @param np Number of parallel cores to use if parallel = TRUE
#' @param RMvalues Vector of non-negative regularization multiplier values.
#' Higher values impose a stronger penalty on model complexity
#' @param feature.classes Character vector of feature classes to be used
#' @param categoricals Vector indicating which (if any) of the input
#' environmental layers are categorical.
#' @param agg.factor Aggregation factor(s) for checkerboard
#' partitioning method.
#' @param algorithm Character vector. Defaults to dismo's maxent.jar
#' @return ENMeval object
#' @export
runENMeval <- function(envs.fg,
bg,
coords,
partition.method,
parallel = FALSE,
np = 2,
RMvalues = seq(0.5, 4, 0.5),
feature.classes = c("L",
"LQ",
"H",
"LQH",
"LQHP",
"LQHPT"),
categoricals = NULL,
agg.factor = 2,
algorithm = "maxent.jar"
){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
if (partition.method == "checkerboard2"){
agg.factor <- c(agg.factor, agg.factor)
}
if (parallel == TRUE){
eval.par <-
ENMeval::ENMevaluate(
occ = coords,
env = envs.fg,
bg.coords = bg,
method = partition.method,
RMvalues = RMvalues,
fc = feature.classes,
parallel = parallel,
algorithm = algorithm,
categoricals = categoricals,
aggregation.factor = agg.factor,
numCores = np
)
} else if (parallel == FALSE){
eval.par <-
ENMeval::ENMevaluate(
occ = coords,
env = envs.fg,
bg.coords = bg,
method = partition.method,
RMvalues = RMvalues,
fc = feature.classes,
parallel = parallel,
algorithm = algorithm,
categoricals = categoricals,
aggregation.factor = agg.factor
)
} else {
stop("Parallel must be either TRUE or FALSE")
}
return(eval.par)
}
#' Function to summarize ENMeval output
#' @param eval.par Object returned from runENMeval
#' @param minLat Minimum latitude to plot for predictions
#' @param maxLat Maximum latitude to plot for predictions
#' @param minLon Minimum longitude to plot for predictions
#' @param maxLon Maximum longitude to plot for predictions
#' @param examine.predictions Character vector of feature classes to examine
#' how complexity affects predictions
#' @param RMvalues Vector of non-negative RM values to examine how
#' complexity affects predictions
#' @param plotDIR Directory to save plots to
#' @param niche.overlap Boolean. If TRUE, calculates pairwise niche overlap
#' matrix
#' @param plot.width Integer. Specify plot widths
#' @param plot.height Integer. Specify plot heights
#' @param imp.margins Integer vector. Margins of variable importance barplot.
#' c(bottom, left, top, right)
#' @export
summarize_ENMeval <- function(eval.par,
minLat = 20,
maxLat = 50,
minLon = -110,
maxLon = -40,
examine.predictions = c("L",
"LQ",
"H",
"LQH",
"LQHP",
"LQHPT"),
RMvalues = seq(0.5, 4, 0.5),
plotDIR = "./plots",
niche.overlap = FALSE,
plot.width = 7,
plot.height = 7,
imp.margins = c(10.0, 4.1, 4.1, 2.1)){
dir.create(plotDIR, showWarnings = FALSE)
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
if (niche.overlap){
# Calculate niche overlap.
overlap <- ENMeval::calc.niche.overlap(eval.par@predictions, stat="D")
# Write niche overlap to a file.
write.csv(x = overlap, file = file.path(plotDIR,
"ENMeval_nicheOverlap.csv"))
}
# Write ENMeval results to file.
write.csv(x = eval.par@results, file = file.path(plotDIR,
"ENMeval_results.csv"))
# Get best model.
eval.par@results[which(eval.par@results$delta.AICc==0),]
eval.par@results[which(eval.par@results$delta.AICc==0),]
bestAICc <- eval.par@results[which(eval.par@results$delta.AICc==0),]
# Write to file.
write.csv(bestAICc, file = file.path(plotDIR, "ENMeval_bestAICc.csv"))
# Save it to file.
pdf(file = file.path(plotDIR,
"ENMeval_relativeOccurenceRate_bestModel.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
raster::plot(eval.par@predictions[[which(eval.par@results$delta.AICc==0)]],
main="Relative Occurrence Rate")
dev.off()
# Look at the model object for our "AICc optimal" model:
aic.opt <- eval.par@models[[which(eval.par@results$delta.AICc==0)]]
# Write it to file.
write.csv(x = aic.opt@results,
file = file.path(plotDIR,
"ENMeval_maxentModel_aicOptimal.csv"))
# Get a data.frame of two variable importance metrics:
# percent contribution and permutation importance.
varImportance <- ENMeval::var.importance(aic.opt)
# Write them to file.
write.csv(varImportance, file = file.path(plotDIR,
"ENMeval_varImportance.csv"))
# The "lambdas" slot shows which variables were included in the model.
# If the coefficient is 0, that variable was not included in the model.
lambdas <- aic.opt@lambdas
write.csv(lambdas, file = file.path(plotDIR, "ENMeval_lambdas.csv"))
pdf(file = file.path(plotDIR, "ENMeval_AUC_importancePlots.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
ENMeval::eval.plot(eval.par@results)
ENMeval::eval.plot(eval.par@results, 'avg.test.AUC',
variance ='var.test.AUC')
ENMeval::eval.plot(eval.par@results, "avg.diff.AUC",
variance="var.diff.AUC")
# Plot permutation importance.
df <- ENMeval::var.importance(aic.opt)
par(mar=imp.margins)
raster::barplot(df$permutation.importance,
names.arg = df$variable,
las = 2,
ylab = "Permutation Importance")
dev.off()
# Let's see how model complexity changes the predictions in our example
pdf(file = file.path(plotDIR, "modelPredictions.pdf"),
width = plot.width,
height = plot.height,
onefile = TRUE)
for (i in 1:length(examine.predictions)){
for (j in 1:length(RMvalues)){
raster::plot(eval.par@predictions[[paste(examine.predictions[i],
RMvalues[j],
sep = "_")]],
ylim=c(minLat,maxLat),
xlim=c(minLon, maxLon),
main=paste0(examine.predictions[i],
"_",
RMvalues[j],
" Prediction"),
)
}
}
dev.off()
}
#' Function to extract raster values at each sample point for raster stack
#' @param env.list List object generated from prepare_rasters()
#' @return List of data.frames with raster values at each sample locality
#' for each raster layer, and list of raster names
#' @export
extractPointValues <- function(env.list){
if (!requireNamespace("raster", quietly = TRUE)){
warning("The raster package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("sp", quietly = TRUE)){
warning("The sp package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("dismo", quietly = TRUE)){
warning("The dismo package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("rJava", quietly = TRUE)){
warning("The rJava package must be installed to use this functionality")
return(NULL)
}
if(!requireNamespace("ENMeval", quietly = TRUE)){
warning("The ENMeval package must be installed to use this functionality")
return(NULL)
}
envs.cropped <- env.list[[1]]
p <- env.list[[4]]
inds <- env.list[[6]]
raster.points <- list()
writeLines("\nExtracting raster values at sample points")
# Extract raster values at each sample point.
for (i in 1:raster::nlayers(envs.cropped)){
raster.points[[i]] <- raster::extract(envs.cropped[[i]], p)
}
writeLines("\nAdding sampleIDs to raster dataframe")
# Add sample IDs to point/raster value dataframe.
rp.df <- lapply(raster.points,
function(x) {
cbind.data.frame(
inds,
raster::coordinates(p),
x
)
}
)
rasterNames <- env.list[[1]]@data@names
# Use raster name as x colname
for (i in 1:length(rasterNames)){
colnames(rp.df[[i]]) <- c("inds", "lng", "lat", rasterNames[i])
}
rm(raster.points)
gc(verbose = FALSE)
# Get which samples are on NA raster values.
test4na <- lapply(rp.df, function(x) x[raster::rowSums(is.na(x)) > 0,])
# If some samples are on NA raster values: Print them and stop with error.
allEmpty <- lapply(test4na, function(x) nrow(x) == 0)
allEmpty <- unlist(allEmpty)
if(!all(allEmpty)){
writeLines("\n\nThe following samples are located on NA raster values:\n")
print(test4na)
stop("The above samples have NA raster values. Please remove the them.")
}
rm(test4na)
gc(verbose = FALSE)
return(rp.df)
}
|
library(MNM)
### Name: affine.trans
### Title: Function For Affine Data Transformation
### Aliases: affine.trans
### Keywords: multivariate
### ** Examples
data(iris)
IRIS <- iris[,1:4]
colMeans(IRIS)
cov(IRIS)
IRIS.trans <- affine.trans(IRIS, solve(cov(IRIS)), colMeans(IRIS),TRUE)
colMeans(IRIS.trans)
cov(IRIS.trans)
| /data/genthat_extracted_code/MNM/examples/affine.trans.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 327 | r | library(MNM)
### Name: affine.trans
### Title: Function For Affine Data Transformation
### Aliases: affine.trans
### Keywords: multivariate
### ** Examples
data(iris)
IRIS <- iris[,1:4]
colMeans(IRIS)
cov(IRIS)
IRIS.trans <- affine.trans(IRIS, solve(cov(IRIS)), colMeans(IRIS),TRUE)
colMeans(IRIS.trans)
cov(IRIS.trans)
|
library(ggplot2)
data = read.csv("../strongscaling.csv")
data$Fragments = as.factor(data$Fragments)
str(data)
ggplot(data, aes(x = Processors, y = Efficiency.S, color = Fragments)) +
geom_point(size = 3, aes(shape = Fragments)) +
scale_shape_manual(values = c(15,19,17,8)) +
geom_line(size = 1) +
scale_x_log10(breaks = c(128, 256, 512, 1024, 2048, 4096, 8192, 16384)) +
#scale_y_continuous(breaks = c(1, 2, 3, 4, 5)) +
xlab("Number of Cores") +
ylab("Efficiency") +
#ggtitle("") +
theme_bw()
ggsave("../../strong-efficiency.pdf", last_plot()) | /plots/scripts/strong-efficiency.r | no_license | sumitkrpandit/DNASequencing | R | false | false | 572 | r | library(ggplot2)
data = read.csv("../strongscaling.csv")
data$Fragments = as.factor(data$Fragments)
str(data)
ggplot(data, aes(x = Processors, y = Efficiency.S, color = Fragments)) +
geom_point(size = 3, aes(shape = Fragments)) +
scale_shape_manual(values = c(15,19,17,8)) +
geom_line(size = 1) +
scale_x_log10(breaks = c(128, 256, 512, 1024, 2048, 4096, 8192, 16384)) +
#scale_y_continuous(breaks = c(1, 2, 3, 4, 5)) +
xlab("Number of Cores") +
ylab("Efficiency") +
#ggtitle("") +
theme_bw()
ggsave("../../strong-efficiency.pdf", last_plot()) |
##### Global: options #####
Production = T
options(scipen = 1000, expressions = 10000)
appVersion = "v2.0"
appName = "COVID-19 Data Visualization Platform"
appLongName = "COVID-19 Data Visualization Platform"
lastUpdate = "2020-04-07"
loader <- tagList(
waiter::spin_loaders(42),
br(),
h3("Loading data")
)
jsToggleFS <- 'shinyjs.toggleFullScreen = function() {
var element = document.documentElement,
enterFS = element.requestFullscreen || element.msRequestFullscreen || element.mozRequestFullScreen || element.webkitRequestFullscreen,
exitFS = document.exitFullscreen || document.msExitFullscreen || document.mozCancelFullScreen || document.webkitExitFullscreen;
if (!document.fullscreenElement && !document.msFullscreenElement && !document.mozFullScreenElement && !document.webkitFullscreenElement) {
enterFS.call(element);
} else {
exitFS.call(document);
}
}'
source("appFiles/packageLoad.R")
source("appFiles/dataLoad.R")
source("appFiles/CSS.R", local = TRUE)
source("appFiles/dashboardPage.R", local = TRUE)
##### User interface #####
ui <- tagList( # dependencies
use_waiter(),
useSweetAlert(),
useShinyjs(),
extendShinyjs(text = jsToggleFS),
waiter::waiter_show_on_load(loader, color = "#000"),
# shows before anything else
##### CSS and style functions #####
CSS, #CSS.R
# Loading message
argonDash::argonDashPage(
title = appLongName,
header = argonDash::argonDashHeader(
gradient = T,
color = NULL,
top_padding = 2,
bottom_padding = 0,
background_img = "coronavirus.jpg",
height = 70,
argonRow(
argonColumn(width = 8,
h4(appLongName, style = 'color:white;
text-align:left;
vertical-align: middle;
font-size:40px;')
),
argonColumn(
width = 4,
h6(HTML(paste0("Creator & Maintainer: <a href='https://www.shubhrampandey.com' target = '_blank'>Shubhram Pandey</a>")), style = 'color:white;
text-align: right;
font-size:15px;
margin-bottom: 0em'),
h6(HTML(paste0("<a href='https://www.3ai.in' target = '_blank'> - 3AI Ambassdor</a>")), style = 'color:white;text-align: right;font-size:15px;')
),
fixedPanel(
div(
actionBttn("fullScreen",
style = "material-circle",
icon = icon("arrows-alt"),
size = "xs",
color = "warning"),
bsPopover("fullScreen", title = NULL, content = "Click to view in full screen", placement = "left", trigger = "hover",
options = NULL),
onclick = "shinyjs.toggleFullScreen();"
),
top = 55,
right = 10
),
fixedPanel(
div(
actionBttn("kofi",
style = "material-circle",
icon = icon("coffee"),
size = "xs",
color = "success"),
bsPopover("kofi", title = NULL, content = "Buy me a coffee", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://ko-fi.com/shubhrampandey', '_blank')"
),
top = 55,
right = 40
),
fixedPanel(
div(
actionBttn("userGuide",
style = "material-circle",
icon = icon("info"),
size = "xs",
color = "royal"),
bsPopover("userGuide", title = NULL, content = "Go to app help page", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://sites.google.com/view/covid-19-userguide/home', '_blank')"
),
top = 55,
right = 70
),
fixedPanel(
div(
actionBttn("webSite",
style = "material-circle",
icon = icon("address-card"),
size = "xs",
color = "primary"),
bsPopover("webSite", title = NULL, content = "About developer", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://www.shubhrampandey.com', '_blank')"
),
top = 55,
right = 100
)
)
),
sidebar = NULL,
body = argonDashBody(
tags$head( tags$meta(name = "viewport", content = "width=1600"),uiOutput("body")),
tags$br(),
dashboardUI
)
)
)
##### server #####
server <- function(input, output, session) {
printLogJs = function(x, ...) {
logjs(x)
T
}
# addHandler(printLogJs)
if (!Production) options(shiny.error = recover)
options(shiny.sanitize.errors = TRUE, width = 160)
session$onSessionEnded(function() {
stopApp()
# q("no")
})
source("appFiles/dashboardServer.R", local = TRUE)
# Hide the loading message when the rest of the server function has executed
waiter_hide() # will hide *on_load waiter
}
# Run the application
shinyApp(ui = ui, server = server) | /app.R | no_license | champasoft/coronaVirus-dataViz | R | false | false | 5,380 | r | ##### Global: options #####
Production = T
options(scipen = 1000, expressions = 10000)
appVersion = "v2.0"
appName = "COVID-19 Data Visualization Platform"
appLongName = "COVID-19 Data Visualization Platform"
lastUpdate = "2020-04-07"
loader <- tagList(
waiter::spin_loaders(42),
br(),
h3("Loading data")
)
jsToggleFS <- 'shinyjs.toggleFullScreen = function() {
var element = document.documentElement,
enterFS = element.requestFullscreen || element.msRequestFullscreen || element.mozRequestFullScreen || element.webkitRequestFullscreen,
exitFS = document.exitFullscreen || document.msExitFullscreen || document.mozCancelFullScreen || document.webkitExitFullscreen;
if (!document.fullscreenElement && !document.msFullscreenElement && !document.mozFullScreenElement && !document.webkitFullscreenElement) {
enterFS.call(element);
} else {
exitFS.call(document);
}
}'
source("appFiles/packageLoad.R")
source("appFiles/dataLoad.R")
source("appFiles/CSS.R", local = TRUE)
source("appFiles/dashboardPage.R", local = TRUE)
##### User interface #####
ui <- tagList( # dependencies
use_waiter(),
useSweetAlert(),
useShinyjs(),
extendShinyjs(text = jsToggleFS),
waiter::waiter_show_on_load(loader, color = "#000"),
# shows before anything else
##### CSS and style functions #####
CSS, #CSS.R
# Loading message
argonDash::argonDashPage(
title = appLongName,
header = argonDash::argonDashHeader(
gradient = T,
color = NULL,
top_padding = 2,
bottom_padding = 0,
background_img = "coronavirus.jpg",
height = 70,
argonRow(
argonColumn(width = 8,
h4(appLongName, style = 'color:white;
text-align:left;
vertical-align: middle;
font-size:40px;')
),
argonColumn(
width = 4,
h6(HTML(paste0("Creator & Maintainer: <a href='https://www.shubhrampandey.com' target = '_blank'>Shubhram Pandey</a>")), style = 'color:white;
text-align: right;
font-size:15px;
margin-bottom: 0em'),
h6(HTML(paste0("<a href='https://www.3ai.in' target = '_blank'> - 3AI Ambassdor</a>")), style = 'color:white;text-align: right;font-size:15px;')
),
fixedPanel(
div(
actionBttn("fullScreen",
style = "material-circle",
icon = icon("arrows-alt"),
size = "xs",
color = "warning"),
bsPopover("fullScreen", title = NULL, content = "Click to view in full screen", placement = "left", trigger = "hover",
options = NULL),
onclick = "shinyjs.toggleFullScreen();"
),
top = 55,
right = 10
),
fixedPanel(
div(
actionBttn("kofi",
style = "material-circle",
icon = icon("coffee"),
size = "xs",
color = "success"),
bsPopover("kofi", title = NULL, content = "Buy me a coffee", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://ko-fi.com/shubhrampandey', '_blank')"
),
top = 55,
right = 40
),
fixedPanel(
div(
actionBttn("userGuide",
style = "material-circle",
icon = icon("info"),
size = "xs",
color = "royal"),
bsPopover("userGuide", title = NULL, content = "Go to app help page", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://sites.google.com/view/covid-19-userguide/home', '_blank')"
),
top = 55,
right = 70
),
fixedPanel(
div(
actionBttn("webSite",
style = "material-circle",
icon = icon("address-card"),
size = "xs",
color = "primary"),
bsPopover("webSite", title = NULL, content = "About developer", placement = "left", trigger = "hover",
options = NULL),
onclick = "window.open('https://www.shubhrampandey.com', '_blank')"
),
top = 55,
right = 100
)
)
),
sidebar = NULL,
body = argonDashBody(
tags$head( tags$meta(name = "viewport", content = "width=1600"),uiOutput("body")),
tags$br(),
dashboardUI
)
)
)
##### server #####
server <- function(input, output, session) {
printLogJs = function(x, ...) {
logjs(x)
T
}
# addHandler(printLogJs)
if (!Production) options(shiny.error = recover)
options(shiny.sanitize.errors = TRUE, width = 160)
session$onSessionEnded(function() {
stopApp()
# q("no")
})
source("appFiles/dashboardServer.R", local = TRUE)
# Hide the loading message when the rest of the server function has executed
waiter_hide() # will hide *on_load waiter
}
# Run the application
shinyApp(ui = ui, server = server) |
make_time_aligner <- function(alignment_variable, ahead, threshold = 500){
days_since_threshold_attained_first_time_aligner <- function(df_use, forecast_date){
stopifnot(alignment_variable %in% unique(df_use %>% pull(variable_name)))
df_alignment_variable <- df_use %>% filter(variable_name == alignment_variable)
day0 <- df_alignment_variable %>%
arrange(geo_value, time_value) %>%
group_by(location, geo_value, variable_name) %>%
mutate(cumul_value = cumsum(value)) %>%
arrange(variable_name, geo_value, time_value) %>%
filter(cumul_value >= threshold) %>%
group_by(location) %>%
summarize(value = min(time_value), .groups = "drop")
locations <- df_use %>% pull(location) %>% unique
train_dates <- df_use %>% pull(time_value) %>% unique
target_dates <- get_target_period(forecast_date, incidence_period = "epiweek", ahead) %$%
seq(start, end, by = "days")
dates <- unique(c(train_dates, target_dates))
df_align <- expand_grid(location = locations, time_value = dates) %>%
left_join(day0, by = "location") %>%
mutate(align_date = ifelse(time_value - value >= 0, time_value - value, NA)) %>%
select(-value)
return(df_align)
}
}
| /forecasters/aardvark/R/time_alignment.R | permissive | mjl2241/covid-19-forecast | R | false | false | 1,250 | r | make_time_aligner <- function(alignment_variable, ahead, threshold = 500){
days_since_threshold_attained_first_time_aligner <- function(df_use, forecast_date){
stopifnot(alignment_variable %in% unique(df_use %>% pull(variable_name)))
df_alignment_variable <- df_use %>% filter(variable_name == alignment_variable)
day0 <- df_alignment_variable %>%
arrange(geo_value, time_value) %>%
group_by(location, geo_value, variable_name) %>%
mutate(cumul_value = cumsum(value)) %>%
arrange(variable_name, geo_value, time_value) %>%
filter(cumul_value >= threshold) %>%
group_by(location) %>%
summarize(value = min(time_value), .groups = "drop")
locations <- df_use %>% pull(location) %>% unique
train_dates <- df_use %>% pull(time_value) %>% unique
target_dates <- get_target_period(forecast_date, incidence_period = "epiweek", ahead) %$%
seq(start, end, by = "days")
dates <- unique(c(train_dates, target_dates))
df_align <- expand_grid(location = locations, time_value = dates) %>%
left_join(day0, by = "location") %>%
mutate(align_date = ifelse(time_value - value >= 0, time_value - value, NA)) %>%
select(-value)
return(df_align)
}
}
|
readType <- function(x)
{
ret <- list()
ret$type <- "character"
ret$mode <- NULL
ret$labels <- NULL
ret$min <- -Inf
ret$max <- +Inf
if (xmlName(x) == "type") {
## FIXME: why is the seq_along() statement needed??
x <- x[sapply(x[seq_along(x)],
xmlName) != "text"][[1]]
ret$type <- xmlName(x)
if (ret$type == "categorical") {
## mode
ret$mode <- xmlAttrs(x)["mode"]
## labels
ind <- 1
for (k in 1:xmlSize(x)) {
if (xmlName(x[[k]]) != "text") {
code <- as.integer(xmlAttrs(x[[k]])["code"])
children <- xmlChildren(x[[k]])
lab <- if (length(children)) children else NULL
ret$labels[code] <-
if (is.null(lab)) ind else xmlValue(lab[[1]])
ind <- ind + 1
}
}
}
if (ret$type == "numeric") {
if (!length(xmlChildren(x)))
ret$mode <- "real"
else {
x <- x[sapply(x[seq_along(x)],
xmlName) != "text"][[1]]
ret$mode <- xmlName(x)
l <- length(xmlChildren(x))
if (ret$mode %in% c("integer", "real") && l)
for (i in seq_len(l))
if (xmlName(x[[i]]) != "text")
ret[[xmlName(x[[i]])]] <- getDataSDML(x[i])
}
}
}
ret
}
| /R/readTypeSDML.R | no_license | cran/StatDataML | R | false | false | 1,564 | r | readType <- function(x)
{
ret <- list()
ret$type <- "character"
ret$mode <- NULL
ret$labels <- NULL
ret$min <- -Inf
ret$max <- +Inf
if (xmlName(x) == "type") {
## FIXME: why is the seq_along() statement needed??
x <- x[sapply(x[seq_along(x)],
xmlName) != "text"][[1]]
ret$type <- xmlName(x)
if (ret$type == "categorical") {
## mode
ret$mode <- xmlAttrs(x)["mode"]
## labels
ind <- 1
for (k in 1:xmlSize(x)) {
if (xmlName(x[[k]]) != "text") {
code <- as.integer(xmlAttrs(x[[k]])["code"])
children <- xmlChildren(x[[k]])
lab <- if (length(children)) children else NULL
ret$labels[code] <-
if (is.null(lab)) ind else xmlValue(lab[[1]])
ind <- ind + 1
}
}
}
if (ret$type == "numeric") {
if (!length(xmlChildren(x)))
ret$mode <- "real"
else {
x <- x[sapply(x[seq_along(x)],
xmlName) != "text"][[1]]
ret$mode <- xmlName(x)
l <- length(xmlChildren(x))
if (ret$mode %in% c("integer", "real") && l)
for (i in seq_len(l))
if (xmlName(x[[i]]) != "text")
ret[[xmlName(x[[i]])]] <- getDataSDML(x[i])
}
}
}
ret
}
|
source("../R.xtables/fun.R")
source("../R.xtables/xtabularAssay.R")
source("../R.xtables/xtablesAssay.R")
source("../R.xtables/xtableHead.R")
source("../R.xtables/xtableLabels.R")
source("../R.xtables/xtableLatinSquare.R")
source("../R.xtables/xtableTableCS.R")
source("../R.xtables/xtableTable.R")
source("../R.xtables/xtableModel.R")
source("../R.xtables/xtableAnova.R")
source("../R.xtables/xtableParameters.R")
source("../R.xtables/xtableValidity.R")
source("../R.xtables/xtablePotency.R")
| /pla/inst/scripts/R/xtables.bak.R | no_license | ingted/R-Examples | R | false | false | 494 | r | source("../R.xtables/fun.R")
source("../R.xtables/xtabularAssay.R")
source("../R.xtables/xtablesAssay.R")
source("../R.xtables/xtableHead.R")
source("../R.xtables/xtableLabels.R")
source("../R.xtables/xtableLatinSquare.R")
source("../R.xtables/xtableTableCS.R")
source("../R.xtables/xtableTable.R")
source("../R.xtables/xtableModel.R")
source("../R.xtables/xtableAnova.R")
source("../R.xtables/xtableParameters.R")
source("../R.xtables/xtableValidity.R")
source("../R.xtables/xtablePotency.R")
|
### R code from vignette source 'compareGroups_vignette.rnw'
### Encoding: ISO8859-1
###################################################
### code chunk number 1: compareGroups_vignette.rnw:106-107
###################################################
library(compareGroups)
###################################################
### code chunk number 2: compareGroups_vignette.rnw:152-153
###################################################
data(predimed)
###################################################
### code chunk number 3: compareGroups_vignette.rnw:160-168
###################################################
dicc<-data.frame(
"Name"=I(names(predimed)),
"Label"=I(unlist(lapply(predimed,label))),
"Codes"=I(unlist(lapply(predimed,function(x) paste(levels(x),collapse="; "))))
)
dicc$Codes<-sub(">=","$\\\\geq$",dicc$Codes)
#print(xtable(dicc,align=rep("l",4)),include.rownames=FALSE,size="small",tabular.environment="longtable", sanitize.text.function=function(x) x)
print(xtable(dicc,align=rep("l",4)),include.rownames=FALSE,size="small", sanitize.text.function=function(x) x)
###################################################
### code chunk number 4: compareGroups_vignette.rnw:194-196
###################################################
predimed$tmain <- with(predimed, Surv(toevent, event == 'Yes'))
label(predimed$tmain) <- "AMI, stroke, or CV Death"
###################################################
### code chunk number 5: compareGroups_vignette.rnw:216-217
###################################################
compareGroups(group ~ . , data=predimed)
###################################################
### code chunk number 6: compareGroups_vignette.rnw:228-229
###################################################
compareGroups(group ~ . -toevent - event, data=predimed)
###################################################
### code chunk number 7: compareGroups_vignette.rnw:236-238
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
res
###################################################
### code chunk number 8: compareGroups_vignette.rnw:257-259
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
subset = sex=='Female')
###################################################
### code chunk number 9: compareGroups_vignette.rnw:266-267
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 10: compareGroups_vignette.rnw:270-272
###################################################
compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed,
selec = list(hormo= sex=="Female", waist = waist>20 ))
###################################################
### code chunk number 11: compareGroups_vignette.rnw:277-278
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 12: compareGroups_vignette.rnw:281-283
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
selec = list(waist= !is.na(hormo)), subset = sex=="Female")
###################################################
### code chunk number 13: compareGroups_vignette.rnw:289-290
###################################################
options(width=80,keep.source=TRUE)
###################################################
### code chunk number 14: compareGroups_vignette.rnw:292-294
###################################################
compareGroups(group ~ age + sex + bmi + bmi + waist + hormo, data=predimed,
selec = list(bmi.1=!is.na(hormo)))
###################################################
### code chunk number 15: compareGroups_vignette.rnw:307-308
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 16: compareGroups_vignette.rnw:310-312
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2))
###################################################
### code chunk number 17: compareGroups_vignette.rnw:314-315
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 18: compareGroups_vignette.rnw:330-331
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 19: compareGroups_vignette.rnw:333-335
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=NA), alpha= 0.01)
###################################################
### code chunk number 20: compareGroups_vignette.rnw:337-338
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 21: compareGroups_vignette.rnw:347-351
###################################################
cuts<-"lo:55=1; 56:60=2; 61:65=3; 66:70=4; 71:75=5; 76:80=6; 81:hi=7"
predimed$age7gr<-car::recode(predimed$age, cuts)
compareGroups(group ~ age7gr, data=predimed, method = c(age7gr=NA))
compareGroups(group ~ age7gr, data=predimed, method = c(age7gr=NA), min.dis=8)
###################################################
### code chunk number 22: compareGroups_vignette.rnw:367-368
###################################################
compareGroups(age7gr ~ sex + bmi + waist, data=predimed, max.ylev=7)
###################################################
### code chunk number 23: compareGroups_vignette.rnw:373-374
###################################################
compareGroups(group ~ sex + age7gr, method= (age7gr=3), data=predimed, max.xlev=5)
###################################################
### code chunk number 24: compareGroups_vignette.rnw:392-394
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
include.label= FALSE)
###################################################
### code chunk number 25: compareGroups_vignette.rnw:401-402
###################################################
options(width=80, keep.source=FALSE)
###################################################
### code chunk number 26: compareGroups_vignette.rnw:405-408
###################################################
resu1<-compareGroups(group ~ age + waist, data=predimed,
method = c(waist=2))
createTable(resu1)
###################################################
### code chunk number 27: compareGroups_vignette.rnw:415-418
###################################################
resu2<-compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2), Q1=0.025, Q3=0.975)
createTable(resu2)
###################################################
### code chunk number 28: compareGroups_vignette.rnw:425-426
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 29: compareGroups_vignette.rnw:430-431
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 30: compareGroups_vignette.rnw:433-435
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2), Q1=0, Q3=1)
###################################################
### code chunk number 31: compareGroups_vignette.rnw:437-438
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 32: compareGroups_vignette.rnw:445-449
###################################################
predimed$smk<-predimed$smoke
levels(predimed$smk)<- c("Never smoker", "Current or former < 1y", "Never or former >= 1y", "Unknown")
label(predimed$smk)<-"Smoking 4 cat."
cbind(table(predimed$smk))
###################################################
### code chunk number 33: compareGroups_vignette.rnw:482-483
###################################################
compareGroups(group ~ age + smk, data=predimed, simplify=FALSE)
###################################################
### code chunk number 34: compareGroups_vignette.rnw:514-517
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, method = c(waist=2),
data=predimed)
summary(res[c(1, 2, 4)])
###################################################
### code chunk number 35: compareGroups_vignette.rnw:530-531
###################################################
plot(res[c(1,2)], file="./figures/univar/", type="pdf")
###################################################
### code chunk number 36: compareGroups_vignette.rnw:549-550
###################################################
plot(res[c(1,2)], bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 37: compareGroups_vignette.rnw:574-576
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
res
###################################################
### code chunk number 38: compareGroups_vignette.rnw:581-582
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 39: compareGroups_vignette.rnw:584-586
###################################################
res<-update(res, . ~. - sex + bmi + toevent, subset = sex=='Female',
method = c(waist=2, tovent=2), selec = list(bmi=!is.na(hormo)))
###################################################
### code chunk number 40: compareGroups_vignette.rnw:588-589
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 41: compareGroups_vignette.rnw:591-592
###################################################
res
###################################################
### code chunk number 42: compareGroups_vignette.rnw:606-612
###################################################
library(SNPassoc)
data(SNPs)
tab <- createTable(compareGroups(casco ~ snp10001 + snp10002 + snp10005
+ snp10008 + snp10009, SNPs))
pvals <- getResults(tab, "p.overall")
p.adjust(pvals, method = "BH")
###################################################
### code chunk number 43: compareGroups_vignette.rnw:629-631
###################################################
res1<-compareGroups(htn ~ age + sex + bmi + smoke, data=predimed, ref=1)
createTable(res1, show.ratio=TRUE)
###################################################
### code chunk number 44: compareGroups_vignette.rnw:637-638
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 45: compareGroups_vignette.rnw:640-643
###################################################
res2<-compareGroups(htn ~ age + sex + bmi + smoke, data=predimed,
ref=c(smoke=1, sex=2))
createTable(res2, show.ratio=TRUE)
###################################################
### code chunk number 46: compareGroups_vignette.rnw:648-649
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 47: compareGroups_vignette.rnw:656-659
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hormo + hyperchol, data=predimed,
ref.no='NO')
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 48: compareGroups_vignette.rnw:668-670
###################################################
res<-compareGroups(htn ~ age + bmi, data=predimed)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 49: compareGroups_vignette.rnw:675-676
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 50: compareGroups_vignette.rnw:678-681
###################################################
res<-compareGroups(htn ~ age + bmi, data=predimed,
fact.ratio= c(age=10, bmi=2))
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 51: compareGroups_vignette.rnw:683-684
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 52: compareGroups_vignette.rnw:693-695
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hyperchol, data=predimed)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 53: compareGroups_vignette.rnw:701-703
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hyperchol, data=predimed, ref.y=2)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 54: compareGroups_vignette.rnw:717-719
###################################################
plot(compareGroups(tmain ~ sex, data=predimed), bivar=TRUE, file="./figures/bivar/")
plot(compareGroups(tmain ~ age, data=predimed), bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 55: compareGroups_vignette.rnw:754-755
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 56: compareGroups_vignette.rnw:757-760
###################################################
res<-compareGroups(sex ~ age + tmain, timemax=c(tmain=3),
data=predimed)
res
###################################################
### code chunk number 57: compareGroups_vignette.rnw:762-763
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 58: compareGroups_vignette.rnw:772-774
###################################################
plot(res[2], file="./figures/univar/")
plot(res[2], bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 59: compareGroups_vignette.rnw:799-800
###################################################
options(width=100,keep.source=FALSE)
###################################################
### code chunk number 60: compareGroups_vignette.rnw:803-806
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed,
selec = list(hormo=sex=="Female"))
restab<-createTable(res)
###################################################
### code chunk number 61: compareGroups_vignette.rnw:811-812
###################################################
print(restab,which.table='descr')
###################################################
### code chunk number 62: compareGroups_vignette.rnw:817-818
###################################################
print(restab,which.table='avail')
###################################################
### code chunk number 63: compareGroups_vignette.rnw:834-835
###################################################
update(restab, hide = c(sex="Male"))
###################################################
### code chunk number 64: compareGroups_vignette.rnw:844-846
###################################################
res<-compareGroups(group ~ age + sex + htn + diab, data=predimed)
createTable(res, hide.no='no', hide = c(sex="Male"))
###################################################
### code chunk number 65: compareGroups_vignette.rnw:855-856
###################################################
createTable(res, digits= c(age=2, sex = 3))
###################################################
### code chunk number 66: compareGroups_vignette.rnw:865-866
###################################################
createTable(res, type=1)
###################################################
### code chunk number 67: compareGroups_vignette.rnw:871-872
###################################################
createTable(res, type=3)
###################################################
### code chunk number 68: compareGroups_vignette.rnw:884-885
###################################################
createTable(res, show.n=TRUE)
###################################################
### code chunk number 69: compareGroups_vignette.rnw:892-893
###################################################
createTable(res, show.descr=FALSE)
###################################################
### code chunk number 70: compareGroups_vignette.rnw:900-901
###################################################
createTable(res, show.all=TRUE)
###################################################
### code chunk number 71: compareGroups_vignette.rnw:907-908
###################################################
createTable(res, show.p.overall=FALSE)
###################################################
### code chunk number 72: compareGroups_vignette.rnw:915-916
###################################################
createTable(res, show.p.trend=TRUE)
###################################################
### code chunk number 73: compareGroups_vignette.rnw:926-927
###################################################
createTable(res, show.p.mul=TRUE)
###################################################
### code chunk number 74: compareGroups_vignette.rnw:933-934
###################################################
createTable(update(res, subset= group!="Control diet"), show.ratio=TRUE)
###################################################
### code chunk number 75: compareGroups_vignette.rnw:939-941
###################################################
createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE)
###################################################
### code chunk number 76: compareGroups_vignette.rnw:950-952
###################################################
createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE, digits.ratio= 3)
###################################################
### code chunk number 77: compareGroups_vignette.rnw:959-962
###################################################
tab<-createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.all = TRUE)
print(tab, header.labels = c("p.overall" = "p-value", "all" = "All"))
###################################################
### code chunk number 78: compareGroups_vignette.rnw:974-977
###################################################
restab1 <- createTable(compareGroups(group ~ age + sex, data=predimed))
restab2 <- createTable(compareGroups(group ~ bmi + smoke, data=predimed))
rbind("Non-modifiable risk factors"=restab1, "Modifiable risk factors"=restab2)
###################################################
### code chunk number 79: compareGroups_vignette.rnw:990-991
###################################################
rbind("Non-modifiable"=restab1,"Modifiable"=restab2)[c(1,4)]
###################################################
### code chunk number 80: compareGroups_vignette.rnw:996-997
###################################################
rbind("Modifiable"=restab1,"Non-modifiable"=restab2)[c(4,3,2,1)]
###################################################
### code chunk number 81: compareGroups_vignette.rnw:1008-1013
###################################################
res<-compareGroups(group ~ age + smoke + bmi + htn , data=predimed)
alltab <- createTable(res, show.p.overall = FALSE)
femaletab <- createTable(update(res,subset=sex=='Female'), show.p.overall = FALSE)
maletab <- createTable(update(res,subset=sex=='Male'), show.p.overall = FALSE)
cbind("ALL"=alltab,"FEMALE"=femaletab,"MALE"=maletab)
###################################################
### code chunk number 82: compareGroups_vignette.rnw:1018-1019
###################################################
cbind(alltab,femaletab,maletab,caption=NULL)
###################################################
### code chunk number 83: compareGroups_vignette.rnw:1024-1025
###################################################
cbind(alltab,femaletab,maletab)
###################################################
### code chunk number 84: compareGroups_vignette.rnw:1038-1040
###################################################
print(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)), which.table='both')
###################################################
### code chunk number 85: compareGroups_vignette.rnw:1043-1045
###################################################
print(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)), nmax=FALSE)
###################################################
### code chunk number 86: compareGroups_vignette.rnw:1051-1053
###################################################
summary(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)))
###################################################
### code chunk number 87: compareGroups_vignette.rnw:1058-1062
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
restab<-createTable(res, type=1, show.ratio=TRUE )
restab
update(restab, show.n=TRUE)
###################################################
### code chunk number 88: compareGroups_vignette.rnw:1068-1069
###################################################
update(restab, x = update(res, subset=c(sex=='Female')), show.n=TRUE)
###################################################
### code chunk number 89: compareGroups_vignette.rnw:1080-1081
###################################################
createTable(compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed))
###################################################
### code chunk number 90: compareGroups_vignette.rnw:1083-1084
###################################################
createTable(compareGroups(group ~ age + sex + bmi, data=predimed))[1:2, ]
###################################################
### code chunk number 91: compareGroups_vignette.rnw:1130-1133
###################################################
restab<-createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed))
export2latex(restab)
###################################################
### code chunk number 92: compareGroups_vignette.rnw:1170-1172 (eval = FALSE)
###################################################
## ?report # to know more about report function
## ?regicor # info about REGICOR data set
###################################################
### code chunk number 93: compareGroups_vignette.rnw:1187-1191
###################################################
# from a compareGroups object
data(regicor)
res <- compareGroups(year ~ .-id, regicor)
missingTable(res)
###################################################
### code chunk number 94: compareGroups_vignette.rnw:1194-1197 (eval = FALSE)
###################################################
## # or from createTable objects
## restab <- createTable(res, hide.no = 'no')
## missingTable(restab)
###################################################
### code chunk number 95: compareGroups_vignette.rnw:1203-1209
###################################################
# first create time-to-cardiovascular event
regicor$tcv<-with(regicor,Surv(tocv,cv=='Yes'))
# create the table
res <- compareGroups(tcv ~ . -id-tocv-cv-todeath-death, regicor, include.miss = TRUE)
restab <- createTable(res, hide.no = 'no')
restab
###################################################
### code chunk number 96: compareGroups_vignette.rnw:1226-1228
###################################################
data(SNPs)
head(SNPs)
###################################################
### code chunk number 97: compareGroups_vignette.rnw:1233-1235
###################################################
res<-compareSNPs(casco ~ snp10001 + snp10002 + snp10003, data=SNPs)
res
###################################################
### code chunk number 98: compareGroups_vignette.rnw:1243-1245
###################################################
res<-compareSNPs(~ snp10001 + snp10002 + snp10003, data=SNPs)
res
###################################################
### code chunk number 99: compareGroups_vignette.rnw:1267-1270
###################################################
export2latex(createTable(compareGroups(group ~ age + sex + smoke + bmi + waist +
wth + htn + diab + hyperchol + famhist + hormo + p14 + toevent + event,
data=predimed), hide.no="No",hide = c(sex="Male")))
###################################################
### code chunk number 100: compareGroups_vignette.rnw:1332-1337
###################################################
export2latex(createTable(compareGroups(htn ~ age + sex + smoke + bmi + waist +
wth + diab + hyperchol + famhist + hormo +
p14 + toevent + event,
data=predimed), hide.no="No",hide = c(sex="Male"),
show.ratio=TRUE, show.descr=FALSE))
###################################################
### code chunk number 101: compareGroups_vignette.rnw:1350-1352
###################################################
export2latex(createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE))
| /compareGroups/inst/doc/compareGroups_vignette.R | no_license | ingted/R-Examples | R | false | false | 27,321 | r | ### R code from vignette source 'compareGroups_vignette.rnw'
### Encoding: ISO8859-1
###################################################
### code chunk number 1: compareGroups_vignette.rnw:106-107
###################################################
library(compareGroups)
###################################################
### code chunk number 2: compareGroups_vignette.rnw:152-153
###################################################
data(predimed)
###################################################
### code chunk number 3: compareGroups_vignette.rnw:160-168
###################################################
dicc<-data.frame(
"Name"=I(names(predimed)),
"Label"=I(unlist(lapply(predimed,label))),
"Codes"=I(unlist(lapply(predimed,function(x) paste(levels(x),collapse="; "))))
)
dicc$Codes<-sub(">=","$\\\\geq$",dicc$Codes)
#print(xtable(dicc,align=rep("l",4)),include.rownames=FALSE,size="small",tabular.environment="longtable", sanitize.text.function=function(x) x)
print(xtable(dicc,align=rep("l",4)),include.rownames=FALSE,size="small", sanitize.text.function=function(x) x)
###################################################
### code chunk number 4: compareGroups_vignette.rnw:194-196
###################################################
predimed$tmain <- with(predimed, Surv(toevent, event == 'Yes'))
label(predimed$tmain) <- "AMI, stroke, or CV Death"
###################################################
### code chunk number 5: compareGroups_vignette.rnw:216-217
###################################################
compareGroups(group ~ . , data=predimed)
###################################################
### code chunk number 6: compareGroups_vignette.rnw:228-229
###################################################
compareGroups(group ~ . -toevent - event, data=predimed)
###################################################
### code chunk number 7: compareGroups_vignette.rnw:236-238
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
res
###################################################
### code chunk number 8: compareGroups_vignette.rnw:257-259
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
subset = sex=='Female')
###################################################
### code chunk number 9: compareGroups_vignette.rnw:266-267
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 10: compareGroups_vignette.rnw:270-272
###################################################
compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed,
selec = list(hormo= sex=="Female", waist = waist>20 ))
###################################################
### code chunk number 11: compareGroups_vignette.rnw:277-278
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 12: compareGroups_vignette.rnw:281-283
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
selec = list(waist= !is.na(hormo)), subset = sex=="Female")
###################################################
### code chunk number 13: compareGroups_vignette.rnw:289-290
###################################################
options(width=80,keep.source=TRUE)
###################################################
### code chunk number 14: compareGroups_vignette.rnw:292-294
###################################################
compareGroups(group ~ age + sex + bmi + bmi + waist + hormo, data=predimed,
selec = list(bmi.1=!is.na(hormo)))
###################################################
### code chunk number 15: compareGroups_vignette.rnw:307-308
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 16: compareGroups_vignette.rnw:310-312
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2))
###################################################
### code chunk number 17: compareGroups_vignette.rnw:314-315
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 18: compareGroups_vignette.rnw:330-331
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 19: compareGroups_vignette.rnw:333-335
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=NA), alpha= 0.01)
###################################################
### code chunk number 20: compareGroups_vignette.rnw:337-338
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 21: compareGroups_vignette.rnw:347-351
###################################################
cuts<-"lo:55=1; 56:60=2; 61:65=3; 66:70=4; 71:75=5; 76:80=6; 81:hi=7"
predimed$age7gr<-car::recode(predimed$age, cuts)
compareGroups(group ~ age7gr, data=predimed, method = c(age7gr=NA))
compareGroups(group ~ age7gr, data=predimed, method = c(age7gr=NA), min.dis=8)
###################################################
### code chunk number 22: compareGroups_vignette.rnw:367-368
###################################################
compareGroups(age7gr ~ sex + bmi + waist, data=predimed, max.ylev=7)
###################################################
### code chunk number 23: compareGroups_vignette.rnw:373-374
###################################################
compareGroups(group ~ sex + age7gr, method= (age7gr=3), data=predimed, max.xlev=5)
###################################################
### code chunk number 24: compareGroups_vignette.rnw:392-394
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
include.label= FALSE)
###################################################
### code chunk number 25: compareGroups_vignette.rnw:401-402
###################################################
options(width=80, keep.source=FALSE)
###################################################
### code chunk number 26: compareGroups_vignette.rnw:405-408
###################################################
resu1<-compareGroups(group ~ age + waist, data=predimed,
method = c(waist=2))
createTable(resu1)
###################################################
### code chunk number 27: compareGroups_vignette.rnw:415-418
###################################################
resu2<-compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2), Q1=0.025, Q3=0.975)
createTable(resu2)
###################################################
### code chunk number 28: compareGroups_vignette.rnw:425-426
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 29: compareGroups_vignette.rnw:430-431
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 30: compareGroups_vignette.rnw:433-435
###################################################
compareGroups(group ~ age + smoke + waist + hormo, data=predimed,
method = c(waist=2), Q1=0, Q3=1)
###################################################
### code chunk number 31: compareGroups_vignette.rnw:437-438
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 32: compareGroups_vignette.rnw:445-449
###################################################
predimed$smk<-predimed$smoke
levels(predimed$smk)<- c("Never smoker", "Current or former < 1y", "Never or former >= 1y", "Unknown")
label(predimed$smk)<-"Smoking 4 cat."
cbind(table(predimed$smk))
###################################################
### code chunk number 33: compareGroups_vignette.rnw:482-483
###################################################
compareGroups(group ~ age + smk, data=predimed, simplify=FALSE)
###################################################
### code chunk number 34: compareGroups_vignette.rnw:514-517
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, method = c(waist=2),
data=predimed)
summary(res[c(1, 2, 4)])
###################################################
### code chunk number 35: compareGroups_vignette.rnw:530-531
###################################################
plot(res[c(1,2)], file="./figures/univar/", type="pdf")
###################################################
### code chunk number 36: compareGroups_vignette.rnw:549-550
###################################################
plot(res[c(1,2)], bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 37: compareGroups_vignette.rnw:574-576
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
res
###################################################
### code chunk number 38: compareGroups_vignette.rnw:581-582
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 39: compareGroups_vignette.rnw:584-586
###################################################
res<-update(res, . ~. - sex + bmi + toevent, subset = sex=='Female',
method = c(waist=2, tovent=2), selec = list(bmi=!is.na(hormo)))
###################################################
### code chunk number 40: compareGroups_vignette.rnw:588-589
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 41: compareGroups_vignette.rnw:591-592
###################################################
res
###################################################
### code chunk number 42: compareGroups_vignette.rnw:606-612
###################################################
library(SNPassoc)
data(SNPs)
tab <- createTable(compareGroups(casco ~ snp10001 + snp10002 + snp10005
+ snp10008 + snp10009, SNPs))
pvals <- getResults(tab, "p.overall")
p.adjust(pvals, method = "BH")
###################################################
### code chunk number 43: compareGroups_vignette.rnw:629-631
###################################################
res1<-compareGroups(htn ~ age + sex + bmi + smoke, data=predimed, ref=1)
createTable(res1, show.ratio=TRUE)
###################################################
### code chunk number 44: compareGroups_vignette.rnw:637-638
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 45: compareGroups_vignette.rnw:640-643
###################################################
res2<-compareGroups(htn ~ age + sex + bmi + smoke, data=predimed,
ref=c(smoke=1, sex=2))
createTable(res2, show.ratio=TRUE)
###################################################
### code chunk number 46: compareGroups_vignette.rnw:648-649
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 47: compareGroups_vignette.rnw:656-659
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hormo + hyperchol, data=predimed,
ref.no='NO')
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 48: compareGroups_vignette.rnw:668-670
###################################################
res<-compareGroups(htn ~ age + bmi, data=predimed)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 49: compareGroups_vignette.rnw:675-676
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 50: compareGroups_vignette.rnw:678-681
###################################################
res<-compareGroups(htn ~ age + bmi, data=predimed,
fact.ratio= c(age=10, bmi=2))
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 51: compareGroups_vignette.rnw:683-684
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 52: compareGroups_vignette.rnw:693-695
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hyperchol, data=predimed)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 53: compareGroups_vignette.rnw:701-703
###################################################
res<-compareGroups(htn ~ age + sex + bmi + hyperchol, data=predimed, ref.y=2)
createTable(res, show.ratio=TRUE)
###################################################
### code chunk number 54: compareGroups_vignette.rnw:717-719
###################################################
plot(compareGroups(tmain ~ sex, data=predimed), bivar=TRUE, file="./figures/bivar/")
plot(compareGroups(tmain ~ age, data=predimed), bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 55: compareGroups_vignette.rnw:754-755
###################################################
options(width=80,keep.source=FALSE)
###################################################
### code chunk number 56: compareGroups_vignette.rnw:757-760
###################################################
res<-compareGroups(sex ~ age + tmain, timemax=c(tmain=3),
data=predimed)
res
###################################################
### code chunk number 57: compareGroups_vignette.rnw:762-763
###################################################
options(width=120,keep.source=FALSE)
###################################################
### code chunk number 58: compareGroups_vignette.rnw:772-774
###################################################
plot(res[2], file="./figures/univar/")
plot(res[2], bivar=TRUE, file="./figures/bivar/")
###################################################
### code chunk number 59: compareGroups_vignette.rnw:799-800
###################################################
options(width=100,keep.source=FALSE)
###################################################
### code chunk number 60: compareGroups_vignette.rnw:803-806
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed,
selec = list(hormo=sex=="Female"))
restab<-createTable(res)
###################################################
### code chunk number 61: compareGroups_vignette.rnw:811-812
###################################################
print(restab,which.table='descr')
###################################################
### code chunk number 62: compareGroups_vignette.rnw:817-818
###################################################
print(restab,which.table='avail')
###################################################
### code chunk number 63: compareGroups_vignette.rnw:834-835
###################################################
update(restab, hide = c(sex="Male"))
###################################################
### code chunk number 64: compareGroups_vignette.rnw:844-846
###################################################
res<-compareGroups(group ~ age + sex + htn + diab, data=predimed)
createTable(res, hide.no='no', hide = c(sex="Male"))
###################################################
### code chunk number 65: compareGroups_vignette.rnw:855-856
###################################################
createTable(res, digits= c(age=2, sex = 3))
###################################################
### code chunk number 66: compareGroups_vignette.rnw:865-866
###################################################
createTable(res, type=1)
###################################################
### code chunk number 67: compareGroups_vignette.rnw:871-872
###################################################
createTable(res, type=3)
###################################################
### code chunk number 68: compareGroups_vignette.rnw:884-885
###################################################
createTable(res, show.n=TRUE)
###################################################
### code chunk number 69: compareGroups_vignette.rnw:892-893
###################################################
createTable(res, show.descr=FALSE)
###################################################
### code chunk number 70: compareGroups_vignette.rnw:900-901
###################################################
createTable(res, show.all=TRUE)
###################################################
### code chunk number 71: compareGroups_vignette.rnw:907-908
###################################################
createTable(res, show.p.overall=FALSE)
###################################################
### code chunk number 72: compareGroups_vignette.rnw:915-916
###################################################
createTable(res, show.p.trend=TRUE)
###################################################
### code chunk number 73: compareGroups_vignette.rnw:926-927
###################################################
createTable(res, show.p.mul=TRUE)
###################################################
### code chunk number 74: compareGroups_vignette.rnw:933-934
###################################################
createTable(update(res, subset= group!="Control diet"), show.ratio=TRUE)
###################################################
### code chunk number 75: compareGroups_vignette.rnw:939-941
###################################################
createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE)
###################################################
### code chunk number 76: compareGroups_vignette.rnw:950-952
###################################################
createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE, digits.ratio= 3)
###################################################
### code chunk number 77: compareGroups_vignette.rnw:959-962
###################################################
tab<-createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.all = TRUE)
print(tab, header.labels = c("p.overall" = "p-value", "all" = "All"))
###################################################
### code chunk number 78: compareGroups_vignette.rnw:974-977
###################################################
restab1 <- createTable(compareGroups(group ~ age + sex, data=predimed))
restab2 <- createTable(compareGroups(group ~ bmi + smoke, data=predimed))
rbind("Non-modifiable risk factors"=restab1, "Modifiable risk factors"=restab2)
###################################################
### code chunk number 79: compareGroups_vignette.rnw:990-991
###################################################
rbind("Non-modifiable"=restab1,"Modifiable"=restab2)[c(1,4)]
###################################################
### code chunk number 80: compareGroups_vignette.rnw:996-997
###################################################
rbind("Modifiable"=restab1,"Non-modifiable"=restab2)[c(4,3,2,1)]
###################################################
### code chunk number 81: compareGroups_vignette.rnw:1008-1013
###################################################
res<-compareGroups(group ~ age + smoke + bmi + htn , data=predimed)
alltab <- createTable(res, show.p.overall = FALSE)
femaletab <- createTable(update(res,subset=sex=='Female'), show.p.overall = FALSE)
maletab <- createTable(update(res,subset=sex=='Male'), show.p.overall = FALSE)
cbind("ALL"=alltab,"FEMALE"=femaletab,"MALE"=maletab)
###################################################
### code chunk number 82: compareGroups_vignette.rnw:1018-1019
###################################################
cbind(alltab,femaletab,maletab,caption=NULL)
###################################################
### code chunk number 83: compareGroups_vignette.rnw:1024-1025
###################################################
cbind(alltab,femaletab,maletab)
###################################################
### code chunk number 84: compareGroups_vignette.rnw:1038-1040
###################################################
print(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)), which.table='both')
###################################################
### code chunk number 85: compareGroups_vignette.rnw:1043-1045
###################################################
print(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)), nmax=FALSE)
###################################################
### code chunk number 86: compareGroups_vignette.rnw:1051-1053
###################################################
summary(createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed)))
###################################################
### code chunk number 87: compareGroups_vignette.rnw:1058-1062
###################################################
res<-compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed)
restab<-createTable(res, type=1, show.ratio=TRUE )
restab
update(restab, show.n=TRUE)
###################################################
### code chunk number 88: compareGroups_vignette.rnw:1068-1069
###################################################
update(restab, x = update(res, subset=c(sex=='Female')), show.n=TRUE)
###################################################
### code chunk number 89: compareGroups_vignette.rnw:1080-1081
###################################################
createTable(compareGroups(group ~ age + sex + smoke + waist + hormo, data=predimed))
###################################################
### code chunk number 90: compareGroups_vignette.rnw:1083-1084
###################################################
createTable(compareGroups(group ~ age + sex + bmi, data=predimed))[1:2, ]
###################################################
### code chunk number 91: compareGroups_vignette.rnw:1130-1133
###################################################
restab<-createTable(compareGroups(group ~ age + sex + smoke + waist + hormo,
data=predimed))
export2latex(restab)
###################################################
### code chunk number 92: compareGroups_vignette.rnw:1170-1172 (eval = FALSE)
###################################################
## ?report # to know more about report function
## ?regicor # info about REGICOR data set
###################################################
### code chunk number 93: compareGroups_vignette.rnw:1187-1191
###################################################
# from a compareGroups object
data(regicor)
res <- compareGroups(year ~ .-id, regicor)
missingTable(res)
###################################################
### code chunk number 94: compareGroups_vignette.rnw:1194-1197 (eval = FALSE)
###################################################
## # or from createTable objects
## restab <- createTable(res, hide.no = 'no')
## missingTable(restab)
###################################################
### code chunk number 95: compareGroups_vignette.rnw:1203-1209
###################################################
# first create time-to-cardiovascular event
regicor$tcv<-with(regicor,Surv(tocv,cv=='Yes'))
# create the table
res <- compareGroups(tcv ~ . -id-tocv-cv-todeath-death, regicor, include.miss = TRUE)
restab <- createTable(res, hide.no = 'no')
restab
###################################################
### code chunk number 96: compareGroups_vignette.rnw:1226-1228
###################################################
data(SNPs)
head(SNPs)
###################################################
### code chunk number 97: compareGroups_vignette.rnw:1233-1235
###################################################
res<-compareSNPs(casco ~ snp10001 + snp10002 + snp10003, data=SNPs)
res
###################################################
### code chunk number 98: compareGroups_vignette.rnw:1243-1245
###################################################
res<-compareSNPs(~ snp10001 + snp10002 + snp10003, data=SNPs)
res
###################################################
### code chunk number 99: compareGroups_vignette.rnw:1267-1270
###################################################
export2latex(createTable(compareGroups(group ~ age + sex + smoke + bmi + waist +
wth + htn + diab + hyperchol + famhist + hormo + p14 + toevent + event,
data=predimed), hide.no="No",hide = c(sex="Male")))
###################################################
### code chunk number 100: compareGroups_vignette.rnw:1332-1337
###################################################
export2latex(createTable(compareGroups(htn ~ age + sex + smoke + bmi + waist +
wth + diab + hyperchol + famhist + hormo +
p14 + toevent + event,
data=predimed), hide.no="No",hide = c(sex="Male"),
show.ratio=TRUE, show.descr=FALSE))
###################################################
### code chunk number 101: compareGroups_vignette.rnw:1350-1352
###################################################
export2latex(createTable(compareGroups(tmain ~ group + age + sex, data=predimed),
show.ratio=TRUE))
|
context("classif_neuralnet")
test_that("classif_neuralnet", {
requirePackagesOrSkip("neuralnet", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
capture.output({
# neuralnet is not dealing with formula with `.` well
nms = names(binaryclass.train)
formula_head = as.character(binaryclass.formula)[2]
varnames = nms[nms!=formula_head]
formula_head = paste('as.numeric(',formula_head,')~')
formula_expand = paste(formula_head, paste(varnames, collapse = "+"))
formula_expand = as.formula(formula_expand)
traindat = binaryclass.train
traindat[[binaryclass.target]] = as.numeric(traindat[[binaryclass.target]])-1
m = neuralnet::neuralnet(formula_expand, hidden=7, data=traindat, err.fct="ce",
linear.output = FALSE)
p = neuralnet::compute(m, covariate = binaryclass.test[,-ncol(binaryclass.test)])
p = as.numeric(as.vector(p[[2]])>0.5)
p = factor(p, labels = binaryclass.class.levs)
})
set.seed(getOption("mlr.debug.seed"))
testSimple("classif.neuralnet", binaryclass.df, binaryclass.target, binaryclass.train.inds, p,
parset = list(hidden = 7, err.fct = "ce"))
# Neuralnet doesn't have the `predict` method
# set.seed(getOption("mlr.debug.seed"))
# lrn = makeLearner("classif.neuralnet",hidden=7)
# task = makeClassifTask(data = binaryclass.df, target = binaryclass.target)
# m2 = try(train(lrn, task, subset = binaryclass.train.inds))
# p2 = predictLearner(.learner=lrn,.model=m2,
# .newdata = binaryclass.test[,-ncol(binaryclass.test)])
# expect_equal(p,p2,tol=1e-4)
})
| /tests/testthat/test_classif_neuralnet.R | no_license | jimhester/mlr | R | false | false | 1,606 | r | context("classif_neuralnet")
test_that("classif_neuralnet", {
requirePackagesOrSkip("neuralnet", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
capture.output({
# neuralnet is not dealing with formula with `.` well
nms = names(binaryclass.train)
formula_head = as.character(binaryclass.formula)[2]
varnames = nms[nms!=formula_head]
formula_head = paste('as.numeric(',formula_head,')~')
formula_expand = paste(formula_head, paste(varnames, collapse = "+"))
formula_expand = as.formula(formula_expand)
traindat = binaryclass.train
traindat[[binaryclass.target]] = as.numeric(traindat[[binaryclass.target]])-1
m = neuralnet::neuralnet(formula_expand, hidden=7, data=traindat, err.fct="ce",
linear.output = FALSE)
p = neuralnet::compute(m, covariate = binaryclass.test[,-ncol(binaryclass.test)])
p = as.numeric(as.vector(p[[2]])>0.5)
p = factor(p, labels = binaryclass.class.levs)
})
set.seed(getOption("mlr.debug.seed"))
testSimple("classif.neuralnet", binaryclass.df, binaryclass.target, binaryclass.train.inds, p,
parset = list(hidden = 7, err.fct = "ce"))
# Neuralnet doesn't have the `predict` method
# set.seed(getOption("mlr.debug.seed"))
# lrn = makeLearner("classif.neuralnet",hidden=7)
# task = makeClassifTask(data = binaryclass.df, target = binaryclass.target)
# m2 = try(train(lrn, task, subset = binaryclass.train.inds))
# p2 = predictLearner(.learner=lrn,.model=m2,
# .newdata = binaryclass.test[,-ncol(binaryclass.test)])
# expect_equal(p,p2,tol=1e-4)
})
|
library(ggplot2)
library(grid)
library(RColorBrewer)
load('data/wind_year.Rda')
wind <- wind.year
load("data/bathy.Rdata")
load("data/night.Rdata")
load("data/red_sea_inside.Rdata")
load("data/red_sea_outside.Rdata")
x_min <- 31
x_max <- 45
y_min <- 10
y_max <- 31
#----- Axis layer ----#
axis_p <-ggplot(wind, aes(x=long, y=lat))
axis_p <- axis_p + coord_cartesian(ylim=c(y_min,y_max),xlim=c(x_min,x_max))
#----- Theme layer -----#
theme_p <- theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
plot.margin = unit(c(-.1, -.1, -1, -1), "lines"))
# ----- Bathymetry layer ------#
bathy_p <-stat_contour(data=bathy_df, aes(x, y, z=topo1),
color="black", breaks=c(-30), size=.15)
# color="#00BAFFFF", breaks=c(-30), size=.15)
#------ Red Sea layer -----#
red.sea.outside_p <- geom_polygon(data=red.sea.outside,
aes(x=long, y=lat, group=group),
colour="#1c5c6b", fill="magenta", size=.5)
#------ Wind Layer ------#
wind_p <- axis_p + geom_tile(aes(fill = class50), height=0.1)
wind_p <- wind_p + scale_fill_manual(values=rev(brewer.pal(7, "RdYlBu")),
labels=seq(7))
wind_p <- wind_p + bathy_p + red.sea.outside_p + theme_p
ggsave(filename="results/posterfig/wind.png", plot=wind_p, width=568.3, height=826, dpi=300,
units="mm")
#------ Night Layer ------#
night_p <- axis_p + geom_raster(data=night_df,
aes(x = x, y = y, fill = nightearth))
night_p <- night_p + scale_fill_gradient(low='black', high='white',
trans='log', limits=c(.1,5000),
na.value='black')
night_p <- night_p + theme_p
ggsave(filename="results/posterfig/night.png", plot=night_p, units="mm")
| /scripts/posterBackground/do.R | no_license | XResearch/redSeaWind | R | false | false | 2,350 | r | library(ggplot2)
library(grid)
library(RColorBrewer)
load('data/wind_year.Rda')
wind <- wind.year
load("data/bathy.Rdata")
load("data/night.Rdata")
load("data/red_sea_inside.Rdata")
load("data/red_sea_outside.Rdata")
x_min <- 31
x_max <- 45
y_min <- 10
y_max <- 31
#----- Axis layer ----#
axis_p <-ggplot(wind, aes(x=long, y=lat))
axis_p <- axis_p + coord_cartesian(ylim=c(y_min,y_max),xlim=c(x_min,x_max))
#----- Theme layer -----#
theme_p <- theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
plot.margin = unit(c(-.1, -.1, -1, -1), "lines"))
# ----- Bathymetry layer ------#
bathy_p <-stat_contour(data=bathy_df, aes(x, y, z=topo1),
color="black", breaks=c(-30), size=.15)
# color="#00BAFFFF", breaks=c(-30), size=.15)
#------ Red Sea layer -----#
red.sea.outside_p <- geom_polygon(data=red.sea.outside,
aes(x=long, y=lat, group=group),
colour="#1c5c6b", fill="magenta", size=.5)
#------ Wind Layer ------#
wind_p <- axis_p + geom_tile(aes(fill = class50), height=0.1)
wind_p <- wind_p + scale_fill_manual(values=rev(brewer.pal(7, "RdYlBu")),
labels=seq(7))
wind_p <- wind_p + bathy_p + red.sea.outside_p + theme_p
ggsave(filename="results/posterfig/wind.png", plot=wind_p, width=568.3, height=826, dpi=300,
units="mm")
#------ Night Layer ------#
night_p <- axis_p + geom_raster(data=night_df,
aes(x = x, y = y, fill = nightearth))
night_p <- night_p + scale_fill_gradient(low='black', high='white',
trans='log', limits=c(.1,5000),
na.value='black')
night_p <- night_p + theme_p
ggsave(filename="results/posterfig/night.png", plot=night_p, units="mm")
|
library(plyr)
library(reshape2)
library(ggplot2)
library(grid)
library(scales)
set.seed(420)
theme_set(theme_bw())
cbp <- c('#E69F00', '#56B4E9', '#009E73', '#F0E442',
'#0072B2', '#D55E00', '#CC79A7')
theme_update(axis.text = element_text(size = 32),
axis.title = element_text(size = 35),
axis.title.y = element_text(hjust = 0.1),
legend.text = element_text(size = 25),
legend.title = element_text(size = 26),
legend.key.size = unit(2, 'cm'),
strip.text = element_text(size = 30))
# exponential
lam <- 1
k <- 1
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gd <- ggplot(survival, aes(x = durs))
gd <- gd + geom_histogram(aes(y = ..density..), fill = 'grey')
gd <- gd + stat_function(fun = dweibull, colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gd <- gd + labs(x = 'Duration', y = 'Density')
ggsave(gd, filename = '../doc/figure/dur_exp.png', width = 15, height = 10)
gsv <- ggplot(survival, aes(x = durs, y = surv))
gsv <- gsv + geom_line(size = 3)
gsv <- gsv + scale_y_continuous(trans = log10_trans())
gsv <- gsv + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsv, filename = '../doc/figure/sur_exp.png', width = 15, height = 10)
ghz <- ggplot(survival, aes(x = durs, y = haz))
ghz <- ghz + geom_line(size = 3)
ghz <- ghz + scale_y_continuous(trans = log10_trans())
ghz <- ghz + labs(x = 'Duration', y = 'h(t)')
ggsave(ghz, filename = '../doc/figure/haz_exp.png', width = 15, height = 10)
# dec
lam <- 1
k <- 0.5
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gdd <- ggplot(survival, aes(x = durs))
gdd <- gdd + geom_histogram(aes(y = ..density..), fill = 'grey')
gdd <- gdd + stat_function(fun = dweibull,
data = data.frame(ss = seq(0, 50, 0.1)),
mapping = aes(x = ss),
colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gdd <- gdd + labs(x = 'Duration', y = 'Density')
ggsave(gdd, filename = '../doc/figure/dur_dec.png', width = 15, height = 10)
gsvd <- ggplot(survival, aes(x = durs, y = surv))
gsvd <- gsvd + geom_line(size = 3)
gsvd <- gsvd + scale_y_continuous(trans = log10_trans())
gsvd <- gsvd + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsvd, filename = '../doc/figure/sur_dec.png', width = 15, height = 10)
ghzd <- ggplot(survival, aes(x = durs, y = haz))
ghzd <- ghzd + geom_line(size = 3)
ghzd <- ghzd + scale_y_continuous(trans = log10_trans())
ghzd <- ghzd + labs(x = 'Duration', y = 'h(t)')
ggsave(ghzd, filename = '../doc/figure/haz_dec.png', width = 15, height = 10)
# acc
lam <- 1
k <- 1.5
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gda <- ggplot(survival, aes(x = durs))
gda <- gda + geom_histogram(aes(y = ..density..), fill = 'grey')
gda <- gda + stat_function(fun = dweibull, colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gda <- gda + labs(x = 'Duration', y = 'Density')
ggsave(gda, filename = '../doc/figure/dur_acc.png', width = 15, height = 10)
gsva <- ggplot(survival, aes(x = durs, y = surv))
gsva <- gsva + geom_line(size = 3)
gsva <- gsva + scale_y_continuous(trans = log10_trans())
gsva <- gsva + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsva, filename = '../doc/figure/sur_acc.png', width = 15, height = 10)
ghza <- ggplot(survival, aes(x = durs, y = haz))
ghza <- ghza + geom_line(size = 3)
ghza <- ghza + scale_y_continuous(trans = log10_trans())
ghza <- ghza + labs(x = 'Duration', y = 'h(t)')
ggsave(ghza, filename = '../doc/figure/haz_acc.png', width = 15, height = 10)
| /R/gambler.r | no_license | psmits/survivor | R | false | false | 3,933 | r | library(plyr)
library(reshape2)
library(ggplot2)
library(grid)
library(scales)
set.seed(420)
theme_set(theme_bw())
cbp <- c('#E69F00', '#56B4E9', '#009E73', '#F0E442',
'#0072B2', '#D55E00', '#CC79A7')
theme_update(axis.text = element_text(size = 32),
axis.title = element_text(size = 35),
axis.title.y = element_text(hjust = 0.1),
legend.text = element_text(size = 25),
legend.title = element_text(size = 26),
legend.key.size = unit(2, 'cm'),
strip.text = element_text(size = 30))
# exponential
lam <- 1
k <- 1
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gd <- ggplot(survival, aes(x = durs))
gd <- gd + geom_histogram(aes(y = ..density..), fill = 'grey')
gd <- gd + stat_function(fun = dweibull, colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gd <- gd + labs(x = 'Duration', y = 'Density')
ggsave(gd, filename = '../doc/figure/dur_exp.png', width = 15, height = 10)
gsv <- ggplot(survival, aes(x = durs, y = surv))
gsv <- gsv + geom_line(size = 3)
gsv <- gsv + scale_y_continuous(trans = log10_trans())
gsv <- gsv + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsv, filename = '../doc/figure/sur_exp.png', width = 15, height = 10)
ghz <- ggplot(survival, aes(x = durs, y = haz))
ghz <- ghz + geom_line(size = 3)
ghz <- ghz + scale_y_continuous(trans = log10_trans())
ghz <- ghz + labs(x = 'Duration', y = 'h(t)')
ggsave(ghz, filename = '../doc/figure/haz_exp.png', width = 15, height = 10)
# dec
lam <- 1
k <- 0.5
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gdd <- ggplot(survival, aes(x = durs))
gdd <- gdd + geom_histogram(aes(y = ..density..), fill = 'grey')
gdd <- gdd + stat_function(fun = dweibull,
data = data.frame(ss = seq(0, 50, 0.1)),
mapping = aes(x = ss),
colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gdd <- gdd + labs(x = 'Duration', y = 'Density')
ggsave(gdd, filename = '../doc/figure/dur_dec.png', width = 15, height = 10)
gsvd <- ggplot(survival, aes(x = durs, y = surv))
gsvd <- gsvd + geom_line(size = 3)
gsvd <- gsvd + scale_y_continuous(trans = log10_trans())
gsvd <- gsvd + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsvd, filename = '../doc/figure/sur_dec.png', width = 15, height = 10)
ghzd <- ggplot(survival, aes(x = durs, y = haz))
ghzd <- ghzd + geom_line(size = 3)
ghzd <- ghzd + scale_y_continuous(trans = log10_trans())
ghzd <- ghzd + labs(x = 'Duration', y = 'h(t)')
ggsave(ghzd, filename = '../doc/figure/haz_dec.png', width = 15, height = 10)
# acc
lam <- 1
k <- 1.5
durs <- rweibull(1000, k, lam)
surv <- exp(-lam * durs^k)
haz <- lam * k * durs^(k - 1)
survival <- data.frame(durs = durs, surv = surv, haz = haz)
gda <- ggplot(survival, aes(x = durs))
gda <- gda + geom_histogram(aes(y = ..density..), fill = 'grey')
gda <- gda + stat_function(fun = dweibull, colour = 'blue', size = 3,
arg = list(shape = k, scale = lam))
gda <- gda + labs(x = 'Duration', y = 'Density')
ggsave(gda, filename = '../doc/figure/dur_acc.png', width = 15, height = 10)
gsva <- ggplot(survival, aes(x = durs, y = surv))
gsva <- gsva + geom_line(size = 3)
gsva <- gsva + scale_y_continuous(trans = log10_trans())
gsva <- gsva + labs(x = 'Duration', y = 'P(T > t)')
ggsave(gsva, filename = '../doc/figure/sur_acc.png', width = 15, height = 10)
ghza <- ggplot(survival, aes(x = durs, y = haz))
ghza <- ghza + geom_line(size = 3)
ghza <- ghza + scale_y_continuous(trans = log10_trans())
ghza <- ghza + labs(x = 'Duration', y = 'h(t)')
ggsave(ghza, filename = '../doc/figure/haz_acc.png', width = 15, height = 10)
|
library(data.table)
library(lubridate)
library(dplyr)
setwd ("H:/Coursera/Exploratory data analysis/exdata-data-household_power_consumption")
data<-read.table("H:/Coursera/Exploratory data analysis/exdata-data-household_power_consumption/household_power_consumption.txt"
,header = TRUE, sep = ";", dec = ".",stringsAsFactors=FALSE, na.strings=c("NA", "-", "?"))
Subset<-dmy(data$Date)>=ymd("2007-02-01") & dmy(data$Date)<=ymd("2007-02-02")
data_subset <- data[Subset,]
data_subset2 <- mutate(data_subset, Time =dmy_hms(paste(data_subset$Date, data_subset$Time)))
png(width = 480, height = 480, file ="H:/Coursera/Exploratory data analysis/plot4.png")
par(mfrow=c(2,2))
plot2<-plot(data_subset2$Time,data_subset2$Global_active_power, xlab= "", ylab= "Global Active Power (kilowatts)", type="n")
lines(data_subset2$Time,data_subset2$Global_active_power)
plot4<-plot(data_subset2$Time,data_subset2$Voltage, xlab= "datetime", ylab= "Voltage", type="n")
lines(data_subset2$Time, data_subset2$Voltage)
plot3<-plot(data_subset2$Time, data_subset2$Sub_metering_1, xlab= "", ylab= "Energy sub metering", type="n")
lines(data_subset2$Time, data_subset2$Sub_metering_1)
lines(data_subset2$Time, data_subset2$Sub_metering_2, col="red")
lines(data_subset2$Time, data_subset2$Sub_metering_3, col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), bty="n", col=c("black", "red", "blue"), lty = c(1,1,1), cex=0.6)
plot5<-plot(data_subset2$Time,data_subset2$Global_reactive_power, xlab= "datetime", ylab= "Global_reactive_power", type="n")
lines(data_subset2$Time, data_subset2$Global_reactive_power)
dev.off()
| /plot4.R | no_license | aliciafs/ExData_Plotting1 | R | false | false | 1,665 | r | library(data.table)
library(lubridate)
library(dplyr)
setwd ("H:/Coursera/Exploratory data analysis/exdata-data-household_power_consumption")
data<-read.table("H:/Coursera/Exploratory data analysis/exdata-data-household_power_consumption/household_power_consumption.txt"
,header = TRUE, sep = ";", dec = ".",stringsAsFactors=FALSE, na.strings=c("NA", "-", "?"))
Subset<-dmy(data$Date)>=ymd("2007-02-01") & dmy(data$Date)<=ymd("2007-02-02")
data_subset <- data[Subset,]
data_subset2 <- mutate(data_subset, Time =dmy_hms(paste(data_subset$Date, data_subset$Time)))
png(width = 480, height = 480, file ="H:/Coursera/Exploratory data analysis/plot4.png")
par(mfrow=c(2,2))
plot2<-plot(data_subset2$Time,data_subset2$Global_active_power, xlab= "", ylab= "Global Active Power (kilowatts)", type="n")
lines(data_subset2$Time,data_subset2$Global_active_power)
plot4<-plot(data_subset2$Time,data_subset2$Voltage, xlab= "datetime", ylab= "Voltage", type="n")
lines(data_subset2$Time, data_subset2$Voltage)
plot3<-plot(data_subset2$Time, data_subset2$Sub_metering_1, xlab= "", ylab= "Energy sub metering", type="n")
lines(data_subset2$Time, data_subset2$Sub_metering_1)
lines(data_subset2$Time, data_subset2$Sub_metering_2, col="red")
lines(data_subset2$Time, data_subset2$Sub_metering_3, col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), bty="n", col=c("black", "red", "blue"), lty = c(1,1,1), cex=0.6)
plot5<-plot(data_subset2$Time,data_subset2$Global_reactive_power, xlab= "datetime", ylab= "Global_reactive_power", type="n")
lines(data_subset2$Time, data_subset2$Global_reactive_power)
dev.off()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Hilmo_import_csv.R
\name{Hilmo_import_csv}
\alias{Hilmo_import_csv}
\title{A Hilmo import .csv function}
\usage{
Hilmo_import_csv(filename)
}
\arguments{
\item{filename}{the name of your file you wish to import}
}
\description{
This function allows you to import csv files in more tidy format and it creates ID column from lahtopaiva and tnro
}
\examples{
Hilmo_import_csv()
}
\keyword{csv}
\keyword{hilmo}
\keyword{import}
| /Hilmo/man/Hilmo_import_csv.Rd | no_license | vilponk/hilmoGIT | R | false | true | 502 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Hilmo_import_csv.R
\name{Hilmo_import_csv}
\alias{Hilmo_import_csv}
\title{A Hilmo import .csv function}
\usage{
Hilmo_import_csv(filename)
}
\arguments{
\item{filename}{the name of your file you wish to import}
}
\description{
This function allows you to import csv files in more tidy format and it creates ID column from lahtopaiva and tnro
}
\examples{
Hilmo_import_csv()
}
\keyword{csv}
\keyword{hilmo}
\keyword{import}
|
loadBands<-function(file = "../../genomic_info/chromosome_bands.csv")
{
t = read.table(file, sep=",", header=T)
b = cbind(t$chromosome, t$band, t$start, t$end)
b = b[order(b$chromosome),]
return(b)
}
loadBreakpoints<-function(bpfile, use_sex = FALSE, ignore_subbands = TRUE)
{
bp = read.table(bpfile, sep="\t", comment="#", header=T)
if (nrow(bp) <= 0) { stop("Breakpoint file is empty") }
if (ignore_subbands)
{
## Ignoring subbands (11.1) and just group them by the major band designation (11)
#bp$Breakpoint = sub("\\.[0-9]+", "", bp$Breakpoint)
bp$Breakpoint = clearSubbands(bp$Breakpoint)
}
if (!use_sex)
{
# Not using sex chromosomes generally,
bp = dropSexChr(bp)
}
return(bp)
}
## if I care enough I can make one function for this stuff...
loadFragments<-function(file, use_sex = FALSE, ignore_subbands = TRUE)
{
## Fragments ##
fg = read.table(file, header=T, sep="\t")
if (nrow(fg) <= 0) { stop("Fragments file is empty") }
fg = fg[order(fg$Chr),]
if (!use_sex)
{
# Not using sex chromosomes generally,
fg = dropSexChr(fg)
}
if (ignore_subbands)
{
## Lets ignore subbands (11.1) and just group them by the major band designation (11)
fg$Start = clearSubbands(fg$Start)
fg$End = clearSubbands(fg$End)
}
return(fg)
}
loadChromosomeInfo<-function(chrfile = "../../genomic_info/chromosome_gene_info_2012.txt")
{
chrinfo = read.table(chrfile, sep="\t", row.names=1, header=T)
if (nrow(chrinfo) <= 0) { stop("Failed to load chromosome info file") }
# don't need the mtDNA row
chrinfo = chrinfo[ -(nrow(chrinfo)), ]
for (i in 1:nrow(chrinfo))
{
row = chrinfo[i,]
# leave pseudogenes out of it
chrinfo[i,'Total.Prot.RNA'] = sum(row$Confirmed.proteins, row$Putative.proteins, row$miRNA, row$rRNA, row$snRNA, row$snoRNA, row$Misc.ncRNA)
}
return(chrinfo)
}
dropSexChr<-function(df, colname = "Chr")
{
df = df[ which(df[[colname]]!="X" & df[[colname]]!="Y"),]
return(df)
}
clearSubbands<-function(col)
{
## Ignoring subbands (11.1) and just group them by the major band designation (11)
col = sub("\\.[0-9]+", "", col)
return(col)
}
sampleCancers<-function(df, colname = "Cancer", cancers)
{
message(nrow(df))
dfcnc = df[ which(df[[colname]] %in% cancers ) ,]
sampled = dfcnc[sample(1:nrow(dfcnc), 500, replace=FALSE),]
noncnc = df[which(df[[colname]] %nin% cancers), ]
df = rbind(noncnc, sampled)
message(nrow(df))
return(df)
}
| /R/lib/load_files.R | no_license | kavitarege/CancerCytogenetics | R | false | false | 2,539 | r |
loadBands<-function(file = "../../genomic_info/chromosome_bands.csv")
{
t = read.table(file, sep=",", header=T)
b = cbind(t$chromosome, t$band, t$start, t$end)
b = b[order(b$chromosome),]
return(b)
}
loadBreakpoints<-function(bpfile, use_sex = FALSE, ignore_subbands = TRUE)
{
bp = read.table(bpfile, sep="\t", comment="#", header=T)
if (nrow(bp) <= 0) { stop("Breakpoint file is empty") }
if (ignore_subbands)
{
## Ignoring subbands (11.1) and just group them by the major band designation (11)
#bp$Breakpoint = sub("\\.[0-9]+", "", bp$Breakpoint)
bp$Breakpoint = clearSubbands(bp$Breakpoint)
}
if (!use_sex)
{
# Not using sex chromosomes generally,
bp = dropSexChr(bp)
}
return(bp)
}
## if I care enough I can make one function for this stuff...
loadFragments<-function(file, use_sex = FALSE, ignore_subbands = TRUE)
{
## Fragments ##
fg = read.table(file, header=T, sep="\t")
if (nrow(fg) <= 0) { stop("Fragments file is empty") }
fg = fg[order(fg$Chr),]
if (!use_sex)
{
# Not using sex chromosomes generally,
fg = dropSexChr(fg)
}
if (ignore_subbands)
{
## Lets ignore subbands (11.1) and just group them by the major band designation (11)
fg$Start = clearSubbands(fg$Start)
fg$End = clearSubbands(fg$End)
}
return(fg)
}
loadChromosomeInfo<-function(chrfile = "../../genomic_info/chromosome_gene_info_2012.txt")
{
chrinfo = read.table(chrfile, sep="\t", row.names=1, header=T)
if (nrow(chrinfo) <= 0) { stop("Failed to load chromosome info file") }
# don't need the mtDNA row
chrinfo = chrinfo[ -(nrow(chrinfo)), ]
for (i in 1:nrow(chrinfo))
{
row = chrinfo[i,]
# leave pseudogenes out of it
chrinfo[i,'Total.Prot.RNA'] = sum(row$Confirmed.proteins, row$Putative.proteins, row$miRNA, row$rRNA, row$snRNA, row$snoRNA, row$Misc.ncRNA)
}
return(chrinfo)
}
dropSexChr<-function(df, colname = "Chr")
{
df = df[ which(df[[colname]]!="X" & df[[colname]]!="Y"),]
return(df)
}
clearSubbands<-function(col)
{
## Ignoring subbands (11.1) and just group them by the major band designation (11)
col = sub("\\.[0-9]+", "", col)
return(col)
}
sampleCancers<-function(df, colname = "Cancer", cancers)
{
message(nrow(df))
dfcnc = df[ which(df[[colname]] %in% cancers ) ,]
sampled = dfcnc[sample(1:nrow(dfcnc), 500, replace=FALSE),]
noncnc = df[which(df[[colname]] %nin% cancers), ]
df = rbind(noncnc, sampled)
message(nrow(df))
return(df)
}
|
## setwd in UCI HAR DATASET, load dplyr and tidyr libraries
## READ DATASETS FOR TRAIN AND TEST
#TRAIN
sub_1<- read.delim("./train/subject_train.txt", header = FALSE)
act_1<- read.delim("./train/y_train.txt", header = FALSE)
set_1 <- read.delim("./train/X_train.txt", header = FALSE)
features <- unlist(read.delim("features.txt", header = FALSE))
for(i in 1:7352){
set_1[i,1]<- trimws(set_1[i,1])
set_1[i,1]<- gsub(" ", " ", set_1[i,1])
}
set_1 <- separate(set_1, V1, into=features, sep=" ")
#TEST
sub_2<- read.delim("./test/subject_test.txt", header = FALSE)
act_2<- read.delim("./test/y_test.txt", header = FALSE)
set_2 <- read.delim("./test/X_test.txt", header = FALSE)
for(i in 1:2947){
set_2[i,1]<- trimws(set_2[i,1])
set_2[i,1]<- gsub(" ", " ", set_2[i,1])
}
set_2 <- separate(set_2, V1, into=features, sep=" ")
## FIND AND EXTRACT ONLY MEASUREMENTS ON MEAN AND STD
c1 <- c(grep("mean\\(\\)$|std\\(\\)$", features))
set_1 <- select(set_1, c1)
set_2 <- select(set_2, c1)
for(i in 1:18){
set_1[,i] <- as.numeric(set_1[,i])
set_2[,i] <- as.numeric(set_2[,i])
}
## MERGE DATASETS
data<-rbind(cbind(sub_1,act_1, set_1),cbind(sub_2, act_2, set_2))
##LABELING ACTIVITIES
data[,2] <- factor(data[,2], levels = c(1,2,3,4,5,6), labels = c("WALKING",
"WALKING_UPSTAIRS","WALKING_DOWNSTAIRS", "SITTING",
"STANDING", "LAYING"))
##LABELING DATA
names(data)[1:2] <- c("subject", "activity")
m<- names(data)[3:20]
m<- as.data.frame(strsplit(m, " "))
m<- as.vector(gsub("-", "",m[2,]))
names(data)[3:20]<-m
##CREATING MEANS
data %>% group_by(subject, activity) %>% summarize_at(vars(1:18), list(mean=mean)) | /run_analysis.R | no_license | adanclop/tidy-data | R | false | false | 1,776 | r | ## setwd in UCI HAR DATASET, load dplyr and tidyr libraries
## READ DATASETS FOR TRAIN AND TEST
#TRAIN
sub_1<- read.delim("./train/subject_train.txt", header = FALSE)
act_1<- read.delim("./train/y_train.txt", header = FALSE)
set_1 <- read.delim("./train/X_train.txt", header = FALSE)
features <- unlist(read.delim("features.txt", header = FALSE))
for(i in 1:7352){
set_1[i,1]<- trimws(set_1[i,1])
set_1[i,1]<- gsub(" ", " ", set_1[i,1])
}
set_1 <- separate(set_1, V1, into=features, sep=" ")
#TEST
sub_2<- read.delim("./test/subject_test.txt", header = FALSE)
act_2<- read.delim("./test/y_test.txt", header = FALSE)
set_2 <- read.delim("./test/X_test.txt", header = FALSE)
for(i in 1:2947){
set_2[i,1]<- trimws(set_2[i,1])
set_2[i,1]<- gsub(" ", " ", set_2[i,1])
}
set_2 <- separate(set_2, V1, into=features, sep=" ")
## FIND AND EXTRACT ONLY MEASUREMENTS ON MEAN AND STD
c1 <- c(grep("mean\\(\\)$|std\\(\\)$", features))
set_1 <- select(set_1, c1)
set_2 <- select(set_2, c1)
for(i in 1:18){
set_1[,i] <- as.numeric(set_1[,i])
set_2[,i] <- as.numeric(set_2[,i])
}
## MERGE DATASETS
data<-rbind(cbind(sub_1,act_1, set_1),cbind(sub_2, act_2, set_2))
##LABELING ACTIVITIES
data[,2] <- factor(data[,2], levels = c(1,2,3,4,5,6), labels = c("WALKING",
"WALKING_UPSTAIRS","WALKING_DOWNSTAIRS", "SITTING",
"STANDING", "LAYING"))
##LABELING DATA
names(data)[1:2] <- c("subject", "activity")
m<- names(data)[3:20]
m<- as.data.frame(strsplit(m, " "))
m<- as.vector(gsub("-", "",m[2,]))
names(data)[3:20]<-m
##CREATING MEANS
data %>% group_by(subject, activity) %>% summarize_at(vars(1:18), list(mean=mean)) |
## This is a pair of functions that cache the inverse of a matrix.
## This function creates a special "matrix" object
## that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
## set the m to NULL for starters
m <- NULL
## create a setter that caches x and m
set <- function(y) {
x <<- y
m <<- NULL
}
## create a getter that returns the cached x,
## which is the inverse of a matrix
get <- function() x
setsolve <- function(solve) m <<- solve
## create a getsolve function that returns the inverse of matrix,
# only calculating it if necessary
getsolve <- function() m
## return the CacheMatrix object as a list of 4 functions
list(set = set, get = get,
setsolve = setsolve,
getsolve = getsolve)
}
}
## this function computes the inverse of the special "matrix" returned
##by makeCacheMatrix above. If the inverse has already been
##calculated (and the matrix has not changed),
##then the cachesolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
#subset list of 4 functions, if the inverse has already been
##calculated, return the cached inverse
m <- x$getsolve()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
#pass matrix to calculation
data <- x$get()
m <- solve(data, ...)
x$setsolve(m)
m
}
###Test Case:
# create cacheable matrix object
m<- makeCacheMatrix( )
# initailize with a an easy to inspect matrix
m$set( matrix( c(0, 2, 2, 0 ), 2, 2))
# note use of parens to retrive the matrix part of the object
m$get()
# test the inverse cacher
cacheSolve( m )
# should be cached now
cacheSolve( m )
| /cachematrix.R | no_license | ShuangyuanWei/ProgrammingAssignment2 | R | false | false | 1,929 | r | ## This is a pair of functions that cache the inverse of a matrix.
## This function creates a special "matrix" object
## that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
## set the m to NULL for starters
m <- NULL
## create a setter that caches x and m
set <- function(y) {
x <<- y
m <<- NULL
}
## create a getter that returns the cached x,
## which is the inverse of a matrix
get <- function() x
setsolve <- function(solve) m <<- solve
## create a getsolve function that returns the inverse of matrix,
# only calculating it if necessary
getsolve <- function() m
## return the CacheMatrix object as a list of 4 functions
list(set = set, get = get,
setsolve = setsolve,
getsolve = getsolve)
}
}
## this function computes the inverse of the special "matrix" returned
##by makeCacheMatrix above. If the inverse has already been
##calculated (and the matrix has not changed),
##then the cachesolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
#subset list of 4 functions, if the inverse has already been
##calculated, return the cached inverse
m <- x$getsolve()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
#pass matrix to calculation
data <- x$get()
m <- solve(data, ...)
x$setsolve(m)
m
}
###Test Case:
# create cacheable matrix object
m<- makeCacheMatrix( )
# initailize with a an easy to inspect matrix
m$set( matrix( c(0, 2, 2, 0 ), 2, 2))
# note use of parens to retrive the matrix part of the object
m$get()
# test the inverse cacher
cacheSolve( m )
# should be cached now
cacheSolve( m )
|
###################
# Nelly Amenyogbe
# HEU Manuscript Analysis: Microbiome data ordination
###################
# In this script, we will prepare ordinations (Non-metric multidimensional scaling; NMDS) for HEU and HUU children together, and for each study site separately. We will then perform statistical analyses (PERMANOVA test) to determine the variance explained by HIV exposure on the stool microbiome composition in each study site separately.
# Load packages
library(phyloseq)
library(ggplot2)
library(vegan)
library(plyr)
# load data
sample.cols <- read.csv("HEU_manuscript_analysis/Rdata/luminex/heu_pca_colours.csv")
physeq <- readRDS("HEU_manuscript_analysis/Rdata/microbiome/gc_heu_phyloseq.rds")
# Subset data by site ####
cad <- subset_samples(physeq, Site == "Canada")
cad <- prune_taxa(taxa_sums(cad) > 0, cad)
blg <- subset_samples(physeq, Site == "Belgium")
blg <- prune_taxa(taxa_sums(blg) > 0, blg)
saf <- subset_samples(physeq, Site == "South Africa")
saf <- prune_taxa(taxa_sums(saf) > 0, saf)
# Ordination of samples ####
# get.nmds.data
# Input: ps = phyloseq object
# output: data frame friendly for plotting via ggplot2
get.nmds.data <- function(ps){
ord <- ordinate(ps, method = "NMDS", distance = "bray")
p <- plot_ordination(physeq, ord)
dat <- p$data
dat$Exposure <- gsub("Control", "HUU", dat$Exposure)
dat$Exposure <- factor(dat$Exposure, levels = c("HUU", "HEU"))
dat$site.exposure <- paste(dat$Site, dat$Exposure)
return(dat)
}
# All samples together ####
heu.nmds.dat <- get.nmds.data(physeq)
# set aesthetics
# colour
sample.cols$site.heu <- gsub("Control", "HUU", sample.cols$site.heu)
cols <- as.character(sample.cols$colour)
names(cols) <- sample.cols$site.heu
# shape
shapes <- sample.cols$shape
names(shapes) <- sample.cols$site.heu
shapes.f <- c("Canada HEU" = 24, "Canada HUU" = 24, "Belgium HEU" = 21, "Belgium HUU" = 21, "South Africa HEU" = 22, "South Africa HUU" = 22) # this is for shapes where the fill is specified rather than the colour
# set factor levels
heu.nmds.dat$site.exposure <- factor(heu.nmds.dat$site.exposure, levels = c("Belgium HUU", "Belgium HEU", "Canada HUU", "Canada HEU", "South Africa HUU", "South Africa HEU"))
p.heu <- ggplot(heu.nmds.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, color = "black") +
theme_classic() +
scale_shape_manual("", values = shapes.f) +
scale_fill_manual("", values = cols) +
theme(axis.text.x = element_text(size = 18, face = "bold"),
axis.text.y = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
legend.position = "bottom")
p.heu
#ggsave("HEU_manuscript_analysis/figures/microbiome/heu_nmds.pdf", device = "pdf", dpi = 300, width = 6.5, height = 6)
# Ordination by Site ####
# CAD ord ####
cad.dat <- get.nmds.data(cad)
cad.p <- ggplot(cad.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 24) +
theme_classic() +
scale_shape_manual("", values = shapes[3:4]) +
scale_fill_manual("", values = cols[3:4]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
cad.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/cad_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# BLG ord ####
blg.dat <- get.nmds.data(blg)
blg.p <- ggplot(blg.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 21) +
theme_classic() +
scale_shape_manual("", values = shapes[1:2]) +
scale_fill_manual("", values = cols[1:2]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
blg.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/blg_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# SAF ord ####
saf.dat <- get.nmds.data(saf)
saf.p <- ggplot(saf.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 22) +
theme_classic() +
scale_shape_manual("", values = shapes[5:6]) +
scale_fill_manual("", values = cols[5:6]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
saf.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/saf_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# Adonis Test for Clustering ####
# determine the variance explained by HIV exposure for each site separately
physeqs <- list(cad, blg, saf)
names(physeqs) <- c("cad", "blg", "saf")
adonis.tests <- llply(as.list(physeqs), function(i){
df <- data.frame(sample_data(i))
dist <- phyloseq::distance(i, "bray")
adonis(dist ~ Exposure, data = df)
})
names(adonis.tests) <- names(physeqs)
adonis.tests
# CAD R2 = 0.03527, p = 0.043
# BLG R2 = 0.03824, p = 0.579
# SAF R2 = 0.0724, p = 0.311
# END ####
| /HEU_manuscript_analysis/scripts/microbiome/ms_microbiome_ordination.R | no_license | nelly-amenyogbe/Global_HEU_immune_microbiome | R | false | false | 6,167 | r | ###################
# Nelly Amenyogbe
# HEU Manuscript Analysis: Microbiome data ordination
###################
# In this script, we will prepare ordinations (Non-metric multidimensional scaling; NMDS) for HEU and HUU children together, and for each study site separately. We will then perform statistical analyses (PERMANOVA test) to determine the variance explained by HIV exposure on the stool microbiome composition in each study site separately.
# Load packages
library(phyloseq)
library(ggplot2)
library(vegan)
library(plyr)
# load data
sample.cols <- read.csv("HEU_manuscript_analysis/Rdata/luminex/heu_pca_colours.csv")
physeq <- readRDS("HEU_manuscript_analysis/Rdata/microbiome/gc_heu_phyloseq.rds")
# Subset data by site ####
cad <- subset_samples(physeq, Site == "Canada")
cad <- prune_taxa(taxa_sums(cad) > 0, cad)
blg <- subset_samples(physeq, Site == "Belgium")
blg <- prune_taxa(taxa_sums(blg) > 0, blg)
saf <- subset_samples(physeq, Site == "South Africa")
saf <- prune_taxa(taxa_sums(saf) > 0, saf)
# Ordination of samples ####
# get.nmds.data
# Input: ps = phyloseq object
# output: data frame friendly for plotting via ggplot2
get.nmds.data <- function(ps){
ord <- ordinate(ps, method = "NMDS", distance = "bray")
p <- plot_ordination(physeq, ord)
dat <- p$data
dat$Exposure <- gsub("Control", "HUU", dat$Exposure)
dat$Exposure <- factor(dat$Exposure, levels = c("HUU", "HEU"))
dat$site.exposure <- paste(dat$Site, dat$Exposure)
return(dat)
}
# All samples together ####
heu.nmds.dat <- get.nmds.data(physeq)
# set aesthetics
# colour
sample.cols$site.heu <- gsub("Control", "HUU", sample.cols$site.heu)
cols <- as.character(sample.cols$colour)
names(cols) <- sample.cols$site.heu
# shape
shapes <- sample.cols$shape
names(shapes) <- sample.cols$site.heu
shapes.f <- c("Canada HEU" = 24, "Canada HUU" = 24, "Belgium HEU" = 21, "Belgium HUU" = 21, "South Africa HEU" = 22, "South Africa HUU" = 22) # this is for shapes where the fill is specified rather than the colour
# set factor levels
heu.nmds.dat$site.exposure <- factor(heu.nmds.dat$site.exposure, levels = c("Belgium HUU", "Belgium HEU", "Canada HUU", "Canada HEU", "South Africa HUU", "South Africa HEU"))
p.heu <- ggplot(heu.nmds.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, color = "black") +
theme_classic() +
scale_shape_manual("", values = shapes.f) +
scale_fill_manual("", values = cols) +
theme(axis.text.x = element_text(size = 18, face = "bold"),
axis.text.y = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
legend.position = "bottom")
p.heu
#ggsave("HEU_manuscript_analysis/figures/microbiome/heu_nmds.pdf", device = "pdf", dpi = 300, width = 6.5, height = 6)
# Ordination by Site ####
# CAD ord ####
cad.dat <- get.nmds.data(cad)
cad.p <- ggplot(cad.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 24) +
theme_classic() +
scale_shape_manual("", values = shapes[3:4]) +
scale_fill_manual("", values = cols[3:4]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
cad.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/cad_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# BLG ord ####
blg.dat <- get.nmds.data(blg)
blg.p <- ggplot(blg.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 21) +
theme_classic() +
scale_shape_manual("", values = shapes[1:2]) +
scale_fill_manual("", values = cols[1:2]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
blg.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/blg_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# SAF ord ####
saf.dat <- get.nmds.data(saf)
saf.p <- ggplot(saf.dat, aes(x = NMDS1, y =NMDS2, fill = site.exposure, shape = site.exposure)) +
geom_point(alpha = 0.9, size = 4, shape = 22) +
theme_classic() +
scale_shape_manual("", values = shapes[5:6]) +
scale_fill_manual("", values = cols[5:6]) +
theme(axis.text.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.line = element_line(size = 0.8),
legend.text = element_text(size = 16),
legend.title = element_text(size = 12),
legend.position = "none")
saf.p
#ggsave("HEU_manuscript_analysis/figures/microbiome/saf_heu_nmds.pdf", device = "pdf", dpi = 300, width = 5, height = 4)
# Adonis Test for Clustering ####
# determine the variance explained by HIV exposure for each site separately
physeqs <- list(cad, blg, saf)
names(physeqs) <- c("cad", "blg", "saf")
adonis.tests <- llply(as.list(physeqs), function(i){
df <- data.frame(sample_data(i))
dist <- phyloseq::distance(i, "bray")
adonis(dist ~ Exposure, data = df)
})
names(adonis.tests) <- names(physeqs)
adonis.tests
# CAD R2 = 0.03527, p = 0.043
# BLG R2 = 0.03824, p = 0.579
# SAF R2 = 0.0724, p = 0.311
# END ####
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catboost.R
\name{catboost.save_pool}
\alias{catboost.save_pool}
\title{Save the dataset}
\usage{
catboost.save_pool(
data,
label = NULL,
weight = NULL,
baseline = NULL,
pool_path = "data.pool",
cd_path = "cd.pool"
)
}
\arguments{
\item{data}{A data.frame with features.
The following column types are supported:
\itemize{
\item double
\item factor.
It is assumed that categorical features are given in this type of columns.
A standard CatBoost processing procedure is applied to this type of columns:
\describe{
\item{1.}{The values are converted to strings.}
\item{2.}{The ConvertCatFeatureToFloat function is applied to the resulting string.}
}
}
Default value: Required argument}
\item{label}{The label vector.}
\item{weight}{The weights of the label vector.}
\item{baseline}{Vector of initial (raw) values of the label function for the object.
Used in the calculation of final values of trees.}
\item{pool_path}{The path to the output file that contains the dataset description.}
\item{cd_path}{The path to the output file that contains the column descriptions.}
}
\value{
Nothing. This method writes a dataset to disk.
}
\description{
Save the dataset to the CatBoost format.
Files with the following data are created:
\itemize{
\item Dataset description
\item Column descriptions
}
Use the catboost.load_pool function to read the resulting files.
These files can also be used in the
\href{https://catboost.ai/docs/concepts/cli-installation.html}{Command-line version}
and the \href{https://catboost.ai/docs/concepts/python-installation.html}{Python library}.
}
| /catboost/R-package/man/catboost.save_pool.Rd | permissive | catboost/catboost | R | false | true | 1,832 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catboost.R
\name{catboost.save_pool}
\alias{catboost.save_pool}
\title{Save the dataset}
\usage{
catboost.save_pool(
data,
label = NULL,
weight = NULL,
baseline = NULL,
pool_path = "data.pool",
cd_path = "cd.pool"
)
}
\arguments{
\item{data}{A data.frame with features.
The following column types are supported:
\itemize{
\item double
\item factor.
It is assumed that categorical features are given in this type of columns.
A standard CatBoost processing procedure is applied to this type of columns:
\describe{
\item{1.}{The values are converted to strings.}
\item{2.}{The ConvertCatFeatureToFloat function is applied to the resulting string.}
}
}
Default value: Required argument}
\item{label}{The label vector.}
\item{weight}{The weights of the label vector.}
\item{baseline}{Vector of initial (raw) values of the label function for the object.
Used in the calculation of final values of trees.}
\item{pool_path}{The path to the output file that contains the dataset description.}
\item{cd_path}{The path to the output file that contains the column descriptions.}
}
\value{
Nothing. This method writes a dataset to disk.
}
\description{
Save the dataset to the CatBoost format.
Files with the following data are created:
\itemize{
\item Dataset description
\item Column descriptions
}
Use the catboost.load_pool function to read the resulting files.
These files can also be used in the
\href{https://catboost.ai/docs/concepts/cli-installation.html}{Command-line version}
and the \href{https://catboost.ai/docs/concepts/python-installation.html}{Python library}.
}
|
input <- scan("stdin")
cat(input[1]-input[2]) | /q1001.r | no_license | taehyunkim2/practice | R | false | false | 45 | r | input <- scan("stdin")
cat(input[1]-input[2]) |
#' dimsum__identify_double_aa_mutations
#'
#' Identify and annotate double AA substitutions.
#'
#' @param input_dt input data.table (required)
#' @param singles_dt singles data.table (required)
#' @param wt_AAseq WT amino acid sequence (required)
#'
#' @return data.table with double amino acid variants
#' @export
#' @import data.table
dimsum__identify_double_aa_mutations <- function(
input_dt,
singles_dt,
wt_AAseq
){
#WT AA sequences
wt_AAseq_split <- strsplit(wt_AAseq,"")[[1]]
### Identify position and identity of double AA mutations
###########################
#Double AA mutants
doubles <- input_dt[Nham_aa==2]
#Add position, mutant AA, WT AA and mean input count
doubles[,Pos1 := which(strsplit(aa_seq,"")[[1]] !=wt_AAseq_split)[1],aa_seq]
doubles[,Pos2 := which(strsplit(aa_seq,"")[[1]] !=wt_AAseq_split)[2],aa_seq]
doubles[,Mut1 := strsplit(aa_seq,"")[[1]][Pos1],aa_seq]
doubles[,Mut2 := strsplit(aa_seq,"")[[1]][Pos2],aa_seq]
doubles[,WT_AA1 := wt_AAseq_split[Pos1],aa_seq]
doubles[,WT_AA2 := wt_AAseq_split[Pos2],aa_seq]
#Mean counts
doubles <- merge(doubles,singles_dt[,.(Pos,Mut,s1_mean_count = mean_count)],by.x = c("Pos1","Mut1"),by.y = c("Pos","Mut"))
doubles <- merge(doubles,singles_dt[,.(Pos,Mut,s2_mean_count = mean_count)],by.x = c("Pos2","Mut2"),by.y = c("Pos","Mut"))
return(doubles)
}
| /R/dimsum__identify_double_aa_mutations.R | permissive | jschmiedel/DiMSum | R | false | false | 1,360 | r |
#' dimsum__identify_double_aa_mutations
#'
#' Identify and annotate double AA substitutions.
#'
#' @param input_dt input data.table (required)
#' @param singles_dt singles data.table (required)
#' @param wt_AAseq WT amino acid sequence (required)
#'
#' @return data.table with double amino acid variants
#' @export
#' @import data.table
dimsum__identify_double_aa_mutations <- function(
input_dt,
singles_dt,
wt_AAseq
){
#WT AA sequences
wt_AAseq_split <- strsplit(wt_AAseq,"")[[1]]
### Identify position and identity of double AA mutations
###########################
#Double AA mutants
doubles <- input_dt[Nham_aa==2]
#Add position, mutant AA, WT AA and mean input count
doubles[,Pos1 := which(strsplit(aa_seq,"")[[1]] !=wt_AAseq_split)[1],aa_seq]
doubles[,Pos2 := which(strsplit(aa_seq,"")[[1]] !=wt_AAseq_split)[2],aa_seq]
doubles[,Mut1 := strsplit(aa_seq,"")[[1]][Pos1],aa_seq]
doubles[,Mut2 := strsplit(aa_seq,"")[[1]][Pos2],aa_seq]
doubles[,WT_AA1 := wt_AAseq_split[Pos1],aa_seq]
doubles[,WT_AA2 := wt_AAseq_split[Pos2],aa_seq]
#Mean counts
doubles <- merge(doubles,singles_dt[,.(Pos,Mut,s1_mean_count = mean_count)],by.x = c("Pos1","Mut1"),by.y = c("Pos","Mut"))
doubles <- merge(doubles,singles_dt[,.(Pos,Mut,s2_mean_count = mean_count)],by.x = c("Pos2","Mut2"),by.y = c("Pos","Mut"))
return(doubles)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expandFunctions.R
\name{coefPlot}
\alias{coefPlot}
\title{Plots coefficients in an impulse response format}
\usage{
coefPlot(xObj, includeIntercept = FALSE, type = "h", main = NULL, ...)
}
\arguments{
\item{xObj}{Output of a fitting model.}
\item{includeIntercept}{Should the 1st coefficient be plotted?
Default is FALSE.}
\item{type}{Graphics type. Default is "h", which
results in an impulse-like plot.}
\item{main}{"main" title; default is the relative
number of non-zero coefficients,
a measure of sparsity.}
\item{...}{Optional additional graphical parameters,
for instance to set ylim to a fixed value.}
}
\value{
Invisibly returns TRUE. Used for its
graphic side effects only.
}
\description{
Given a model xObj for which coef(xObj)
returns a set of coefficients, plot the coefficients.
The plots make it easier to compare which features are large,
which are set to zero, and how features change from run
to run in a graphical manner.
If the fitting process is linear (e.g. lm, glmnet, etc.)
and the original features are appropriately ordered lags,
this will generate an impulse response.
Any coefficients that are \emph{exactly} zero (for instance,
set that way by LASSO) will appear as red X's; non-zero
points will be black O's.
}
\details{
If includeIntercept==TRUE, the intercept of the model
will be plotted as index 0.
Changing the type using \code{type="b"}
will result in a parallel coordinate-like plot rather
than an impulse-like plot. It is sometimes easier to
see the differences in coefficients with type="b"
rather than type="h".
}
\examples{
set.seed(1)
nObs <- 100
X <- distMat(nObs,6)
A <- cbind(c(1,0,-1,rep(0,3))) # Y will only depend on X[,1] and X[,3]
Y <- X \%*\% A + 0.1*rnorm(nObs)
lassoObj <- easyLASSO(X,Y)
Yhat <- predict(lassoObj,newx=X)
yyHatPlot(Y,Yhat)
coef( lassoObj ) # Sparse coefficients
coefPlot( lassoObj )
coefPlot( lassoObj, includeIntercept=TRUE )
coefPlot( lassoObj, type="b" )
}
| /man/coefPlot.Rd | no_license | cran/expandFunctions | R | false | true | 2,122 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expandFunctions.R
\name{coefPlot}
\alias{coefPlot}
\title{Plots coefficients in an impulse response format}
\usage{
coefPlot(xObj, includeIntercept = FALSE, type = "h", main = NULL, ...)
}
\arguments{
\item{xObj}{Output of a fitting model.}
\item{includeIntercept}{Should the 1st coefficient be plotted?
Default is FALSE.}
\item{type}{Graphics type. Default is "h", which
results in an impulse-like plot.}
\item{main}{"main" title; default is the relative
number of non-zero coefficients,
a measure of sparsity.}
\item{...}{Optional additional graphical parameters,
for instance to set ylim to a fixed value.}
}
\value{
Invisibly returns TRUE. Used for its
graphic side effects only.
}
\description{
Given a model xObj for which coef(xObj)
returns a set of coefficients, plot the coefficients.
The plots make it easier to compare which features are large,
which are set to zero, and how features change from run
to run in a graphical manner.
If the fitting process is linear (e.g. lm, glmnet, etc.)
and the original features are appropriately ordered lags,
this will generate an impulse response.
Any coefficients that are \emph{exactly} zero (for instance,
set that way by LASSO) will appear as red X's; non-zero
points will be black O's.
}
\details{
If includeIntercept==TRUE, the intercept of the model
will be plotted as index 0.
Changing the type using \code{type="b"}
will result in a parallel coordinate-like plot rather
than an impulse-like plot. It is sometimes easier to
see the differences in coefficients with type="b"
rather than type="h".
}
\examples{
set.seed(1)
nObs <- 100
X <- distMat(nObs,6)
A <- cbind(c(1,0,-1,rep(0,3))) # Y will only depend on X[,1] and X[,3]
Y <- X \%*\% A + 0.1*rnorm(nObs)
lassoObj <- easyLASSO(X,Y)
Yhat <- predict(lassoObj,newx=X)
yyHatPlot(Y,Yhat)
coef( lassoObj ) # Sparse coefficients
coefPlot( lassoObj )
coefPlot( lassoObj, includeIntercept=TRUE )
coefPlot( lassoObj, type="b" )
}
|
library(sp)
library(ggplot2)
segntos <- read.csv(file = "C:/Users/Giuseppe Antonelli/Desktop/tesi/segntos.csv")
date <- segntos[,32]
date <- as.Date(date, format = '%Y%m%d')
date <- na.omit(date)
mesi <- format(date, '%m')
mesi <- as.numeric(mesi)
segnalaz <- table(mesi)
barplot(segnalaz, main="Segnalazioni per mese", ylab="segnalazioni", xlab="mesi",
names.arg=c("JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"), cex.names=1.8)
| /extract_mesi_wikiplantbase.R | no_license | interacquas/Optmised-sampling | R | false | false | 484 | r | library(sp)
library(ggplot2)
segntos <- read.csv(file = "C:/Users/Giuseppe Antonelli/Desktop/tesi/segntos.csv")
date <- segntos[,32]
date <- as.Date(date, format = '%Y%m%d')
date <- na.omit(date)
mesi <- format(date, '%m')
mesi <- as.numeric(mesi)
segnalaz <- table(mesi)
barplot(segnalaz, main="Segnalazioni per mese", ylab="segnalazioni", xlab="mesi",
names.arg=c("JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"), cex.names=1.8)
|
###################################################
## 1.3.2017
## Exploratory Analysis Project 1 - Part 3 of 4
## UC Irvine Data set: Electric power consumption
## Reproduce a histogram of Global Active Power
## Create plot3.png
## Restrict Dates to: 2007-02-01 and 2007-02-02
## Note: missing data is coded as ? in dataset
###################################################
## Get data
fileloc <- "./exdata_data_household_power_consumption/household_power_consumption.txt"
readLines(fileloc,10)
## Note: setting column class to "date" expects a certain ordering "year/month/day"
## however the data is set up as "day/month/year". can use read.zoo to set date format,
## but chose to import as character and convert to date
columnClasses <- c("character","character","numeric","numeric","numeric","numeric","numeric", "numeric","numeric")
EPCdata <- read.table(fileloc, header = TRUE, sep = ";", na.strings = "?", colClasses = columnClasses)
##Data checks
summary(EPCdata)
colSums(is.na(EPCdata))
table(EPCdata$Date)
## subset data on dates: 2007-02-01 & 2007-02-02
## "Date" is still character vector, will convert after subsetting
## current format: "d/m/yyyy"
EPC <- EPCdata[EPCdata$Date == "1/2/2007"|EPCdata$Date == "2/2/2007", ]
## add datetime; not necessary for plot 1 but needed for the others
EPC$Datetime <- strptime(paste(EPC$Date,EPC$Time, sep =" "),format = "%d/%m/%Y %H:%M:%S")
## Open graphical device: png
png(filename = "ExData_Plotting1/plot3.png", width = 480, height = 480)
## Recreate graph
with(EPC, plot(Datetime,Sub_metering_1, type = "n", xlab = "",
ylab = "Energy sub metering"))
with(EPC, points(Datetime, Sub_metering_1, type = "l"))
with(EPC, points(Datetime, Sub_metering_2, type = "l", col = "red"))
with(EPC, points(Datetime, Sub_metering_3, type = "l", col = "blue"))
legend("topright", lty = 1,col = c("black","red","blue"),
legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
## Close graphics device
dev.off()
| /plot3.R | no_license | Michelle-Stutey-Henderson/ExData_Plotting1 | R | false | false | 2,004 | r | ###################################################
## 1.3.2017
## Exploratory Analysis Project 1 - Part 3 of 4
## UC Irvine Data set: Electric power consumption
## Reproduce a histogram of Global Active Power
## Create plot3.png
## Restrict Dates to: 2007-02-01 and 2007-02-02
## Note: missing data is coded as ? in dataset
###################################################
## Get data
fileloc <- "./exdata_data_household_power_consumption/household_power_consumption.txt"
readLines(fileloc,10)
## Note: setting column class to "date" expects a certain ordering "year/month/day"
## however the data is set up as "day/month/year". can use read.zoo to set date format,
## but chose to import as character and convert to date
columnClasses <- c("character","character","numeric","numeric","numeric","numeric","numeric", "numeric","numeric")
EPCdata <- read.table(fileloc, header = TRUE, sep = ";", na.strings = "?", colClasses = columnClasses)
##Data checks
summary(EPCdata)
colSums(is.na(EPCdata))
table(EPCdata$Date)
## subset data on dates: 2007-02-01 & 2007-02-02
## "Date" is still character vector, will convert after subsetting
## current format: "d/m/yyyy"
EPC <- EPCdata[EPCdata$Date == "1/2/2007"|EPCdata$Date == "2/2/2007", ]
## add datetime; not necessary for plot 1 but needed for the others
EPC$Datetime <- strptime(paste(EPC$Date,EPC$Time, sep =" "),format = "%d/%m/%Y %H:%M:%S")
## Open graphical device: png
png(filename = "ExData_Plotting1/plot3.png", width = 480, height = 480)
## Recreate graph
with(EPC, plot(Datetime,Sub_metering_1, type = "n", xlab = "",
ylab = "Energy sub metering"))
with(EPC, points(Datetime, Sub_metering_1, type = "l"))
with(EPC, points(Datetime, Sub_metering_2, type = "l", col = "red"))
with(EPC, points(Datetime, Sub_metering_3, type = "l", col = "blue"))
legend("topright", lty = 1,col = c("black","red","blue"),
legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
## Close graphics device
dev.off()
|
library(caret)
### Name: as.matrix.confusionMatrix
### Title: Confusion matrix as a table
### Aliases: as.matrix.confusionMatrix as.table.confusionMatrix
### Keywords: utilities
### ** Examples
###################
## 2 class example
lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
levels = rev(lvs))
pred <- factor(
c(
rep(lvs, times = c(54, 32)),
rep(lvs, times = c(27, 231))),
levels = rev(lvs))
xtab <- table(pred, truth)
results <- confusionMatrix(xtab)
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
###################
## 3 class example
xtab <- confusionMatrix(iris$Species, sample(iris$Species))
as.matrix(xtab)
| /data/genthat_extracted_code/caret/examples/as.matrix.confusionMatrix.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 806 | r | library(caret)
### Name: as.matrix.confusionMatrix
### Title: Confusion matrix as a table
### Aliases: as.matrix.confusionMatrix as.table.confusionMatrix
### Keywords: utilities
### ** Examples
###################
## 2 class example
lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
levels = rev(lvs))
pred <- factor(
c(
rep(lvs, times = c(54, 32)),
rep(lvs, times = c(27, 231))),
levels = rev(lvs))
xtab <- table(pred, truth)
results <- confusionMatrix(xtab)
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
###################
## 3 class example
xtab <- confusionMatrix(iris$Species, sample(iris$Species))
as.matrix(xtab)
|
# Travel-app06
# setup.R
library(shiny)
library(rsconnect)
shinyapps::setAccountInfo(name='tohweizhong',
token='DF9614818065B5DAAF22AE7D78C38089',
secret='uSZ0rCRf6/gKj2ltBXrGENb4kn0y8Cw2bZaA87zk')
deployApp(appName = "Travel-app06", account = "tohweizhong")
| /Travel-app06/setup.R | no_license | tohweizhong/Travel-app | R | false | false | 315 | r |
# Travel-app06
# setup.R
library(shiny)
library(rsconnect)
shinyapps::setAccountInfo(name='tohweizhong',
token='DF9614818065B5DAAF22AE7D78C38089',
secret='uSZ0rCRf6/gKj2ltBXrGENb4kn0y8Cw2bZaA87zk')
deployApp(appName = "Travel-app06", account = "tohweizhong")
|
library(scater)
library(stringr)
options(stringsAsFactors=FALSE)
library(pheatmap)
library(gtools)
library(ggplot2)
source("../utils.R")
source("runGSEA_preRank.R")
args <- commandArgs()
tumor <- args[6]
outDir <- file.path("dataset",tumor)
if(!dir.exists(outDir) ) dir.create(outDir,recursive=TRUE)
pathway_file <- "../Data/KEGG_metabolism.gmt"
#1. Loading the data
selected_sce <- readRDS(file.path("../1-ReadData/dataset/",tumor,"selected_sce.rds"))
selected_nontumor_sce <- selected_sce[,selected_sce$cellType!="Malignant"]
selected_nontumor_metabolic_sce <- selected_nontumor_sce[rowData(selected_nontumor_sce)$metabolic,]
#=========================================================================
celltypes <- unique(selected_nontumor_metabolic_sce$cellType)
#2.Tumor cells
enrich_data_df <- data.frame(x=NULL,y=NULL,NES=NULL,PVAL=NULL)
pc_plotdata <- data.frame(x=numeric(),y=numeric(),
sel=character(),types=character())
for (t in celltypes){
t2 <- str_replace(t," ","")
each_metabolic_sce <- selected_nontumor_metabolic_sce[,selected_nontumor_metabolic_sce$cellType==t]
each_metabolic_tpm <- assay(each_metabolic_sce,"exprs")
each_metabolic_tpm <- each_metabolic_tpm[rowSums(each_metabolic_tpm)>0,]
x <- each_metabolic_tpm
ntop <- nrow(x)
rv <- rowVars(x)
select <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))]
pca <- prcomp(t(x[select,]))
percentVar <- pca$sdev^2 / sum( pca$sdev^2 )
###select PCs that explain at least 80% of the variance
cum_var <- cumsum(percentVar)
select_pcs <- which(cum_var>0.8)[1]
###plot the PCA and explained variances
tmp_plotdata <- data.frame(x=1:length(percentVar),y=percentVar,
sel=c(rep("y",select_pcs),rep("n",length(percentVar)-select_pcs)),
types=rep(t,length(percentVar)))
pc_plotdata <- rbind(pc_plotdata,tmp_plotdata)
###
pre_rank_matrix <- as.matrix(rowSums(abs(pca$rotation[,1:select_pcs])))
runGSEA_preRank(pre_rank_matrix,pathway_file,t2)
#get the result
result_dir <- list.files(path="preRankResults",pattern = paste0("^",t2,".GseaPreranked(.*)"),full.names=T)
result_file <- list.files(path=result_dir,pattern="gsea_report_for_na_pos_(.*).xls",full.names=T)
gsea_result <- read.table(result_file,header = T,sep="\t",row.names=1)
gsea_pathways <- str_to_title(rownames(gsea_result))
gsea_pathways <- str_replace(gsea_pathways,"Tca","TCA")
gsea_pathways <- str_replace(gsea_pathways,"Gpi","GPI")
enrich_data_df <- rbind(enrich_data_df,data.frame(x=t2,y=gsea_pathways,NES=gsea_result$NES,PVAL=gsea_result$NOM.p.val))
}
#remove pvalue <0.01 pathways
min_pval <- by(enrich_data_df$PVAL, enrich_data_df$y, FUN=min)
select_pathways <- names(min_pval)[(min_pval<=0.01)]
select_enrich_data_df <- enrich_data_df[enrich_data_df$y%in% select_pathways,]
#converto pvalue to -log10
pvals <- select_enrich_data_df$PVAL
pvals[pvals<=0] = 1e-10
select_enrich_data_df$PVAL <- -log10(pvals)
#sort
pathway_pv_sum <- by(select_enrich_data_df$PVAL,select_enrich_data_df$y,FUN=sum)
pathway_order <- names(pathway_pv_sum)[order(pathway_pv_sum,decreasing = T)]
###########################top 10
##check before doing this
pathway_order <- pathway_order[1:10]
select_enrich_data_df <- select_enrich_data_df[select_enrich_data_df$y %in% pathway_order,]
########################################
select_enrich_data_df$y <- factor(select_enrich_data_df$y,levels = pathway_order)
# #buble plot
p <- ggplot(select_enrich_data_df, aes(x = x, y = y, size = PVAL, color = NES)) +
geom_point(shape=19) +
#ggtitle("pathway heterogeneity") +
labs(x = NULL, y = NULL,
size = "-log10 pvalue", color = "NES") +
scale_size(range = c(0, 2.5)) +
scale_color_gradient( low = "white", high = "red") +
#scale_color_gradient2(low="red",mid="white",high="blue",midpoint = 1) +
theme(legend.position = "bottom", legend.direction = "horizontal",
legend.box = "horizontal",
legend.key.size = unit(0.1, "cm"),
legend.text = element_text(colour="black",size=6),
axis.line = element_line(size=0.3, colour = "black"),
#panel.grid.major = element_line(colour = "#d3d3d3"),
#panel.grid.minor = element_blank(),
axis.ticks = element_line(colour = "black", size = 0.3),
panel.border = element_blank(), panel.background = element_blank(),
axis.text.x=element_text(colour="black", size = 6,angle=90,hjust=1,vjust=0.5),
axis.text.y=element_text(colour="black", size = 6)) +
theme(plot.margin = unit(rep(1,4),"lines"))
ggsave(file.path(outDir,"non-malignant_enriched_pathway.pdf"),p,width = 3.6,height=2.5,units="in",device="pdf",useDingbats=FALSE)
##plot variance
p <- ggplot(pc_plotdata) + geom_point(aes(x,y,colour=factor(sel)),size=0.5) +
scale_color_manual(values=c("gray","#ff4000")) +
facet_wrap(~factor(types),scales="free",ncol = 4) + theme_bw() +
labs(x="Principal components", y="Explained variance (%)") +
theme(legend.position="none",panel.grid.major = element_blank(),
panel.grid.minor= element_blank(),
axis.line=element_line(size=0.2,colour="black"),
axis.ticks = element_line(colour = "black",size=0.2),
axis.text.x=element_text(colour="black", size = 6),
axis.text.y=element_text(colour="black", size = 6),
strip.background = element_rect(fill="white",size=0.2,colour = NULL),
strip.text=element_text(size=6))
ggsave(file.path(outDir,"non-malignant_PC_variance_plot.pdf"),p,width = 7.5,height=2.7,units="in",device="pdf",useDingbats=FALSE)
unlink("preRankResults",recursive=T)
unlink("prerank.rnk")
date_string <- Sys.Date()
date_split <- strsplit(as.character(date_string),"-")[[1]]
unlink(paste0(tolower(month.abb[as.numeric(date_split[2])]),date_split[3]),recursive=T) | /6-PathwayHeterogeneity/intra_non-malignant_heterogeneity.R.R | permissive | zhengtaoxiao/Single-Cell-Metabolic-Landscape | R | false | false | 5,818 | r | library(scater)
library(stringr)
options(stringsAsFactors=FALSE)
library(pheatmap)
library(gtools)
library(ggplot2)
source("../utils.R")
source("runGSEA_preRank.R")
args <- commandArgs()
tumor <- args[6]
outDir <- file.path("dataset",tumor)
if(!dir.exists(outDir) ) dir.create(outDir,recursive=TRUE)
pathway_file <- "../Data/KEGG_metabolism.gmt"
#1. Loading the data
selected_sce <- readRDS(file.path("../1-ReadData/dataset/",tumor,"selected_sce.rds"))
selected_nontumor_sce <- selected_sce[,selected_sce$cellType!="Malignant"]
selected_nontumor_metabolic_sce <- selected_nontumor_sce[rowData(selected_nontumor_sce)$metabolic,]
#=========================================================================
celltypes <- unique(selected_nontumor_metabolic_sce$cellType)
#2.Tumor cells
enrich_data_df <- data.frame(x=NULL,y=NULL,NES=NULL,PVAL=NULL)
pc_plotdata <- data.frame(x=numeric(),y=numeric(),
sel=character(),types=character())
for (t in celltypes){
t2 <- str_replace(t," ","")
each_metabolic_sce <- selected_nontumor_metabolic_sce[,selected_nontumor_metabolic_sce$cellType==t]
each_metabolic_tpm <- assay(each_metabolic_sce,"exprs")
each_metabolic_tpm <- each_metabolic_tpm[rowSums(each_metabolic_tpm)>0,]
x <- each_metabolic_tpm
ntop <- nrow(x)
rv <- rowVars(x)
select <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))]
pca <- prcomp(t(x[select,]))
percentVar <- pca$sdev^2 / sum( pca$sdev^2 )
###select PCs that explain at least 80% of the variance
cum_var <- cumsum(percentVar)
select_pcs <- which(cum_var>0.8)[1]
###plot the PCA and explained variances
tmp_plotdata <- data.frame(x=1:length(percentVar),y=percentVar,
sel=c(rep("y",select_pcs),rep("n",length(percentVar)-select_pcs)),
types=rep(t,length(percentVar)))
pc_plotdata <- rbind(pc_plotdata,tmp_plotdata)
###
pre_rank_matrix <- as.matrix(rowSums(abs(pca$rotation[,1:select_pcs])))
runGSEA_preRank(pre_rank_matrix,pathway_file,t2)
#get the result
result_dir <- list.files(path="preRankResults",pattern = paste0("^",t2,".GseaPreranked(.*)"),full.names=T)
result_file <- list.files(path=result_dir,pattern="gsea_report_for_na_pos_(.*).xls",full.names=T)
gsea_result <- read.table(result_file,header = T,sep="\t",row.names=1)
gsea_pathways <- str_to_title(rownames(gsea_result))
gsea_pathways <- str_replace(gsea_pathways,"Tca","TCA")
gsea_pathways <- str_replace(gsea_pathways,"Gpi","GPI")
enrich_data_df <- rbind(enrich_data_df,data.frame(x=t2,y=gsea_pathways,NES=gsea_result$NES,PVAL=gsea_result$NOM.p.val))
}
#remove pvalue <0.01 pathways
min_pval <- by(enrich_data_df$PVAL, enrich_data_df$y, FUN=min)
select_pathways <- names(min_pval)[(min_pval<=0.01)]
select_enrich_data_df <- enrich_data_df[enrich_data_df$y%in% select_pathways,]
#converto pvalue to -log10
pvals <- select_enrich_data_df$PVAL
pvals[pvals<=0] = 1e-10
select_enrich_data_df$PVAL <- -log10(pvals)
#sort
pathway_pv_sum <- by(select_enrich_data_df$PVAL,select_enrich_data_df$y,FUN=sum)
pathway_order <- names(pathway_pv_sum)[order(pathway_pv_sum,decreasing = T)]
###########################top 10
##check before doing this
pathway_order <- pathway_order[1:10]
select_enrich_data_df <- select_enrich_data_df[select_enrich_data_df$y %in% pathway_order,]
########################################
select_enrich_data_df$y <- factor(select_enrich_data_df$y,levels = pathway_order)
# #buble plot
p <- ggplot(select_enrich_data_df, aes(x = x, y = y, size = PVAL, color = NES)) +
geom_point(shape=19) +
#ggtitle("pathway heterogeneity") +
labs(x = NULL, y = NULL,
size = "-log10 pvalue", color = "NES") +
scale_size(range = c(0, 2.5)) +
scale_color_gradient( low = "white", high = "red") +
#scale_color_gradient2(low="red",mid="white",high="blue",midpoint = 1) +
theme(legend.position = "bottom", legend.direction = "horizontal",
legend.box = "horizontal",
legend.key.size = unit(0.1, "cm"),
legend.text = element_text(colour="black",size=6),
axis.line = element_line(size=0.3, colour = "black"),
#panel.grid.major = element_line(colour = "#d3d3d3"),
#panel.grid.minor = element_blank(),
axis.ticks = element_line(colour = "black", size = 0.3),
panel.border = element_blank(), panel.background = element_blank(),
axis.text.x=element_text(colour="black", size = 6,angle=90,hjust=1,vjust=0.5),
axis.text.y=element_text(colour="black", size = 6)) +
theme(plot.margin = unit(rep(1,4),"lines"))
ggsave(file.path(outDir,"non-malignant_enriched_pathway.pdf"),p,width = 3.6,height=2.5,units="in",device="pdf",useDingbats=FALSE)
##plot variance
p <- ggplot(pc_plotdata) + geom_point(aes(x,y,colour=factor(sel)),size=0.5) +
scale_color_manual(values=c("gray","#ff4000")) +
facet_wrap(~factor(types),scales="free",ncol = 4) + theme_bw() +
labs(x="Principal components", y="Explained variance (%)") +
theme(legend.position="none",panel.grid.major = element_blank(),
panel.grid.minor= element_blank(),
axis.line=element_line(size=0.2,colour="black"),
axis.ticks = element_line(colour = "black",size=0.2),
axis.text.x=element_text(colour="black", size = 6),
axis.text.y=element_text(colour="black", size = 6),
strip.background = element_rect(fill="white",size=0.2,colour = NULL),
strip.text=element_text(size=6))
ggsave(file.path(outDir,"non-malignant_PC_variance_plot.pdf"),p,width = 7.5,height=2.7,units="in",device="pdf",useDingbats=FALSE)
unlink("preRankResults",recursive=T)
unlink("prerank.rnk")
date_string <- Sys.Date()
date_split <- strsplit(as.character(date_string),"-")[[1]]
unlink(paste0(tolower(month.abb[as.numeric(date_split[2])]),date_split[3]),recursive=T) |
\encoding{UTF8}
\name{granplot}
\alias{granplot}
\title{
Histogram with a cumulative percentage curve
}
\description{
This function provides a histogram of the grain-size distribution with a cumulative percentage curve
}
\usage{
granplot(x, xc = 1, meshmin=1, hist = TRUE, cum = TRUE, main = "",
col.cum = "red", col.hist="darkgray", cexname=0.9,
cexlab=1.3,decreasing=FALSE)
}
\arguments{
\item{x}{
A numeric matrix or data frame (see the shape of data(granulo))
}
\item{xc}{
A numeric value or a numeric vector to define columns
}
\item{meshmin}{
Define the size of the smallest meshsize if it is 0 in raw data
}
\item{hist}{
If TRUE, display a histogram; if FALSE, do not display a histogram (only for only one column)
}
\item{cum}{
If TRUE, display a cumulative percentage curve; if FALSE do not display a cumulative percentage curve (only for only one column)
}
\item{main}{
Add a title to the current plot
}
\item{col.cum}{
Color in which cumulative percentage curve will be drawn
}
\item{col.hist}{
Color in which histogram will be drawn
}
\item{cexname}{
A numerical value giving the amount by which plotting text and symbols should be magnified
relative to the default.
}
\item{cexlab}{
A numerical value giving the amount by which axis labels should be magnified
relative to the default.
}
\item{decreasing}{
A logical value defining the order increasing or decreasing
}
}
\details{
The obtained graph is the most commonly used by Sedimentologists
}
\value{
A histogram with a cumulative percentage curve
}
\author{
Regis K. Gallon (MNHN) \email{reg.gallon@gmail.com},
Jerome Fournier (CNRS) \email{fournier@mnhn.fr}
}
\seealso{
\code{\link[G2Sd]{grandistrib}}
}
\examples{
data(granulo)
granplot(granulo,xc=1,hist=TRUE,cum=TRUE,main="Grain-size Distribution",
col.hist="gray",col.cum="red")
granplot(granulo,xc=2:4,main="Grain-size Distribution")
} | /man/granplot.Rd | no_license | gallonr/G2Sd | R | false | false | 1,893 | rd | \encoding{UTF8}
\name{granplot}
\alias{granplot}
\title{
Histogram with a cumulative percentage curve
}
\description{
This function provides a histogram of the grain-size distribution with a cumulative percentage curve
}
\usage{
granplot(x, xc = 1, meshmin=1, hist = TRUE, cum = TRUE, main = "",
col.cum = "red", col.hist="darkgray", cexname=0.9,
cexlab=1.3,decreasing=FALSE)
}
\arguments{
\item{x}{
A numeric matrix or data frame (see the shape of data(granulo))
}
\item{xc}{
A numeric value or a numeric vector to define columns
}
\item{meshmin}{
Define the size of the smallest meshsize if it is 0 in raw data
}
\item{hist}{
If TRUE, display a histogram; if FALSE, do not display a histogram (only for only one column)
}
\item{cum}{
If TRUE, display a cumulative percentage curve; if FALSE do not display a cumulative percentage curve (only for only one column)
}
\item{main}{
Add a title to the current plot
}
\item{col.cum}{
Color in which cumulative percentage curve will be drawn
}
\item{col.hist}{
Color in which histogram will be drawn
}
\item{cexname}{
A numerical value giving the amount by which plotting text and symbols should be magnified
relative to the default.
}
\item{cexlab}{
A numerical value giving the amount by which axis labels should be magnified
relative to the default.
}
\item{decreasing}{
A logical value defining the order increasing or decreasing
}
}
\details{
The obtained graph is the most commonly used by Sedimentologists
}
\value{
A histogram with a cumulative percentage curve
}
\author{
Regis K. Gallon (MNHN) \email{reg.gallon@gmail.com},
Jerome Fournier (CNRS) \email{fournier@mnhn.fr}
}
\seealso{
\code{\link[G2Sd]{grandistrib}}
}
\examples{
data(granulo)
granplot(granulo,xc=1,hist=TRUE,cum=TRUE,main="Grain-size Distribution",
col.hist="gray",col.cum="red")
granplot(granulo,xc=2:4,main="Grain-size Distribution")
} |
# Copyright (C) 2016-2017,2019 Iñaki Ucar
#
# This file is part of simmer.
#
# simmer is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# simmer is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with simmer. If not, see <http://www.gnu.org/licenses/>.
context("resource-preemption")
test_that("a lower priority arrival gets rejected before accessing the server", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 1, 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(as.character(arrs[!arrs$finished, ]$name), "p0a1")
expect_equal(arrs_ordered$end_time, c(30, 3, 12, 22))
})
test_that("tasks are NOT restarted", {
t0 <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
t1 <- trajectory() %>%
seize("dummy", 2) %>%
timeout(10) %>%
release("dummy", 2)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t0, at(0, 0), restart = FALSE) %>%
add_generator("p1a", t1, at(2, 15), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(30, 30, 12, 25))
expect_equal(arrs_ordered$activity_time, c(10, 10, 10, 10))
})
test_that("tasks are restarted", {
t0 <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
t1 <- trajectory() %>%
seize("dummy", 2) %>%
timeout(10) %>%
release("dummy", 2)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t0, at(0, 0), restart = TRUE) %>%
add_generator("p1a", t1, at(2, 15), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(35, 35, 12, 25))
expect_equal(arrs_ordered$activity_time, c(15, 15, 10, 10))
})
test_that("tasks are preempted in a FIFO basis", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 1), restart = TRUE) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE, preempt_order = "fifo") %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(22, 23, 12, 13))
expect_equal(arrs_ordered$activity_time, c(12, 12, 10, 10))
})
test_that("tasks are preempted in a LIFO basis", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 1), restart = TRUE) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE, preempt_order = "lifo") %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(22, 23, 12, 13))
expect_equal(arrs_ordered$activity_time, c(13, 11, 10, 10))
})
test_that("queue can exceed queue_size by default", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(1), priority = 1) %>%
add_resource("dummy", 1, 1, preemptive = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 20, 30))
expect_equal(res$server, c(1, 1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 2, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(20, 30, 11))
expect_equal(arr_ordered$activity_time, c(10, 10, 10))
expect_equal(arr_ordered$finished, c(TRUE, TRUE, TRUE))
})
test_that("queue cannot exceed queue_size with hard limit (preempted rejected)", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(1), priority = 1) %>%
add_resource("dummy", 1, 1, preemptive = TRUE, queue_size_strict = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 21))
expect_equal(res$server, c(1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(1, 21, 11))
expect_equal(arr_ordered$activity_time, c(1, 10, 10))
expect_equal(arr_ordered$finished, c(FALSE, TRUE, TRUE))
})
test_that("queue cannot exceed queue_size with hard limit (preempted to queue)", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy")
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0), priority = 0) %>%
add_generator("p1a", t, at(0), priority = 1) %>%
add_generator("p2a", t, at(1), priority = 2) %>%
add_resource("dummy", 1, 1, preemptive = TRUE, queue_size_strict = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 20))
expect_equal(res$server, c(1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(1, 20, 11))
expect_equal(arr_ordered$activity_time, c(0, 10, 10))
expect_equal(arr_ordered$finished, c(FALSE, TRUE, TRUE))
})
test_that("preemption works in non-saturated multi-server resources", {
low_prio <- trajectory() %>%
seize("res", 1) %>%
timeout(10) %>%
release("res", 1)
high_prio <- trajectory() %>%
seize("res", 7) %>%
timeout(10) %>%
release("res", 7)
env <- simmer(verbose = TRUE) %>%
add_resource("res", 10, preemptive = TRUE) %>%
add_generator("low_prio", low_prio, at(rep(0, 5))) %>%
add_generator("high_prio", high_prio, at(1), priority = 1) %>%
run()
arr <- get_mon_arrivals(env)
expect_equal(arr$start_time, c(0, 0, 0, 1, 0, 0))
expect_equal(arr$end_time, c(10, 10, 10, 11, 19, 19))
expect_equal(arr$activity_time, rep(10, 6))
})
test_that("preemption works properly for a previously stopped arrival", {
new_timeout <- trajectory() %>%
timeout(1)
customer <- trajectory() %>%
seize("res") %>%
trap("signal", new_timeout) %>%
timeout(5) %>%
release("res")
blocker <- trajectory() %>%
send("signal") %>%
seize("res") %>%
timeout(20) %>%
release("res")
arr <- simmer(verbose=TRUE) %>%
add_resource("res", preemptive=TRUE) %>%
add_generator("customer", customer, at(0)) %>%
add_generator("blocker", blocker, at(2), priority=10) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(2, 0))
expect_equal(arr$end_time, c(22, 23))
expect_equal(arr$activity_time, c(20, 3))
})
test_that("arrivals wait until dequeued from all resources", {
lprio <- trajectory() %>%
seize("one") %>% # "one" seized
seize("two") %>% # enqueued in "two"
timeout(10) %>%
release_all()
hprio <- trajectory() %>%
seize("one") %>% # preempts lprio in "one"
set_capacity("two", 1) %>% # dequeues lprio in "two"
timeout(100) %>%
release_all()
arr <- simmer(verbose=TRUE) %>%
add_resource("one", 1, preemptive=TRUE) %>%
add_resource("two", 0) %>%
add_generator("lprio", lprio, at(0), priority=0) %>%
add_generator("hprio", hprio, at(1), priority=1) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(1, 0))
expect_equal(arr$end_time, c(101, 111))
expect_equal(arr$activity_time, c(100, 10))
expect_equal(arr$finished, c(TRUE, TRUE))
})
test_that("rejected arrivals leave all queues", {
out <- trajectory() %>%
timeout(1)
lprio <- trajectory() %>%
handle_unfinished(out) %>%
seize("one") %>% # "one" seized
seize("two") %>% # enqueued in "two"
timeout(10) %>%
release_all()
hprio <- trajectory() %>%
seize("one") %>% # preempts and rejects lprio from "one"
timeout(100) %>%
release_all()
arr <- simmer(verbose=TRUE) %>%
add_resource("one", 1, 0, preemptive=TRUE, queue_size_strict=TRUE) %>%
add_resource("two", 0) %>%
add_generator("lprio", lprio, at(0), priority=0) %>%
add_generator("hprio", hprio, at(1), priority=1) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(0, 1))
expect_equal(arr$end_time, c(2, 101))
expect_equal(arr$activity_time, c(1, 100))
expect_equal(arr$finished, c(TRUE, TRUE))
})
| /simmer/tests/testthat/test-simmer-resource-preemption.R | no_license | akhikolla/TestedPackages-NoIssues | R | false | false | 9,401 | r | # Copyright (C) 2016-2017,2019 Iñaki Ucar
#
# This file is part of simmer.
#
# simmer is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# simmer is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with simmer. If not, see <http://www.gnu.org/licenses/>.
context("resource-preemption")
test_that("a lower priority arrival gets rejected before accessing the server", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 1, 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(as.character(arrs[!arrs$finished, ]$name), "p0a1")
expect_equal(arrs_ordered$end_time, c(30, 3, 12, 22))
})
test_that("tasks are NOT restarted", {
t0 <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
t1 <- trajectory() %>%
seize("dummy", 2) %>%
timeout(10) %>%
release("dummy", 2)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t0, at(0, 0), restart = FALSE) %>%
add_generator("p1a", t1, at(2, 15), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(30, 30, 12, 25))
expect_equal(arrs_ordered$activity_time, c(10, 10, 10, 10))
})
test_that("tasks are restarted", {
t0 <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
t1 <- trajectory() %>%
seize("dummy", 2) %>%
timeout(10) %>%
release("dummy", 2)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t0, at(0, 0), restart = TRUE) %>%
add_generator("p1a", t1, at(2, 15), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE) %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(35, 35, 12, 25))
expect_equal(arrs_ordered$activity_time, c(15, 15, 10, 10))
})
test_that("tasks are preempted in a FIFO basis", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 1), restart = TRUE) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE, preempt_order = "fifo") %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(22, 23, 12, 13))
expect_equal(arrs_ordered$activity_time, c(12, 12, 10, 10))
})
test_that("tasks are preempted in a LIFO basis", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 1), restart = TRUE) %>%
add_generator("p1a", t, at(2, 3), priority = 1) %>%
add_resource("dummy", 2, preemptive = TRUE, preempt_order = "lifo") %>%
run()
arrs <- env %>% get_mon_arrivals()
arrs_ordered <- arrs[order(arrs$name), ]
expect_equal(arrs_ordered$end_time, c(22, 23, 12, 13))
expect_equal(arrs_ordered$activity_time, c(13, 11, 10, 10))
})
test_that("queue can exceed queue_size by default", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(1), priority = 1) %>%
add_resource("dummy", 1, 1, preemptive = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 20, 30))
expect_equal(res$server, c(1, 1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 2, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(20, 30, 11))
expect_equal(arr_ordered$activity_time, c(10, 10, 10))
expect_equal(arr_ordered$finished, c(TRUE, TRUE, TRUE))
})
test_that("queue cannot exceed queue_size with hard limit (preempted rejected)", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy", 1)
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0, 0)) %>%
add_generator("p1a", t, at(1), priority = 1) %>%
add_resource("dummy", 1, 1, preemptive = TRUE, queue_size_strict = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 21))
expect_equal(res$server, c(1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(1, 21, 11))
expect_equal(arr_ordered$activity_time, c(1, 10, 10))
expect_equal(arr_ordered$finished, c(FALSE, TRUE, TRUE))
})
test_that("queue cannot exceed queue_size with hard limit (preempted to queue)", {
t <- trajectory() %>%
seize("dummy", 1) %>%
timeout(10) %>%
release("dummy")
env <- simmer(verbose = TRUE) %>%
add_generator("p0a", t, at(0), priority = 0) %>%
add_generator("p1a", t, at(0), priority = 1) %>%
add_generator("p2a", t, at(1), priority = 2) %>%
add_resource("dummy", 1, 1, preemptive = TRUE, queue_size_strict = TRUE) %>%
run()
res <- env %>% get_mon_resources()
arr <- env %>% get_mon_arrivals()
arr_ordered <- arr[order(arr$name), ]
expect_equal(res$time, c(0, 0, 1, 11, 20))
expect_equal(res$server, c(1, 1, 1, 1, 0))
expect_equal(res$queue, c(0, 1, 1, 0, 0))
expect_equal(arr_ordered$end_time, c(1, 20, 11))
expect_equal(arr_ordered$activity_time, c(0, 10, 10))
expect_equal(arr_ordered$finished, c(FALSE, TRUE, TRUE))
})
test_that("preemption works in non-saturated multi-server resources", {
low_prio <- trajectory() %>%
seize("res", 1) %>%
timeout(10) %>%
release("res", 1)
high_prio <- trajectory() %>%
seize("res", 7) %>%
timeout(10) %>%
release("res", 7)
env <- simmer(verbose = TRUE) %>%
add_resource("res", 10, preemptive = TRUE) %>%
add_generator("low_prio", low_prio, at(rep(0, 5))) %>%
add_generator("high_prio", high_prio, at(1), priority = 1) %>%
run()
arr <- get_mon_arrivals(env)
expect_equal(arr$start_time, c(0, 0, 0, 1, 0, 0))
expect_equal(arr$end_time, c(10, 10, 10, 11, 19, 19))
expect_equal(arr$activity_time, rep(10, 6))
})
test_that("preemption works properly for a previously stopped arrival", {
new_timeout <- trajectory() %>%
timeout(1)
customer <- trajectory() %>%
seize("res") %>%
trap("signal", new_timeout) %>%
timeout(5) %>%
release("res")
blocker <- trajectory() %>%
send("signal") %>%
seize("res") %>%
timeout(20) %>%
release("res")
arr <- simmer(verbose=TRUE) %>%
add_resource("res", preemptive=TRUE) %>%
add_generator("customer", customer, at(0)) %>%
add_generator("blocker", blocker, at(2), priority=10) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(2, 0))
expect_equal(arr$end_time, c(22, 23))
expect_equal(arr$activity_time, c(20, 3))
})
test_that("arrivals wait until dequeued from all resources", {
lprio <- trajectory() %>%
seize("one") %>% # "one" seized
seize("two") %>% # enqueued in "two"
timeout(10) %>%
release_all()
hprio <- trajectory() %>%
seize("one") %>% # preempts lprio in "one"
set_capacity("two", 1) %>% # dequeues lprio in "two"
timeout(100) %>%
release_all()
arr <- simmer(verbose=TRUE) %>%
add_resource("one", 1, preemptive=TRUE) %>%
add_resource("two", 0) %>%
add_generator("lprio", lprio, at(0), priority=0) %>%
add_generator("hprio", hprio, at(1), priority=1) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(1, 0))
expect_equal(arr$end_time, c(101, 111))
expect_equal(arr$activity_time, c(100, 10))
expect_equal(arr$finished, c(TRUE, TRUE))
})
test_that("rejected arrivals leave all queues", {
out <- trajectory() %>%
timeout(1)
lprio <- trajectory() %>%
handle_unfinished(out) %>%
seize("one") %>% # "one" seized
seize("two") %>% # enqueued in "two"
timeout(10) %>%
release_all()
hprio <- trajectory() %>%
seize("one") %>% # preempts and rejects lprio from "one"
timeout(100) %>%
release_all()
arr <- simmer(verbose=TRUE) %>%
add_resource("one", 1, 0, preemptive=TRUE, queue_size_strict=TRUE) %>%
add_resource("two", 0) %>%
add_generator("lprio", lprio, at(0), priority=0) %>%
add_generator("hprio", hprio, at(1), priority=1) %>%
run() %>%
get_mon_arrivals()
expect_equal(arr$start_time, c(0, 1))
expect_equal(arr$end_time, c(2, 101))
expect_equal(arr$activity_time, c(1, 100))
expect_equal(arr$finished, c(TRUE, TRUE))
})
|
library(qrcmNP)
### Name: summary.piqr
### Title: Summary After Penalized Quantile Regression Coefficients
### Modeling
### Aliases: summary.piqr
### ** Examples
# using simulated data
set.seed(1234)
n <- 300
x1 <- rexp(n)
x2 <- runif(n, 0, 5)
x <- cbind(x1,x2)
b <- function(p){matrix(cbind(1, qnorm(p), slp(p, 2)), nrow=4, byrow=TRUE)}
theta <- matrix(0, nrow=3, ncol=4); theta[, 1] <- 1; theta[1,2] <- 1; theta[2:3,3] <- 2
qy <- function(p, theta, b, x){rowSums(x * t(theta %*% b(p)))}
y <- qy(runif(n), theta, b, cbind(1, x))
s <- matrix(1, nrow=3, ncol=4); s[1,3:4] <- 0
obj <- piqr(y ~ x1 + x2, formula.p = ~ I(qnorm(p)) + slp(p, 2), s=s, nlambda=50)
best <- gof.piqr(obj, method="AIC", plot=FALSE)
best2 <- gof.piqr(obj, method="BIC", plot=FALSE)
summary(obj, best$minLambda)
summary(obj, best2$minLambda)
| /data/genthat_extracted_code/qrcmNP/examples/summary.piqr.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 829 | r | library(qrcmNP)
### Name: summary.piqr
### Title: Summary After Penalized Quantile Regression Coefficients
### Modeling
### Aliases: summary.piqr
### ** Examples
# using simulated data
set.seed(1234)
n <- 300
x1 <- rexp(n)
x2 <- runif(n, 0, 5)
x <- cbind(x1,x2)
b <- function(p){matrix(cbind(1, qnorm(p), slp(p, 2)), nrow=4, byrow=TRUE)}
theta <- matrix(0, nrow=3, ncol=4); theta[, 1] <- 1; theta[1,2] <- 1; theta[2:3,3] <- 2
qy <- function(p, theta, b, x){rowSums(x * t(theta %*% b(p)))}
y <- qy(runif(n), theta, b, cbind(1, x))
s <- matrix(1, nrow=3, ncol=4); s[1,3:4] <- 0
obj <- piqr(y ~ x1 + x2, formula.p = ~ I(qnorm(p)) + slp(p, 2), s=s, nlambda=50)
best <- gof.piqr(obj, method="AIC", plot=FALSE)
best2 <- gof.piqr(obj, method="BIC", plot=FALSE)
summary(obj, best$minLambda)
summary(obj, best2$minLambda)
|
install.packages("caret")
install.packages("rpart")
install.packages("tree")
install.packages("randomForest")
install.packages("e1071")
install.packages("ggplot2")
| /assignment5/setup.r | no_license | reidy-p/datasci_course_materials | R | false | false | 164 | r | install.packages("caret")
install.packages("rpart")
install.packages("tree")
install.packages("randomForest")
install.packages("e1071")
install.packages("ggplot2")
|
#' RM2C2: Scoring, Summarizing
#' @name filter_cog_duplicate_records
#' @param df class: numeric; original X
#' @param group_vars class: vector; factors to group data by
#' @param time_var class: vector; variable for time
#' @keywords m2c2, cognition
#' @import tidyverse
#' @examples
#' filter_cog_duplicate_records(df)
#' @export
filter_cog_duplicate_records <- function(df) {
return(df %>% distinct())
} | /R/filter_cog_duplicate_records.R | permissive | nelsonroque/surveydolphinr | R | false | false | 412 | r | #' RM2C2: Scoring, Summarizing
#' @name filter_cog_duplicate_records
#' @param df class: numeric; original X
#' @param group_vars class: vector; factors to group data by
#' @param time_var class: vector; variable for time
#' @keywords m2c2, cognition
#' @import tidyverse
#' @examples
#' filter_cog_duplicate_records(df)
#' @export
filter_cog_duplicate_records <- function(df) {
return(df %>% distinct())
} |
## 8 R로 데이터 읽어오기
## 8.0.1 주요 내용
install.packages('AER')
library('AER')
# R 내장 데이터 : data()
# 기본적인 방법 : read.table/write.table,load/save
# 텍스트로 저장된 화일 읽어오기
# -read.csv
# -빅데이터: data.table::fread,readr::read_csv
# 엑셀화일: readxl::read_excel
# 웹에서 긁어오기: htmltab, readHTMLTable
## 8.1 R 내장 데이터
data(mtcars)
head(mtcars, n=3)
data("BankWages", package='AER')
head(BankWages, n=3)
## 8.2 들어가기 : write.table/read.table, save/load
dat <- mtcars
head(dat, n=3)
class(dat)
write.table(dat, file='dat.txt')
dat02 <- read.table(file='dat.txt')
all.equal(dat, dat02)
dat <- mtcars
save(dat, file='dat.RData')
datBackup <- dat
rm(dat)
head(dat)
# Error in head(dat) : object 'dat' not found
load(file='dat.RData')
head(dat, n=3)
all.equal(dat, datBackup)
file.size('dat.txt')
file.size('dat.RData')
# ubuntu에서는 3835(아마도 데이터가 크지 않아서) vs 1780
object.size(dat)
## 8.3 텍스트로 저장된 데이터 화일 읽기
## 8.3.1 직접 텍스트 데이터 화일을 작성해 보기
datMsg <-
data.frame(
name = c("BTS", "트와이스", "케이티 킴"),
phone = c('010-4342-5842', '010-5821-4433', '010-5532-4432'),
usageLastMonth = c(38000, 58000,31000),
message = c('안녕, 날씨 좋다! "가즈아!"라고 말하고 싶다.',
'달빛 아래 춤추자! \'너무너무너무\'라고 노래 부를래.',
'Memorable'),
price = c(30, 10, NA),
stringsAsFactors=FALSE)
datMsg
## 8.3.2 확장자 csv
write.csv(datMsg, file='dat.csv')
datMsg02 <- read.csv(file='dat.csv')
all.equal(datMsg, datMsg02)
head(datMsg02, 3)
datMsg03 <-
read.csv(file='dat.csv', row.names=1, stringsAsFactors=FALSE)
all.equal(datMsg, datMsg03)
## 8.3.3 텍스트 데이터 화일을 불러읽기
# 1. 텍스트 인코딩
# readr::guess_encoding 을 통해 유추 가능. 하지만 확실치 않음
#
# notepad++^3 등의 문서작성 프로그램을 활용하여 인코딩을 확인할 수도 있다.
# 특히 UTF-8BOM과 UTF-8의 구분은 readr::guess_encoding()에서는 불가능 하지만 notepad++에서는 가능
#
# 2. 전체적인 형식: 아래에서 c(,) 로 묶인 원소 중 하나를 선택해야 한다.
# 예) header=TRUE 또는 header = FALSE
#
# 행이름을 포함하는가? header=c(TRUE,FALSE)
# 열이름을 포함하는가? row.names = c(1,NULL)
# 열 구분자(delimiter) sep=c('\t',',','')
#
# 3.데이터를 표기하는 방법
# 주석은 어떻게 구분하는가? comment.char =
# 따옴표(quotation mark; 문자열 속에 열 구분자를 포함시켜야 할 경우를 생각해보자): quote=
# 소수점 표기 방법(decimal seperator): dec=(나라마다 소수점 표기방법이 다르다.)
#
# 4.그밖에
# stringsAsFactors = c(TRUE,FALSE)
# 파일 불러오는 경우 디렉토리 getwd()를 통해 확인해야함
# dat01 <- read.csv('Seoul_Hangang_Tourist_2009_2013.csv', fileEncoding = 'UTF-8')
# dat01 <- read.csv('서울시 한강공원 이용객 현황 (2009_2013년).csv', fileEncoding = 'UTF-8')
#dat01 <- read.csv('Seoul_Hangang_Tourist_2009_2013.csv', header = TRUE)
dat01 <- read.csv('서울시 한강공원 이용객 현황 (2009_2013년).csv', fileEncoding = 'UTF-8')
# windows에서는 에러. ubuntu에서는 정상적으로 읽어들임.
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
fileEncoding = 'cp949', quote="'") # unbuntu
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
quote="'")
# windows에서는 정상 작동.
# ubuntu에서는,
# Error in make.names(col.names, unique = TRUE) :
# invalid multibyte string at '<b4>뿩<c0><cf><c0><da>'
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
quote="'",
fileEncoding = 'EUC-KR') # ubuntu에서 정상 작동
dat03 <- read.csv('http://www.nber.org/data/population-birthplace-diversity/JoEG_BP_diversity_data.csv')
head(dat03, n=3)
## 8.3.5 윈도우에서 인코딩 문제
dat1 <- read.table('UTF-8test.txt',
sep=',',
fileEncoding = 'UTF-8',
stringsAsFactors = FALSE); dat1
dat2 <- readr::read_delim('UTF-8test.txt',
delim = ',',
col_names = FALSE); dat2
dat3 <- data.table::fread('UTF-8test.txt',
sep = ',',
header = FALSE,
encoding = 'UTF-8'); dat3
dat1 <- read.table('UTF-8test.txt', sep = ',',
fileEncoding = 'UTF-8',
stringsAsFactors = FALSE)
dat1 ## ubuntu에서는 문제 없음
dat2 <- readr::read_delim('UTF-8test.txt',
delim = ',',
col_names = FALSE)
dat2
dat3 <- data.table::fread('UTF-8test.txt',
sep = ',',
header = FALSE,
encoding = 'UTF-8'); dat3
dat1$V1 # ubuntu에서는 정상 작동
dat3$V1
dat3df <- as.data.frame(dat3); dat3tb <- tibble::as_tibble(dat3)
print(dat3df)
print(dat3df$V1)
## 8.4 EXCEL 화일 읽기
# install.packages("xlsx")
# ubuntu>
# sudo apt update -y
# sudo apt install -y openjdk-8-jdk openjdk-8-jre
# sudo R CMD javareconf
# *** JAVA_HOME is not a valid path, ignoring
# Java interpreter : /usr/bin/java
# Java version : 1.8.0_252
# Java home path : /usr/lib/jvm/java-8-openjdk-amd64/jre
# Java compiler : /usr/bin/javac
# Java headers gen.: /usr/bin/javah
# Java archive tool: /usr/bin/jar
# trying to compile and link a JNI program
# detected JNI cpp flags : -I$(JAVA_HOME)/../include -I$(JAVA_HOME)/../include/linux
# detected JNI linker flags : -L$(JAVA_HOME)/lib/amd64/server -ljvm
# gcc -std=gnu99 -I"/usr/share/R/include" -DNDEBUG -I/usr/lib/jvm/java-8-openjdk-amd64/jre/../include -I/usr/lib/jvm/java-8-openjdk-amd64/jre/../include/linux -fpic -g -O2 -fdebug-prefix-map=/home/jranke/git/r-backports/stretch/r-base-3.6.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g -c conftest.c -o conftest.o
# gcc -std=gnu99 -shared -L/usr/lib/R/lib -Wl,-z,relro -o conftest.so conftest.o -L/usr/lib/jvm/java-8-openjdk-amd64/jre/lib/amd64/server -ljvm -L/usr/lib/R/lib -lR
#
#
# JAVA_HOME : /usr/lib/jvm/java-8-openjdk-amd64/jre
# Java library path: $(JAVA_HOME)/lib/amd64/server
# JNI cpp flags : -I$(JAVA_HOME)/../include -I$(JAVA_HOME)/../include/linux
# JNI linker flags : -L$(JAVA_HOME)/lib/amd64/server -ljvm
# Updating Java configuration in /usr/lib/R
# Done.
#install.packages("rJava")
#java libs : '-L/usr/lib/jvm/default-java/jre/lib/amd64/server -ljvm'
#checking whether Java run-time works... ./configure: line 3796: /usr/lib/jvm/default-java/jre/bin/java: No such file or directory
#no
#configure: error: Java interpreter '/usr/lib/jvm/default-java/jre/bin/java' does not work
#ERROR: configuration failed for package ‘rJava’
#* removing ‘/home/master/R/x86_64-pc-linux-gnu-library/3.6/rJava’
#Warning in install.packages :
# installation of package ‘rJava’ had non-zero exit status
#
require(rJava) # ubuntu와 windows에서 java 설치 방법???
#Loading required package: rJava
#Error: package or namespace load failed for ‘rJava’:
# .onLoad failed in loadNamespace() for 'rJava', details:
# call: dyn.load(file, DLLpath = DLLpath, ...)
#error: unable to load shared object '/home/master/R/x86_64-pc-linux-gnu-library/3.6/rJava/libs/rJava.so':
# libjvm.so: cannot open shared object file: No such file or directory
# rJava 설치 on ubuntu
# https://wikidocs.net/52630
#install.packages('xlsx')
## 8.4 EXCEL 화일 읽기
# readxl::excel_sheets(path= )
# readxl::read_excel(path= , sheet= )
library(readxl)
readxl::read_excel('서울시 한강공원 이용객 현황 (2009_2013년).xls', sheet=1)
#Error(Ubuntu):
# filepath: 서울시 한강공원 이용객 현황 (2009_2013년).xls
# libxls error: Unable to open file
readxl::read_xlsx('서울시 한강공원 이용객 현황 (2009_2013년).xls', sheet=1)
# 8.4.1
library(readxl)
rm(list=ls())
fn = "excel_example.xls"
vSh <- excel_sheets(fn)
#li <- vector(mode="list", length=length(vSh)-1)
if ( length(vSh) > 0 ) {
for (iSh in 1:(length(vSh))) {
vname <- vSh[iSh]
if (exists(vname)) {
cat('\b\b변수 ', vname, '이(가) 이미 존재합니다.\n')
break
}
assign(vname, read_excel(fn, sheet=vSh[iSh]))
}
} else {
cat('No Sheet!!!\n')
}
vSh
ls()
# 8.5
install.packages('foreign')
install.packages('haven')
# 기본값이 없는 인수에 대한 오류
library(foreign)
#read.spss() # SPSS
#read.dta() # Stata
#read.ssd() # SAS
#read.octave() # Octave
#read.mtp() # Minitab
#read.systat() # Systat
library(haven)
#read_dta() # Stata
#read_por() # SPSS .por
#read_sas() # SAS
#read_sav() # SPSS .sav, .zsav
#read_stata() # Stata
#read_xpt() # SAS transport files
url =
'http://www.nber.org/data/population-birthplace-diversity/JoEG_BP_diversity_data.dta'
# 8.5.1
# 8.5.2
#install.packages('htmltab')
library(htmltab)
url <-
"https://en.wikipedia.org/wiki/List_of_most_common_surnames_in_Europe"
surnames <- htmltab(doc = url, which = 13)
head(surnames, n=10)
#install.packages('XML')
#install.packages("RCurl")
#install.packages("rlist")
library(XML)
library(RCurl)
library(rlist)
url <-
"https://en.wikipedia.org/wiki/List_of_most_common_surnames_in_Europe"
theurl <- getURL(url, .opts = list(ssl.verifypeer = FALSE) )
# Windows에서 다음과 같은 에러가 발생(ubuntu는 정상작동)
# error:1407742E:SSL routines:SSL23_GET_SERVER_HELLO:tlsv1 alert protocol version
# RCurl은 더 이상 관리되지 않는다고 함.
#https://stackoverflow.com/questions/31504983/rcurl-geturl-ssl-error
library(curl)
con <- curl(url)
html <- readLines(con)
df <- readHTMLTable(html, header = TRUE, which = 13,
stringsAsFactors = FALSE, encoding = "UTF-8")
head(df, n=10)
| /08_ImportData_seolbin_edited.R | no_license | Sumeun/DSwithRv2 | R | false | false | 10,210 | r | ## 8 R로 데이터 읽어오기
## 8.0.1 주요 내용
install.packages('AER')
library('AER')
# R 내장 데이터 : data()
# 기본적인 방법 : read.table/write.table,load/save
# 텍스트로 저장된 화일 읽어오기
# -read.csv
# -빅데이터: data.table::fread,readr::read_csv
# 엑셀화일: readxl::read_excel
# 웹에서 긁어오기: htmltab, readHTMLTable
## 8.1 R 내장 데이터
data(mtcars)
head(mtcars, n=3)
data("BankWages", package='AER')
head(BankWages, n=3)
## 8.2 들어가기 : write.table/read.table, save/load
dat <- mtcars
head(dat, n=3)
class(dat)
write.table(dat, file='dat.txt')
dat02 <- read.table(file='dat.txt')
all.equal(dat, dat02)
dat <- mtcars
save(dat, file='dat.RData')
datBackup <- dat
rm(dat)
head(dat)
# Error in head(dat) : object 'dat' not found
load(file='dat.RData')
head(dat, n=3)
all.equal(dat, datBackup)
file.size('dat.txt')
file.size('dat.RData')
# ubuntu에서는 3835(아마도 데이터가 크지 않아서) vs 1780
object.size(dat)
## 8.3 텍스트로 저장된 데이터 화일 읽기
## 8.3.1 직접 텍스트 데이터 화일을 작성해 보기
datMsg <-
data.frame(
name = c("BTS", "트와이스", "케이티 킴"),
phone = c('010-4342-5842', '010-5821-4433', '010-5532-4432'),
usageLastMonth = c(38000, 58000,31000),
message = c('안녕, 날씨 좋다! "가즈아!"라고 말하고 싶다.',
'달빛 아래 춤추자! \'너무너무너무\'라고 노래 부를래.',
'Memorable'),
price = c(30, 10, NA),
stringsAsFactors=FALSE)
datMsg
## 8.3.2 확장자 csv
write.csv(datMsg, file='dat.csv')
datMsg02 <- read.csv(file='dat.csv')
all.equal(datMsg, datMsg02)
head(datMsg02, 3)
datMsg03 <-
read.csv(file='dat.csv', row.names=1, stringsAsFactors=FALSE)
all.equal(datMsg, datMsg03)
## 8.3.3 텍스트 데이터 화일을 불러읽기
# 1. 텍스트 인코딩
# readr::guess_encoding 을 통해 유추 가능. 하지만 확실치 않음
#
# notepad++^3 등의 문서작성 프로그램을 활용하여 인코딩을 확인할 수도 있다.
# 특히 UTF-8BOM과 UTF-8의 구분은 readr::guess_encoding()에서는 불가능 하지만 notepad++에서는 가능
#
# 2. 전체적인 형식: 아래에서 c(,) 로 묶인 원소 중 하나를 선택해야 한다.
# 예) header=TRUE 또는 header = FALSE
#
# 행이름을 포함하는가? header=c(TRUE,FALSE)
# 열이름을 포함하는가? row.names = c(1,NULL)
# 열 구분자(delimiter) sep=c('\t',',','')
#
# 3.데이터를 표기하는 방법
# 주석은 어떻게 구분하는가? comment.char =
# 따옴표(quotation mark; 문자열 속에 열 구분자를 포함시켜야 할 경우를 생각해보자): quote=
# 소수점 표기 방법(decimal seperator): dec=(나라마다 소수점 표기방법이 다르다.)
#
# 4.그밖에
# stringsAsFactors = c(TRUE,FALSE)
# 파일 불러오는 경우 디렉토리 getwd()를 통해 확인해야함
# dat01 <- read.csv('Seoul_Hangang_Tourist_2009_2013.csv', fileEncoding = 'UTF-8')
# dat01 <- read.csv('서울시 한강공원 이용객 현황 (2009_2013년).csv', fileEncoding = 'UTF-8')
#dat01 <- read.csv('Seoul_Hangang_Tourist_2009_2013.csv', header = TRUE)
dat01 <- read.csv('서울시 한강공원 이용객 현황 (2009_2013년).csv', fileEncoding = 'UTF-8')
# windows에서는 에러. ubuntu에서는 정상적으로 읽어들임.
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
fileEncoding = 'cp949', quote="'") # unbuntu
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
quote="'")
# windows에서는 정상 작동.
# ubuntu에서는,
# Error in make.names(col.names, unique = TRUE) :
# invalid multibyte string at '<b4>뿩<c0><cf><c0><da>'
dat02 <- read.csv('서울특별시 공공자전거 대여소별 이용정보(월간)_2017_1_12.csv',
quote="'",
fileEncoding = 'EUC-KR') # ubuntu에서 정상 작동
dat03 <- read.csv('http://www.nber.org/data/population-birthplace-diversity/JoEG_BP_diversity_data.csv')
head(dat03, n=3)
## 8.3.5 윈도우에서 인코딩 문제
dat1 <- read.table('UTF-8test.txt',
sep=',',
fileEncoding = 'UTF-8',
stringsAsFactors = FALSE); dat1
dat2 <- readr::read_delim('UTF-8test.txt',
delim = ',',
col_names = FALSE); dat2
dat3 <- data.table::fread('UTF-8test.txt',
sep = ',',
header = FALSE,
encoding = 'UTF-8'); dat3
dat1 <- read.table('UTF-8test.txt', sep = ',',
fileEncoding = 'UTF-8',
stringsAsFactors = FALSE)
dat1 ## ubuntu에서는 문제 없음
dat2 <- readr::read_delim('UTF-8test.txt',
delim = ',',
col_names = FALSE)
dat2
dat3 <- data.table::fread('UTF-8test.txt',
sep = ',',
header = FALSE,
encoding = 'UTF-8'); dat3
dat1$V1 # ubuntu에서는 정상 작동
dat3$V1
dat3df <- as.data.frame(dat3); dat3tb <- tibble::as_tibble(dat3)
print(dat3df)
print(dat3df$V1)
## 8.4 EXCEL 화일 읽기
# install.packages("xlsx")
# ubuntu>
# sudo apt update -y
# sudo apt install -y openjdk-8-jdk openjdk-8-jre
# sudo R CMD javareconf
# *** JAVA_HOME is not a valid path, ignoring
# Java interpreter : /usr/bin/java
# Java version : 1.8.0_252
# Java home path : /usr/lib/jvm/java-8-openjdk-amd64/jre
# Java compiler : /usr/bin/javac
# Java headers gen.: /usr/bin/javah
# Java archive tool: /usr/bin/jar
# trying to compile and link a JNI program
# detected JNI cpp flags : -I$(JAVA_HOME)/../include -I$(JAVA_HOME)/../include/linux
# detected JNI linker flags : -L$(JAVA_HOME)/lib/amd64/server -ljvm
# gcc -std=gnu99 -I"/usr/share/R/include" -DNDEBUG -I/usr/lib/jvm/java-8-openjdk-amd64/jre/../include -I/usr/lib/jvm/java-8-openjdk-amd64/jre/../include/linux -fpic -g -O2 -fdebug-prefix-map=/home/jranke/git/r-backports/stretch/r-base-3.6.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g -c conftest.c -o conftest.o
# gcc -std=gnu99 -shared -L/usr/lib/R/lib -Wl,-z,relro -o conftest.so conftest.o -L/usr/lib/jvm/java-8-openjdk-amd64/jre/lib/amd64/server -ljvm -L/usr/lib/R/lib -lR
#
#
# JAVA_HOME : /usr/lib/jvm/java-8-openjdk-amd64/jre
# Java library path: $(JAVA_HOME)/lib/amd64/server
# JNI cpp flags : -I$(JAVA_HOME)/../include -I$(JAVA_HOME)/../include/linux
# JNI linker flags : -L$(JAVA_HOME)/lib/amd64/server -ljvm
# Updating Java configuration in /usr/lib/R
# Done.
#install.packages("rJava")
#java libs : '-L/usr/lib/jvm/default-java/jre/lib/amd64/server -ljvm'
#checking whether Java run-time works... ./configure: line 3796: /usr/lib/jvm/default-java/jre/bin/java: No such file or directory
#no
#configure: error: Java interpreter '/usr/lib/jvm/default-java/jre/bin/java' does not work
#ERROR: configuration failed for package ‘rJava’
#* removing ‘/home/master/R/x86_64-pc-linux-gnu-library/3.6/rJava’
#Warning in install.packages :
# installation of package ‘rJava’ had non-zero exit status
#
require(rJava) # ubuntu와 windows에서 java 설치 방법???
#Loading required package: rJava
#Error: package or namespace load failed for ‘rJava’:
# .onLoad failed in loadNamespace() for 'rJava', details:
# call: dyn.load(file, DLLpath = DLLpath, ...)
#error: unable to load shared object '/home/master/R/x86_64-pc-linux-gnu-library/3.6/rJava/libs/rJava.so':
# libjvm.so: cannot open shared object file: No such file or directory
# rJava 설치 on ubuntu
# https://wikidocs.net/52630
#install.packages('xlsx')
## 8.4 EXCEL 화일 읽기
# readxl::excel_sheets(path= )
# readxl::read_excel(path= , sheet= )
library(readxl)
readxl::read_excel('서울시 한강공원 이용객 현황 (2009_2013년).xls', sheet=1)
#Error(Ubuntu):
# filepath: 서울시 한강공원 이용객 현황 (2009_2013년).xls
# libxls error: Unable to open file
readxl::read_xlsx('서울시 한강공원 이용객 현황 (2009_2013년).xls', sheet=1)
# 8.4.1
library(readxl)
rm(list=ls())
fn = "excel_example.xls"
vSh <- excel_sheets(fn)
#li <- vector(mode="list", length=length(vSh)-1)
if ( length(vSh) > 0 ) {
for (iSh in 1:(length(vSh))) {
vname <- vSh[iSh]
if (exists(vname)) {
cat('\b\b변수 ', vname, '이(가) 이미 존재합니다.\n')
break
}
assign(vname, read_excel(fn, sheet=vSh[iSh]))
}
} else {
cat('No Sheet!!!\n')
}
vSh
ls()
# 8.5
install.packages('foreign')
install.packages('haven')
# 기본값이 없는 인수에 대한 오류
library(foreign)
#read.spss() # SPSS
#read.dta() # Stata
#read.ssd() # SAS
#read.octave() # Octave
#read.mtp() # Minitab
#read.systat() # Systat
library(haven)
#read_dta() # Stata
#read_por() # SPSS .por
#read_sas() # SAS
#read_sav() # SPSS .sav, .zsav
#read_stata() # Stata
#read_xpt() # SAS transport files
url =
'http://www.nber.org/data/population-birthplace-diversity/JoEG_BP_diversity_data.dta'
# 8.5.1
# 8.5.2
#install.packages('htmltab')
library(htmltab)
url <-
"https://en.wikipedia.org/wiki/List_of_most_common_surnames_in_Europe"
surnames <- htmltab(doc = url, which = 13)
head(surnames, n=10)
#install.packages('XML')
#install.packages("RCurl")
#install.packages("rlist")
library(XML)
library(RCurl)
library(rlist)
url <-
"https://en.wikipedia.org/wiki/List_of_most_common_surnames_in_Europe"
theurl <- getURL(url, .opts = list(ssl.verifypeer = FALSE) )
# Windows에서 다음과 같은 에러가 발생(ubuntu는 정상작동)
# error:1407742E:SSL routines:SSL23_GET_SERVER_HELLO:tlsv1 alert protocol version
# RCurl은 더 이상 관리되지 않는다고 함.
#https://stackoverflow.com/questions/31504983/rcurl-geturl-ssl-error
library(curl)
con <- curl(url)
html <- readLines(con)
df <- readHTMLTable(html, header = TRUE, which = 13,
stringsAsFactors = FALSE, encoding = "UTF-8")
head(df, n=10)
|
# Plot Of Twitter User Hashtag Count
# Original source: http://decisionsandr.blogspot.com/2013/11/using-r-to-find-obamas-most-frequent.html & https://github.com/chrisalbon/code_r/blob/master/twitter.r
# Note: This r code will have to be run in two parts since you will have to get a code from the Twitter website and enter it into R manually.
# Load twitteR package
library(twitteR)
# create object of request token URL
reqURL <- "https://api.twitter.com/oauth/request_token"
# create object of access URL
accessURL <- "http://api.twitter.com/oauth/access_token"
# create object of authorization URL
authURL <- "http://api.twitter.com/oauth/authorize"
# create objects with keys (get these from dev.twitter.com)
consumerKey <- "XXXXX"
consumerSecret <- "XXXXX"
# add them to twitcred
twitCred <- OAuthFactory$new(consumerKey=consumerKey,consumerSecret=consumerSecret,requestURL=reqURL,accessURL=accessURL,authURL=authURL)
# Prepare for the hangshake
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# conduct the handshake, then enter in the number code
twitCred$handshake(cainfo="cacert.pem")
# -------------
# Enter number from browser into R
# -------------
# test to make sure it works
registerTwitterOAuth(twitCred)
# create object of 3200 tweets in ChrisAlbon's timeline
tw <- userTimeline("ChrisAlbon", n = 3200)
# create a object that is a data frame of tw
tw <- twListToDF(tw)
# create an object of the tweet texts
vec1 <- tw$text
# create an object that extract the hashtags
extract.hashes <- function(vec){
hash.pattern = "#[[:alpha:]]+"
have.hash = grep(x = vec, pattern = hash.pattern)
hash.matches = gregexpr(pattern = hash.pattern, text = vec[have.hash])
extracted.hash = regmatches(x = vec[have.hash], m = hash.matches)
df = data.frame(table(tolower(unlist(extracted.hash))))
colnames(df) = c("tag","freq")
df = df[order(df$freq,decreasing = TRUE),]
return(df)
}
# create object of 50 of vec1's hashtags
dat = head(extract.hashes(vec1),50)
# create object the hashtags ordered by frequency
dat2 = transform(dat,tag = reorder(tag,freq))
# load the ggplot2 package
library(ggplot2)
# plot the hashtags by frequency
p = ggplot(dat2, aes(x = tag, y = freq)) + geom_bar(fill = "blue")
# redo it a bit and add a title
p + coord_flip() + labs(title = "Hashtag frequencies in the tweets of ChrisAlbon)
| /R/api/twitter.R | no_license | mitchelllisle/code_snippets | R | false | false | 2,375 | r | # Plot Of Twitter User Hashtag Count
# Original source: http://decisionsandr.blogspot.com/2013/11/using-r-to-find-obamas-most-frequent.html & https://github.com/chrisalbon/code_r/blob/master/twitter.r
# Note: This r code will have to be run in two parts since you will have to get a code from the Twitter website and enter it into R manually.
# Load twitteR package
library(twitteR)
# create object of request token URL
reqURL <- "https://api.twitter.com/oauth/request_token"
# create object of access URL
accessURL <- "http://api.twitter.com/oauth/access_token"
# create object of authorization URL
authURL <- "http://api.twitter.com/oauth/authorize"
# create objects with keys (get these from dev.twitter.com)
consumerKey <- "XXXXX"
consumerSecret <- "XXXXX"
# add them to twitcred
twitCred <- OAuthFactory$new(consumerKey=consumerKey,consumerSecret=consumerSecret,requestURL=reqURL,accessURL=accessURL,authURL=authURL)
# Prepare for the hangshake
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# conduct the handshake, then enter in the number code
twitCred$handshake(cainfo="cacert.pem")
# -------------
# Enter number from browser into R
# -------------
# test to make sure it works
registerTwitterOAuth(twitCred)
# create object of 3200 tweets in ChrisAlbon's timeline
tw <- userTimeline("ChrisAlbon", n = 3200)
# create a object that is a data frame of tw
tw <- twListToDF(tw)
# create an object of the tweet texts
vec1 <- tw$text
# create an object that extract the hashtags
extract.hashes <- function(vec){
hash.pattern = "#[[:alpha:]]+"
have.hash = grep(x = vec, pattern = hash.pattern)
hash.matches = gregexpr(pattern = hash.pattern, text = vec[have.hash])
extracted.hash = regmatches(x = vec[have.hash], m = hash.matches)
df = data.frame(table(tolower(unlist(extracted.hash))))
colnames(df) = c("tag","freq")
df = df[order(df$freq,decreasing = TRUE),]
return(df)
}
# create object of 50 of vec1's hashtags
dat = head(extract.hashes(vec1),50)
# create object the hashtags ordered by frequency
dat2 = transform(dat,tag = reorder(tag,freq))
# load the ggplot2 package
library(ggplot2)
# plot the hashtags by frequency
p = ggplot(dat2, aes(x = tag, y = freq)) + geom_bar(fill = "blue")
# redo it a bit and add a title
p + coord_flip() + labs(title = "Hashtag frequencies in the tweets of ChrisAlbon)
|
# install packages
# install.packages("dplyr")
# install.packages('hflights')
# Load Library
library(dplyr)
library(hflights)
dim(hflights)
hflights_df <- tbl_df(hflights)
# filter 함수
# 조건에 따라(Month가 1 또는 2) 데이터 행 추출 (subset()함수와 비슷)
filter(hflights_df, Month == 1 | Month == 2)
filter(hflights_df, Month == 1 , DayofMonth == 1)
# arrange 함수
# 지정한 열 기준으로 작은값 부터 큰 값 순으로 데이터 정렬 (역순을 원할 땐, desc() 함께 사용)
arrange(hflights_df, ArrDelay, Month, Year) # ArrDelay, Month, Year 순으로 정렬)
arrange(hflights_df, desc(Month)) # Month의 큰 값부터 작은 값으로 정렬
# select 함수
# 열을 추출할 때 사용, 복수 열을 추출할 때에는 콤마(,)로 구분하고, 인접한 열은 (:) 연산자로 이용하여 추출
select(hflights_df, Year, Month, DayofMonth)
select(hflights_df, -(Year:DayOfWeek)) # Year부터 DayOfWeek를 제외한 나머지 열 추출
# mutate 함수
# 열을 추가할 때 사용(transform() 함수와 비슷)
mutate(hflights_df, gain = ArrDelay - DepDelay, gain_per_hour = gain/(AirTime/60))
# mutate 함수에서 생성한 열인 gain을 바로 다음 gain_per_hour에서 바로 사용할 수 있음
# transform 함수에서는 위와 같은 동시활동(계산)을 사용할 수 없음)
# summarise 함수
# mean(), sd(), var(), median() 등의 함수를 지정하여 기초 통계량을 구할 수 있음, 결과값은 data.frame형식으로 출력
summarise(hflights_df, delay = mean(DepDelay, na.rm = TRUE))
summarise(hflights_df, plane = n_distinct(TailNum)) # n_distinct 함수는 열(변수)의 유니크 값의 수를 산출
hflights_df %>% group_by(FlightNum, Dest) %>% summarise(n = n()) # n()은 관측값의 갯수를 구해줌(group_by와 함꼐 쓰일 떄 유용함)
summarise(hflights_df, first = first(DepTime)) # first() 함수는 해당 열(변수)의 첫번째 값을 산출
summarise(hflights_df, last = last(DepTime)) # last() 함수는 해당 열(변수)의 마지막 행 값을 산출
summarise(hflights_df, data = nth(DepTime, 10)) # nth(x, n) 함수는 원하는 행의 해당 열(변수 : x)의 값을 산출
# group_by 함수
# 지정한 열의 수준(Level)별로 그룹화된 결과를 얻을 수 있음
#아래 코드는 비행편수 20편 이상, 평군 비행거리 2,000마일 이상인 항공사별 평균 연착시간을 계산하여 그림으로 표현
planes <- group_by(hflights_df, TailNum)
delay <- summarise(planes, count = n(), dist = mean(Distance, na.rm = TeRUE), delay = mean(ArrDelay, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)
library(ggplot2)
ggplot(delay, aes(dist, delay))+geom_point(aes(size = count), alpha = 1/2)+geom_smooth()+scale_size_area()
#group_by의 많은 예제는
vignette("introduction", package = 'dplyr') # package 간단히 공부할때는 도움이 많이 될 듯 !! (vignette : 삽화)
# chain 함수 : 코드를 줄일 수 있는 획기적인 방법 또한 코드도 직관적으로 이해할 수 있음
# (1)
# hflight 데이터를 a1) Year, Month, DayofMonth의 수준별로 그룹화, a2) Year부터 DayofMonth, ArrDelay, DepDelay열을 선택,
# a3) 평균 연착시간과 평균 출발 지연시간을 구하고, a4) 평균 연착시간과 평균 출발지연시간이 30분 이상인 데이터를 추출한
# 결과
a1 <- group_by(hflights_df, Year, Month, DayofMonth)
a2 <- select(a1, Year:DayofMonth, ArrDelay, DepDelay)
a3 <- summarise(a2, arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)
# (2)
#위 예제를 " %>% " 를 이용하면 코드가 아래와 같이 단순해짐
hflights_df %>% group_by(Year, Month, DayofMonth) %>% summarise(arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE)) %>% filter(arr > 30 | dep > 30)
# (3)
# 위 예제 3번째 표현식 : 데이터 input 자리에 함수를 이용해 데이터를 자동 입력
filter(
summarise(
select(
group_by(hflights_df, Year, Month, DayofMonth),
Year:DayofMonth, ArrDelay, DepDelay
),
arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE)
), arr > 30 | dep > 30
)
# distinct 함수
#데이터의 유니크한 변수들을 찾아줌 (unique() 함수와 유사함)
distinct(hflights_df, Year, DepDelay)
# sample_n / sample_frac 함수
sample_n(hflights_df, 10) # 10개의 행을 랜덤해서 추출
sample_frac(hflights_df, 0.01) # 전체 행에서 0.01 (1%) 비율로 데이터(행)를 추출
# 예제 (* 주의 *)
# 이 예제의 각 결과들을 보면 처음에 3개의 변수(Year, Month, DayofMonth)를 기준으로 group설정 후, summarise 함수는
# 하나의 수준씩 그룹에서 제외시켜 결과를 산출
# per_month는 DayofMonth의 변수가 제외되어 결과가 산출
# per_year는 Month 변수가 벗겨져(peel off) 결과를 산출
daily <- group_by(hflights_df, Year, Month, DayofMonth)
per_day <- summarise(daily, flights = n())
per_month <- summarise(per_day, flights = sum(flights))
per_year <- summarise(per_month, flights = sum(flights))
| /dplyr-exercise.R | no_license | sys505moon/R_Study | R | false | false | 5,162 | r | # install packages
# install.packages("dplyr")
# install.packages('hflights')
# Load Library
library(dplyr)
library(hflights)
dim(hflights)
hflights_df <- tbl_df(hflights)
# filter 함수
# 조건에 따라(Month가 1 또는 2) 데이터 행 추출 (subset()함수와 비슷)
filter(hflights_df, Month == 1 | Month == 2)
filter(hflights_df, Month == 1 , DayofMonth == 1)
# arrange 함수
# 지정한 열 기준으로 작은값 부터 큰 값 순으로 데이터 정렬 (역순을 원할 땐, desc() 함께 사용)
arrange(hflights_df, ArrDelay, Month, Year) # ArrDelay, Month, Year 순으로 정렬)
arrange(hflights_df, desc(Month)) # Month의 큰 값부터 작은 값으로 정렬
# select 함수
# 열을 추출할 때 사용, 복수 열을 추출할 때에는 콤마(,)로 구분하고, 인접한 열은 (:) 연산자로 이용하여 추출
select(hflights_df, Year, Month, DayofMonth)
select(hflights_df, -(Year:DayOfWeek)) # Year부터 DayOfWeek를 제외한 나머지 열 추출
# mutate 함수
# 열을 추가할 때 사용(transform() 함수와 비슷)
mutate(hflights_df, gain = ArrDelay - DepDelay, gain_per_hour = gain/(AirTime/60))
# mutate 함수에서 생성한 열인 gain을 바로 다음 gain_per_hour에서 바로 사용할 수 있음
# transform 함수에서는 위와 같은 동시활동(계산)을 사용할 수 없음)
# summarise 함수
# mean(), sd(), var(), median() 등의 함수를 지정하여 기초 통계량을 구할 수 있음, 결과값은 data.frame형식으로 출력
summarise(hflights_df, delay = mean(DepDelay, na.rm = TRUE))
summarise(hflights_df, plane = n_distinct(TailNum)) # n_distinct 함수는 열(변수)의 유니크 값의 수를 산출
hflights_df %>% group_by(FlightNum, Dest) %>% summarise(n = n()) # n()은 관측값의 갯수를 구해줌(group_by와 함꼐 쓰일 떄 유용함)
summarise(hflights_df, first = first(DepTime)) # first() 함수는 해당 열(변수)의 첫번째 값을 산출
summarise(hflights_df, last = last(DepTime)) # last() 함수는 해당 열(변수)의 마지막 행 값을 산출
summarise(hflights_df, data = nth(DepTime, 10)) # nth(x, n) 함수는 원하는 행의 해당 열(변수 : x)의 값을 산출
# group_by 함수
# 지정한 열의 수준(Level)별로 그룹화된 결과를 얻을 수 있음
#아래 코드는 비행편수 20편 이상, 평군 비행거리 2,000마일 이상인 항공사별 평균 연착시간을 계산하여 그림으로 표현
planes <- group_by(hflights_df, TailNum)
delay <- summarise(planes, count = n(), dist = mean(Distance, na.rm = TeRUE), delay = mean(ArrDelay, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)
library(ggplot2)
ggplot(delay, aes(dist, delay))+geom_point(aes(size = count), alpha = 1/2)+geom_smooth()+scale_size_area()
#group_by의 많은 예제는
vignette("introduction", package = 'dplyr') # package 간단히 공부할때는 도움이 많이 될 듯 !! (vignette : 삽화)
# chain 함수 : 코드를 줄일 수 있는 획기적인 방법 또한 코드도 직관적으로 이해할 수 있음
# (1)
# hflight 데이터를 a1) Year, Month, DayofMonth의 수준별로 그룹화, a2) Year부터 DayofMonth, ArrDelay, DepDelay열을 선택,
# a3) 평균 연착시간과 평균 출발 지연시간을 구하고, a4) 평균 연착시간과 평균 출발지연시간이 30분 이상인 데이터를 추출한
# 결과
a1 <- group_by(hflights_df, Year, Month, DayofMonth)
a2 <- select(a1, Year:DayofMonth, ArrDelay, DepDelay)
a3 <- summarise(a2, arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)
# (2)
#위 예제를 " %>% " 를 이용하면 코드가 아래와 같이 단순해짐
hflights_df %>% group_by(Year, Month, DayofMonth) %>% summarise(arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE)) %>% filter(arr > 30 | dep > 30)
# (3)
# 위 예제 3번째 표현식 : 데이터 input 자리에 함수를 이용해 데이터를 자동 입력
filter(
summarise(
select(
group_by(hflights_df, Year, Month, DayofMonth),
Year:DayofMonth, ArrDelay, DepDelay
),
arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE)
), arr > 30 | dep > 30
)
# distinct 함수
#데이터의 유니크한 변수들을 찾아줌 (unique() 함수와 유사함)
distinct(hflights_df, Year, DepDelay)
# sample_n / sample_frac 함수
sample_n(hflights_df, 10) # 10개의 행을 랜덤해서 추출
sample_frac(hflights_df, 0.01) # 전체 행에서 0.01 (1%) 비율로 데이터(행)를 추출
# 예제 (* 주의 *)
# 이 예제의 각 결과들을 보면 처음에 3개의 변수(Year, Month, DayofMonth)를 기준으로 group설정 후, summarise 함수는
# 하나의 수준씩 그룹에서 제외시켜 결과를 산출
# per_month는 DayofMonth의 변수가 제외되어 결과가 산출
# per_year는 Month 변수가 벗겨져(peel off) 결과를 산출
daily <- group_by(hflights_df, Year, Month, DayofMonth)
per_day <- summarise(daily, flights = n())
per_month <- summarise(per_day, flights = sum(flights))
per_year <- summarise(per_month, flights = sum(flights))
|
# Analyze bird counts
# All rights reserved
Read data file into dataframe
Run analysis using p-val 0.5
Save table with bold black header
Save small figure, thick blue line
Save references 1 through 12
WOW I'VE MADE SUCH CHANGES
same here!
| /script.R | no_license | brad2x/birdsurvey | R | false | false | 247 | r | # Analyze bird counts
# All rights reserved
Read data file into dataframe
Run analysis using p-val 0.5
Save table with bold black header
Save small figure, thick blue line
Save references 1 through 12
WOW I'VE MADE SUCH CHANGES
same here!
|
library(hddtools)
### Name: getContent
### Title: Extracts links from ftp page
### Aliases: getContent
### ** Examples
## Not run:
##D # Retrieve mopex daily catalogue
##D url <- "ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/"
##D getContent(dirs = url)
## End(Not run)
| /data/genthat_extracted_code/hddtools/examples/getContent.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 305 | r | library(hddtools)
### Name: getContent
### Title: Extracts links from ftp page
### Aliases: getContent
### ** Examples
## Not run:
##D # Retrieve mopex daily catalogue
##D url <- "ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/"
##D getContent(dirs = url)
## End(Not run)
|
## The function makecache creats a special "matrix".
makeCache <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setinverse <- function(inverse) i <<- inverse
getinverse <- function() i
list(set = set,
get = get,
setinverse = setinverse,
getinverse = getinverse)
}
#The function "cacheSolve" computes the inverse of the special “matrix” returned by makeCacheMatrix above.
#If the inverse has already been calculated (and the matrix has not changed), then cacheSolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
i <- x$getinverse()
if (!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data, ...)
x$setinverse(i)
i
}
#testing
a<-matrix(c(1,2,3,4),3,3)
b<-makeCache(a)
cacheSolve(b)
| /asm2.R | no_license | tracyli6/cocowalnut | R | false | false | 1,022 | r | ## The function makecache creats a special "matrix".
makeCache <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setinverse <- function(inverse) i <<- inverse
getinverse <- function() i
list(set = set,
get = get,
setinverse = setinverse,
getinverse = getinverse)
}
#The function "cacheSolve" computes the inverse of the special “matrix” returned by makeCacheMatrix above.
#If the inverse has already been calculated (and the matrix has not changed), then cacheSolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
i <- x$getinverse()
if (!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data, ...)
x$setinverse(i)
i
}
#testing
a<-matrix(c(1,2,3,4),3,3)
b<-makeCache(a)
cacheSolve(b)
|
#reading and transforming the data
df1= read.csv(file="QueryResults1.csv",header=TRUE)
df2<- read.csv(file="QueryResults2.csv",header=TRUE)
StackOverflow<- rbind(df1,df2)
#str(StackOverflow)
#names(StackOverflow)
nrow(StackOverflow)
#replacing NA with 1001
StackOverflow [is.na(StackOverflow)] <- 1001
#removing column that contain text or are unrelated to this question
StackOverflowdata<- StackOverflow[,-c(1, 2,3,12,14,15,22,23,25,26,27,28,29,30,31,32,33,34)]
#str(StackOverflowdata)
nrow(StackOverflowdata)
#to check uniqueness of data
lapply(StackOverflowdata["RejectionReasonId"], unique)
StackOverflowdata$IsSpam= ifelse(StackOverflowdata$RejectionReasonId==101,1,0)
#str(StackOverflowdata)
#names(StackOverflowdata)
StackOverflowdata<- StackOverflowdata[,-c(16)]
lapply(StackOverflowdata["IsSpam"], unique)
#dividing data for training and testing purpose
smp_size<- floor(0.75*nrow(StackOverflowdata))
set.seed(123)
train_ind<- sample(seq_len(nrow(StackOverflowdata)), size=smp_size)
training_data<- StackOverflowdata[train_ind,]
testing_data<- StackOverflowdata[-train_ind,]
testing_y<- testing_data$IsSpam
nrow(training_data)
nrow(testing_data)
#str(training_data)
#names(training_data)
#GLM
logistics_model<-glm(IsSpam~.,data=training_data,family="binomial")
#to discover better model
#step(logistics_model, direction = "forward")
logistics_model = glm(IsSpam ~ PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary + owner_views +
owner_upvotes + owner_downvotes + editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount + has_code +post_views +
UserId, family = "binomial", data = training_data)
summary(logistics_model)
#performing trial modelling
#step(logistics_model, direction = "backward")
#final regression model
logistics_model = glm(formula = IsSpam ~ PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views, family = "binomial", data = training_data)
summary(logistics_model)
#Bayesian information criterion
BIC(logistics_model)
logistics_probs<-predict(logistics_model,training_data,type="response")
head(logistics_probs)
logistics_pred_y=rep(0,length(testing_y))
logistics_pred_y[logistics_probs>0.55]=1
training_y=training_data$IsSpam
table(logistics_pred_y,training_y)
mean(logistics_pred_y!=training_y,na.rm=TRUE)
logistics_probs<- predict(logistics_model,testing_data, type="response")
head(logistics_probs)
logistics_pred_y=rep(0,length(testing_y))
logistics_pred_y[logistics_probs>0.55]=1
table(logistics_pred_y,testing_y)
mean(logistics_pred_y!=testing_y,na.rm=TRUE)
#kfold for regression
library(boot)
MSE_10_Fold_CV=cv.glm(training_data,logistics_model,K=10)$delta[1]
MSE_10_Fold_CV
MSE_10_Fold_CV=NULL
for(i in 1:10){
model=glm(IsSpam~poly(PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views),data=training_data)
MSE_10_Fold_CV[i]=cv.glm(training_data,model,K=10)$delta[1]
}
#summary(model)
MSE_10_Fold_CV
#ROC logistic regression
#install.packagesll.packages("ROCR")
library(ROCR)
ROCRpred=prediction(logistics_probs,testing_y)
ROCRperf=performance(ROCRpred,"tpr","fpr")
#plot(ROCRperf)
#plot(ROCRperf,colorize=TRUE)
#plot(ROCRperf,colorize=TRUE,print.cutoffs.at=seq(0,1,0.05),text.adj=c(-0.2,1.7))
as.numeric(performance(ROCRpred,"auc")@y.values)
#Linear Discriminant Analysis
library(MASS)
lda.model<- lda(IsSpam~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data)
#lda.model
#summary(lda.model)
lda_pred<- predict(lda.model,training_data)
lda_class<- lda_pred$class
table(lda_class,training_data$IsSpam)
mean(lda_class!=training_data$IsSpam)
lda_pred<- predict(lda.model,testing_data)
lda_class<- lda_pred$class
table(lda_class,testing_data$IsSpam)
mean(lda_class!=testing_data$IsSpam)
#Cross Validation LDA
library(rpart)
library(ipred)
ip.lda <- function(object, newdata) predict(object, newdata = newdata)$class
errorest(factor(training_data$IsSpam)~ training_data$PostTypeId + training_data$PostScore + training_data$post_length +
training_data$owner_reputation + training_data$owner_profile_summary +
training_data$owner_upvotes+ training_data$editDurationAfterCreation +
training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount+ training_data$post_views, data=training_data, model=lda, estimator="cv",est.para=control.errorest(k=10), predict=ip.lda)$err
#ROC LDA
S=lda_pred$posterior[,2]
roc.curve=function(s,print=FALSE){
Ps=(S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.53
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for LDA")
abline(0,1)
#Quadratic Discriminant Analysis
qda.model<- qda(IsSpam~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data)
#qda.model
qda_pred=predict(qda.model,training_data)
qda_class=qda_pred$class
table(qda_class,training_data$IsSpam)
mean(qda_class!=training_data$IsSpam)
qda_pred1=predict(qda.model,testing_data)
qda_class_n=qda_pred1$class
table(qda_class_n,testing_data$IsSpam)
mean(qda_class_n!=testing_data$IsSpam)
#Cross Validation QDA
ip.qda <- function(object, newdata) predict(object, newdata = newdata)$class
errorest(factor(training_data$IsSpam)~ training_data$PostTypeId + training_data$PostScore + training_data$post_length +
training_data$owner_reputation + training_data$owner_profile_summary +
training_data$owner_upvotes+ training_data$editDurationAfterCreation +
training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount+ training_data$post_views, data=training_data, model=qda, estimator="cv",est.para=control.errorest(k=10), predict=ip.qda)$err
#ROC QDA
qda.S=qda_pred1$posterior[,2]
roc.curve=function(s,print=FALSE){
Ps=(qda.S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.85
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for LDA")
abline(0,1)
#k nearest neighbours
library(class)
#install.packagesll.packages("cars")
#library(cars)
train.x<- cbind(training_data$PostTypeId + training_data$PostScore + training_data$post_length + training_data$owner_reputation + training_data$owner_profile_summary + training_data$owner_upvotes + training_data$editDurationAfterCreation + training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount + training_data$post_views)
test.x<- cbind(testing_data$PostTypeId + testing_data$PostScore + testing_data$post_length + testing_data$owner_reputation + testing_data$owner_profile_summary + testing_data$owner_upvotes + testing_data$editDurationAfterCreation + testing_data$q_num_tags + testing_data$AnswerCount + testing_data$CommentCount + testing_data$post_views)
train1.x=train.x[!duplicated(train.x),drop=FALSE]
test1.x=test.x[!duplicated(test.x), drop=FALSE]
tt<- training_data$IsSpam[duplicated(train.x)=='FALSE']
head(tt)
length(tt)
knn.pred<- knn(data.frame(train1.x),data.frame(test1.x),tt,k=1)
tt1<- testing_data$IsSpam[duplicated(test.x)=='FALSE']
length(tt1)
table(knn.pred,tt1)
mean(knn.pred!=tt1)
knn.pred<- knn(data.frame(train1.x),data.frame(test1.x),tt,k=2)
table(knn.pred,tt1)
mean(knn.pred!=tt1)
#Classification and Regression Trees
#CART Modeling:
#install.packagesll.packages("tree")
library(tree)
tree.training_data=tree(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,training_data)
text(tree.training_data,pretty=0)
summary(tree.training_data)
plot(tree.training_data)
text(tree.training_data,pretty=0)
lf<- seq(1,nrow(training_data))
tree.training_data=tree(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,training_data,subset=lf)
tree.pred=predict(tree.training_data,testing_data,type="class")
table(tree.pred,testing_y)
mean(tree.pred!=testing_data$IsSpam)
#Cross Validation and Pruning for the Classification Tree:
cv.training_data=cv.tree(tree.training_data,FUN=prune.misclass)
names(cv.training_data)
#cv.training_data
par(mfrow=c(1,2))
plot(cv.training_data$size,cv.training_data$dev,type="b")
plot(cv.training_data$k,cv.training_data$dev,type="b")
par(mfrow=c(1,1))
prune.training_data=prune.misclass(tree.training_data,best=5)
plot(prune.training_data)
text(prune.training_data,pretty=0)
tree.pred=predict(prune.training_data,testing_data,type="class")
table(tree.pred,testing_y)
mean(tree.pred!=testing_data$IsSpam)
#ROC for CART:
tree.pred=predict(tree.training_data,testing_data,type="vector",prob=TRUE)
#tree.pred
tree.S=tree.pred[,2]
roc.curve=function(s,print=FALSE){
Ps=(tree.S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.55
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for CART")
abline(0,1)
#Random Forest Modeling:
#install.packagesll.packages("randomForest")
library(randomForest)
bag.training_data=randomForest(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data,subset=lf,importance=TRUE)
#bag.training_data
xyz = predict(bag.training_data, newdata = testing_data)
table(testing_y, xyz)
mean(xyz!=testing_y)
#C5.0
#install.packagesll.packages("C50")
library(C50)
c50_model <- C5.0(training_data[-16], as.factor(training_data$IsSpam))
c50_model
#summary(c50_model)
#testing C50
c50_pred <- predict(c50_model, testing_data)
#install.packagesll.packages("gmodels")
library(gmodels)
CrossTable(testing_data$IsSpam, c50_pred,
prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
dnn = c('actual default', 'predicted default'))
#improving C50 with adaptive boosting
c50_boost10 <- C5.0(training_data[-16], as.factor(training_data$IsSpam),
trials = 10)
c50_boost10
c50_pred10 <- predict(c50_boost10, testing_data)
CrossTable(testing_data$IsSpam, c50_pred10,
prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
dnn = c('actual default', 'predicted default'))
#Dimensional Reduction
#install.packages("ISLR")
library(ISLR)
#fix(StackOverflowdata)
#names(StackOverflowdata)
dim(StackOverflowdata)
sum(is.na(StackOverflowdata$IsSpam))
StackOverflowdata=na.omit(StackOverflowdata)
dim(StackOverflowdata)
sum(is.na(StackOverflowdata))
#install.packages("pls")
library(pls)
set.seed(2)
pcr.fit=pcr(IsSpam~., data=StackOverflowdata,scale=TRUE,validation="CV")
summary(pcr.fit)
validationplot(pcr.fit,val.type="MSEP")
set.seed(1)
pcr.fit=pcr(IsSpam~., data=training_data,scale=TRUE, validation="CV")
validationplot(pcr.fit,val.type="MSEP")
pcr.pred=predict(pcr.fit,testing_data,ncomp=7)
mean((pcr.pred-testing_y)^2)
pcr.fit=pcr(IsSpam~.,data=testing_data, scale=TRUE,ncomp=7)
mean((pcr.pred-testing_y)^2)
summary(pcr.fit)
# Partial Least Squares
set.seed(1)
pls.fit=plsr(IsSpam~., data=StackOverflowdata,scale=TRUE, validation="CV")
summary(pls.fit)
validationplot(pls.fit,val.type="MSEP")
pls.pred=predict(pls.fit,testing_data,ncomp=2)
mean((pls.pred-testing_y)^2)
pls.fit=plsr(IsSpam~., data=StackOverflowdata,scale=TRUE,ncomp=2)
summary(pls.fit)
# Subset Selection Methods
# Best Subset Selection
#install.packages("ISLR")
library(ISLR)
#sum(is.na(StackOverflow$IsSpam))
lapply(StackOverflowdata["IsSpam"], unique)
#Confirms NO NA data
#install.packages("leaps")
library(leaps)
regfit.full=regsubsets(IsSpam~.,StackOverflowdata)
summary(regfit.full)
regfit.full=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19)
reg.summary=summary(regfit.full)
names(reg.summary)
reg.summary$rsq
par(mfrow=c(2,2))
plot(reg.summary$rss,xlab="Number of Variables",ylab="RSS",type="l")
plot(reg.summary$adjr2,xlab="Number of Variables",ylab="Adjusted
RSq",type="l")
which.max(reg.summary$adjr2)
points(11,reg.summary$adjr2[11], col="red",cex=2,pch=20)
plot(reg.summary$cp,xlab="Number of Variables",ylab="Cp",type='l')
which.min(reg.summary$cp)
points(10,reg.summary$cp[10],col="red",cex=2,pch=20)
which.min(reg.summary$bic)
plot(reg.summary$bic,xlab="Number of Variables",ylab="BIC",type='l')
points(6,reg.summary$bic[6],col="red",cex=2,pch=20)
coef(regfit.full,6)
# Forward and Backward Stepwise Selection
regfit.fwd=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19,method="forward")
summary(regfit.fwd)
regfit.bwd=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19,method="backward"
)
summary(regfit.bwd)
coef(regfit.full,7)
coef(regfit.fwd,7)
coef(regfit.bwd,7)
# Choosing Among Models
set.seed(1)
train=sample(c(TRUE,FALSE), nrow(StackOverflowdata),rep=TRUE)
test=(!train)
regfit.best=regsubsets(IsSpam~.,data=StackOverflowdata[train,],nvmax=19)
test.mat=model.matrix(IsSpam~.,data=StackOverflowdata[test,])
val.errors=rep(NA,19)
for(i in 1:11){
coefi=coef(regfit.best,id=i)
pred=test.mat[,names(coefi)]%*%coefi
val.errors[i]=mean((StackOverflowdata$IsSpam[test]-pred)^2)
}
val.errors
which.min(val.errors)
coef(regfit.best,10)
predict.regsubsets=function(object,newdata,id,...){
form=as.formula(object$call[[2]])
mat=model.matrix(form,newdata)
coefi=coef(object,id=id)
xvars=names(coefi)
mat[,xvars]%*%coefi
}
regfit.best=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19)
coef(regfit.best,10)
k=10
set.seed(1)
folds=sample(1:k,nrow(StackOverflowdata),replace=TRUE)
cv.errors=matrix(NA,k,11, dimnames=list(NULL, paste(1:11)))
for(j in 1:k){
best.fit=regsubsets(IsSpam~.,data=StackOverflowdata[folds!=j,],nvmax=11)
for(i in 1:11){
pred=predict(best.fit,StackOverflowdata[folds==j,],id=i)
cv.errors[j,i]=mean( (StackOverflowdata$IsSpam[folds==j]-pred)^2)
}
}
mean.cv.errors=apply(cv.errors,2,mean)
mean.cv.errors
par(mfrow=c(1,1))
plot(mean.cv.errors,type='b')
reg.best=regsubsets(IsSpam~.,data=StackOverflowdata, nvmax=11)
coef(reg.best,11)
# Ridge Regression and LASSO
#install.packages("ISLR")
library(ISLR)
fix(Hitters)
names(Hitters)
dim(Hitters)
sum(is.na(Hitters$Salary))
Hitters=na.omit(Hitters)
dim(Hitters)
sum(is.na(Hitters))
#install.packages("glmnet")
library(glmnet)
#the package invokes inputs and outputs separately unlike lm and glm
x=model.matrix(Salary~.,Hitters)[,-1]
y=Hitters$Salary
# set vector of lambda values to study range from 10^10 to 0.01, total length=100
grid=10^seq(10,-2,length=100)
ridge.mod=glmnet(x,y,alpha=0,lambda=grid)
dim(coef(ridge.mod))
# let us look at a few results here
#first lambda=50
ridge.mod$lambda[50]
coef(ridge.mod)[,50]
sqrt(sum(coef(ridge.mod)[-1,50]^2))
#next, lambda=60
ridge.mod$lambda[60]
coef(ridge.mod)[,60]
sqrt(sum(coef(ridge.mod)[-1,60]^2))
#prediction of the coefficients for lambda=50 (play with this)
predict(ridge.mod,s=500,type="coefficients")[1:20,]
#prepare for training and validation set testing
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
ridge.mod=glmnet(x[train,],y[train],alpha=0,lambda=grid, thresh=1e-12)
ridge.pred=predict(ridge.mod,s=4,newx=x[test,])
#evaluate and compare test MSE and the spread of y.test
mean((ridge.pred-y.test)^2)
mean((mean(y[train])-y.test)^2)
#test wth two other lambdas
ridge.pred=predict(ridge.mod,s=1e10,newx=x[test,])
mean((ridge.pred-y.test)^2)
ridge.pred=predict(ridge.mod,s=0,newx=x[test,],exact=T)
mean((ridge.pred-y.test)^2)
# compare with lm
# The following two are the same
lm(y~x, subset=train)
predict(ridge.mod,s=0,exact=T,type="coefficients")[1:20,]
#Cross validation to get the best lambda
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=0)
plot(cv.out)
bestlam=cv.out$lambda.min
bestlam
#now predict with the best lambda
ridge.pred=predict(ridge.mod,s=bestlam,newx=x[test,])
mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:20,]
# Lasso
#only difference in model building is to use aloha=1
lasso.mod=glmnet(x[train,],y[train],alpha=1,lambda=grid)
plot(lasso.mod)
# use CV to get best lambda
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=1)
plot(cv.out)
bestlam=cv.out$lambda.min
#use best lambda for prediction
lasso.pred=predict(lasso.mod,s=bestlam,newx=x[test,])
mean((lasso.pred-y.test)^2)
out=glmnet(x,y,alpha=1,lambda=grid)
lasso.coef=predict(out,type="coefficients",s=bestlam)[1:20,]
lasso.coef
lasso.coef[lasso.coef!=0]
```
# Support Vector Classifier
set.seed(1)
install.packages("e1071")
librarIsSpam(e1071)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=10,scale=FALSE)
plot(svmfit, training_data)
#which ones are support vectors
svmfit$index
summary(svmfit)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=0.1,scale=FALSE)
plot(svmfit, training_data)
svmfit$index
#cross validation
set.seed(1)
tune.out=tune(svm,IsSpam~.,data=training_data,kernel="linear",ranges=list(cost=c(0.001, 0.01, 0.1, 1,5,10,100)))
summary(tune.out)
bestmod=tune.out$best.model
summary(bestmod)
ypred=predict(bestmod,testing_data)
table(predict=ypred, truth=testing_y)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=.01,scale=FALSE)
ypred=predict(svmfit,testing_data)
table(predict=ypred, truth=testing_y)
plot(x, col=(y+5)/2, pch=19)
dat=data.frame(x=x,y=as.factor(IsSpam))
svmfit=svm(IsSpam~., data=dat, kernel="linear", cost=1e5)
summarIsSpam(svmfit)
plot(svmfit, dat)
svmfit=svm(IsSpam~., data=dat, kernel="linear", cost=1)
summarIsSpam(svmfit)
plot(svmfit,dat)
set.seed(1)
svmfit=svm(IsSpam~., data=training_data, kernel="radial", gamma=1, cost=1)
plot(svmfit, training_data)
summary(svmfit)
svmfit=svm(IsSpam~., data=training_data, kernel="radial",gamma=1,cost=1e5)
plot(svmfit,training_data)
set.seed(1)
tune.out=tune(svm, IsSpam~., data=training_data, kernel="radial", ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4)))
summary(tune.out)
table(true=testing_data, pred=predict(tune.out$best.model,newx=testing_data))
#ROC
install.packages("ROCR")
library(ROCR)
rocplot=function(pred, truth, ...){
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf,...)}
svmfit.opt=svm(IsSpam~., data=training_data, kernel="radial",gamma=2, cost=1,decision.values=T)
fitted=attributes(predict(svmfit.opt,training_data,decision.values=TRUE))$decision.values
par(mfrow=c(1,2))
rocplot(fitted,training_data,main="Training Data")
svmfit.flex=svm(IsSpam~., data=training_data, kernel="radial",gamma=50, cost=1, decision.values=T)
fitted=attributes(predict(svmfit.flex,training_data,decision.values=T))$decision.values
rocplot(fitted,training_data,add=T,col="red")
fitted=attributes(predict(svmfit.opt,testing_y,decision.values=T))$decision.values
rocplot(fitted,testing_y,main="Test Data")
fitted=attributes(predict(svmfit.flex,testing_data,decision.values=T))$decision.values
rocplot(fitted,testing_y,add=T,col="red")
| /R code/Question1 R code.R | no_license | aloksatpathy/EDAStackOverflow | R | false | false | 20,858 | r | #reading and transforming the data
df1= read.csv(file="QueryResults1.csv",header=TRUE)
df2<- read.csv(file="QueryResults2.csv",header=TRUE)
StackOverflow<- rbind(df1,df2)
#str(StackOverflow)
#names(StackOverflow)
nrow(StackOverflow)
#replacing NA with 1001
StackOverflow [is.na(StackOverflow)] <- 1001
#removing column that contain text or are unrelated to this question
StackOverflowdata<- StackOverflow[,-c(1, 2,3,12,14,15,22,23,25,26,27,28,29,30,31,32,33,34)]
#str(StackOverflowdata)
nrow(StackOverflowdata)
#to check uniqueness of data
lapply(StackOverflowdata["RejectionReasonId"], unique)
StackOverflowdata$IsSpam= ifelse(StackOverflowdata$RejectionReasonId==101,1,0)
#str(StackOverflowdata)
#names(StackOverflowdata)
StackOverflowdata<- StackOverflowdata[,-c(16)]
lapply(StackOverflowdata["IsSpam"], unique)
#dividing data for training and testing purpose
smp_size<- floor(0.75*nrow(StackOverflowdata))
set.seed(123)
train_ind<- sample(seq_len(nrow(StackOverflowdata)), size=smp_size)
training_data<- StackOverflowdata[train_ind,]
testing_data<- StackOverflowdata[-train_ind,]
testing_y<- testing_data$IsSpam
nrow(training_data)
nrow(testing_data)
#str(training_data)
#names(training_data)
#GLM
logistics_model<-glm(IsSpam~.,data=training_data,family="binomial")
#to discover better model
#step(logistics_model, direction = "forward")
logistics_model = glm(IsSpam ~ PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary + owner_views +
owner_upvotes + owner_downvotes + editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount + has_code +post_views +
UserId, family = "binomial", data = training_data)
summary(logistics_model)
#performing trial modelling
#step(logistics_model, direction = "backward")
#final regression model
logistics_model = glm(formula = IsSpam ~ PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views, family = "binomial", data = training_data)
summary(logistics_model)
#Bayesian information criterion
BIC(logistics_model)
logistics_probs<-predict(logistics_model,training_data,type="response")
head(logistics_probs)
logistics_pred_y=rep(0,length(testing_y))
logistics_pred_y[logistics_probs>0.55]=1
training_y=training_data$IsSpam
table(logistics_pred_y,training_y)
mean(logistics_pred_y!=training_y,na.rm=TRUE)
logistics_probs<- predict(logistics_model,testing_data, type="response")
head(logistics_probs)
logistics_pred_y=rep(0,length(testing_y))
logistics_pred_y[logistics_probs>0.55]=1
table(logistics_pred_y,testing_y)
mean(logistics_pred_y!=testing_y,na.rm=TRUE)
#kfold for regression
library(boot)
MSE_10_Fold_CV=cv.glm(training_data,logistics_model,K=10)$delta[1]
MSE_10_Fold_CV
MSE_10_Fold_CV=NULL
for(i in 1:10){
model=glm(IsSpam~poly(PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views),data=training_data)
MSE_10_Fold_CV[i]=cv.glm(training_data,model,K=10)$delta[1]
}
#summary(model)
MSE_10_Fold_CV
#ROC logistic regression
#install.packagesll.packages("ROCR")
library(ROCR)
ROCRpred=prediction(logistics_probs,testing_y)
ROCRperf=performance(ROCRpred,"tpr","fpr")
#plot(ROCRperf)
#plot(ROCRperf,colorize=TRUE)
#plot(ROCRperf,colorize=TRUE,print.cutoffs.at=seq(0,1,0.05),text.adj=c(-0.2,1.7))
as.numeric(performance(ROCRpred,"auc")@y.values)
#Linear Discriminant Analysis
library(MASS)
lda.model<- lda(IsSpam~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data)
#lda.model
#summary(lda.model)
lda_pred<- predict(lda.model,training_data)
lda_class<- lda_pred$class
table(lda_class,training_data$IsSpam)
mean(lda_class!=training_data$IsSpam)
lda_pred<- predict(lda.model,testing_data)
lda_class<- lda_pred$class
table(lda_class,testing_data$IsSpam)
mean(lda_class!=testing_data$IsSpam)
#Cross Validation LDA
library(rpart)
library(ipred)
ip.lda <- function(object, newdata) predict(object, newdata = newdata)$class
errorest(factor(training_data$IsSpam)~ training_data$PostTypeId + training_data$PostScore + training_data$post_length +
training_data$owner_reputation + training_data$owner_profile_summary +
training_data$owner_upvotes+ training_data$editDurationAfterCreation +
training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount+ training_data$post_views, data=training_data, model=lda, estimator="cv",est.para=control.errorest(k=10), predict=ip.lda)$err
#ROC LDA
S=lda_pred$posterior[,2]
roc.curve=function(s,print=FALSE){
Ps=(S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.53
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for LDA")
abline(0,1)
#Quadratic Discriminant Analysis
qda.model<- qda(IsSpam~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data)
#qda.model
qda_pred=predict(qda.model,training_data)
qda_class=qda_pred$class
table(qda_class,training_data$IsSpam)
mean(qda_class!=training_data$IsSpam)
qda_pred1=predict(qda.model,testing_data)
qda_class_n=qda_pred1$class
table(qda_class_n,testing_data$IsSpam)
mean(qda_class_n!=testing_data$IsSpam)
#Cross Validation QDA
ip.qda <- function(object, newdata) predict(object, newdata = newdata)$class
errorest(factor(training_data$IsSpam)~ training_data$PostTypeId + training_data$PostScore + training_data$post_length +
training_data$owner_reputation + training_data$owner_profile_summary +
training_data$owner_upvotes+ training_data$editDurationAfterCreation +
training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount+ training_data$post_views, data=training_data, model=qda, estimator="cv",est.para=control.errorest(k=10), predict=ip.qda)$err
#ROC QDA
qda.S=qda_pred1$posterior[,2]
roc.curve=function(s,print=FALSE){
Ps=(qda.S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.85
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for LDA")
abline(0,1)
#k nearest neighbours
library(class)
#install.packagesll.packages("cars")
#library(cars)
train.x<- cbind(training_data$PostTypeId + training_data$PostScore + training_data$post_length + training_data$owner_reputation + training_data$owner_profile_summary + training_data$owner_upvotes + training_data$editDurationAfterCreation + training_data$q_num_tags + training_data$AnswerCount + training_data$CommentCount + training_data$post_views)
test.x<- cbind(testing_data$PostTypeId + testing_data$PostScore + testing_data$post_length + testing_data$owner_reputation + testing_data$owner_profile_summary + testing_data$owner_upvotes + testing_data$editDurationAfterCreation + testing_data$q_num_tags + testing_data$AnswerCount + testing_data$CommentCount + testing_data$post_views)
train1.x=train.x[!duplicated(train.x),drop=FALSE]
test1.x=test.x[!duplicated(test.x), drop=FALSE]
tt<- training_data$IsSpam[duplicated(train.x)=='FALSE']
head(tt)
length(tt)
knn.pred<- knn(data.frame(train1.x),data.frame(test1.x),tt,k=1)
tt1<- testing_data$IsSpam[duplicated(test.x)=='FALSE']
length(tt1)
table(knn.pred,tt1)
mean(knn.pred!=tt1)
knn.pred<- knn(data.frame(train1.x),data.frame(test1.x),tt,k=2)
table(knn.pred,tt1)
mean(knn.pred!=tt1)
#Classification and Regression Trees
#CART Modeling:
#install.packagesll.packages("tree")
library(tree)
tree.training_data=tree(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,training_data)
text(tree.training_data,pretty=0)
summary(tree.training_data)
plot(tree.training_data)
text(tree.training_data,pretty=0)
lf<- seq(1,nrow(training_data))
tree.training_data=tree(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,training_data,subset=lf)
tree.pred=predict(tree.training_data,testing_data,type="class")
table(tree.pred,testing_y)
mean(tree.pred!=testing_data$IsSpam)
#Cross Validation and Pruning for the Classification Tree:
cv.training_data=cv.tree(tree.training_data,FUN=prune.misclass)
names(cv.training_data)
#cv.training_data
par(mfrow=c(1,2))
plot(cv.training_data$size,cv.training_data$dev,type="b")
plot(cv.training_data$k,cv.training_data$dev,type="b")
par(mfrow=c(1,1))
prune.training_data=prune.misclass(tree.training_data,best=5)
plot(prune.training_data)
text(prune.training_data,pretty=0)
tree.pred=predict(prune.training_data,testing_data,type="class")
table(tree.pred,testing_y)
mean(tree.pred!=testing_data$IsSpam)
#ROC for CART:
tree.pred=predict(tree.training_data,testing_data,type="vector",prob=TRUE)
#tree.pred
tree.S=tree.pred[,2]
roc.curve=function(s,print=FALSE){
Ps=(tree.S>s)*1
FP=sum((Ps==1)*(testing_data$IsSpam==0))/sum(testing_data$IsSpam==0)
TP=sum((Ps==1)*(testing_data$IsSpam==1))/sum(testing_data$IsSpam==1)
if(print==TRUE){
print(table(Observed=testing_data$IsSpam,Predicted=Ps))
}
vect=c(FP,TP)
names(vect)=c("FPR","TPR")
return(vect)
}
threshold=0.55
roc.curve(threshold,print=TRUE)
ROC.curve=Vectorize(roc.curve)
M.ROC=ROC.curve(seq(0,1,by=.01))
plot(M.ROC[1,],M.ROC[2,],col="blue",lwd=2,type="l",main="ROC for CART")
abline(0,1)
#Random Forest Modeling:
#install.packagesll.packages("randomForest")
library(randomForest)
bag.training_data=randomForest(as.factor(IsSpam)~PostTypeId + PostScore + post_length +
owner_reputation + owner_profile_summary +
owner_upvotes+ editDurationAfterCreation +
q_num_tags + AnswerCount + CommentCount+ post_views,data=training_data,subset=lf,importance=TRUE)
#bag.training_data
xyz = predict(bag.training_data, newdata = testing_data)
table(testing_y, xyz)
mean(xyz!=testing_y)
#C5.0
#install.packagesll.packages("C50")
library(C50)
c50_model <- C5.0(training_data[-16], as.factor(training_data$IsSpam))
c50_model
#summary(c50_model)
#testing C50
c50_pred <- predict(c50_model, testing_data)
#install.packagesll.packages("gmodels")
library(gmodels)
CrossTable(testing_data$IsSpam, c50_pred,
prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
dnn = c('actual default', 'predicted default'))
#improving C50 with adaptive boosting
c50_boost10 <- C5.0(training_data[-16], as.factor(training_data$IsSpam),
trials = 10)
c50_boost10
c50_pred10 <- predict(c50_boost10, testing_data)
CrossTable(testing_data$IsSpam, c50_pred10,
prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
dnn = c('actual default', 'predicted default'))
#Dimensional Reduction
#install.packages("ISLR")
library(ISLR)
#fix(StackOverflowdata)
#names(StackOverflowdata)
dim(StackOverflowdata)
sum(is.na(StackOverflowdata$IsSpam))
StackOverflowdata=na.omit(StackOverflowdata)
dim(StackOverflowdata)
sum(is.na(StackOverflowdata))
#install.packages("pls")
library(pls)
set.seed(2)
pcr.fit=pcr(IsSpam~., data=StackOverflowdata,scale=TRUE,validation="CV")
summary(pcr.fit)
validationplot(pcr.fit,val.type="MSEP")
set.seed(1)
pcr.fit=pcr(IsSpam~., data=training_data,scale=TRUE, validation="CV")
validationplot(pcr.fit,val.type="MSEP")
pcr.pred=predict(pcr.fit,testing_data,ncomp=7)
mean((pcr.pred-testing_y)^2)
pcr.fit=pcr(IsSpam~.,data=testing_data, scale=TRUE,ncomp=7)
mean((pcr.pred-testing_y)^2)
summary(pcr.fit)
# Partial Least Squares
set.seed(1)
pls.fit=plsr(IsSpam~., data=StackOverflowdata,scale=TRUE, validation="CV")
summary(pls.fit)
validationplot(pls.fit,val.type="MSEP")
pls.pred=predict(pls.fit,testing_data,ncomp=2)
mean((pls.pred-testing_y)^2)
pls.fit=plsr(IsSpam~., data=StackOverflowdata,scale=TRUE,ncomp=2)
summary(pls.fit)
# Subset Selection Methods
# Best Subset Selection
#install.packages("ISLR")
library(ISLR)
#sum(is.na(StackOverflow$IsSpam))
lapply(StackOverflowdata["IsSpam"], unique)
#Confirms NO NA data
#install.packages("leaps")
library(leaps)
regfit.full=regsubsets(IsSpam~.,StackOverflowdata)
summary(regfit.full)
regfit.full=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19)
reg.summary=summary(regfit.full)
names(reg.summary)
reg.summary$rsq
par(mfrow=c(2,2))
plot(reg.summary$rss,xlab="Number of Variables",ylab="RSS",type="l")
plot(reg.summary$adjr2,xlab="Number of Variables",ylab="Adjusted
RSq",type="l")
which.max(reg.summary$adjr2)
points(11,reg.summary$adjr2[11], col="red",cex=2,pch=20)
plot(reg.summary$cp,xlab="Number of Variables",ylab="Cp",type='l')
which.min(reg.summary$cp)
points(10,reg.summary$cp[10],col="red",cex=2,pch=20)
which.min(reg.summary$bic)
plot(reg.summary$bic,xlab="Number of Variables",ylab="BIC",type='l')
points(6,reg.summary$bic[6],col="red",cex=2,pch=20)
coef(regfit.full,6)
# Forward and Backward Stepwise Selection
regfit.fwd=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19,method="forward")
summary(regfit.fwd)
regfit.bwd=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19,method="backward"
)
summary(regfit.bwd)
coef(regfit.full,7)
coef(regfit.fwd,7)
coef(regfit.bwd,7)
# Choosing Among Models
set.seed(1)
train=sample(c(TRUE,FALSE), nrow(StackOverflowdata),rep=TRUE)
test=(!train)
regfit.best=regsubsets(IsSpam~.,data=StackOverflowdata[train,],nvmax=19)
test.mat=model.matrix(IsSpam~.,data=StackOverflowdata[test,])
val.errors=rep(NA,19)
for(i in 1:11){
coefi=coef(regfit.best,id=i)
pred=test.mat[,names(coefi)]%*%coefi
val.errors[i]=mean((StackOverflowdata$IsSpam[test]-pred)^2)
}
val.errors
which.min(val.errors)
coef(regfit.best,10)
predict.regsubsets=function(object,newdata,id,...){
form=as.formula(object$call[[2]])
mat=model.matrix(form,newdata)
coefi=coef(object,id=id)
xvars=names(coefi)
mat[,xvars]%*%coefi
}
regfit.best=regsubsets(IsSpam~.,data=StackOverflowdata,nvmax=19)
coef(regfit.best,10)
k=10
set.seed(1)
folds=sample(1:k,nrow(StackOverflowdata),replace=TRUE)
cv.errors=matrix(NA,k,11, dimnames=list(NULL, paste(1:11)))
for(j in 1:k){
best.fit=regsubsets(IsSpam~.,data=StackOverflowdata[folds!=j,],nvmax=11)
for(i in 1:11){
pred=predict(best.fit,StackOverflowdata[folds==j,],id=i)
cv.errors[j,i]=mean( (StackOverflowdata$IsSpam[folds==j]-pred)^2)
}
}
mean.cv.errors=apply(cv.errors,2,mean)
mean.cv.errors
par(mfrow=c(1,1))
plot(mean.cv.errors,type='b')
reg.best=regsubsets(IsSpam~.,data=StackOverflowdata, nvmax=11)
coef(reg.best,11)
# Ridge Regression and LASSO
#install.packages("ISLR")
library(ISLR)
fix(Hitters)
names(Hitters)
dim(Hitters)
sum(is.na(Hitters$Salary))
Hitters=na.omit(Hitters)
dim(Hitters)
sum(is.na(Hitters))
#install.packages("glmnet")
library(glmnet)
#the package invokes inputs and outputs separately unlike lm and glm
x=model.matrix(Salary~.,Hitters)[,-1]
y=Hitters$Salary
# set vector of lambda values to study range from 10^10 to 0.01, total length=100
grid=10^seq(10,-2,length=100)
ridge.mod=glmnet(x,y,alpha=0,lambda=grid)
dim(coef(ridge.mod))
# let us look at a few results here
#first lambda=50
ridge.mod$lambda[50]
coef(ridge.mod)[,50]
sqrt(sum(coef(ridge.mod)[-1,50]^2))
#next, lambda=60
ridge.mod$lambda[60]
coef(ridge.mod)[,60]
sqrt(sum(coef(ridge.mod)[-1,60]^2))
#prediction of the coefficients for lambda=50 (play with this)
predict(ridge.mod,s=500,type="coefficients")[1:20,]
#prepare for training and validation set testing
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
ridge.mod=glmnet(x[train,],y[train],alpha=0,lambda=grid, thresh=1e-12)
ridge.pred=predict(ridge.mod,s=4,newx=x[test,])
#evaluate and compare test MSE and the spread of y.test
mean((ridge.pred-y.test)^2)
mean((mean(y[train])-y.test)^2)
#test wth two other lambdas
ridge.pred=predict(ridge.mod,s=1e10,newx=x[test,])
mean((ridge.pred-y.test)^2)
ridge.pred=predict(ridge.mod,s=0,newx=x[test,],exact=T)
mean((ridge.pred-y.test)^2)
# compare with lm
# The following two are the same
lm(y~x, subset=train)
predict(ridge.mod,s=0,exact=T,type="coefficients")[1:20,]
#Cross validation to get the best lambda
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=0)
plot(cv.out)
bestlam=cv.out$lambda.min
bestlam
#now predict with the best lambda
ridge.pred=predict(ridge.mod,s=bestlam,newx=x[test,])
mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:20,]
# Lasso
#only difference in model building is to use aloha=1
lasso.mod=glmnet(x[train,],y[train],alpha=1,lambda=grid)
plot(lasso.mod)
# use CV to get best lambda
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=1)
plot(cv.out)
bestlam=cv.out$lambda.min
#use best lambda for prediction
lasso.pred=predict(lasso.mod,s=bestlam,newx=x[test,])
mean((lasso.pred-y.test)^2)
out=glmnet(x,y,alpha=1,lambda=grid)
lasso.coef=predict(out,type="coefficients",s=bestlam)[1:20,]
lasso.coef
lasso.coef[lasso.coef!=0]
```
# Support Vector Classifier
set.seed(1)
install.packages("e1071")
librarIsSpam(e1071)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=10,scale=FALSE)
plot(svmfit, training_data)
#which ones are support vectors
svmfit$index
summary(svmfit)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=0.1,scale=FALSE)
plot(svmfit, training_data)
svmfit$index
#cross validation
set.seed(1)
tune.out=tune(svm,IsSpam~.,data=training_data,kernel="linear",ranges=list(cost=c(0.001, 0.01, 0.1, 1,5,10,100)))
summary(tune.out)
bestmod=tune.out$best.model
summary(bestmod)
ypred=predict(bestmod,testing_data)
table(predict=ypred, truth=testing_y)
svmfit=svm(IsSpam~., data=training_data, kernel="linear", cost=.01,scale=FALSE)
ypred=predict(svmfit,testing_data)
table(predict=ypred, truth=testing_y)
plot(x, col=(y+5)/2, pch=19)
dat=data.frame(x=x,y=as.factor(IsSpam))
svmfit=svm(IsSpam~., data=dat, kernel="linear", cost=1e5)
summarIsSpam(svmfit)
plot(svmfit, dat)
svmfit=svm(IsSpam~., data=dat, kernel="linear", cost=1)
summarIsSpam(svmfit)
plot(svmfit,dat)
set.seed(1)
svmfit=svm(IsSpam~., data=training_data, kernel="radial", gamma=1, cost=1)
plot(svmfit, training_data)
summary(svmfit)
svmfit=svm(IsSpam~., data=training_data, kernel="radial",gamma=1,cost=1e5)
plot(svmfit,training_data)
set.seed(1)
tune.out=tune(svm, IsSpam~., data=training_data, kernel="radial", ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4)))
summary(tune.out)
table(true=testing_data, pred=predict(tune.out$best.model,newx=testing_data))
#ROC
install.packages("ROCR")
library(ROCR)
rocplot=function(pred, truth, ...){
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf,...)}
svmfit.opt=svm(IsSpam~., data=training_data, kernel="radial",gamma=2, cost=1,decision.values=T)
fitted=attributes(predict(svmfit.opt,training_data,decision.values=TRUE))$decision.values
par(mfrow=c(1,2))
rocplot(fitted,training_data,main="Training Data")
svmfit.flex=svm(IsSpam~., data=training_data, kernel="radial",gamma=50, cost=1, decision.values=T)
fitted=attributes(predict(svmfit.flex,training_data,decision.values=T))$decision.values
rocplot(fitted,training_data,add=T,col="red")
fitted=attributes(predict(svmfit.opt,testing_y,decision.values=T))$decision.values
rocplot(fitted,testing_y,main="Test Data")
fitted=attributes(predict(svmfit.flex,testing_data,decision.values=T))$decision.values
rocplot(fitted,testing_y,add=T,col="red")
|
testlist <- list(A = structure(c(2.257160459728e+205, 9.53818252179844e+295 ), .Dim = 1:2), B = structure(c(2.19477802979261e+294, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613124293-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 321 | r | testlist <- list(A = structure(c(2.257160459728e+205, 9.53818252179844e+295 ), .Dim = 1:2), B = structure(c(2.19477802979261e+294, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) |
install.packages('MASS')
library('MASS')
View(cpus)
set.seed(20)
install.packages('neuralnet')
library('neuralnet')
base <-cpus
max_base <- apply(base, 2, max)
min_base <- apply(base, 2, min)
base_scal <- scale(base, center = min_base, scale = max_base - min_base)
index <- sample(1:nrow(base), round(0.80*nrow(base)))
train_base <- as.data.frame(base_scal[index,])
test_base <- as.data.frame(base_scal[-index,])
n <- colnames(base)
f <- as.formula(paste('chmin~', paste(n[!n %in% 'chmin'], collapse = '+')))
n1 <- neuralnet(f,data = train_base, hidden = c(9, 5, 3), linear.output = F)
plot(n1)
pr <- compute(n1, test_base[1:8])
print(pr$net.result)
pr$net.result <- sapply(pr$net.result, round, digits = 0)
test1 <- table(test_base$age, pr$net.result)
test1
sum(test1[1,])
sum(test1[2,])
Accuracy1 <- (test1[1,1] + test1[2, 2])/sum(test1)
Accuracy1
install.packages('RSNNS')
library(RSNNS)
set.seed(20)
base2 <- cpus[sample(1:nrow(cpus), length(1:nrow(cpus))),
1:ncol(cpus)]
base2_Values <- base2[,1]
base2_Target <- base2[, 1:3]
base2 <- splitForTrainingAndTest(base2_Values, base2_Target, ratio = 0.2)
base2 <- normTrainingAndTestSet(base2)
model <- mlp(base2$inputsTrain,
base2$targetsTrain,
size = 5,
maxit = 50,
inputsTest = base2$inputsTest,
targetsTest = base2$targetsTest)
test2 <- confusionMatrix(base2$targetsTrain, encodeClassLabels(fitted.values(model),
method = "402040", l = 0.5, h = 0.51))
test2
sum(test2[1,])
sum(test2[2,])
Accuracy2 <- (test2[1,1] + test2[2, 2])/sum(test2)
Accuracy2
install.packages("kohonen")
library('kohonen')
set.seed(20)
base3 <- base[1:8]
base3_1 <- cpus[1:1]
table(base3_1)
train <- sample(nrow(base3), 151)
X_train <- scale(data3[train,])
X_test <- scale(data3[-train,],
center = attr(X_train, "scaled:center"),
scale = attr(X_train, "scaled:center"))
train_base <- list(measurements = X_train,
base3_1 = base3_1[train,])
test_base <- list(measurements = X_test,
base3_1 = base3_1[-train,])
mygrid <- somgrid(5, 5, 'chmin')
som.base3 <- supersom(train_base, grid = mygrid)
som.predict <- predict(som.base3, newdata = test_base)
test3 <- table(base3_1[-train,], som.predict$predictions$base3_1)
sum(test3[1,])
sum(test3[2,])
Accuracy3 <- (test3[1,1] + test3[2, 2])/sum(test3)
Accuracy3
Accuracy1
Accuracy2
Accuracy3
| /Dmitrieva_E-2041.r | no_license | dmitrieva18/Dmitrieva_KT | R | false | false | 2,645 | r | install.packages('MASS')
library('MASS')
View(cpus)
set.seed(20)
install.packages('neuralnet')
library('neuralnet')
base <-cpus
max_base <- apply(base, 2, max)
min_base <- apply(base, 2, min)
base_scal <- scale(base, center = min_base, scale = max_base - min_base)
index <- sample(1:nrow(base), round(0.80*nrow(base)))
train_base <- as.data.frame(base_scal[index,])
test_base <- as.data.frame(base_scal[-index,])
n <- colnames(base)
f <- as.formula(paste('chmin~', paste(n[!n %in% 'chmin'], collapse = '+')))
n1 <- neuralnet(f,data = train_base, hidden = c(9, 5, 3), linear.output = F)
plot(n1)
pr <- compute(n1, test_base[1:8])
print(pr$net.result)
pr$net.result <- sapply(pr$net.result, round, digits = 0)
test1 <- table(test_base$age, pr$net.result)
test1
sum(test1[1,])
sum(test1[2,])
Accuracy1 <- (test1[1,1] + test1[2, 2])/sum(test1)
Accuracy1
install.packages('RSNNS')
library(RSNNS)
set.seed(20)
base2 <- cpus[sample(1:nrow(cpus), length(1:nrow(cpus))),
1:ncol(cpus)]
base2_Values <- base2[,1]
base2_Target <- base2[, 1:3]
base2 <- splitForTrainingAndTest(base2_Values, base2_Target, ratio = 0.2)
base2 <- normTrainingAndTestSet(base2)
model <- mlp(base2$inputsTrain,
base2$targetsTrain,
size = 5,
maxit = 50,
inputsTest = base2$inputsTest,
targetsTest = base2$targetsTest)
test2 <- confusionMatrix(base2$targetsTrain, encodeClassLabels(fitted.values(model),
method = "402040", l = 0.5, h = 0.51))
test2
sum(test2[1,])
sum(test2[2,])
Accuracy2 <- (test2[1,1] + test2[2, 2])/sum(test2)
Accuracy2
install.packages("kohonen")
library('kohonen')
set.seed(20)
base3 <- base[1:8]
base3_1 <- cpus[1:1]
table(base3_1)
train <- sample(nrow(base3), 151)
X_train <- scale(data3[train,])
X_test <- scale(data3[-train,],
center = attr(X_train, "scaled:center"),
scale = attr(X_train, "scaled:center"))
train_base <- list(measurements = X_train,
base3_1 = base3_1[train,])
test_base <- list(measurements = X_test,
base3_1 = base3_1[-train,])
mygrid <- somgrid(5, 5, 'chmin')
som.base3 <- supersom(train_base, grid = mygrid)
som.predict <- predict(som.base3, newdata = test_base)
test3 <- table(base3_1[-train,], som.predict$predictions$base3_1)
sum(test3[1,])
sum(test3[2,])
Accuracy3 <- (test3[1,1] + test3[2, 2])/sum(test3)
Accuracy3
Accuracy1
Accuracy2
Accuracy3
|
#################
require(lubridate)
# download the data set, if it doesn't already exist in the working directory
# (has already been downloaded previously)
if(!file.exists("exdata_data_household_power_consumption.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
"exdata_data_household_power_consumption.zip")
}
if(!dir.exists("exdata_data_household_power_consumption")) {
unzip("exdata_data_household_power_consumption.zip",
exdir="exdata_data_household_power_consumption")
}
# read the data set into R, unless it has been read into R already
if(!exists("bigTable")) {
classes <- c("character", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric")
bigTable <- read.table("exdata_data_household_power_consumption/household_power_consumption.txt",
sep=";", header=TRUE, na.strings="?", colClasses=classes)
}
# create the data frame of date subsets, unless it already exists
if(!exists("dateSubset")) {
# subset data from only dates within the range 2007-02-01 and 2007-02-02
dateSubset <- bigTable[bigTable$Date == "1/2/2007" | bigTable$Date == "2/2/2007", ]
# convert the date/time columns into date/time class objects
dateSubset$Date <- dmy(dateSubset$Date)
timeVector <- strptime(dateSubset$Time, format="%H:%M:%S", tz="UTC")
timeVector[dateSubset$Date=="2007-02-01"] <- update(timeVector[dateSubset$Date=="2007-02-01"],
years=2007, months=2, days=1)
timeVector[dateSubset$Date=="2007-02-02"] <- update(timeVector[dateSubset$Date=="2007-02-02"],
years=2007, months=2, days=2)
dateSubset$Time <- timeVector
row.names(dateSubset) <- NULL
}
#################
# create the line graph plot 2
png(file="plot2.png", width=480, height=480, bg="transparent")
plot(dateSubset$Time, dateSubset$Global_active_power,
type="l",
ylab="Global Active Power (kilowatts)",
xlab="")
dev.off()
| /plot2.R | no_license | Natasha-R/Exploratory-Data-Analysis-Course-Project-1 | R | false | false | 2,186 | r |
#################
require(lubridate)
# download the data set, if it doesn't already exist in the working directory
# (has already been downloaded previously)
if(!file.exists("exdata_data_household_power_consumption.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
"exdata_data_household_power_consumption.zip")
}
if(!dir.exists("exdata_data_household_power_consumption")) {
unzip("exdata_data_household_power_consumption.zip",
exdir="exdata_data_household_power_consumption")
}
# read the data set into R, unless it has been read into R already
if(!exists("bigTable")) {
classes <- c("character", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric")
bigTable <- read.table("exdata_data_household_power_consumption/household_power_consumption.txt",
sep=";", header=TRUE, na.strings="?", colClasses=classes)
}
# create the data frame of date subsets, unless it already exists
if(!exists("dateSubset")) {
# subset data from only dates within the range 2007-02-01 and 2007-02-02
dateSubset <- bigTable[bigTable$Date == "1/2/2007" | bigTable$Date == "2/2/2007", ]
# convert the date/time columns into date/time class objects
dateSubset$Date <- dmy(dateSubset$Date)
timeVector <- strptime(dateSubset$Time, format="%H:%M:%S", tz="UTC")
timeVector[dateSubset$Date=="2007-02-01"] <- update(timeVector[dateSubset$Date=="2007-02-01"],
years=2007, months=2, days=1)
timeVector[dateSubset$Date=="2007-02-02"] <- update(timeVector[dateSubset$Date=="2007-02-02"],
years=2007, months=2, days=2)
dateSubset$Time <- timeVector
row.names(dateSubset) <- NULL
}
#################
# create the line graph plot 2
png(file="plot2.png", width=480, height=480, bg="transparent")
plot(dateSubset$Time, dateSubset$Global_active_power,
type="l",
ylab="Global Active Power (kilowatts)",
xlab="")
dev.off()
|
## 1. Build the states data.frame----
## Clear the environment
rm(list = ls())
## set working dir
setwd("C:/Users/jayso/OneDrive/Desktop/coviddata")
## call necessary libraries
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(data.table)
## Read the Data
states <- fread("C:/Users/jayso/OneDrive/Documents/GitHub/covid-19-data/us-states.csv")
## Read the population data
statepop <- fread("C:/RWD/countypopbyfips2.csv")
## merge state data with state population
states <- left_join(states, statepop, by = "fips", all.x = TRUE)
## Make the date column a format that R can recognize
states$date <- as.Date(states$date)
## Add a column for yesterday's cases
states$adayago <- sapply(seq_len(nrow(states)),
function(ayer) with(states, sum(cases[date == (date[ayer] - 1) & state == state[ayer]])))
## Add a column for today's new cases
states$newcases <- states$cases - states$adayago
## 7 day average of new cases
states$sevdayavg <- sapply(seq_len(nrow(states)),
function(ayer) with(states, mean(newcases[date >= (date[ayer] - 7) & date <= date[ayer] & state == state[ayer]])))
## 7 day average 7 days ago
states$sevdayavgwkago <- sapply(seq_len(nrow(states)),
function(ayer) with(states, sum(sevdayavg[date == (date[ayer] - 7) & state == state[ayer]])))
## Change in 7 day average since 7 days ago
states$sevdayavgchange <- states$sevdayavg - states$sevdayavgwkago
## Add a column for number of cases one week ago
states$aweekago <- sapply(seq_len(nrow(states)), function(semana) with(states, sum(cases[date == (date[semana] - 7) & state == state[semana]])))
## Add a column for this week's new cases
states$weeklynew <- states$cases - states$aweekago
## Add a column for cases 3 weeks ago
states$weeksago <- sapply(seq_len(nrow(states)), function(act) with(states, sum(cases[date == (date[act] - 21) & state == state[act]])))
## Add a column for the estimated number of active cases (total - total from 3 weeks ago)
states$active <- states$cases - states$weeksago
## Add a column to calculate the active cases per 100,000 people
states$activepp <- states$active / states$population * 100000
## Add a column to calculate the number of recovered cases (total - active - deaths)
states$recovered <- states$cases - states$active - states$deaths
## Add a column to calculate the number of recovered cases per 100,000 people
states$recoveredpp <- states$recovered / states$population * 100000
## Add a column to calculate weekly growth as a percent
states$weeklygrowth <- states$weeklynew / states$aweekago
## Add a column to calculate weekly growth per 100,000 people
states$actgrowthpp <- states$active / states$population *100000
## Add a column for total cases per 100,000
states$casespp <- states$cases / states$population * 100000
## turn states into factor
states$state <-as.factor(states$state)
states$region <- as.factor(states$region)
states$division <- as.factor(states$division)
## Turn a data column into a categorical variable
states$activeppF <- cut(states$activepp, breaks = quantile(states$activepp))
table(states$activeppF)
library(Hmisc)
states$activeppF <- cut2(states$activepp, g = 4)
table(states$activeppF)
## yesno <- sample(c("yes", "no"), size = 10, replace = TRUE)
## yesnofac <- factor(yesno, levels = c("yes", "no"))
## relevel(yesnofac, ref = "yes")
## as.numeric(yesnofac)
library(plyr)
## restData2 <- mutate(restData, zipGroups = cut2(zipCode, g = 4))
## table(restData2$zipGroups)
##-----
## Load purrr
library(purrr)
## Create a 7 day rolling average for new cases
states$newcasesavg <- sapply(seq_len(nrow(states)), function(act) with(states, mean(newcases[date <= (date[act] - 7) & state == state[act]], na.rm = TRUE)))
## Group by division
div_summ = filter(states, date == as.Date(max(states$date)))
div_summ = group_by(div_summ, division)
div_summ = summarise(div_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T),
activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
div_summ2 <- table(today$division, today$activepp)
states$state <- as.factor(states$state)
states2 <- states %>%
group_by(state) %>%
select(date, activepp) %>%
filter(min_rank(desc(activepp)) <= 1) %>%
arrange(state, desc(activepp))
levels(states$state)
## 2. Functions for analyzing and plotting the states data.frame ----
## Load ggplot2
library(ggplot2)
## Function to plot a state's total and active per 100,000 curves
plotpp <- function(st) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st)
t1 <- deparse(substitute(st))
t2 <- paste(t1, "Per 100k People")
ggplot(s1, aes(x = date)) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
geom_line(aes(y = casespp), color = "black", size = 2) +
labs(title = t2, y = "Total / Active", x = "Date")
}
## Function to compare 3 states activepp curves
plot3active <- function(st1, st2, st3) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st1 | state == st2 | state == st3)
t1 <- deparse(substitute(st1))
t2 <- deparse(substitute(st2))
t3 <- deparse(substitute(st3))
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~state) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plot37DA <- function(st1, st2, st3) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st1 | state == st2 | state == st3)
t1 <- deparse(substitute(st1))
t2 <- deparse(substitute(st2))
t3 <- deparse(substitute(st3))
t4 <- paste("7 Day Avg of New Cases")
ggplot(s1, aes(x = date)) +
facet_wrap(~state) +
geom_line(aes(y = sevdayavg), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plotUSA <- function() {
ggplot(states, aes(x = date)) +
facet_wrap(~division) +
geom_line(aes(y = sevdayavg), color = "blue", size = 2)
}
ENCentral <- states %>%
filter(division == "East North Central")
ggplot(ENCentral, aes(x = date)) +
facet_wrap(. ~ state) +
geom_line(aes(y = sevdayavg), color = "darkred", size = 2)
## Filter the latest numbers
date1 <- max(states$date)
statestoday <- filter(states, states$date == date1)
fastestgrowth <- statestoday %>%
select(state, weeklygrowthpp) %>%
arrange(desc(weeklygrowthpp))
library(ggplot2)
ggplot(states2$Oklahoma, aes(x = date)) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
geom_line(aes(y = casespp), color = "black", size = 2) +
geom_line(aes(y = recoveredpp), color = "red", size = 2)
## 3. Load the counties data ----
## **Don't try and load the counties data and the states data at the same time**
# Clear the environment to make some space - this file contains many observations
rm(list = ls())
## set working dir
setwd("C:/Users/jayso/OneDrive/Desktop/covid-data")
## call required libraries
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(data.table)
# Read the counties data
counties <- fread("C:/Users/jayso/OneDrive/Documents/GitHub/covid-19-data/us-counties.csv")
## NYC/KC/territory fix
counties$fips[counties$county == "New York City"] = 361111
counties$fips[counties$county == "Kansas City"] = 291111
counties$fips[counties$state == "Puerto Rico"] = 7200
counties$fips[counties$state == "Guam"] = 6600
counties$fips[counties$state == "Virgin Islands"] = 7800
counties$fips[counties$state == "Northern Mariana Islands"] = 6900
counties <- counties[counties$county != "Unknown"]
## Read county population values
countydemo <- fread("./demobyfips.csv")
## merge county data with county population
counties <- left_join(counties, countydemo, by = "fips", all.x = TRUE)
## filter to cities in/around Mississippi
counties <- counties[counties$state == "Mississippi"]
MScitynames <- countiesMS$csba
MScitynames <- unique(MScitynames)
MScitynames <- MScitynames[MScitynames != ""]
counties <- counties[counties$csba %in% MScitynames]
ggplot(counties, aes(x = date)) +
geom_line(aes(y = activepp), col = "blue", size = 2)
x <- round(runif(1000, 0, 100))
y <- x + rnorm(1000, 0, 30)
summary(y)
sd(y)
range(y)
mean(y)
qqplot(x, y)
plot(ecdf(x))
xy <- sample(x, 200)
mean(x)
summary(xy)
plot(ecdf(xy))
## Make the date column a format that R will recognize
counties$date <- as.Date(counties$date)
## Add a column for yesterday's cases (with timer so I have a benchmark for judging improvements)
counties$adayago2 <- for (i in 1:nrow(counties))
sapply(seq_len(nrow(counties)),
function(x) with(counties,))
rep(c("a", "b", "c"))
num_repeat <- 10
dates <- data.frame(rep(
seq(as.Date('2017-01-01'), as.Date('2017-12-31'), by = 'days'),
times = num_repeat))
system.time({
counties$adayago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
counties$aweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 7) & fips == fips[x]], na.rm = TRUE)))
counties$thrweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 21) & fips == fips[x]], na.rm = TRUE)))})
counties$active <- counties$cases - counties$thrweekago
counties$activepp <- counties$active / counties$population * 100000
counties$newcases <- counties$cases - counties$adayago
system.time({
counties$svnDAnc <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
mean(newcases[date >= (date[x] - 7) & date <= date[x] & fips == fips[x]], na.rm = TRUE)))
counties$svnDAlast <-
sapply(seq_len(nrow(counties)),
function(sumx) with(counties,
sum(svnDAnc[date == (date[sumx] - 7) & fips == fips[sumx]], na.rm = TRUE)))})
system.time({
counties$actadayago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(active[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
counties$actaweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(active[date == (date[x] - 7) & fips == fips[x]], na.rm = TRUE)))
counties$actgrowthpp <- (counties$active - counties$actaweekago) / counties$population *100000
})
countiesDT$newcases <- ifelse(countiesDT$cases - countiesDT$adayago < 0, 0, countiesDT$cases - countiesDT$adayago)
countiesDT$newcasesW <- ifelse(countiesDT$cases - countiesDT$aweekago < 0, 0, countiesDT$cases - countiesDT$aweekago)
countiesDT$active <- ifelse(countiesDT$cases - countiesDT$thrweekago < 0, 0, countiesDT$cases - countiesDT$thrweekago)
data.frame(Count = colSums(df[,-1] == 'Y'), # count of "Y"s in each column
# sum of Values column where A/B/C is "Y"
Sum = sapply(df[,-1], function(x){sum(df$Values[x == 'Y'])}))
RI$adayago <- sapply(seq_len(nrow(RI)),
function(x) with(RI,
sum(cases[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
#### FYI - system.time call - user, 519, system, 109, elapsed 630.33
countiesDT$newcases <- countiesDT$cases - countiesDT$adayago
### A week ago - with system.time call
system.time({
countiesDT$aweekago <- sapply(seq_len(nrow(countiesDT)),
function(ayer) with(countiesDT,
sum(cases[date == (date[ayer] - 7) & fips == fips[ayer]])))})
## user system elapsed
## 526.89 101.89 631.96
system.time({
TX$aweekago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(cases[date == (date[x] - 7) & fips == fips[x]])))})
system.time({
TX$thrwkago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(cases[date == (date[x] - 21) & fips == fips[x]])))})
TX$actthrwkago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(active[date == (date[x] - 21) & fips == fips[x]])))
system.time({
RI$aweekago <- lapply(seq_len(nrow(RI)),
function(ayer) with(RI,
sum(cases[date == (date[ayer] - 7) & fips == fips[ayer]], na.rm = TRUE)))})
## group by city
latest <- max(counties$date)
basic_summ = filter(counties, date == as.Date(latest))
basic_summ$csba <- as.factor(basic_summ$csba)
basic_summ$csa <- as.factor(basic_summ$csa)
basic_summ = group_by(basic_summ, csba, csa)
basic_summ = summarise(basic_summ, sumcases = sum(cases), sumactive = sum(active),
sumpop = sum(population), activepp = sum(active) / sum(population) * 100000)
summary(basic_summ)
## group by state
state_summ = filter(counties, date == as.Date(latest))
state_summ = group_by(state_summ, state)
state_summ = summarise(state_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T), activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
state_summ <- state_summ[state_summ$sumpop > 0,]
## group by division
div_summ = filter(counties, date == as.Date(latest))
div_summ = group_by(div_summ, division)
div_summ = summarise(div_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T),
activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
## filter by state
pkstate <- function(pk) {
stname <- deparse(substitute(pk))
stname2 <- noquote(stname)
sta1 <- filter(counties, state == pk)
assign(stname2, sta1, env=.GlobalEnv)
}
getwd()
## Filter the latest numbers
date1 <- max(TX$date)
countiestoday <- filter(TX, TX$date == date1)
fastestgrowth <- countiestoday %>%
select(county, activegrowth) %>%
arrange(desc(activegrowth))
## 4. Plot some county stuff ----
plot3county <- function(co1, co2, co3) { # co = the counties whose data we wish to plot
s1 <- subset(counties, county == co1 | county == co2 | county == co3)
t1 <- deparse(substitute(co1))
t2 <- deparse(substitute(co2))
t3 <- deparse(substitute(co3))
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~county) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plot3fips <- function(f1, f2, f3) { # co = the counties whose data we wish to plot
s1 <- subset(counties, fips == f1 | fips == f2 | fips == f3)
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~county) +
geom_line(aes(y = active), color = "blue", size = 1) +
labs(title = t4, x = "Date")
}
counties <- counties %>% group_by(csba)
ggplot(counties, aes(x = date)) +
facet_wrap(. ~ csa) +
geom_line(aes(y = activepp), color = "orange", size = 1)
today <- counties %>% filter(date == "2020-08-23")
today$log <- log(today$activepp)
meanlog <- mean(today$log)
today$sd <- sd(today$activepp)
sqrtsd <- sqrt(175.5)
today$log2 <- (today$log - meanlog)/sqrtsd
boxplot(today$log2)
max(counties$date)
cutpoints <- quantile(today$activepp, seq(0, 1, length = 10), na.rm = TRUE)
today$activecut <- cut(today$activepp, cutpoints)
levels(today$activecut)
### 5. Cross validation ------
library(ggplot2)
library(ggthemes)
library(zoo)
library(xts)
library(quantmod)
library(forecast)
library(fpp)
library(fpp2)
library(tidyverse)
library(caret)
set.seed(123)
CFR5 <- read.csv("./CFR3.csv")
train.control <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
model <- train(CFR ~., data = CFRdata1, method = "lm", trControl = train.control)
print(model)
usa$date <- as.Date(usa$date)
usa$thrwksago <-
sapply(seq_len(nrow(usa)),
function(x) with(usa,
sum(cases[date == (date[x] - 21)], na.rm = TRUE)))
## DT
counties$peaktodate <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
max(activepp[date <= date[x] & fips == fips[x]], na.rm = TRUE)))
countiesDT <- data.table(counties)
countiesDT[, peaked := activepp != peaktodate]
countiesDT[, actppvstavg := {tmp <- sapply(seq_len(nrow(countiesDT)),
function(x) with(countiesDT,
mean(activepp[date <= (date[x] - 1) & state == state[x]])));
activepp / tmp}]
x <- round(runif(1000, 0, 100))
x
quantile(x)
data(iris)
head(Iris)
head(iris)
do.call("rbind", tapply(iris$Sepal.Length,
iris$Species,
quantile))
statesquant <- do.call("rbind", tapply(statestoday$activepp,
statestoday$state,
quantile)) | /script.R | no_license | jaysonleek/coviddata | R | false | false | 17,887 | r | ## 1. Build the states data.frame----
## Clear the environment
rm(list = ls())
## set working dir
setwd("C:/Users/jayso/OneDrive/Desktop/coviddata")
## call necessary libraries
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(data.table)
## Read the Data
states <- fread("C:/Users/jayso/OneDrive/Documents/GitHub/covid-19-data/us-states.csv")
## Read the population data
statepop <- fread("C:/RWD/countypopbyfips2.csv")
## merge state data with state population
states <- left_join(states, statepop, by = "fips", all.x = TRUE)
## Make the date column a format that R can recognize
states$date <- as.Date(states$date)
## Add a column for yesterday's cases
states$adayago <- sapply(seq_len(nrow(states)),
function(ayer) with(states, sum(cases[date == (date[ayer] - 1) & state == state[ayer]])))
## Add a column for today's new cases
states$newcases <- states$cases - states$adayago
## 7 day average of new cases
states$sevdayavg <- sapply(seq_len(nrow(states)),
function(ayer) with(states, mean(newcases[date >= (date[ayer] - 7) & date <= date[ayer] & state == state[ayer]])))
## 7 day average 7 days ago
states$sevdayavgwkago <- sapply(seq_len(nrow(states)),
function(ayer) with(states, sum(sevdayavg[date == (date[ayer] - 7) & state == state[ayer]])))
## Change in 7 day average since 7 days ago
states$sevdayavgchange <- states$sevdayavg - states$sevdayavgwkago
## Add a column for number of cases one week ago
states$aweekago <- sapply(seq_len(nrow(states)), function(semana) with(states, sum(cases[date == (date[semana] - 7) & state == state[semana]])))
## Add a column for this week's new cases
states$weeklynew <- states$cases - states$aweekago
## Add a column for cases 3 weeks ago
states$weeksago <- sapply(seq_len(nrow(states)), function(act) with(states, sum(cases[date == (date[act] - 21) & state == state[act]])))
## Add a column for the estimated number of active cases (total - total from 3 weeks ago)
states$active <- states$cases - states$weeksago
## Add a column to calculate the active cases per 100,000 people
states$activepp <- states$active / states$population * 100000
## Add a column to calculate the number of recovered cases (total - active - deaths)
states$recovered <- states$cases - states$active - states$deaths
## Add a column to calculate the number of recovered cases per 100,000 people
states$recoveredpp <- states$recovered / states$population * 100000
## Add a column to calculate weekly growth as a percent
states$weeklygrowth <- states$weeklynew / states$aweekago
## Add a column to calculate weekly growth per 100,000 people
states$actgrowthpp <- states$active / states$population *100000
## Add a column for total cases per 100,000
states$casespp <- states$cases / states$population * 100000
## turn states into factor
states$state <-as.factor(states$state)
states$region <- as.factor(states$region)
states$division <- as.factor(states$division)
## Turn a data column into a categorical variable
states$activeppF <- cut(states$activepp, breaks = quantile(states$activepp))
table(states$activeppF)
library(Hmisc)
states$activeppF <- cut2(states$activepp, g = 4)
table(states$activeppF)
## yesno <- sample(c("yes", "no"), size = 10, replace = TRUE)
## yesnofac <- factor(yesno, levels = c("yes", "no"))
## relevel(yesnofac, ref = "yes")
## as.numeric(yesnofac)
library(plyr)
## restData2 <- mutate(restData, zipGroups = cut2(zipCode, g = 4))
## table(restData2$zipGroups)
##-----
## Load purrr
library(purrr)
## Create a 7 day rolling average for new cases
states$newcasesavg <- sapply(seq_len(nrow(states)), function(act) with(states, mean(newcases[date <= (date[act] - 7) & state == state[act]], na.rm = TRUE)))
## Group by division
div_summ = filter(states, date == as.Date(max(states$date)))
div_summ = group_by(div_summ, division)
div_summ = summarise(div_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T),
activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
div_summ2 <- table(today$division, today$activepp)
states$state <- as.factor(states$state)
states2 <- states %>%
group_by(state) %>%
select(date, activepp) %>%
filter(min_rank(desc(activepp)) <= 1) %>%
arrange(state, desc(activepp))
levels(states$state)
## 2. Functions for analyzing and plotting the states data.frame ----
## Load ggplot2
library(ggplot2)
## Function to plot a state's total and active per 100,000 curves
plotpp <- function(st) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st)
t1 <- deparse(substitute(st))
t2 <- paste(t1, "Per 100k People")
ggplot(s1, aes(x = date)) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
geom_line(aes(y = casespp), color = "black", size = 2) +
labs(title = t2, y = "Total / Active", x = "Date")
}
## Function to compare 3 states activepp curves
plot3active <- function(st1, st2, st3) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st1 | state == st2 | state == st3)
t1 <- deparse(substitute(st1))
t2 <- deparse(substitute(st2))
t3 <- deparse(substitute(st3))
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~state) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plot37DA <- function(st1, st2, st3) { # st = the state whose data we wish to plot
s1 <- subset(states, state == st1 | state == st2 | state == st3)
t1 <- deparse(substitute(st1))
t2 <- deparse(substitute(st2))
t3 <- deparse(substitute(st3))
t4 <- paste("7 Day Avg of New Cases")
ggplot(s1, aes(x = date)) +
facet_wrap(~state) +
geom_line(aes(y = sevdayavg), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plotUSA <- function() {
ggplot(states, aes(x = date)) +
facet_wrap(~division) +
geom_line(aes(y = sevdayavg), color = "blue", size = 2)
}
ENCentral <- states %>%
filter(division == "East North Central")
ggplot(ENCentral, aes(x = date)) +
facet_wrap(. ~ state) +
geom_line(aes(y = sevdayavg), color = "darkred", size = 2)
## Filter the latest numbers
date1 <- max(states$date)
statestoday <- filter(states, states$date == date1)
fastestgrowth <- statestoday %>%
select(state, weeklygrowthpp) %>%
arrange(desc(weeklygrowthpp))
library(ggplot2)
ggplot(states2$Oklahoma, aes(x = date)) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
geom_line(aes(y = casespp), color = "black", size = 2) +
geom_line(aes(y = recoveredpp), color = "red", size = 2)
## 3. Load the counties data ----
## **Don't try and load the counties data and the states data at the same time**
# Clear the environment to make some space - this file contains many observations
rm(list = ls())
## set working dir
setwd("C:/Users/jayso/OneDrive/Desktop/covid-data")
## call required libraries
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(data.table)
# Read the counties data
counties <- fread("C:/Users/jayso/OneDrive/Documents/GitHub/covid-19-data/us-counties.csv")
## NYC/KC/territory fix
counties$fips[counties$county == "New York City"] = 361111
counties$fips[counties$county == "Kansas City"] = 291111
counties$fips[counties$state == "Puerto Rico"] = 7200
counties$fips[counties$state == "Guam"] = 6600
counties$fips[counties$state == "Virgin Islands"] = 7800
counties$fips[counties$state == "Northern Mariana Islands"] = 6900
counties <- counties[counties$county != "Unknown"]
## Read county population values
countydemo <- fread("./demobyfips.csv")
## merge county data with county population
counties <- left_join(counties, countydemo, by = "fips", all.x = TRUE)
## filter to cities in/around Mississippi
counties <- counties[counties$state == "Mississippi"]
MScitynames <- countiesMS$csba
MScitynames <- unique(MScitynames)
MScitynames <- MScitynames[MScitynames != ""]
counties <- counties[counties$csba %in% MScitynames]
ggplot(counties, aes(x = date)) +
geom_line(aes(y = activepp), col = "blue", size = 2)
x <- round(runif(1000, 0, 100))
y <- x + rnorm(1000, 0, 30)
summary(y)
sd(y)
range(y)
mean(y)
qqplot(x, y)
plot(ecdf(x))
xy <- sample(x, 200)
mean(x)
summary(xy)
plot(ecdf(xy))
## Make the date column a format that R will recognize
counties$date <- as.Date(counties$date)
## Add a column for yesterday's cases (with timer so I have a benchmark for judging improvements)
counties$adayago2 <- for (i in 1:nrow(counties))
sapply(seq_len(nrow(counties)),
function(x) with(counties,))
rep(c("a", "b", "c"))
num_repeat <- 10
dates <- data.frame(rep(
seq(as.Date('2017-01-01'), as.Date('2017-12-31'), by = 'days'),
times = num_repeat))
system.time({
counties$adayago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
counties$aweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 7) & fips == fips[x]], na.rm = TRUE)))
counties$thrweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(cases[date == (date[x] - 21) & fips == fips[x]], na.rm = TRUE)))})
counties$active <- counties$cases - counties$thrweekago
counties$activepp <- counties$active / counties$population * 100000
counties$newcases <- counties$cases - counties$adayago
system.time({
counties$svnDAnc <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
mean(newcases[date >= (date[x] - 7) & date <= date[x] & fips == fips[x]], na.rm = TRUE)))
counties$svnDAlast <-
sapply(seq_len(nrow(counties)),
function(sumx) with(counties,
sum(svnDAnc[date == (date[sumx] - 7) & fips == fips[sumx]], na.rm = TRUE)))})
system.time({
counties$actadayago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(active[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
counties$actaweekago <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
sum(active[date == (date[x] - 7) & fips == fips[x]], na.rm = TRUE)))
counties$actgrowthpp <- (counties$active - counties$actaweekago) / counties$population *100000
})
countiesDT$newcases <- ifelse(countiesDT$cases - countiesDT$adayago < 0, 0, countiesDT$cases - countiesDT$adayago)
countiesDT$newcasesW <- ifelse(countiesDT$cases - countiesDT$aweekago < 0, 0, countiesDT$cases - countiesDT$aweekago)
countiesDT$active <- ifelse(countiesDT$cases - countiesDT$thrweekago < 0, 0, countiesDT$cases - countiesDT$thrweekago)
data.frame(Count = colSums(df[,-1] == 'Y'), # count of "Y"s in each column
# sum of Values column where A/B/C is "Y"
Sum = sapply(df[,-1], function(x){sum(df$Values[x == 'Y'])}))
RI$adayago <- sapply(seq_len(nrow(RI)),
function(x) with(RI,
sum(cases[date == (date[x] - 1) & fips == fips[x]], na.rm = TRUE)))
#### FYI - system.time call - user, 519, system, 109, elapsed 630.33
countiesDT$newcases <- countiesDT$cases - countiesDT$adayago
### A week ago - with system.time call
system.time({
countiesDT$aweekago <- sapply(seq_len(nrow(countiesDT)),
function(ayer) with(countiesDT,
sum(cases[date == (date[ayer] - 7) & fips == fips[ayer]])))})
## user system elapsed
## 526.89 101.89 631.96
system.time({
TX$aweekago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(cases[date == (date[x] - 7) & fips == fips[x]])))})
system.time({
TX$thrwkago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(cases[date == (date[x] - 21) & fips == fips[x]])))})
TX$actthrwkago <- sapply(seq_len(nrow(TX)),
function(x) with(TX,
sum(active[date == (date[x] - 21) & fips == fips[x]])))
system.time({
RI$aweekago <- lapply(seq_len(nrow(RI)),
function(ayer) with(RI,
sum(cases[date == (date[ayer] - 7) & fips == fips[ayer]], na.rm = TRUE)))})
## group by city
latest <- max(counties$date)
basic_summ = filter(counties, date == as.Date(latest))
basic_summ$csba <- as.factor(basic_summ$csba)
basic_summ$csa <- as.factor(basic_summ$csa)
basic_summ = group_by(basic_summ, csba, csa)
basic_summ = summarise(basic_summ, sumcases = sum(cases), sumactive = sum(active),
sumpop = sum(population), activepp = sum(active) / sum(population) * 100000)
summary(basic_summ)
## group by state
state_summ = filter(counties, date == as.Date(latest))
state_summ = group_by(state_summ, state)
state_summ = summarise(state_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T), activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
state_summ <- state_summ[state_summ$sumpop > 0,]
## group by division
div_summ = filter(counties, date == as.Date(latest))
div_summ = group_by(div_summ, division)
div_summ = summarise(div_summ, sumcases = sum(cases, na.rm = T), sumactive = sum(active, na.rm = T),
sumpop = sum(population, na.rm = T),
activepp = sum(active, na.rm = T) / sum(population, na.rm = T) * 100000)
## filter by state
pkstate <- function(pk) {
stname <- deparse(substitute(pk))
stname2 <- noquote(stname)
sta1 <- filter(counties, state == pk)
assign(stname2, sta1, env=.GlobalEnv)
}
getwd()
## Filter the latest numbers
date1 <- max(TX$date)
countiestoday <- filter(TX, TX$date == date1)
fastestgrowth <- countiestoday %>%
select(county, activegrowth) %>%
arrange(desc(activegrowth))
## 4. Plot some county stuff ----
plot3county <- function(co1, co2, co3) { # co = the counties whose data we wish to plot
s1 <- subset(counties, county == co1 | county == co2 | county == co3)
t1 <- deparse(substitute(co1))
t2 <- deparse(substitute(co2))
t3 <- deparse(substitute(co3))
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~county) +
geom_line(aes(y = activepp), color = "blue", size = 2) +
labs(title = t4, x = "Date")
}
plot3fips <- function(f1, f2, f3) { # co = the counties whose data we wish to plot
s1 <- subset(counties, fips == f1 | fips == f2 | fips == f3)
t4 <- paste("Active per 100K")
ggplot(s1, aes(x = date)) +
facet_wrap(~county) +
geom_line(aes(y = active), color = "blue", size = 1) +
labs(title = t4, x = "Date")
}
counties <- counties %>% group_by(csba)
ggplot(counties, aes(x = date)) +
facet_wrap(. ~ csa) +
geom_line(aes(y = activepp), color = "orange", size = 1)
today <- counties %>% filter(date == "2020-08-23")
today$log <- log(today$activepp)
meanlog <- mean(today$log)
today$sd <- sd(today$activepp)
sqrtsd <- sqrt(175.5)
today$log2 <- (today$log - meanlog)/sqrtsd
boxplot(today$log2)
max(counties$date)
cutpoints <- quantile(today$activepp, seq(0, 1, length = 10), na.rm = TRUE)
today$activecut <- cut(today$activepp, cutpoints)
levels(today$activecut)
### 5. Cross validation ------
library(ggplot2)
library(ggthemes)
library(zoo)
library(xts)
library(quantmod)
library(forecast)
library(fpp)
library(fpp2)
library(tidyverse)
library(caret)
set.seed(123)
CFR5 <- read.csv("./CFR3.csv")
train.control <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
model <- train(CFR ~., data = CFRdata1, method = "lm", trControl = train.control)
print(model)
usa$date <- as.Date(usa$date)
usa$thrwksago <-
sapply(seq_len(nrow(usa)),
function(x) with(usa,
sum(cases[date == (date[x] - 21)], na.rm = TRUE)))
## DT
counties$peaktodate <-
sapply(seq_len(nrow(counties)),
function(x) with(counties,
max(activepp[date <= date[x] & fips == fips[x]], na.rm = TRUE)))
countiesDT <- data.table(counties)
countiesDT[, peaked := activepp != peaktodate]
countiesDT[, actppvstavg := {tmp <- sapply(seq_len(nrow(countiesDT)),
function(x) with(countiesDT,
mean(activepp[date <= (date[x] - 1) & state == state[x]])));
activepp / tmp}]
x <- round(runif(1000, 0, 100))
x
quantile(x)
data(iris)
head(Iris)
head(iris)
do.call("rbind", tapply(iris$Sepal.Length,
iris$Species,
quantile))
statesquant <- do.call("rbind", tapply(statestoday$activepp,
statestoday$state,
quantile)) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AMOUNTAIN.R
\name{networkSimulation}
\alias{networkSimulation}
\title{Illustration of weighted network simulation}
\usage{
networkSimulation(n, k, theta)
}
\arguments{
\item{n}{number of nodes in the network}
\item{k}{number of nodes in the module, n < k}
\item{theta}{module node score follow the uniform distribution in range [theta,1]}
}
\value{
a list containing network adjacency matrix, node score and module membership
}
\description{
Simulate a single weighted network
}
\examples{
pp <- networkSimulation(100,20,0.5)
moduleid <- pp[[3]]
netid <- 1:100
restp<- netid[-moduleid]
groupdesign=list(moduleid,restp)
names(groupdesign)=c('module','background')
\dontrun{library(qgraph)
pg<-qgraph(pp[[1]],groups=groupdesign,legend=TRUE)}
}
\author{
Dong Li, \email{dxl466@cs.bham.ac.uk}
}
\keyword{simulation}
| /man/networkSimulation.Rd | no_license | fairmiracle/AMOUNTAIN | R | false | true | 893 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AMOUNTAIN.R
\name{networkSimulation}
\alias{networkSimulation}
\title{Illustration of weighted network simulation}
\usage{
networkSimulation(n, k, theta)
}
\arguments{
\item{n}{number of nodes in the network}
\item{k}{number of nodes in the module, n < k}
\item{theta}{module node score follow the uniform distribution in range [theta,1]}
}
\value{
a list containing network adjacency matrix, node score and module membership
}
\description{
Simulate a single weighted network
}
\examples{
pp <- networkSimulation(100,20,0.5)
moduleid <- pp[[3]]
netid <- 1:100
restp<- netid[-moduleid]
groupdesign=list(moduleid,restp)
names(groupdesign)=c('module','background')
\dontrun{library(qgraph)
pg<-qgraph(pp[[1]],groups=groupdesign,legend=TRUE)}
}
\author{
Dong Li, \email{dxl466@cs.bham.ac.uk}
}
\keyword{simulation}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plausibleValues.R
\name{plausibleValues}
\alias{plausibleValues}
\title{Plausible-Values Imputation of Factor Scores Estimated from a lavaan Model}
\usage{
plausibleValues(object, nDraws = 20L, seed = 12345,
omit.imps = c("no.conv", "no.se"), ...)
}
\arguments{
\item{object}{A fitted model of class \code{\linkS4class{lavaan}},
\code{\link[blavaan]{blavaan}}, or \code{\linkS4class{lavaan.mi}}}
\item{nDraws}{\code{integer} specifying the number of draws, analogous to
the number of imputed data sets. If \code{object} is of class
\code{\linkS4class{lavaan.mi}}, this will be the number of draws taken
\emph{per imputation}. Ignored if \code{object} is of class
\code{\link[blavaan]{blavaan}}, in which case the number of draws is the
number of MCMC samples from the posterior.}
\item{seed}{\code{integer} passed to \code{\link{set.seed}()}. Ignored if
\code{object} is of class \code{\link[blavaan]{blavaan}},}
\item{omit.imps}{\code{character} vector specifying criteria for omitting
imputations when \code{object} is of class \code{\linkS4class{lavaan.mi}}.
Can include any of \code{c("no.conv", "no.se", "no.npd")}.}
\item{...}{Optional arguments to pass to \code{\link[lavaan]{lavPredict}}.
\code{assemble} will be ignored because multiple groups are always
assembled into a single \code{data.frame} per draw. \code{type} will be
ignored because it is set internally to \code{type="lv"}.}
}
\value{
A \code{list} of length \code{nDraws}, each of which is a
\code{data.frame} containing plausible values, which can be treated as
a \code{list} of imputed data sets to be passed to \code{\link{runMI}}
(see \bold{Examples}). If \code{object} is of class
\code{\linkS4class{lavaan.mi}}, the \code{list} will be of length
\code{nDraws*m}, where \code{m} is the number of imputations.
}
\description{
Draw plausible values of factor scores estimated from a fitted
\code{\link[lavaan]{lavaan}} model, then treat them as multiple imputations
of missing data using \code{\link{runMI}}.
}
\details{
Because latent variables are unobserved, they can be considered as missing
data, which can be imputed using Monte Carlo methods. This may be of
interest to researchers with sample sizes too small to fit their complex
structural models. Fitting a factor model as a first step,
\code{\link[lavaan]{lavPredict}} provides factor-score estimates, which can
be treated as observed values in a path analysis (Step 2). However, the
resulting standard errors and test statistics could not be trusted because
the Step-2 analysis would not take into account the uncertainty about the
estimated factor scores. Using the asymptotic sampling covariance matrix
of the factor scores provided by \code{\link[lavaan]{lavPredict}},
\code{plausibleValues} draws a set of \code{nDraws} imputations from the
sampling distribution of each factor score, returning a list of data sets
that can be treated like multiple imputations of incomplete data. If the
data were already imputed to handle missing data, \code{plausibleValues}
also accepts an object of class \code{\linkS4class{lavaan.mi}}, and will
draw \code{nDraws} plausible values from each imputation. Step 2 would
then take into account uncertainty about both missing values and factor
scores. Bayesian methods can also be used to generate factor scores, as
available with the \pkg{blavaan} package, in which case plausible
values are simply saved parameters from the posterior distribution. See
Asparouhov and Muthen (2010) for further technical details and references.
Each returned \code{data.frame} includes a \code{case.idx} column that
indicates the corresponding rows in the data set to which the model was
originally fitted (unless the user requests only Level-2 variables). This
can be used to merge the plausible values with the original observed data,
but users should note that including any new variables in a Step-2 model
might not accurately account for their relationship(s) with factor scores
because they were not accounted for in the Step-1 model from which factor
scores were estimated.
If \code{object} is a multilevel \code{lavaan} model, users can request
plausible values for latent variables at particular levels of analysis by
setting the \code{\link[lavaan]{lavPredict}} argument \code{level=1} or
\code{level=2}. If the \code{level} argument is not passed via \dots,
then both levels are returned in a single merged data set per draw. For
multilevel models, each returned \code{data.frame} also includes a column
indicating to which cluster each row belongs (unless the user requests only
Level-2 variables).
}
\examples{
## example from ?cfa and ?lavPredict help pages
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit1 <- cfa(HS.model, data = HolzingerSwineford1939)
fs1 <- plausibleValues(fit1, nDraws = 3,
## lavPredict() can add only the modeled data
append.data = TRUE)
lapply(fs1, head)
## To merge factor scores to original data.frame (not just modeled data)
fs1 <- plausibleValues(fit1, nDraws = 3)
idx <- lavInspect(fit1, "case.idx") # row index for each case
if (is.list(idx)) idx <- do.call(c, idx) # for multigroup models
data(HolzingerSwineford1939) # copy data to workspace
HolzingerSwineford1939$case.idx <- idx # add row index as variable
## loop over draws to merge original data with factor scores
for (i in seq_along(fs1)) {
fs1[[i]] <- merge(fs1[[i]], HolzingerSwineford1939, by = "case.idx")
}
lapply(fs1, head)
## multiple-group analysis, in 2 steps
step1 <- cfa(HS.model, data = HolzingerSwineford1939, group = "school",
group.equal = c("loadings","intercepts"))
PV.list <- plausibleValues(step1)
## subsequent path analysis
path.model <- ' visual ~ c(t1, t2)*textual + c(s1, s2)*speed '
\dontrun{
step2 <- sem.mi(path.model, data = PV.list, group = "school")
## test equivalence of both slopes across groups
lavTestWald.mi(step2, constraints = 't1 == t2 ; s1 == s2')
}
## multilevel example from ?Demo.twolevel help page
model <- '
level: 1
fw =~ y1 + y2 + y3
fw ~ x1 + x2 + x3
level: 2
fb =~ y1 + y2 + y3
fb ~ w1 + w2
'
msem <- sem(model, data = Demo.twolevel, cluster = "cluster")
mlPVs <- plausibleValues(msem, nDraws = 3) # both levels by default
lapply(mlPVs, head, n = 10)
## only Level 1
mlPV1 <- plausibleValues(msem, nDraws = 3, level = 1)
lapply(mlPV1, head)
## only Level 2
mlPV2 <- plausibleValues(msem, nDraws = 3, level = 2)
lapply(mlPV2, head)
}
\references{
Asparouhov, T. & Muthen, B. O. (2010). \emph{Plausible values for latent
variables using M}plus. Technical Report. Retrieved from
www.statmodel.com/download/Plausible.pdf
}
\seealso{
\code{\link{runMI}}, \code{\linkS4class{lavaan.mi}}
}
\author{
Terrence D. Jorgensen (University of Amsterdam;
\email{TJorgensen314@gmail.com})
}
| /semTools/man/plausibleValues.Rd | no_license | HarmanjitS/semTools | R | false | true | 6,979 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plausibleValues.R
\name{plausibleValues}
\alias{plausibleValues}
\title{Plausible-Values Imputation of Factor Scores Estimated from a lavaan Model}
\usage{
plausibleValues(object, nDraws = 20L, seed = 12345,
omit.imps = c("no.conv", "no.se"), ...)
}
\arguments{
\item{object}{A fitted model of class \code{\linkS4class{lavaan}},
\code{\link[blavaan]{blavaan}}, or \code{\linkS4class{lavaan.mi}}}
\item{nDraws}{\code{integer} specifying the number of draws, analogous to
the number of imputed data sets. If \code{object} is of class
\code{\linkS4class{lavaan.mi}}, this will be the number of draws taken
\emph{per imputation}. Ignored if \code{object} is of class
\code{\link[blavaan]{blavaan}}, in which case the number of draws is the
number of MCMC samples from the posterior.}
\item{seed}{\code{integer} passed to \code{\link{set.seed}()}. Ignored if
\code{object} is of class \code{\link[blavaan]{blavaan}},}
\item{omit.imps}{\code{character} vector specifying criteria for omitting
imputations when \code{object} is of class \code{\linkS4class{lavaan.mi}}.
Can include any of \code{c("no.conv", "no.se", "no.npd")}.}
\item{...}{Optional arguments to pass to \code{\link[lavaan]{lavPredict}}.
\code{assemble} will be ignored because multiple groups are always
assembled into a single \code{data.frame} per draw. \code{type} will be
ignored because it is set internally to \code{type="lv"}.}
}
\value{
A \code{list} of length \code{nDraws}, each of which is a
\code{data.frame} containing plausible values, which can be treated as
a \code{list} of imputed data sets to be passed to \code{\link{runMI}}
(see \bold{Examples}). If \code{object} is of class
\code{\linkS4class{lavaan.mi}}, the \code{list} will be of length
\code{nDraws*m}, where \code{m} is the number of imputations.
}
\description{
Draw plausible values of factor scores estimated from a fitted
\code{\link[lavaan]{lavaan}} model, then treat them as multiple imputations
of missing data using \code{\link{runMI}}.
}
\details{
Because latent variables are unobserved, they can be considered as missing
data, which can be imputed using Monte Carlo methods. This may be of
interest to researchers with sample sizes too small to fit their complex
structural models. Fitting a factor model as a first step,
\code{\link[lavaan]{lavPredict}} provides factor-score estimates, which can
be treated as observed values in a path analysis (Step 2). However, the
resulting standard errors and test statistics could not be trusted because
the Step-2 analysis would not take into account the uncertainty about the
estimated factor scores. Using the asymptotic sampling covariance matrix
of the factor scores provided by \code{\link[lavaan]{lavPredict}},
\code{plausibleValues} draws a set of \code{nDraws} imputations from the
sampling distribution of each factor score, returning a list of data sets
that can be treated like multiple imputations of incomplete data. If the
data were already imputed to handle missing data, \code{plausibleValues}
also accepts an object of class \code{\linkS4class{lavaan.mi}}, and will
draw \code{nDraws} plausible values from each imputation. Step 2 would
then take into account uncertainty about both missing values and factor
scores. Bayesian methods can also be used to generate factor scores, as
available with the \pkg{blavaan} package, in which case plausible
values are simply saved parameters from the posterior distribution. See
Asparouhov and Muthen (2010) for further technical details and references.
Each returned \code{data.frame} includes a \code{case.idx} column that
indicates the corresponding rows in the data set to which the model was
originally fitted (unless the user requests only Level-2 variables). This
can be used to merge the plausible values with the original observed data,
but users should note that including any new variables in a Step-2 model
might not accurately account for their relationship(s) with factor scores
because they were not accounted for in the Step-1 model from which factor
scores were estimated.
If \code{object} is a multilevel \code{lavaan} model, users can request
plausible values for latent variables at particular levels of analysis by
setting the \code{\link[lavaan]{lavPredict}} argument \code{level=1} or
\code{level=2}. If the \code{level} argument is not passed via \dots,
then both levels are returned in a single merged data set per draw. For
multilevel models, each returned \code{data.frame} also includes a column
indicating to which cluster each row belongs (unless the user requests only
Level-2 variables).
}
\examples{
## example from ?cfa and ?lavPredict help pages
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit1 <- cfa(HS.model, data = HolzingerSwineford1939)
fs1 <- plausibleValues(fit1, nDraws = 3,
## lavPredict() can add only the modeled data
append.data = TRUE)
lapply(fs1, head)
## To merge factor scores to original data.frame (not just modeled data)
fs1 <- plausibleValues(fit1, nDraws = 3)
idx <- lavInspect(fit1, "case.idx") # row index for each case
if (is.list(idx)) idx <- do.call(c, idx) # for multigroup models
data(HolzingerSwineford1939) # copy data to workspace
HolzingerSwineford1939$case.idx <- idx # add row index as variable
## loop over draws to merge original data with factor scores
for (i in seq_along(fs1)) {
fs1[[i]] <- merge(fs1[[i]], HolzingerSwineford1939, by = "case.idx")
}
lapply(fs1, head)
## multiple-group analysis, in 2 steps
step1 <- cfa(HS.model, data = HolzingerSwineford1939, group = "school",
group.equal = c("loadings","intercepts"))
PV.list <- plausibleValues(step1)
## subsequent path analysis
path.model <- ' visual ~ c(t1, t2)*textual + c(s1, s2)*speed '
\dontrun{
step2 <- sem.mi(path.model, data = PV.list, group = "school")
## test equivalence of both slopes across groups
lavTestWald.mi(step2, constraints = 't1 == t2 ; s1 == s2')
}
## multilevel example from ?Demo.twolevel help page
model <- '
level: 1
fw =~ y1 + y2 + y3
fw ~ x1 + x2 + x3
level: 2
fb =~ y1 + y2 + y3
fb ~ w1 + w2
'
msem <- sem(model, data = Demo.twolevel, cluster = "cluster")
mlPVs <- plausibleValues(msem, nDraws = 3) # both levels by default
lapply(mlPVs, head, n = 10)
## only Level 1
mlPV1 <- plausibleValues(msem, nDraws = 3, level = 1)
lapply(mlPV1, head)
## only Level 2
mlPV2 <- plausibleValues(msem, nDraws = 3, level = 2)
lapply(mlPV2, head)
}
\references{
Asparouhov, T. & Muthen, B. O. (2010). \emph{Plausible values for latent
variables using M}plus. Technical Report. Retrieved from
www.statmodel.com/download/Plausible.pdf
}
\seealso{
\code{\link{runMI}}, \code{\linkS4class{lavaan.mi}}
}
\author{
Terrence D. Jorgensen (University of Amsterdam;
\email{TJorgensen314@gmail.com})
}
|
# plot tuning performance
library(ggplot2)
library(gridExtra)
source(file = "helperfunctions.R")
load(file = "./data/cv_lists-nnet-tuning.RData")
parameters = expand.grid("size" = seq(from = 3, to = 15, by = 2),
"decay" = c(0.01, 0.1, 0.5, 0.8, 1))
# extract results from tuning with helperfunction
loss = helper.cvlist.tune(cv.list.u)
x = 1:length(loss[[1]])
# 3x2 plot for each tau-category
df = data.frame(loss[[1]])/10000
blank.x = theme(axis.title.x = element_blank())
caption.b = labs(y = "loss [10.000]") # only y-axis
caption = labs(x = "parameter index", # x- and y-axis
y = "loss [10.000]")
# text inside plot (for tau-category 1)
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 1")
# sub-plot for tau-category 1
g1 = ggplot(df, aes(x=x, y=loss[[1]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
# sub-plots for tau-category 2:6
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 2")
g2 = ggplot(df, aes(x=x, y=loss[[2]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 3")
g3 = ggplot(df, aes(x=x, y=loss[[3]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 4")
g4 = ggplot(df, aes(x=x, y=loss[[4]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 5")
g5 = ggplot(df, aes(x=x, y=loss[[5]]/10000)) + geom_point() + caption + text + theme_bw()
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 6")
g6 = ggplot(df, aes(x=x, y=loss[[6]]/10000)) + geom_point() + caption + text + theme_bw()
# combine plots
plot = grid.arrange(g1, g2 , g3, g4, g5, g6, nrow = 3, ncol = 2)
# save plot as pdf
width = 2
hight = 1
b = 11
ggsave(file="./graphs/plots/nnet_tuning.pdf", plot,
width = b*width, height = b*hight, dpi = 150, units = "cm", device='pdf')
dev.off()
# combine tau-categories
# when combining the graphs from previous plot, it is important to
# normalize the loss of each category to 1 since the loss depends on
# the item_price, as well as the tau-category.
# higher tau-category leads naturally to a higher loss
su = (df[,1]*(-1))/max(df[,1]*(-1)) # tau-category 1
su = loss[[1]]*(-1)/max(loss[[1]]*(-1))
for (i in 2:6){
su = su + (loss[[i]]*(-1))/max(loss[[i]]*(-1))
}
su = su/6 * (-1) # normalize and change sign
# to compare shape to previous plots
su2 = data.frame(su) # change to data.frame for ggplot()
caption = labs(x = "parameter index",
y = "normed loss")
plot = ggplot(su2, aes(x=x, y=su2)) + geom_point() + caption + theme_bw()
width = 1
hight = 1/2
b = 11
ggsave(file="./graphs/plots/nnet-normed_sum.pdf", plot,
width = b*width, height = b*hight, dpi = 150, units = "cm", device='pdf')
plot
dev.off()
parameters[which.max(su2[[1]]),] # best settings for nnet
df[which.max(su2[[1]]),] # results for best settings
| /submission/graphs/nnet_tuning_graph.R | no_license | fractaldust/SPL_DFK | R | false | false | 3,666 | r | # plot tuning performance
library(ggplot2)
library(gridExtra)
source(file = "helperfunctions.R")
load(file = "./data/cv_lists-nnet-tuning.RData")
parameters = expand.grid("size" = seq(from = 3, to = 15, by = 2),
"decay" = c(0.01, 0.1, 0.5, 0.8, 1))
# extract results from tuning with helperfunction
loss = helper.cvlist.tune(cv.list.u)
x = 1:length(loss[[1]])
# 3x2 plot for each tau-category
df = data.frame(loss[[1]])/10000
blank.x = theme(axis.title.x = element_blank())
caption.b = labs(y = "loss [10.000]") # only y-axis
caption = labs(x = "parameter index", # x- and y-axis
y = "loss [10.000]")
# text inside plot (for tau-category 1)
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 1")
# sub-plot for tau-category 1
g1 = ggplot(df, aes(x=x, y=loss[[1]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
# sub-plots for tau-category 2:6
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 2")
g2 = ggplot(df, aes(x=x, y=loss[[2]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 3")
g3 = ggplot(df, aes(x=x, y=loss[[3]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 4")
g4 = ggplot(df, aes(x=x, y=loss[[4]]/10000)) + geom_point() + caption.b + text + theme_bw() + blank.x
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 5")
g5 = ggplot(df, aes(x=x, y=loss[[5]]/10000)) + geom_point() + caption + text + theme_bw()
text = annotate(geom = "text", x=Inf, y=-Inf,hjust = 1, vjust = 0,
color = "red", size = 4,
label = "tau_c = 6")
g6 = ggplot(df, aes(x=x, y=loss[[6]]/10000)) + geom_point() + caption + text + theme_bw()
# combine plots
plot = grid.arrange(g1, g2 , g3, g4, g5, g6, nrow = 3, ncol = 2)
# save plot as pdf
width = 2
hight = 1
b = 11
ggsave(file="./graphs/plots/nnet_tuning.pdf", plot,
width = b*width, height = b*hight, dpi = 150, units = "cm", device='pdf')
dev.off()
# combine tau-categories
# when combining the graphs from previous plot, it is important to
# normalize the loss of each category to 1 since the loss depends on
# the item_price, as well as the tau-category.
# higher tau-category leads naturally to a higher loss
su = (df[,1]*(-1))/max(df[,1]*(-1)) # tau-category 1
su = loss[[1]]*(-1)/max(loss[[1]]*(-1))
for (i in 2:6){
su = su + (loss[[i]]*(-1))/max(loss[[i]]*(-1))
}
su = su/6 * (-1) # normalize and change sign
# to compare shape to previous plots
su2 = data.frame(su) # change to data.frame for ggplot()
caption = labs(x = "parameter index",
y = "normed loss")
plot = ggplot(su2, aes(x=x, y=su2)) + geom_point() + caption + theme_bw()
width = 1
hight = 1/2
b = 11
ggsave(file="./graphs/plots/nnet-normed_sum.pdf", plot,
width = b*width, height = b*hight, dpi = 150, units = "cm", device='pdf')
plot
dev.off()
parameters[which.max(su2[[1]]),] # best settings for nnet
df[which.max(su2[[1]]),] # results for best settings
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/sampling.R
\name{betasUnder}
\alias{betasUnder}
\title{betasUnder}
\usage{
betasUnder(y, positive = 1, N = 10, method = "perc")
}
\arguments{
\item{y}{response variable}
\item{positive}{value of the positive (minority) class}
\item{N}{number of values of beta to return}
\item{method}{method to compute beta: perc or prob}
}
\value{
values of beta for a give response variable
}
\description{
Defines the possible levels of undersampling given the class proporiton
}
\examples{
y <- rep(c(1, 0, 1, 0, 0, 0), 100)
betasUnder(y, positive=1, N = 10, method="perc")
}
| /man/betasUnder.Rd | no_license | dalpozz/warping | R | false | false | 654 | rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/sampling.R
\name{betasUnder}
\alias{betasUnder}
\title{betasUnder}
\usage{
betasUnder(y, positive = 1, N = 10, method = "perc")
}
\arguments{
\item{y}{response variable}
\item{positive}{value of the positive (minority) class}
\item{N}{number of values of beta to return}
\item{method}{method to compute beta: perc or prob}
}
\value{
values of beta for a give response variable
}
\description{
Defines the possible levels of undersampling given the class proporiton
}
\examples{
y <- rep(c(1, 0, 1, 0, 0, 0), 100)
betasUnder(y, positive=1, N = 10, method="perc")
}
|
#### Subsetting ####
#### 1. Integers ####
# Positive integers behave just like *ij* notation in linear algebra.
# Lets work with an example data frame `df` inspired from the Beatles.
df <- data.frame(name=c("John","Paul","George","Ringo"),
birth=c(1940, 1942, 1943, 1940),
insturment=c("guitar", "bass","guitar","drums") )
df
# The following gives the element in 2nd row and 3rd column.
df[2,3]
#### Multiple integers to subset ####
# By giving vectors as indices, we can also get multiple rows and columns, giving multiple elements.
# The following gives the elements that are in 2nd and 4th rows and the 3rd column.
df[c(2,4),3]
# The following gives the elements that are in 2nd & 4th rows and 2nd & 3rd columns.
df[c(2,4),c(2,3)]
# When selecting a series of rows and columns in a sequential manner, `:` becomes very handy.
# (Remember: `start:end` gives us a sequence of integers. For example `1:10` gives a vector of integers from 1 to 10.)
# Select rows from 1 to 3 and columns from 1 to 2.
df[1:3, 1:2]
#### Repeating integers ####
# repeating input repeats output.
df[c(1,1,1,2,2), 1:3]
#### Integer `0` ####
# As an index, **zero will return nothing** from a dimension. This creates an empty object.
df[1:2,0]
#### Negative integers ####
# Negative integers return **everything but** the elements at the specified locations.
# You cannot use both negative and positive integers in the same dimension
# Exclude rows 2 to 4 and select columns 2 to 3.
df[-c(2:4), 2:3]
# Exclude rows 2 to 4 and select columns 2 to 3.
df[-c(2:4), 2:3]
#### 2. Blank Spaces ####
# Blank spaces return **everything**
# (i.e., no subsetting occurs on that dimension)
vec <- c(6,1,3,6,10,5)
vec[]
# Return every element on row 1
df[1,]
# Return every element on column 2
df[,2]
#### 3. Names ####
# If your object has names, you can ask for elements or columns back by name. (We talked about this in the first session)
names(vec) <- c("a","b","c","d","e","f")
vec
# Now I can call elements in `vec` by their names
vec[c("a","b","d")]
# Same applies to columns and column names
df[ ,"birth"]
df[ ,c("name","birth")]
#### 4. Logical ####
# You can subset with a logical vector of the **same length as the dimension** you are subsetting. Each element that corresponds to a TRUE will be returned.
# The following will return 2nd, 4th and 5th elements in `vec`
vec[c(FALSE,TRUE,FALSE,TRUE,TRUE,FALSE)]
# The following will return **2nd, 3rd rows** in `df`
df[c(FALSE,TRUE,TRUE,FALSE), ]
#### Subsetting Lists ####
# Subsetting lists can get tricky, since a list can contain objects that has one or more dimensions.
lst <- list(c(1,2), TRUE, c("a", "b", "c"))
lst
# What is the difference?
lst[c(1,2)] # outputs a list with 2 objects
lst[1] # outputs a list with 1 object
lst[[1]] # outputs a vector
### Easiest way to subset data frames and lists: `$` sign
# The most common syntax for subsetting lists and data frames:
# When we have a names list such as
names(lst) <- c("alpha", "beta", "gamma")
lst
# then we can subset objects with `$`
lst$alpha
lst$beta
lst$gamma
# It also works to subset columns of a data frame
df$birth
#### R Packages ####
install.packages(c("ggplot2", "maps", "RColorBrewer"))
library("ggplot2")
library("maps")
library("RColorBrewer")
#### Diamonds ####
diamonds <- data.frame(diamonds)
# first 6 rows
diamonds[1:6,]
# last 6 rows
dim(diamonds)
ncol(diamonds) # number of columns
#one way
nrow(diamonds) # number of rows
nrow(diamonds) - 6
diamonds[53934:53940,]
# another way
diamonds[(nrow(diamonds)-6):nrow(diamonds),]
diamonds[-( 1: (nrow(diamonds)-6) ), ]
# easiest way to check the first and last 5 rows
head(diamonds, 20)
tail(diamonds)
# view data
View(diamonds) # notice: Capital V
## Help pages ##
# You can open the help page for any R object (including functions) by typing `?`
# followed by the object's name
?diamonds
#### Logical tests ####
#### Logical comparisons ####
# What will these return?
1< 3
1> 3
c(1, 2, 3, 4, 5) > 3
#### %in% ####
# What does this do?
1 %in% c(1, 2, 3, 4)
1 %in% c(2, 3, 4)
c(3,4,5,6) %in% c(2, 3, 4)
#### Boolean operators ####
?Logic
x <- 4
# & -- and
x > 2 & x < 9
x > 5 & x < 9
x > 5 & x < 2
TRUE & TRUE
TRUE & TRUE & TRUE & TRUE
TRUE & TRUE & FALSE & TRUE
# | -- or
TRUE | TRUE
TRUE | FALSE
FALSE | FALSE
x > 2 | x < 9
x > 5 | x < 9
x > 5 | x < 2
# xor -- Is either condition 1 or condition 2 true, but not both?
xor(TRUE, TRUE)
xor(TRUE, FALSE)
xor(FALSE, TRUE)
xor(FALSE, FALSE)
xor(x > 2, x < 9)
xor(x > 5, x < 9)
xor(x > 5, x < 2)
# ! -- Negation
!c(TRUE, TRUE)
!c(TRUE, FALSE)
!c(FALSE, FALSE)
!(x > 2, x < 9)
!(x > 5, x < 9)
!(x > 5, x < 2)
# any -- is any condition true?
any(c(TRUE, FALSE, FALSE))
any(c(FALSE, FALSE, FALSE))
any(x > 5, x < 2)
any(x > 2, x < 9)
# all -- is every condition TRUE?
all(c(TRUE, TRUE, TRUE))
all(c(TRUE, FALSE, TRUE))
all(x > 5, x < 2)
all(x > 2, x < 9)
#### Your turn ####
w <- c(-1, 0, 1)
x <- c(5, 15)
y <- "February"
z <- c("Monday", "Tuesday", "Friday")
# Is w positive?
# Is x greater than 10 and less than 20? Is object y the word February?
# Is every value in z a day of the week?
## Answers
# Is w positive?
w> 0
# Is x greater than 10 and less than 20?
10 < x & x < 20
#Is object y the word February?
y == "February"
# Is every value in z a day of the week?
all(z %in% c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday", "Sunday"))
# Common mistakes
x > 10 & < 20
y = "February"
all(z == "Monday" | "Tuesday" | "Wednesday"...)
#### Logical subsetting #####
x_zeroes <- diamonds$x == 0
head(x_zeroes)
# What will this return?
diamonds[x_zeroes, ]
#### Saving results ####
# Prints to screen
diamonds[diamonds$x > 10, ]
# Saves to new data frame
big <- diamonds[diamonds$x > 10, ]
# Overwrites existing data frame. Dangerous!
diamonds <- diamonds[diamonds$x < 10,]
diamonds <- diamonds[1, 1]
diamonds # Uh oh!
rm(diamonds)
diamonds <- data.frame(diamonds) # Phew!
#### NA behavior ####
a <- c(1, NA)
a == NA
is.na(a)
b <- c(1, 2, 3, 4, NA)
sum(b)
sum(b, na.rm = TRUE)
#### NA Assignment ####
# Before removing outliers
qplot(x, y, data = diamonds)
## Let's remove outliers ##
?diamonds
# Let's start with x
summary(diamonds$x)
# there are 0s in x
#but x is supposed to be the lenght of the diamond, it can't be 0, a diamond is a 3d object in real life
diamonds$x[diamonds$x == 0]
# we can assign NA to 0s to let qplot know that these are missing values
diamonds$x[diamonds$x == 0] <- NA
# now 0s gone from the Min. and we have NA's
summary(diamonds$x)
# Now let's do y
summary(diamonds$y)
# we can assign NA to 0s to let qplot know that these are missing values
diamonds$y[diamonds$y == 0] <- NA
# very big diamonds
y_big <- diamonds$y > 20
diamonds$y[y_big] <- NA
# outliers are assigned as NA!
summary(diamonds$y)
# let's look at our plot again
qplot(x, y, data = diamonds)
| /IntroToRSessions/RIntroductionWorkshop_July2017/Session2/Session2.R | no_license | spiralelucide/R | R | false | false | 6,997 | r |
#### Subsetting ####
#### 1. Integers ####
# Positive integers behave just like *ij* notation in linear algebra.
# Lets work with an example data frame `df` inspired from the Beatles.
df <- data.frame(name=c("John","Paul","George","Ringo"),
birth=c(1940, 1942, 1943, 1940),
insturment=c("guitar", "bass","guitar","drums") )
df
# The following gives the element in 2nd row and 3rd column.
df[2,3]
#### Multiple integers to subset ####
# By giving vectors as indices, we can also get multiple rows and columns, giving multiple elements.
# The following gives the elements that are in 2nd and 4th rows and the 3rd column.
df[c(2,4),3]
# The following gives the elements that are in 2nd & 4th rows and 2nd & 3rd columns.
df[c(2,4),c(2,3)]
# When selecting a series of rows and columns in a sequential manner, `:` becomes very handy.
# (Remember: `start:end` gives us a sequence of integers. For example `1:10` gives a vector of integers from 1 to 10.)
# Select rows from 1 to 3 and columns from 1 to 2.
df[1:3, 1:2]
#### Repeating integers ####
# repeating input repeats output.
df[c(1,1,1,2,2), 1:3]
#### Integer `0` ####
# As an index, **zero will return nothing** from a dimension. This creates an empty object.
df[1:2,0]
#### Negative integers ####
# Negative integers return **everything but** the elements at the specified locations.
# You cannot use both negative and positive integers in the same dimension
# Exclude rows 2 to 4 and select columns 2 to 3.
df[-c(2:4), 2:3]
# Exclude rows 2 to 4 and select columns 2 to 3.
df[-c(2:4), 2:3]
#### 2. Blank Spaces ####
# Blank spaces return **everything**
# (i.e., no subsetting occurs on that dimension)
vec <- c(6,1,3,6,10,5)
vec[]
# Return every element on row 1
df[1,]
# Return every element on column 2
df[,2]
#### 3. Names ####
# If your object has names, you can ask for elements or columns back by name. (We talked about this in the first session)
names(vec) <- c("a","b","c","d","e","f")
vec
# Now I can call elements in `vec` by their names
vec[c("a","b","d")]
# Same applies to columns and column names
df[ ,"birth"]
df[ ,c("name","birth")]
#### 4. Logical ####
# You can subset with a logical vector of the **same length as the dimension** you are subsetting. Each element that corresponds to a TRUE will be returned.
# The following will return 2nd, 4th and 5th elements in `vec`
vec[c(FALSE,TRUE,FALSE,TRUE,TRUE,FALSE)]
# The following will return **2nd, 3rd rows** in `df`
df[c(FALSE,TRUE,TRUE,FALSE), ]
#### Subsetting Lists ####
# Subsetting lists can get tricky, since a list can contain objects that has one or more dimensions.
lst <- list(c(1,2), TRUE, c("a", "b", "c"))
lst
# What is the difference?
lst[c(1,2)] # outputs a list with 2 objects
lst[1] # outputs a list with 1 object
lst[[1]] # outputs a vector
### Easiest way to subset data frames and lists: `$` sign
# The most common syntax for subsetting lists and data frames:
# When we have a names list such as
names(lst) <- c("alpha", "beta", "gamma")
lst
# then we can subset objects with `$`
lst$alpha
lst$beta
lst$gamma
# It also works to subset columns of a data frame
df$birth
#### R Packages ####
install.packages(c("ggplot2", "maps", "RColorBrewer"))
library("ggplot2")
library("maps")
library("RColorBrewer")
#### Diamonds ####
diamonds <- data.frame(diamonds)
# first 6 rows
diamonds[1:6,]
# last 6 rows
dim(diamonds)
ncol(diamonds) # number of columns
#one way
nrow(diamonds) # number of rows
nrow(diamonds) - 6
diamonds[53934:53940,]
# another way
diamonds[(nrow(diamonds)-6):nrow(diamonds),]
diamonds[-( 1: (nrow(diamonds)-6) ), ]
# easiest way to check the first and last 5 rows
head(diamonds, 20)
tail(diamonds)
# view data
View(diamonds) # notice: Capital V
## Help pages ##
# You can open the help page for any R object (including functions) by typing `?`
# followed by the object's name
?diamonds
#### Logical tests ####
#### Logical comparisons ####
# What will these return?
1< 3
1> 3
c(1, 2, 3, 4, 5) > 3
#### %in% ####
# What does this do?
1 %in% c(1, 2, 3, 4)
1 %in% c(2, 3, 4)
c(3,4,5,6) %in% c(2, 3, 4)
#### Boolean operators ####
?Logic
x <- 4
# & -- and
x > 2 & x < 9
x > 5 & x < 9
x > 5 & x < 2
TRUE & TRUE
TRUE & TRUE & TRUE & TRUE
TRUE & TRUE & FALSE & TRUE
# | -- or
TRUE | TRUE
TRUE | FALSE
FALSE | FALSE
x > 2 | x < 9
x > 5 | x < 9
x > 5 | x < 2
# xor -- Is either condition 1 or condition 2 true, but not both?
xor(TRUE, TRUE)
xor(TRUE, FALSE)
xor(FALSE, TRUE)
xor(FALSE, FALSE)
xor(x > 2, x < 9)
xor(x > 5, x < 9)
xor(x > 5, x < 2)
# ! -- Negation
!c(TRUE, TRUE)
!c(TRUE, FALSE)
!c(FALSE, FALSE)
!(x > 2, x < 9)
!(x > 5, x < 9)
!(x > 5, x < 2)
# any -- is any condition true?
any(c(TRUE, FALSE, FALSE))
any(c(FALSE, FALSE, FALSE))
any(x > 5, x < 2)
any(x > 2, x < 9)
# all -- is every condition TRUE?
all(c(TRUE, TRUE, TRUE))
all(c(TRUE, FALSE, TRUE))
all(x > 5, x < 2)
all(x > 2, x < 9)
#### Your turn ####
w <- c(-1, 0, 1)
x <- c(5, 15)
y <- "February"
z <- c("Monday", "Tuesday", "Friday")
# Is w positive?
# Is x greater than 10 and less than 20? Is object y the word February?
# Is every value in z a day of the week?
## Answers
# Is w positive?
w> 0
# Is x greater than 10 and less than 20?
10 < x & x < 20
#Is object y the word February?
y == "February"
# Is every value in z a day of the week?
all(z %in% c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday", "Sunday"))
# Common mistakes
x > 10 & < 20
y = "February"
all(z == "Monday" | "Tuesday" | "Wednesday"...)
#### Logical subsetting #####
x_zeroes <- diamonds$x == 0
head(x_zeroes)
# What will this return?
diamonds[x_zeroes, ]
#### Saving results ####
# Prints to screen
diamonds[diamonds$x > 10, ]
# Saves to new data frame
big <- diamonds[diamonds$x > 10, ]
# Overwrites existing data frame. Dangerous!
diamonds <- diamonds[diamonds$x < 10,]
diamonds <- diamonds[1, 1]
diamonds # Uh oh!
rm(diamonds)
diamonds <- data.frame(diamonds) # Phew!
#### NA behavior ####
a <- c(1, NA)
a == NA
is.na(a)
b <- c(1, 2, 3, 4, NA)
sum(b)
sum(b, na.rm = TRUE)
#### NA Assignment ####
# Before removing outliers
qplot(x, y, data = diamonds)
## Let's remove outliers ##
?diamonds
# Let's start with x
summary(diamonds$x)
# there are 0s in x
#but x is supposed to be the lenght of the diamond, it can't be 0, a diamond is a 3d object in real life
diamonds$x[diamonds$x == 0]
# we can assign NA to 0s to let qplot know that these are missing values
diamonds$x[diamonds$x == 0] <- NA
# now 0s gone from the Min. and we have NA's
summary(diamonds$x)
# Now let's do y
summary(diamonds$y)
# we can assign NA to 0s to let qplot know that these are missing values
diamonds$y[diamonds$y == 0] <- NA
# very big diamonds
y_big <- diamonds$y > 20
diamonds$y[y_big] <- NA
# outliers are assigned as NA!
summary(diamonds$y)
# let's look at our plot again
qplot(x, y, data = diamonds)
|
################################################################################
##
## R package clusrank by Mei-Ling Ting Lee, Jun Yan, and Yujing Jiang
## Copyright (C) 2015
##
## This file is part of the R package clusrank.
##
## The R package clusrank is free software: you can redistribute it and/or
## modify it under the terms of the GNU General Public License as published
## by the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package clusrank is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with the R package reda. If not, see <http://www.gnu.org/licenses/>.
##
################################################################################
#' The Wilcoxon Signed Rank Test for Clustered Data
#'
#' Performs one-sample Wilcoxon test on vectors of data using
#' large sample.
#'
#' @note This function is able to deal with data with
#' clusterentitical or variable cluster size. When the data
#' is unbalanced, adjusted signed rank statistic is used.
#' Ties are dropped in the test.
#' @examples
#' data(crsd)
#' cluswilcox.test(z, cluster = id, data = crsd)
#' data(crsdUnb)
#' cluswilcox.test(z, cluster = id, data = crsdUnb)
#' @author Yujing Jiang
#' @references
#' Bernard Rosner, Robert J. Glynn, Mei-Ling Ting Lee(2006)
#' \emph{The Wilcoxon Signed Rank Test for Paired Comparisons of
#' Clustered Data.} Biometrics, \bold{62}, 185-192.
#' @describeIn cluswilcox.test numeric interface for signed rank test.
#' @importFrom stats complete.cases
#' @export
cluswilcox.test.numeric <- function(x, y = NULL,
cluster = NULL,
data = parent.frame(),
alternative = c("two.sided", "less", "greater"),
mu = 0, permutation = FALSE,
n.rep = 500, ...) {
## Process the input arguments before feeding them to
## signed rank test . Assign a class (better to
## be S4) to the processed arguments for them to be
## sent to the corresponding functions.
##
## Inputs:
## The same as cluswilcox.test.
## x: numeric vector of data values. Non-finite
## (e.g., infinite or missing) values will be omitted.
##
##
## y: an optional numeric vector of data values:
## as with x non-finite values will be omitted.
##
##
## cluster: an integer vector. Cluster cluster.If not provclustered,
## assume there is no cluster.
##
## data: an optional matrix or data frame
## (or similar: see model.frame) containing the variables.
## By default the variables are taken from environment(formula).
##
##
## alternative: a character string specifying the
## alternative hypothesis, must be one of
## "two.sclustered" (default), "greater" or "less".
## You can specify just the initial letter.
##
## mu: a number specifying an optional parameter
## used to form the null hypothesis.
##
## paired: a logical indicating whether you want a paired test.
##
## permuation:
METHOD <- "Wilcoxon signed rank test for clutered data"
pars <- as.list(match.call()[-1])
## If data name existed, take out the x (and y) observations,
## group cluster, cluster cluster, stratum cluster, otherwise, no need to
## take values from a data frame.
if(!is.null(pars$data)) {
x <- data[, as.character(pars$x)]
DNAME <- (pars$x)
if(!is.null(pars$y)) {
y <- data[, as.character(pars$y)]
DNAME <- paste(DNAME, "and", pars$y)
} else {
y <- NULL
}
if(!is.null(pars$cluster)) {
cluster <- data[, as.character(pars$cluster)]
DNAME <- paste0(DNAME, ", cluster: ", pars$cluster)
} else {
cluster <- NULL
}
DNAME <- paste0(DNAME, " from ", pars$data)
} else {
DNAME <- deparse(substitute(x))
if(!is.null(y)) {
DNAME <- paste(DNAME, "and", deparse(substitute(y)))
}
if(!is.null(cluster)) {
DNAME <- paste0(DNAME, ", cluster id: ", deparse(substitute(cluster)))
}
}
## Check and initialize cluster if not given,
## transform it to numeric if given as characters.
l.x <- length(x)
if( is.null(cluster)) {
cluster <- c(1 : l.x)
} else {
if(!is.numeric(cluster)) {
if(!is.character(cluster)) {
stop("'cluster' has to be numeric or characters")
}
if(length(cluster) != l.x) {
stop("'cluster' and 'x' must have the same lengths")
}
uniq.cluster <- unique(cluster)
l.uniq.cluster <- length(uniq.cluster)
cluster <- as.numeric(recoderFunc(cluster, uniq.cluster, c(1 : l.uniq.cluster)))
}
}
## Check x.
if ( !is.numeric(x))
stop("'x' must be numeric")
## Check data for paired test, paired test
## do not deal with stratified data.
if( !is.null(y)) {
if (!is.numeric(y))
stop("'y' must be numeric")
l.y <- length(y)
if( l.y != l.x) {
stop("'x' and 'y' must have the same
lengths for signed rank test.")
}
OK <- complete.cases(x, y, cluster)
x <- x[OK] - y[OK] - mu
cluster <- cluster[OK]
finite.x <- is.finite(x)
x <- x[finite.x]
cluster <- cluster[finite.x]
if(length(x) < 1L) {
stop("not enough (finite) 'x' observations")
}
} else {
## If only x is given, it is the difference score.
OK <- complete.cases(x, cluster)
x <- x[OK]
cluster <- cluster[OK]
finite.x <- is.finite(x)
x <- x[finite.x] - mu
cluster <- cluster[finite.x]
if(length(x) < 1L) {
stop("not enough (finite) 'x' observations")
}
}
alternative <- match.arg(alternative)
if(!missing(mu) && ((length(mu) > 1L) || !is.finite(mu))) {
stop("'mu' must be a single number")
}
if(permutation == FALSE) {
return(cluswilcox.test.signedrank(x, cluster, alternative, mu, DNAME, METHOD))
} else {
METHOD <- paste(METHOD, "using permutation")
return(cluswilcox.test.signedrank.permutation(x, cluster, alternative, mu,
n.rep,
DNAME, METHOD))
}
}
| /clusrank/R/clusnumeric.R | no_license | ingted/R-Examples | R | false | false | 6,530 | r | ################################################################################
##
## R package clusrank by Mei-Ling Ting Lee, Jun Yan, and Yujing Jiang
## Copyright (C) 2015
##
## This file is part of the R package clusrank.
##
## The R package clusrank is free software: you can redistribute it and/or
## modify it under the terms of the GNU General Public License as published
## by the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package clusrank is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with the R package reda. If not, see <http://www.gnu.org/licenses/>.
##
################################################################################
#' The Wilcoxon Signed Rank Test for Clustered Data
#'
#' Performs one-sample Wilcoxon test on vectors of data using
#' large sample.
#'
#' @note This function is able to deal with data with
#' clusterentitical or variable cluster size. When the data
#' is unbalanced, adjusted signed rank statistic is used.
#' Ties are dropped in the test.
#' @examples
#' data(crsd)
#' cluswilcox.test(z, cluster = id, data = crsd)
#' data(crsdUnb)
#' cluswilcox.test(z, cluster = id, data = crsdUnb)
#' @author Yujing Jiang
#' @references
#' Bernard Rosner, Robert J. Glynn, Mei-Ling Ting Lee(2006)
#' \emph{The Wilcoxon Signed Rank Test for Paired Comparisons of
#' Clustered Data.} Biometrics, \bold{62}, 185-192.
#' @describeIn cluswilcox.test numeric interface for signed rank test.
#' @importFrom stats complete.cases
#' @export
cluswilcox.test.numeric <- function(x, y = NULL,
cluster = NULL,
data = parent.frame(),
alternative = c("two.sided", "less", "greater"),
mu = 0, permutation = FALSE,
n.rep = 500, ...) {
## Process the input arguments before feeding them to
## signed rank test . Assign a class (better to
## be S4) to the processed arguments for them to be
## sent to the corresponding functions.
##
## Inputs:
## The same as cluswilcox.test.
## x: numeric vector of data values. Non-finite
## (e.g., infinite or missing) values will be omitted.
##
##
## y: an optional numeric vector of data values:
## as with x non-finite values will be omitted.
##
##
## cluster: an integer vector. Cluster cluster.If not provclustered,
## assume there is no cluster.
##
## data: an optional matrix or data frame
## (or similar: see model.frame) containing the variables.
## By default the variables are taken from environment(formula).
##
##
## alternative: a character string specifying the
## alternative hypothesis, must be one of
## "two.sclustered" (default), "greater" or "less".
## You can specify just the initial letter.
##
## mu: a number specifying an optional parameter
## used to form the null hypothesis.
##
## paired: a logical indicating whether you want a paired test.
##
## permuation:
METHOD <- "Wilcoxon signed rank test for clutered data"
pars <- as.list(match.call()[-1])
## If data name existed, take out the x (and y) observations,
## group cluster, cluster cluster, stratum cluster, otherwise, no need to
## take values from a data frame.
if(!is.null(pars$data)) {
x <- data[, as.character(pars$x)]
DNAME <- (pars$x)
if(!is.null(pars$y)) {
y <- data[, as.character(pars$y)]
DNAME <- paste(DNAME, "and", pars$y)
} else {
y <- NULL
}
if(!is.null(pars$cluster)) {
cluster <- data[, as.character(pars$cluster)]
DNAME <- paste0(DNAME, ", cluster: ", pars$cluster)
} else {
cluster <- NULL
}
DNAME <- paste0(DNAME, " from ", pars$data)
} else {
DNAME <- deparse(substitute(x))
if(!is.null(y)) {
DNAME <- paste(DNAME, "and", deparse(substitute(y)))
}
if(!is.null(cluster)) {
DNAME <- paste0(DNAME, ", cluster id: ", deparse(substitute(cluster)))
}
}
## Check and initialize cluster if not given,
## transform it to numeric if given as characters.
l.x <- length(x)
if( is.null(cluster)) {
cluster <- c(1 : l.x)
} else {
if(!is.numeric(cluster)) {
if(!is.character(cluster)) {
stop("'cluster' has to be numeric or characters")
}
if(length(cluster) != l.x) {
stop("'cluster' and 'x' must have the same lengths")
}
uniq.cluster <- unique(cluster)
l.uniq.cluster <- length(uniq.cluster)
cluster <- as.numeric(recoderFunc(cluster, uniq.cluster, c(1 : l.uniq.cluster)))
}
}
## Check x.
if ( !is.numeric(x))
stop("'x' must be numeric")
## Check data for paired test, paired test
## do not deal with stratified data.
if( !is.null(y)) {
if (!is.numeric(y))
stop("'y' must be numeric")
l.y <- length(y)
if( l.y != l.x) {
stop("'x' and 'y' must have the same
lengths for signed rank test.")
}
OK <- complete.cases(x, y, cluster)
x <- x[OK] - y[OK] - mu
cluster <- cluster[OK]
finite.x <- is.finite(x)
x <- x[finite.x]
cluster <- cluster[finite.x]
if(length(x) < 1L) {
stop("not enough (finite) 'x' observations")
}
} else {
## If only x is given, it is the difference score.
OK <- complete.cases(x, cluster)
x <- x[OK]
cluster <- cluster[OK]
finite.x <- is.finite(x)
x <- x[finite.x] - mu
cluster <- cluster[finite.x]
if(length(x) < 1L) {
stop("not enough (finite) 'x' observations")
}
}
alternative <- match.arg(alternative)
if(!missing(mu) && ((length(mu) > 1L) || !is.finite(mu))) {
stop("'mu' must be a single number")
}
if(permutation == FALSE) {
return(cluswilcox.test.signedrank(x, cluster, alternative, mu, DNAME, METHOD))
} else {
METHOD <- paste(METHOD, "using permutation")
return(cluswilcox.test.signedrank.permutation(x, cluster, alternative, mu,
n.rep,
DNAME, METHOD))
}
}
|
library(ggplot2)
library(reshape2)
library(BEST)
## Read our datasets
areas_wide <- read.csv("./results/buffer_areas.csv")
symdiffs_wide <- read.csv("./results/buffer_symdiffs.csv")
## Convert to tidy/long format and ft^2 -> km^2
ft2tokm2 <- function(x) x * 9.2903e-8
areas <- melt(areas_wide, value.name="sqft", variable.name="type", id.vars=c("id"))
areas$km2 <- ft2tokm2(areas$sqft)
symdiffs <- melt(symdiffs_wide, value.name="sqft", variable.name="type", id.vars=c("id"))
symdiffs$km2 <- ft2tokm2(symdiffs$sqft)
## Descriptive density plots of areas and symmetric differences
area_dens <- ggplot(areas, aes(x=km2, color=type, linetype=type)) +
scale_color_brewer(type="qual", palette=2) +
geom_density() + theme_bw()
ggsave("./results/area_dens.pdf", width=5, height=2.5)
symdiff_dens <- ggplot(symdiffs, aes(x=km2)) +
geom_density() + theme_bw()
ggsave("./results/symdiff_dens.pdf", width=5, height=2.5)
## Statistically compare areas of pg buffers and esri round ended
## buffers using BEST
best_areas_pg_esr <- BESTmcmc(y1=ft2tokm2(areas_wide$postgis),
y2=ft2tokm2(areas_wide$esri))
## Power analysis, takes quite a while to run
## bpwr_areas_pg_esr <- BESTpower(best_areas_pg_esr,
## N1=length(areas_wide$postgis),
## N2=length(areas_wide$esri_round),
## ROPEm=c(-0.0314,0.0314),
## maxHDIWm=1.0, nRep=1000)
## Plots from BEST
pdf("./results/best_areas_pg_esr_mean.pdf", width=8, height=4)
plot(best_areas_pg_esr, "mean")
dev.off()
pdf("./results/best_areas_pg_esr_sd.pdf", width=8, height=4)
plot(best_areas_pg_esr, "sd")
dev.off()
pdf("./results/best_areas_pg_esr_effect.pdf", width=8, height=4)
plot(best_areas_pg_esr, "effect")
dev.off()
pdf("./results/best_areas_pg_esr_nu.pdf", width=8, height=4)
plot(best_areas_pg_esr, "nu")
dev.off()
pdf("./results/best_areas_pg_esr.pdf", width=8.5, height=11)
plotAll(best_areas_pg_esr)
dev.off()
| /analysis/055-compare-buffers.R | permissive | pschmied/pgismethods | R | false | false | 2,024 | r | library(ggplot2)
library(reshape2)
library(BEST)
## Read our datasets
areas_wide <- read.csv("./results/buffer_areas.csv")
symdiffs_wide <- read.csv("./results/buffer_symdiffs.csv")
## Convert to tidy/long format and ft^2 -> km^2
ft2tokm2 <- function(x) x * 9.2903e-8
areas <- melt(areas_wide, value.name="sqft", variable.name="type", id.vars=c("id"))
areas$km2 <- ft2tokm2(areas$sqft)
symdiffs <- melt(symdiffs_wide, value.name="sqft", variable.name="type", id.vars=c("id"))
symdiffs$km2 <- ft2tokm2(symdiffs$sqft)
## Descriptive density plots of areas and symmetric differences
area_dens <- ggplot(areas, aes(x=km2, color=type, linetype=type)) +
scale_color_brewer(type="qual", palette=2) +
geom_density() + theme_bw()
ggsave("./results/area_dens.pdf", width=5, height=2.5)
symdiff_dens <- ggplot(symdiffs, aes(x=km2)) +
geom_density() + theme_bw()
ggsave("./results/symdiff_dens.pdf", width=5, height=2.5)
## Statistically compare areas of pg buffers and esri round ended
## buffers using BEST
best_areas_pg_esr <- BESTmcmc(y1=ft2tokm2(areas_wide$postgis),
y2=ft2tokm2(areas_wide$esri))
## Power analysis, takes quite a while to run
## bpwr_areas_pg_esr <- BESTpower(best_areas_pg_esr,
## N1=length(areas_wide$postgis),
## N2=length(areas_wide$esri_round),
## ROPEm=c(-0.0314,0.0314),
## maxHDIWm=1.0, nRep=1000)
## Plots from BEST
pdf("./results/best_areas_pg_esr_mean.pdf", width=8, height=4)
plot(best_areas_pg_esr, "mean")
dev.off()
pdf("./results/best_areas_pg_esr_sd.pdf", width=8, height=4)
plot(best_areas_pg_esr, "sd")
dev.off()
pdf("./results/best_areas_pg_esr_effect.pdf", width=8, height=4)
plot(best_areas_pg_esr, "effect")
dev.off()
pdf("./results/best_areas_pg_esr_nu.pdf", width=8, height=4)
plot(best_areas_pg_esr, "nu")
dev.off()
pdf("./results/best_areas_pg_esr.pdf", width=8.5, height=11)
plotAll(best_areas_pg_esr)
dev.off()
|
rm(list = ls()); gc()
# get args
args = commandArgs(TRUE)
batch_no = as.double(args[1])
simulations = as.integer(args[2])
library(extdepth)
source('/home/trevorh2/assimilation-cfr/code/ks_field_functions.R')
source('/home/trevorh2/assimilation-cfr/code/sim_functions.R')
# marginal number of points in the field (field is pts x pts)
pts = 40
# number of regions to subdivide the fields into
regions = 64
# number of time points
time_points = 10
# standard flat prior mean
prior_mu = matrix(0, pts, pts)
post_mu = readRDS("post_mu.rds")
prior_mu = as.vector(prior_mu)
post_mu = as.vector(post_mu)
cat("#### Starting Simulation \n")
upper_de = matrix(0, regions*time_points, simulations)
upper_bf = matrix(0, regions*time_points, simulations)
upper_pw = matrix(0, regions*time_points, simulations)
ks_value = matrix(0, regions*time_points, simulations)
pval_de = rep(0, simulations)
for (i in 1:simulations) {
t0 = Sys.time()
kst = rep(0, regions*time_points)
ksp = matrix(0, regions*time_points, 1000)
for (t in 1:time_points) {
prior = sim_gp(100, mu = prior_mu, l = 5, pts = pts)
post = sim_gp(100, mu = post_mu, l = 5, pts = pts)
# split em all
prior.split = vapply(1:100, function(x) matsplitter(prior[,,x], 5, 5),
FUN.VALUE = array(0, dim = c(5, 5, regions)))
post.split = vapply(1:100, function(x) matsplitter(post[,,x], 5, 5),
FUN.VALUE = array(0, dim = c(5, 5, regions)))
# find the observed kst field
kst[1:regions + regions*(t - 1)] = kst.field(prior.split, post.split, 100)
ksp[1:regions + regions*(t - 1),] = kst.permute(prior.split, post.split, 1000, 1)
}
# Bonferroni central regions
bf_val = (1-(0.05/(regions*time_points)))
# Depth central regions
perm.ed = edepth_set(ksp, depth_function = "rank")
perm.cr = central_region(ksp, perm.ed)
kst.ed = edepth(kst, ksp, depth_function = "rank")
# Upper regions
upper_bf[,i] = sapply(1:length(kst), function(r) quantile(ksp[r,], bf_val))
upper_pw[,i] = sapply(1:length(kst), function(r) quantile(ksp[r,], 0.95))
upper_de[,i] = perm.cr[[2]]
# save ks values
ks_value[,i] = kst
# save ks p values
pval_de[i] = kst.ed
cat(paste0("sim ", i, "\t", Sys.time()-t0, "\n"))
}
| /Archive/code/sim/power_time.R | no_license | trevor-harris/assimilation-cfr | R | false | false | 2,309 | r | rm(list = ls()); gc()
# get args
args = commandArgs(TRUE)
batch_no = as.double(args[1])
simulations = as.integer(args[2])
library(extdepth)
source('/home/trevorh2/assimilation-cfr/code/ks_field_functions.R')
source('/home/trevorh2/assimilation-cfr/code/sim_functions.R')
# marginal number of points in the field (field is pts x pts)
pts = 40
# number of regions to subdivide the fields into
regions = 64
# number of time points
time_points = 10
# standard flat prior mean
prior_mu = matrix(0, pts, pts)
post_mu = readRDS("post_mu.rds")
prior_mu = as.vector(prior_mu)
post_mu = as.vector(post_mu)
cat("#### Starting Simulation \n")
upper_de = matrix(0, regions*time_points, simulations)
upper_bf = matrix(0, regions*time_points, simulations)
upper_pw = matrix(0, regions*time_points, simulations)
ks_value = matrix(0, regions*time_points, simulations)
pval_de = rep(0, simulations)
for (i in 1:simulations) {
t0 = Sys.time()
kst = rep(0, regions*time_points)
ksp = matrix(0, regions*time_points, 1000)
for (t in 1:time_points) {
prior = sim_gp(100, mu = prior_mu, l = 5, pts = pts)
post = sim_gp(100, mu = post_mu, l = 5, pts = pts)
# split em all
prior.split = vapply(1:100, function(x) matsplitter(prior[,,x], 5, 5),
FUN.VALUE = array(0, dim = c(5, 5, regions)))
post.split = vapply(1:100, function(x) matsplitter(post[,,x], 5, 5),
FUN.VALUE = array(0, dim = c(5, 5, regions)))
# find the observed kst field
kst[1:regions + regions*(t - 1)] = kst.field(prior.split, post.split, 100)
ksp[1:regions + regions*(t - 1),] = kst.permute(prior.split, post.split, 1000, 1)
}
# Bonferroni central regions
bf_val = (1-(0.05/(regions*time_points)))
# Depth central regions
perm.ed = edepth_set(ksp, depth_function = "rank")
perm.cr = central_region(ksp, perm.ed)
kst.ed = edepth(kst, ksp, depth_function = "rank")
# Upper regions
upper_bf[,i] = sapply(1:length(kst), function(r) quantile(ksp[r,], bf_val))
upper_pw[,i] = sapply(1:length(kst), function(r) quantile(ksp[r,], 0.95))
upper_de[,i] = perm.cr[[2]]
# save ks values
ks_value[,i] = kst
# save ks p values
pval_de[i] = kst.ed
cat(paste0("sim ", i, "\t", Sys.time()-t0, "\n"))
}
|
context("AMQP Connection")
test_that("rabbitr succesfully stablishes connection with RabbitMQ", {
skip_if_no_rabbitmq()
conn <- rabbitr()
})
| /tests/testthat/test_connection.R | no_license | lecardozo/rabbitr | R | false | false | 150 | r | context("AMQP Connection")
test_that("rabbitr succesfully stablishes connection with RabbitMQ", {
skip_if_no_rabbitmq()
conn <- rabbitr()
})
|
#' Prepare inputs for TMB model.
#'
#' @param naomi_data Naomi data object
#' @param report_likelihood Option to report likelihood in fit object (default true).
#' @param anchor_home_district Option to include random effect home district attractiveness to retain residents on ART within home districts (default true).
#'
#' @return Inputs ready for TMB model
#'
#' @seealso [select_naomi_data]
#' @export
prepare_tmb_inputs <- function(naomi_data,
report_likelihood = 1L) {
stopifnot(is(naomi_data, "naomi_data"))
stopifnot(is(naomi_data, "naomi_mf"))
## ANC observation aggregation matrices
##
## TODO: Refactor code to make the function create_artattend_Amat() more generic.
## Should not refer to 'ART' specific; also useful for ANC attendance,
## fertility, etc.
create_anc_Amat <- function(anc_obs_dat) {
df_attend_anc <- naomi_data$mf_model %>%
dplyr::select(reside_area_id = area_id,
attend_area_id = area_id,
sex,
age_group,
idx)
dat <- dplyr::rename(anc_obs_dat, attend_area_id = area_id)
Amat <- create_artattend_Amat(
dat,
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_attend_anc,
by_residence = FALSE
)
Amat
}
create_survey_Amat <- function(survey_dat) {
df_attend_survey <- naomi_data$mf_model %>%
dplyr::select(reside_area_id = area_id,
attend_area_id = area_id,
sex,
age_group,
idx)
survey_dat$attend_area_id <- survey_dat$area_id
Amat <- create_artattend_Amat(
survey_dat,
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_attend_survey,
by_residence = FALSE,
by_survey = TRUE
)
Amat
}
A_anc_clients_t2 <- create_anc_Amat(naomi_data$anc_clients_t2_dat)
A_anc_prev_t1 <- create_anc_Amat(naomi_data$anc_prev_t1_dat)
A_anc_prev_t2 <- create_anc_Amat(naomi_data$anc_prev_t2_dat)
A_anc_artcov_t1 <- create_anc_Amat(naomi_data$anc_artcov_t1_dat)
A_anc_artcov_t2 <- create_anc_Amat(naomi_data$anc_artcov_t2_dat)
A_prev <- create_survey_Amat(naomi_data$prev_dat)
A_artcov <- create_survey_Amat(naomi_data$artcov_dat)
A_vls <- create_survey_Amat(naomi_data$vls_dat)
A_recent <- create_survey_Amat(naomi_data$recent_dat)
## ART attendance aggregation
# Default model for ART attending: Anchor home district = add random effect for home district
if(naomi_data$model_options$anchor_home_district) {
Xgamma <- naomi:::sparse_model_matrix(~0 + attend_area_idf, naomi_data$mf_artattend)
} else {
Xgamma <- sparse_model_matrix(~0 + attend_area_idf:as.integer(jstar != 1),
naomi_data$mf_artattend)
}
if(naomi_data$artattend_t2) {
Xgamma_t2 <- Xgamma
} else {
Xgamma_t2 <- sparse_model_matrix(~0, naomi_data$mf_artattend)
}
df_art_attend <- naomi_data$mf_model %>%
dplyr::rename(reside_area_id = area_id) %>%
dplyr::left_join(naomi_data$mf_artattend, by = "reside_area_id", multiple = "all") %>%
dplyr::mutate(attend_idf = forcats::as_factor(attend_idx),
idf = forcats::as_factor(idx))
Xart_gamma <- sparse_model_matrix(~0 + attend_idf, df_art_attend)
Xart_idx <- sparse_model_matrix(~0 + idf, df_art_attend)
A_artattend_t1 <- create_artattend_Amat(artnum_df = dplyr::rename(naomi_data$artnum_t1_dat, attend_area_id = area_id),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_artattend_t2 <- create_artattend_Amat(artnum_df = dplyr::rename(naomi_data$artnum_t2_dat, attend_area_id = area_id),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_artattend_mf <- create_artattend_Amat(artnum_df = dplyr::select(naomi_data$mf_model, attend_area_id = area_id, sex, age_group, artnum_idx = idx),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_art_reside_attend <- naomi_data$mf_artattend %>%
dplyr::transmute(
reside_area_id,
attend_area_id,
sex = "both",
age_group = "Y000_999"
) %>%
create_artattend_Amat(age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = TRUE)
## Construct TMB data and initial parameter vectors
df <- naomi_data$mf_model
X_15to49 <- Matrix::t(sparse_model_matrix(~-1 + area_idf:age15to49, naomi_data$mf_model))
## Paediatric prevalence from 15-49 female ratio
X_15to49f <- Matrix::t(Matrix::sparse.model.matrix(~0 + area_idf:age15to49:as.integer(sex == "female"), df))
df$bin_paed_rho_model <- 1 - df$bin_rho_model
X_paed_rho_ratio <- sparse_model_matrix(~-1 + area_idf:paed_rho_ratio:bin_paed_rho_model, df)
paed_rho_ratio_offset <- 0.5 * df$bin_rho_model
X_paed_lambda_ratio_t1 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t1, df)
X_paed_lambda_ratio_t2 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t2, df)
X_paed_lambda_ratio_t3 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t3, df)
X_paed_lambda_ratio_t4 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t4, df)
X_paed_lambda_ratio_t5 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t5, df)
f_rho_a <- if(all(is.na(df$rho_a_fct))) ~0 else ~0 + rho_a_fct
f_alpha_a <- if(all(is.na(df$alpha_a_fct))) ~0 else ~0 + alpha_a_fct
if (naomi_data$rho_paed_x_term) {
f_rho_xa <- ~0 + area_idf
} else {
f_rho_xa <- ~0
}
## Ratio of paediatric incidence rate to 15-49 female prevalence
## If no sex stratified prevalence data, don't estimate spatial variation in
## sex odds ratio
if ( ! all(c("male", "female") %in% naomi_data$prev_dat$sex)) {
f_rho_xs <- ~0
} else {
f_rho_xs <- ~0 + area_idf
}
## If no sex stratified ART coverage data, don't estimate spatial variation in
## sex odds ratio
if ( ! all(c("male", "female") %in% naomi_data$artcov_dat$sex) &&
! all(c("male", "female") %in% naomi_data$artnum_t1_dat$sex) &&
! all(c("male", "female") %in% naomi_data$artnum_t2_dat$sex) ) {
f_alpha_xs <- ~0
} else {
f_alpha_xs <- ~0 + area_idf
}
## If flag **and** has ART by sex data at both times, estimate time x district x
## sex ART odds ratio.
if (naomi_data$alpha_xst_term) {
if (!all(c("male", "female") %in% naomi_data$artnum_t1_dat$sex) &&
!all(c("male", "female") %in% naomi_data$artnum_t2_dat$sex)) {
stop(paste("Sex-stratified ART data are required at both Time 1 and Time 2",
"to estimate district x sex x time interaction for ART coverage"))
}
f_alpha_xst <- ~0 + area_idf
} else {
f_alpha_xst <- ~0
}
## If no ART data at both time points, do not fit a change in ART coverage. Use
## logit difference in ART coverage from Spectrum.
## T1 ART data may be either survey or programme
##
has_t1_art <- nrow(naomi_data$artcov_dat) > 0 | nrow(naomi_data$artnum_t1_dat) > 0
has_t2_art <- nrow(naomi_data$artnum_t2_dat) > 0
if( !has_t1_art | !has_t2_art ) {
f_alpha_t2 <- ~0
f_alpha_xt <- ~0
logit_alpha_t1t2_offset <- naomi_data$mf_model$logit_alpha_t1t2_offset
} else {
f_alpha_t2 <- ~1
f_alpha_xt <- ~0 + area_idf
logit_alpha_t1t2_offset <- numeric(nrow(naomi_data$mf_model))
}
## Paediatric ART coverage random effects
artnum_t1_dat <- naomi_data$artnum_t1_dat %>%
dplyr::left_join(get_age_groups(), by = "age_group") %>%
dplyr::mutate(age_group_end = age_group_start + age_group_span - 1)
artnum_t2_dat <- naomi_data$artnum_t2_dat %>%
dplyr::left_join(get_age_groups(), by = "age_group") %>%
dplyr::mutate(age_group_end = age_group_start + age_group_span - 1)
has_t1_paed_art <- any(artnum_t1_dat$age_group_end < 15)
has_t2_paed_art <- any(artnum_t2_dat$age_group_end < 15)
if(has_t1_paed_art | has_t2_paed_art) {
f_alpha_xa <- ~0 + area_idf
} else {
f_alpha_xa <- ~0
}
if(has_t1_paed_art & has_t2_paed_art) {
f_alpha_t2 <- ~1 + age_below15
f_alpha_xat <- ~0 + area_idf
} else {
f_alpha_xat <- ~0
}
## If no recent infection data, do not estimate incidence sex ratio or
## district random effects
if(nrow(naomi_data$recent_dat) == 0) {
f_lambda <- ~0
f_lambda_x <- ~0
} else {
f_lambda <- ~ 1 + female_15plus
f_lambda_x <- ~0 + area_idf
}
dtmb <- list(
population_t1 = df$population_t1,
population_t2 = df$population_t2,
Lproj_hivpop_t1t2 = naomi_data$Lproj_t1t2$Lproj_hivpop,
Lproj_incid_t1t2 = naomi_data$Lproj_t1t2$Lproj_incid,
Lproj_paed_t1t2 = naomi_data$Lproj_t1t2$Lproj_paed,
X_rho = as.matrix(sparse_model_matrix(~female_15plus, df, "bin_rho_model", TRUE)),
X_alpha = stats::model.matrix(~female_15plus, df),
X_alpha_t2 = stats::model.matrix(f_alpha_t2, df),
X_lambda = stats::model.matrix(f_lambda, df),
X_asfr = stats::model.matrix(~1, df),
X_ancrho = stats::model.matrix(~1, df),
X_ancalpha = stats::model.matrix(~1, df),
Z_x = sparse_model_matrix(~0 + area_idf, df),
Z_rho_x = sparse_model_matrix(~0 + area_idf, df, "bin_rho_model", TRUE),
Z_rho_xs = sparse_model_matrix(f_rho_xs, df, "female_15plus", TRUE),
Z_rho_a = sparse_model_matrix(f_rho_a, df, "bin_rho_model", TRUE),
Z_rho_as = sparse_model_matrix(f_rho_a, df, "female_15plus", TRUE),
Z_rho_xa = sparse_model_matrix(f_rho_xa, df, "age_below15"),
Z_alpha_x = sparse_model_matrix(~0 + area_idf, df),
Z_alpha_xs = sparse_model_matrix(f_alpha_xs, df, "female_15plus", TRUE),
Z_alpha_a = sparse_model_matrix(f_alpha_a, df),
Z_alpha_as = sparse_model_matrix(f_alpha_a, df, "female_15plus", TRUE),
Z_alpha_xt = sparse_model_matrix(f_alpha_xt, df),
Z_alpha_xa = sparse_model_matrix(f_alpha_xa, df, "age_below15"),
Z_alpha_xat = sparse_model_matrix(f_alpha_xat, df, "age_below15"),
Z_alpha_xst = sparse_model_matrix(f_alpha_xst, df, "female_15plus", TRUE),
Z_lambda_x = sparse_model_matrix(f_lambda_x, df),
## Z_xa = Matrix::sparse.model.matrix(~0 + area_idf:age_group_idf, df),
Z_asfr_x = sparse_model_matrix(~0 + area_idf, df),
Z_ancrho_x = sparse_model_matrix(~0 + area_idf, df),
Z_ancalpha_x = sparse_model_matrix(~0 + area_idf, df),
log_asfr_t1_offset = log(df$asfr_t1),
log_asfr_t2_offset = log(df$asfr_t2),
log_asfr_t3_offset = log(df$asfr_t3),
logit_anc_rho_t1_offset = log(df$frr_plhiv_t1),
logit_anc_rho_t2_offset = log(df$frr_plhiv_t2),
logit_anc_rho_t3_offset = log(df$frr_plhiv_t3),
logit_anc_alpha_t1_offset = log(df$frr_already_art_t1),
logit_anc_alpha_t2_offset = log(df$frr_already_art_t2),
logit_anc_alpha_t3_offset = log(df$frr_already_art_t3),
##
logit_rho_offset = naomi_data$mf_model$logit_rho_offset * naomi_data$mf_model$bin_rho_model,
logit_alpha_offset = naomi_data$mf_model$logit_alpha_offset,
logit_alpha_t1t2_offset = logit_alpha_t1t2_offset,
##
unaware_untreated_prop_t1 = df$spec_unaware_untreated_prop_t1,
unaware_untreated_prop_t2 = df$spec_unaware_untreated_prop_t2,
unaware_untreated_prop_t3 = df$spec_unaware_untreated_prop_t3,
##
Q_x = methods::as(naomi_data$Q, "dgCMatrix"),
Q_x_rankdef = ncol(naomi_data$Q) - as.integer(Matrix::rankMatrix(naomi_data$Q)),
n_nb = naomi_data$mf_areas$n_neighbors,
adj_i = naomi_data$mf_artattend$reside_area_idx - 1L,
adj_j = naomi_data$mf_artattend$attend_area_idx - 1L,
Xgamma = Xgamma,
Xgamma_t2 = Xgamma_t2,
log_gamma_offset = naomi_data$mf_artattend$log_gamma_offset,
Xart_idx = Xart_idx,
Xart_gamma = Xart_gamma,
##
omega = naomi_data$omega,
OmegaT0 = naomi_data$rita_param$OmegaT0,
sigma_OmegaT = naomi_data$rita_param$sigma_OmegaT,
betaT0 = naomi_data$rita_param$betaT0,
sigma_betaT = naomi_data$rita_param$sigma_betaT,
ritaT = naomi_data$rita_param$ritaT,
##
logit_nu_mean = naomi_data$logit_nu_mean,
logit_nu_sd = naomi_data$logit_nu_sd,
##
X_15to49 = X_15to49,
log_lambda_t1_offset = naomi_data$mf_model$log_lambda_t1_offset,
log_lambda_t2_offset = naomi_data$mf_model$log_lambda_t2_offset,
##
X_15to49f = X_15to49f,
X_paed_rho_ratio = X_paed_rho_ratio,
paed_rho_ratio_offset = paed_rho_ratio_offset,
##
X_paed_lambda_ratio_t1 = X_paed_lambda_ratio_t1,
X_paed_lambda_ratio_t2 = X_paed_lambda_ratio_t2,
X_paed_lambda_ratio_t3 = X_paed_lambda_ratio_t3,
X_paed_lambda_ratio_t4 = X_paed_lambda_ratio_t4,
X_paed_lambda_ratio_t5 = X_paed_lambda_ratio_t5,
##
## Household survey input data
x_prev = naomi_data$prev_dat$x_eff,
n_prev = naomi_data$prev_dat$n_eff,
A_prev = A_prev,
x_artcov = naomi_data$artcov_dat$x_eff,
n_artcov = naomi_data$artcov_dat$n_eff,
A_artcov = A_artcov,
x_vls = naomi_data$vls_dat$x_eff,
n_vls = naomi_data$vls_dat$n_eff,
A_vls = A_vls,
x_recent = naomi_data$recent_dat$x_eff,
n_recent = naomi_data$recent_dat$n_eff,
A_recent = A_recent,
##
## ANC testing input data
x_anc_clients_t2 = naomi_data$anc_clients_t2_dat$anc_clients_x,
offset_anc_clients_t2 = naomi_data$anc_clients_t2_dat$anc_clients_pys_offset,
A_anc_clients_t2 = A_anc_clients_t2,
x_anc_prev_t1 = naomi_data$anc_prev_t1_dat$anc_prev_x,
n_anc_prev_t1 = naomi_data$anc_prev_t1_dat$anc_prev_n,
A_anc_prev_t1 = A_anc_prev_t1,
x_anc_artcov_t1 = naomi_data$anc_artcov_t1_dat$anc_artcov_x,
n_anc_artcov_t1 = naomi_data$anc_artcov_t1_dat$anc_artcov_n,
A_anc_artcov_t1 = A_anc_artcov_t1,
x_anc_prev_t2 = naomi_data$anc_prev_t2_dat$anc_prev_x,
n_anc_prev_t2 = naomi_data$anc_prev_t2_dat$anc_prev_n,
A_anc_prev_t2 = A_anc_prev_t2,
x_anc_artcov_t2 = naomi_data$anc_artcov_t2_dat$anc_artcov_x,
n_anc_artcov_t2 = naomi_data$anc_artcov_t2_dat$anc_artcov_n,
A_anc_artcov_t2 = A_anc_artcov_t2,
##
## Number on ART input data
A_artattend_t1 = A_artattend_t1,
x_artnum_t1 = naomi_data$artnum_t1_dat$art_current,
A_artattend_t2 = A_artattend_t2,
x_artnum_t2 = naomi_data$artnum_t2_dat$art_current,
A_artattend_mf = A_artattend_mf,
A_art_reside_attend = A_art_reside_attend,
##
## Time 3 projection inputs
population_t3 = df$population_t3,
Lproj_hivpop_t2t3 = naomi_data$Lproj_t2t3$Lproj_hivpop,
Lproj_incid_t2t3 = naomi_data$Lproj_t2t3$Lproj_incid,
Lproj_paed_t2t3 = naomi_data$Lproj_t2t3$Lproj_paed,
logit_alpha_t2t3_offset = df$logit_alpha_t2t3_offset,
log_lambda_t3_offset = df$log_lambda_t3_offset,
##
## Time 4 projection inputs
population_t4 = df$population_t4,
Lproj_hivpop_t3t4 = naomi_data$Lproj_t3t4$Lproj_hivpop,
Lproj_incid_t3t4 = naomi_data$Lproj_t3t4$Lproj_incid,
Lproj_paed_t3t4 = naomi_data$Lproj_t3t4$Lproj_paed,
logit_alpha_t3t4_offset = df$logit_alpha_t3t4_offset,
log_lambda_t4_offset = df$log_lambda_t4_offset,
##
## Time 5 projection inputs
population_t5 = df$population_t5,
Lproj_hivpop_t4t5 = naomi_data$Lproj_t4t5$Lproj_hivpop,
Lproj_incid_t4t5 = naomi_data$Lproj_t4t5$Lproj_incid,
Lproj_paed_t4t5 = naomi_data$Lproj_t4t5$Lproj_paed,
logit_alpha_t4t5_offset = df$logit_alpha_t4t5_offset,
log_lambda_t5_offset = df$log_lambda_t5_offset,
##
A_out = naomi_data$A_out,
A_anc_out = naomi_data$A_anc_out,
calc_outputs = 1L,
report_likelihood = report_likelihood
)
ptmb <- list(
beta_rho = numeric(ncol(dtmb$X_rho)),
beta_alpha = numeric(ncol(dtmb$X_alpha)),
beta_alpha_t2 = numeric(ncol(dtmb$X_alpha_t2)),
beta_lambda = numeric(ncol(dtmb$X_lambda)),
beta_asfr = numeric(1),
beta_anc_rho = numeric(1),
beta_anc_alpha = numeric(1),
beta_anc_rho_t2 = numeric(1),
beta_anc_alpha_t2 = numeric(1),
u_rho_x = numeric(ncol(dtmb$Z_rho_x)),
us_rho_x = numeric(ncol(dtmb$Z_rho_x)),
u_rho_xs = numeric(ncol(dtmb$Z_rho_xs)),
us_rho_xs = numeric(ncol(dtmb$Z_rho_xs)),
u_rho_a = numeric(ncol(dtmb$Z_rho_a)),
u_rho_as = numeric(ncol(dtmb$Z_rho_as)),
u_rho_xa = numeric(ncol(dtmb$Z_rho_xa)),
ui_asfr_x = numeric(ncol(dtmb$Z_asfr_x)),
ui_anc_rho_x = numeric(ncol(dtmb$Z_ancrho_x)),
ui_anc_alpha_x = numeric(ncol(dtmb$Z_ancalpha_x)),
ui_anc_rho_xt = numeric(ncol(dtmb$Z_ancrho_x)),
ui_anc_alpha_xt = numeric(ncol(dtmb$Z_ancalpha_x)),
##
u_alpha_x = numeric(ncol(dtmb$Z_alpha_x)),
us_alpha_x = numeric(ncol(dtmb$Z_alpha_x)),
u_alpha_xs = numeric(ncol(dtmb$Z_alpha_xs)),
us_alpha_xs = numeric(ncol(dtmb$Z_alpha_xs)),
u_alpha_a = numeric(ncol(dtmb$Z_alpha_a)),
u_alpha_as = numeric(ncol(dtmb$Z_alpha_as)),
u_alpha_xt = numeric(ncol(dtmb$Z_alpha_xt)),
u_alpha_xa = numeric(ncol(dtmb$Z_alpha_xa)),
u_alpha_xat = numeric(ncol(dtmb$Z_alpha_xat)),
u_alpha_xst = numeric(ncol(dtmb$Z_alpha_xst)),
##
log_sigma_lambda_x = log(1.0),
ui_lambda_x = numeric(ncol(dtmb$Z_lambda_x)),
##
logit_phi_rho_a = 0,
log_sigma_rho_a = log(2.5),
logit_phi_rho_as = 2.582,
log_sigma_rho_as = log(2.5),
logit_phi_rho_x = 0,
log_sigma_rho_x = log(2.5),
logit_phi_rho_xs = 0,
log_sigma_rho_xs = log(2.5),
log_sigma_rho_xa = log(0.5),
##
logit_phi_alpha_a = 0,
log_sigma_alpha_a = log(2.5),
logit_phi_alpha_as = 2.582,
log_sigma_alpha_as = log(2.5),
logit_phi_alpha_x = 0,
log_sigma_alpha_x = log(2.5),
logit_phi_alpha_xs = 0,
log_sigma_alpha_xs = log(2.5),
log_sigma_alpha_xt = log(2.5),
log_sigma_alpha_xa = log(2.5),
log_sigma_alpha_xat = log(2.5),
log_sigma_alpha_xst = log(2.5),
##
OmegaT_raw = 0,
log_betaT = 0,
logit_nu_raw = 0,
##
log_sigma_asfr_x = log(0.5),
log_sigma_ancrho_x = log(2.5),
log_sigma_ancalpha_x = log(2.5),
log_sigma_ancrho_xt = log(2.5),
log_sigma_ancalpha_xt = log(2.5),
##
log_or_gamma = numeric(ncol(dtmb$Xgamma)),
log_sigma_or_gamma = log(2.5),
log_or_gamma_t1t2 = numeric(ncol(dtmb$Xgamma_t2)),
log_sigma_or_gamma_t1t2 = log(2.5)
)
v <- list(data = dtmb,
par_init = ptmb)
class(v) <- "naomi_tmb_input"
v
}
sparse_model_matrix <- function(formula, data, binary_interaction = 1,
drop_zero_cols = FALSE) {
if(is.character(binary_interaction))
binary_interaction <- data[[binary_interaction]]
stopifnot(length(binary_interaction) %in% c(1, nrow(data)))
mm <- Matrix::sparse.model.matrix(formula, data)
mm <- mm * binary_interaction
mm <- Matrix::drop0(mm)
if(drop_zero_cols)
mm <- mm[ , apply(mm, 2, Matrix::nnzero) > 0]
mm
}
make_tmb_obj <- function(data, par, calc_outputs = 1L, inner_verbose = FALSE,
progress = NULL) {
data$calc_outputs <- as.integer(calc_outputs)
obj <- TMB::MakeADFun(data = data,
parameters = par,
DLL = "naomi",
silent = !inner_verbose,
random = c("beta_rho",
"beta_alpha", "beta_alpha_t2",
"beta_lambda",
"beta_asfr",
"beta_anc_rho", "beta_anc_alpha",
"beta_anc_rho_t2", "beta_anc_alpha_t2",
"u_rho_x", "us_rho_x",
"u_rho_xs", "us_rho_xs",
"u_rho_a", "u_rho_as",
"u_rho_xa",
##
"u_alpha_x", "us_alpha_x",
"u_alpha_xs", "us_alpha_xs",
"u_alpha_a", "u_alpha_as",
"u_alpha_xt",
"u_alpha_xa", "u_alpha_xat", "u_alpha_xst",
##
"ui_lambda_x",
"logit_nu_raw",
##
"ui_asfr_x",
"ui_anc_rho_x", "ui_anc_alpha_x",
"ui_anc_rho_xt", "ui_anc_alpha_xt",
##
"log_or_gamma", "log_or_gamma_t1t2"))
if (!is.null(progress)) {
obj$fn <- report_progress(obj$fn, progress)
}
obj
}
report_progress <- function(fun, progress) {
fun <- match.fun(fun)
function(...) {
progress$iterate_fit()
fun(...)
}
}
#' Fit TMB model
#'
#' @param tmb_input Model input data
#' @param outer_verbose If TRUE print function and parameters every iteration
#' @param inner_verbose If TRUE then disable tracing information from TMB
#' @param max_iter maximum number of iterations
#' @param progress Progress printer, if null no progress printed
#'
#' @return Fit model.
#' @export
fit_tmb <- function(tmb_input,
outer_verbose = TRUE,
inner_verbose = FALSE,
max_iter = 250,
progress = NULL
) {
stopifnot(inherits(tmb_input, "naomi_tmb_input"))
obj <- make_tmb_obj(tmb_input$data, tmb_input$par_init, calc_outputs = 0L,
inner_verbose, progress)
trace <- if(outer_verbose) 1 else 0
f <- withCallingHandlers(
stats::nlminb(obj$par, obj$fn, obj$gr,
control = list(trace = trace,
iter.max = max_iter)),
warning = function(w) {
if(grepl("NA/NaN function evaluation", w$message))
invokeRestart("muffleWarning")
}
)
if(f$convergence != 0)
warning(paste("convergence error:", f$message))
if(outer_verbose)
message(paste("converged:", f$message))
f$par.fixed <- f$par
f$par.full <- obj$env$last.par
objout <- make_tmb_obj(tmb_input$data, tmb_input$par_init, calc_outputs = 1L, inner_verbose)
f$mode <- objout$report(f$par.full)
val <- c(f, obj = list(objout))
class(val) <- "naomi_fit"
val
}
#' Calculate Posterior Mean and Uncertainty Via TMB `sdreport()`
#'
#' @param naomi_fit Fitted TMB model.
#'
#' @export
report_tmb <- function(naomi_fit) {
stopifnot(methods::is(fit, "naomi_fit"))
naomi_fit$sdreport <- TMB::sdreport(naomi_fit$obj, naomi_fit$par,
getReportCovariance = FALSE,
bias.correct = TRUE)
naomi_fit
}
#' Sample TMB fit
#'
#' @param fit The TMB fit
#' @param nsample Number of samples
#' @param rng_seed seed passed to set.seed.
#' @param random_only Random only
#' @param verbose If TRUE prints additional information.
#'
#' @return Sampled fit.
#' @export
sample_tmb <- function(fit, nsample = 1000, rng_seed = NULL,
random_only = TRUE, verbose = FALSE) {
set.seed(rng_seed)
stopifnot(methods::is(fit, "naomi_fit"))
stopifnot(nsample > 1)
to_tape <- TMB:::isNullPointer(fit$obj$env$ADFun$ptr)
if (to_tape)
fit$obj$retape(FALSE)
if(!random_only) {
if(verbose) print("Calculating joint precision")
hess <- sdreport_joint_precision(fit$obj, fit$par.fixed)
if(verbose) print("Inverting precision for joint covariance")
cov <- solve(hess)
if(verbose) print("Drawing sample")
## TODO: write a version of rmvnorm that uses precision instead of covariance
smp <- mvtnorm::rmvnorm(nsample, fit$par.full, cov)
} else {
r <- fit$obj$env$random
par_f <- fit$par.full[-r]
par_r <- fit$par.full[r]
hess_r <- fit$obj$env$spHess(fit$par.full, random = TRUE)
smp_r <- rmvnorm_sparseprec(nsample, par_r, hess_r)
smp <- matrix(0, nsample, length(fit$par.full))
smp[ , r] <- smp_r
smp[ ,-r] <- matrix(par_f, nsample, length(par_f), byrow = TRUE)
colnames(smp)[r] <- colnames(smp_r)
colnames(smp)[-r] <- names(par_f)
}
if(verbose) print("Simulating outputs")
sim <- apply(smp, 1, fit$obj$report)
r <- fit$obj$report()
if(verbose) print("Returning sample")
fit$sample <- Map(vapply, list(sim), "[[", lapply(lengths(r), numeric), names(r))
is_vector <- vapply(fit$sample, inherits, logical(1), "numeric")
fit$sample[is_vector] <- lapply(fit$sample[is_vector], matrix, nrow = 1)
names(fit$sample) <- names(r)
fit
}
rmvnorm_sparseprec <- function(
n,
mean = rep(0, nrow(prec)),
prec = diag(length(mean))
) {
z = matrix(stats::rnorm(n * length(mean)), ncol = n)
L_inv = Matrix::Cholesky(prec)
v <- mean + Matrix::solve(as(L_inv, "pMatrix"), Matrix::solve(Matrix::t(as(L_inv, "Matrix")), z))
as.matrix(Matrix::t(v))
}
create_artattend_Amat <- function(artnum_df, age_groups, sexes, area_aggregation,
df_art_attend, by_residence = FALSE, by_survey = FALSE) {
## If by_residence = TRUE, merge by reside_area_id, else aggregate over all
## reside_area_id
by_vars <- c("attend_area_id", "sex", "age_group")
if (by_residence) {
by_vars <- c(by_vars, "reside_area_id")
}
id_vars <- by_vars
if (by_survey) {
id_vars <- c(id_vars, "survey_id")
}
if(!("artnum_idx" %in% colnames(artnum_df))) {
artnum_df$artnum_idx <- seq_len(nrow(artnum_df))
}
A_artnum <- artnum_df %>%
dplyr::select(tidyselect::all_of(id_vars), artnum_idx) %>%
dplyr::rename(artdat_age_group = age_group,
artdat_sex = sex) %>%
dplyr::left_join(
get_age_groups() %>%
dplyr::transmute(
artdat_age_group = age_group,
artdat_age_start = age_group_start,
artdat_age_end = age_group_start + age_group_span
),
by = "artdat_age_group"
) %>%
## Note: this would be much faster with tree data structure for age rather than crossing...
tidyr::crossing(
get_age_groups() %>%
dplyr::filter(age_group %in% age_groups)
) %>%
dplyr::filter(
artdat_age_start <= age_group_start,
age_group_start + age_group_span <= artdat_age_end
) %>%
dplyr::left_join(
data.frame(artdat_sex = c("male", "female", "both", "both", "both"),
sex = c("male", "female", "male", "female", "both"),
stringsAsFactors = FALSE) %>%
dplyr::filter(sex %in% sexes),
by = "artdat_sex",
multiple = "all"
)
## Map artattend_area_id to model_area_id
A_artnum <- A_artnum %>%
dplyr::left_join(
area_aggregation,
by = c("attend_area_id" = "area_id"),
multiple = "all"
) %>%
dplyr::mutate(attend_area_id = model_area_id,
model_area_id = NULL)
## Check no areas with duplicated reporting
art_duplicated_check <- A_artnum %>%
dplyr::group_by_at(id_vars) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::filter(n > 1)
if (nrow(art_duplicated_check)) {
stop(paste("ART or ANC data multiply reported for some age/sex strata in areas:",
paste(unique(art_duplicated_check$attend_area_id), collapse = ", ")))
}
## Merge to ART attendance data frame
df_art_attend <- df_art_attend %>%
dplyr::select(tidyselect::all_of(by_vars)) %>%
dplyr::mutate(
Aidx = dplyr::row_number(),
value = 1
)
A_artnum <- dplyr::left_join(A_artnum, df_art_attend, by = by_vars, multiple = "all")
A_artnum <- A_artnum %>%
{
Matrix::spMatrix(nrow(artnum_df),
nrow(df_art_attend),
.$artnum_idx,
.$Aidx,
.$value)
}
A_artnum
}
| /R/tmb-model.R | permissive | mrc-ide/naomi | R | false | false | 29,092 | r | #' Prepare inputs for TMB model.
#'
#' @param naomi_data Naomi data object
#' @param report_likelihood Option to report likelihood in fit object (default true).
#' @param anchor_home_district Option to include random effect home district attractiveness to retain residents on ART within home districts (default true).
#'
#' @return Inputs ready for TMB model
#'
#' @seealso [select_naomi_data]
#' @export
prepare_tmb_inputs <- function(naomi_data,
report_likelihood = 1L) {
stopifnot(is(naomi_data, "naomi_data"))
stopifnot(is(naomi_data, "naomi_mf"))
## ANC observation aggregation matrices
##
## TODO: Refactor code to make the function create_artattend_Amat() more generic.
## Should not refer to 'ART' specific; also useful for ANC attendance,
## fertility, etc.
create_anc_Amat <- function(anc_obs_dat) {
df_attend_anc <- naomi_data$mf_model %>%
dplyr::select(reside_area_id = area_id,
attend_area_id = area_id,
sex,
age_group,
idx)
dat <- dplyr::rename(anc_obs_dat, attend_area_id = area_id)
Amat <- create_artattend_Amat(
dat,
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_attend_anc,
by_residence = FALSE
)
Amat
}
create_survey_Amat <- function(survey_dat) {
df_attend_survey <- naomi_data$mf_model %>%
dplyr::select(reside_area_id = area_id,
attend_area_id = area_id,
sex,
age_group,
idx)
survey_dat$attend_area_id <- survey_dat$area_id
Amat <- create_artattend_Amat(
survey_dat,
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_attend_survey,
by_residence = FALSE,
by_survey = TRUE
)
Amat
}
A_anc_clients_t2 <- create_anc_Amat(naomi_data$anc_clients_t2_dat)
A_anc_prev_t1 <- create_anc_Amat(naomi_data$anc_prev_t1_dat)
A_anc_prev_t2 <- create_anc_Amat(naomi_data$anc_prev_t2_dat)
A_anc_artcov_t1 <- create_anc_Amat(naomi_data$anc_artcov_t1_dat)
A_anc_artcov_t2 <- create_anc_Amat(naomi_data$anc_artcov_t2_dat)
A_prev <- create_survey_Amat(naomi_data$prev_dat)
A_artcov <- create_survey_Amat(naomi_data$artcov_dat)
A_vls <- create_survey_Amat(naomi_data$vls_dat)
A_recent <- create_survey_Amat(naomi_data$recent_dat)
## ART attendance aggregation
# Default model for ART attending: Anchor home district = add random effect for home district
if(naomi_data$model_options$anchor_home_district) {
Xgamma <- naomi:::sparse_model_matrix(~0 + attend_area_idf, naomi_data$mf_artattend)
} else {
Xgamma <- sparse_model_matrix(~0 + attend_area_idf:as.integer(jstar != 1),
naomi_data$mf_artattend)
}
if(naomi_data$artattend_t2) {
Xgamma_t2 <- Xgamma
} else {
Xgamma_t2 <- sparse_model_matrix(~0, naomi_data$mf_artattend)
}
df_art_attend <- naomi_data$mf_model %>%
dplyr::rename(reside_area_id = area_id) %>%
dplyr::left_join(naomi_data$mf_artattend, by = "reside_area_id", multiple = "all") %>%
dplyr::mutate(attend_idf = forcats::as_factor(attend_idx),
idf = forcats::as_factor(idx))
Xart_gamma <- sparse_model_matrix(~0 + attend_idf, df_art_attend)
Xart_idx <- sparse_model_matrix(~0 + idf, df_art_attend)
A_artattend_t1 <- create_artattend_Amat(artnum_df = dplyr::rename(naomi_data$artnum_t1_dat, attend_area_id = area_id),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_artattend_t2 <- create_artattend_Amat(artnum_df = dplyr::rename(naomi_data$artnum_t2_dat, attend_area_id = area_id),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_artattend_mf <- create_artattend_Amat(artnum_df = dplyr::select(naomi_data$mf_model, attend_area_id = area_id, sex, age_group, artnum_idx = idx),
age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = FALSE)
A_art_reside_attend <- naomi_data$mf_artattend %>%
dplyr::transmute(
reside_area_id,
attend_area_id,
sex = "both",
age_group = "Y000_999"
) %>%
create_artattend_Amat(age_groups = naomi_data$age_groups,
sexes = naomi_data$sexes,
area_aggregation = naomi_data$area_aggregation,
df_art_attend = df_art_attend,
by_residence = TRUE)
## Construct TMB data and initial parameter vectors
df <- naomi_data$mf_model
X_15to49 <- Matrix::t(sparse_model_matrix(~-1 + area_idf:age15to49, naomi_data$mf_model))
## Paediatric prevalence from 15-49 female ratio
X_15to49f <- Matrix::t(Matrix::sparse.model.matrix(~0 + area_idf:age15to49:as.integer(sex == "female"), df))
df$bin_paed_rho_model <- 1 - df$bin_rho_model
X_paed_rho_ratio <- sparse_model_matrix(~-1 + area_idf:paed_rho_ratio:bin_paed_rho_model, df)
paed_rho_ratio_offset <- 0.5 * df$bin_rho_model
X_paed_lambda_ratio_t1 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t1, df)
X_paed_lambda_ratio_t2 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t2, df)
X_paed_lambda_ratio_t3 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t3, df)
X_paed_lambda_ratio_t4 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t4, df)
X_paed_lambda_ratio_t5 <- sparse_model_matrix(~-1 + area_idf:paed_lambda_ratio_t5, df)
f_rho_a <- if(all(is.na(df$rho_a_fct))) ~0 else ~0 + rho_a_fct
f_alpha_a <- if(all(is.na(df$alpha_a_fct))) ~0 else ~0 + alpha_a_fct
if (naomi_data$rho_paed_x_term) {
f_rho_xa <- ~0 + area_idf
} else {
f_rho_xa <- ~0
}
## Ratio of paediatric incidence rate to 15-49 female prevalence
## If no sex stratified prevalence data, don't estimate spatial variation in
## sex odds ratio
if ( ! all(c("male", "female") %in% naomi_data$prev_dat$sex)) {
f_rho_xs <- ~0
} else {
f_rho_xs <- ~0 + area_idf
}
## If no sex stratified ART coverage data, don't estimate spatial variation in
## sex odds ratio
if ( ! all(c("male", "female") %in% naomi_data$artcov_dat$sex) &&
! all(c("male", "female") %in% naomi_data$artnum_t1_dat$sex) &&
! all(c("male", "female") %in% naomi_data$artnum_t2_dat$sex) ) {
f_alpha_xs <- ~0
} else {
f_alpha_xs <- ~0 + area_idf
}
## If flag **and** has ART by sex data at both times, estimate time x district x
## sex ART odds ratio.
if (naomi_data$alpha_xst_term) {
if (!all(c("male", "female") %in% naomi_data$artnum_t1_dat$sex) &&
!all(c("male", "female") %in% naomi_data$artnum_t2_dat$sex)) {
stop(paste("Sex-stratified ART data are required at both Time 1 and Time 2",
"to estimate district x sex x time interaction for ART coverage"))
}
f_alpha_xst <- ~0 + area_idf
} else {
f_alpha_xst <- ~0
}
## If no ART data at both time points, do not fit a change in ART coverage. Use
## logit difference in ART coverage from Spectrum.
## T1 ART data may be either survey or programme
##
has_t1_art <- nrow(naomi_data$artcov_dat) > 0 | nrow(naomi_data$artnum_t1_dat) > 0
has_t2_art <- nrow(naomi_data$artnum_t2_dat) > 0
if( !has_t1_art | !has_t2_art ) {
f_alpha_t2 <- ~0
f_alpha_xt <- ~0
logit_alpha_t1t2_offset <- naomi_data$mf_model$logit_alpha_t1t2_offset
} else {
f_alpha_t2 <- ~1
f_alpha_xt <- ~0 + area_idf
logit_alpha_t1t2_offset <- numeric(nrow(naomi_data$mf_model))
}
## Paediatric ART coverage random effects
artnum_t1_dat <- naomi_data$artnum_t1_dat %>%
dplyr::left_join(get_age_groups(), by = "age_group") %>%
dplyr::mutate(age_group_end = age_group_start + age_group_span - 1)
artnum_t2_dat <- naomi_data$artnum_t2_dat %>%
dplyr::left_join(get_age_groups(), by = "age_group") %>%
dplyr::mutate(age_group_end = age_group_start + age_group_span - 1)
has_t1_paed_art <- any(artnum_t1_dat$age_group_end < 15)
has_t2_paed_art <- any(artnum_t2_dat$age_group_end < 15)
if(has_t1_paed_art | has_t2_paed_art) {
f_alpha_xa <- ~0 + area_idf
} else {
f_alpha_xa <- ~0
}
if(has_t1_paed_art & has_t2_paed_art) {
f_alpha_t2 <- ~1 + age_below15
f_alpha_xat <- ~0 + area_idf
} else {
f_alpha_xat <- ~0
}
## If no recent infection data, do not estimate incidence sex ratio or
## district random effects
if(nrow(naomi_data$recent_dat) == 0) {
f_lambda <- ~0
f_lambda_x <- ~0
} else {
f_lambda <- ~ 1 + female_15plus
f_lambda_x <- ~0 + area_idf
}
dtmb <- list(
population_t1 = df$population_t1,
population_t2 = df$population_t2,
Lproj_hivpop_t1t2 = naomi_data$Lproj_t1t2$Lproj_hivpop,
Lproj_incid_t1t2 = naomi_data$Lproj_t1t2$Lproj_incid,
Lproj_paed_t1t2 = naomi_data$Lproj_t1t2$Lproj_paed,
X_rho = as.matrix(sparse_model_matrix(~female_15plus, df, "bin_rho_model", TRUE)),
X_alpha = stats::model.matrix(~female_15plus, df),
X_alpha_t2 = stats::model.matrix(f_alpha_t2, df),
X_lambda = stats::model.matrix(f_lambda, df),
X_asfr = stats::model.matrix(~1, df),
X_ancrho = stats::model.matrix(~1, df),
X_ancalpha = stats::model.matrix(~1, df),
Z_x = sparse_model_matrix(~0 + area_idf, df),
Z_rho_x = sparse_model_matrix(~0 + area_idf, df, "bin_rho_model", TRUE),
Z_rho_xs = sparse_model_matrix(f_rho_xs, df, "female_15plus", TRUE),
Z_rho_a = sparse_model_matrix(f_rho_a, df, "bin_rho_model", TRUE),
Z_rho_as = sparse_model_matrix(f_rho_a, df, "female_15plus", TRUE),
Z_rho_xa = sparse_model_matrix(f_rho_xa, df, "age_below15"),
Z_alpha_x = sparse_model_matrix(~0 + area_idf, df),
Z_alpha_xs = sparse_model_matrix(f_alpha_xs, df, "female_15plus", TRUE),
Z_alpha_a = sparse_model_matrix(f_alpha_a, df),
Z_alpha_as = sparse_model_matrix(f_alpha_a, df, "female_15plus", TRUE),
Z_alpha_xt = sparse_model_matrix(f_alpha_xt, df),
Z_alpha_xa = sparse_model_matrix(f_alpha_xa, df, "age_below15"),
Z_alpha_xat = sparse_model_matrix(f_alpha_xat, df, "age_below15"),
Z_alpha_xst = sparse_model_matrix(f_alpha_xst, df, "female_15plus", TRUE),
Z_lambda_x = sparse_model_matrix(f_lambda_x, df),
## Z_xa = Matrix::sparse.model.matrix(~0 + area_idf:age_group_idf, df),
Z_asfr_x = sparse_model_matrix(~0 + area_idf, df),
Z_ancrho_x = sparse_model_matrix(~0 + area_idf, df),
Z_ancalpha_x = sparse_model_matrix(~0 + area_idf, df),
log_asfr_t1_offset = log(df$asfr_t1),
log_asfr_t2_offset = log(df$asfr_t2),
log_asfr_t3_offset = log(df$asfr_t3),
logit_anc_rho_t1_offset = log(df$frr_plhiv_t1),
logit_anc_rho_t2_offset = log(df$frr_plhiv_t2),
logit_anc_rho_t3_offset = log(df$frr_plhiv_t3),
logit_anc_alpha_t1_offset = log(df$frr_already_art_t1),
logit_anc_alpha_t2_offset = log(df$frr_already_art_t2),
logit_anc_alpha_t3_offset = log(df$frr_already_art_t3),
##
logit_rho_offset = naomi_data$mf_model$logit_rho_offset * naomi_data$mf_model$bin_rho_model,
logit_alpha_offset = naomi_data$mf_model$logit_alpha_offset,
logit_alpha_t1t2_offset = logit_alpha_t1t2_offset,
##
unaware_untreated_prop_t1 = df$spec_unaware_untreated_prop_t1,
unaware_untreated_prop_t2 = df$spec_unaware_untreated_prop_t2,
unaware_untreated_prop_t3 = df$spec_unaware_untreated_prop_t3,
##
Q_x = methods::as(naomi_data$Q, "dgCMatrix"),
Q_x_rankdef = ncol(naomi_data$Q) - as.integer(Matrix::rankMatrix(naomi_data$Q)),
n_nb = naomi_data$mf_areas$n_neighbors,
adj_i = naomi_data$mf_artattend$reside_area_idx - 1L,
adj_j = naomi_data$mf_artattend$attend_area_idx - 1L,
Xgamma = Xgamma,
Xgamma_t2 = Xgamma_t2,
log_gamma_offset = naomi_data$mf_artattend$log_gamma_offset,
Xart_idx = Xart_idx,
Xart_gamma = Xart_gamma,
##
omega = naomi_data$omega,
OmegaT0 = naomi_data$rita_param$OmegaT0,
sigma_OmegaT = naomi_data$rita_param$sigma_OmegaT,
betaT0 = naomi_data$rita_param$betaT0,
sigma_betaT = naomi_data$rita_param$sigma_betaT,
ritaT = naomi_data$rita_param$ritaT,
##
logit_nu_mean = naomi_data$logit_nu_mean,
logit_nu_sd = naomi_data$logit_nu_sd,
##
X_15to49 = X_15to49,
log_lambda_t1_offset = naomi_data$mf_model$log_lambda_t1_offset,
log_lambda_t2_offset = naomi_data$mf_model$log_lambda_t2_offset,
##
X_15to49f = X_15to49f,
X_paed_rho_ratio = X_paed_rho_ratio,
paed_rho_ratio_offset = paed_rho_ratio_offset,
##
X_paed_lambda_ratio_t1 = X_paed_lambda_ratio_t1,
X_paed_lambda_ratio_t2 = X_paed_lambda_ratio_t2,
X_paed_lambda_ratio_t3 = X_paed_lambda_ratio_t3,
X_paed_lambda_ratio_t4 = X_paed_lambda_ratio_t4,
X_paed_lambda_ratio_t5 = X_paed_lambda_ratio_t5,
##
## Household survey input data
x_prev = naomi_data$prev_dat$x_eff,
n_prev = naomi_data$prev_dat$n_eff,
A_prev = A_prev,
x_artcov = naomi_data$artcov_dat$x_eff,
n_artcov = naomi_data$artcov_dat$n_eff,
A_artcov = A_artcov,
x_vls = naomi_data$vls_dat$x_eff,
n_vls = naomi_data$vls_dat$n_eff,
A_vls = A_vls,
x_recent = naomi_data$recent_dat$x_eff,
n_recent = naomi_data$recent_dat$n_eff,
A_recent = A_recent,
##
## ANC testing input data
x_anc_clients_t2 = naomi_data$anc_clients_t2_dat$anc_clients_x,
offset_anc_clients_t2 = naomi_data$anc_clients_t2_dat$anc_clients_pys_offset,
A_anc_clients_t2 = A_anc_clients_t2,
x_anc_prev_t1 = naomi_data$anc_prev_t1_dat$anc_prev_x,
n_anc_prev_t1 = naomi_data$anc_prev_t1_dat$anc_prev_n,
A_anc_prev_t1 = A_anc_prev_t1,
x_anc_artcov_t1 = naomi_data$anc_artcov_t1_dat$anc_artcov_x,
n_anc_artcov_t1 = naomi_data$anc_artcov_t1_dat$anc_artcov_n,
A_anc_artcov_t1 = A_anc_artcov_t1,
x_anc_prev_t2 = naomi_data$anc_prev_t2_dat$anc_prev_x,
n_anc_prev_t2 = naomi_data$anc_prev_t2_dat$anc_prev_n,
A_anc_prev_t2 = A_anc_prev_t2,
x_anc_artcov_t2 = naomi_data$anc_artcov_t2_dat$anc_artcov_x,
n_anc_artcov_t2 = naomi_data$anc_artcov_t2_dat$anc_artcov_n,
A_anc_artcov_t2 = A_anc_artcov_t2,
##
## Number on ART input data
A_artattend_t1 = A_artattend_t1,
x_artnum_t1 = naomi_data$artnum_t1_dat$art_current,
A_artattend_t2 = A_artattend_t2,
x_artnum_t2 = naomi_data$artnum_t2_dat$art_current,
A_artattend_mf = A_artattend_mf,
A_art_reside_attend = A_art_reside_attend,
##
## Time 3 projection inputs
population_t3 = df$population_t3,
Lproj_hivpop_t2t3 = naomi_data$Lproj_t2t3$Lproj_hivpop,
Lproj_incid_t2t3 = naomi_data$Lproj_t2t3$Lproj_incid,
Lproj_paed_t2t3 = naomi_data$Lproj_t2t3$Lproj_paed,
logit_alpha_t2t3_offset = df$logit_alpha_t2t3_offset,
log_lambda_t3_offset = df$log_lambda_t3_offset,
##
## Time 4 projection inputs
population_t4 = df$population_t4,
Lproj_hivpop_t3t4 = naomi_data$Lproj_t3t4$Lproj_hivpop,
Lproj_incid_t3t4 = naomi_data$Lproj_t3t4$Lproj_incid,
Lproj_paed_t3t4 = naomi_data$Lproj_t3t4$Lproj_paed,
logit_alpha_t3t4_offset = df$logit_alpha_t3t4_offset,
log_lambda_t4_offset = df$log_lambda_t4_offset,
##
## Time 5 projection inputs
population_t5 = df$population_t5,
Lproj_hivpop_t4t5 = naomi_data$Lproj_t4t5$Lproj_hivpop,
Lproj_incid_t4t5 = naomi_data$Lproj_t4t5$Lproj_incid,
Lproj_paed_t4t5 = naomi_data$Lproj_t4t5$Lproj_paed,
logit_alpha_t4t5_offset = df$logit_alpha_t4t5_offset,
log_lambda_t5_offset = df$log_lambda_t5_offset,
##
A_out = naomi_data$A_out,
A_anc_out = naomi_data$A_anc_out,
calc_outputs = 1L,
report_likelihood = report_likelihood
)
ptmb <- list(
beta_rho = numeric(ncol(dtmb$X_rho)),
beta_alpha = numeric(ncol(dtmb$X_alpha)),
beta_alpha_t2 = numeric(ncol(dtmb$X_alpha_t2)),
beta_lambda = numeric(ncol(dtmb$X_lambda)),
beta_asfr = numeric(1),
beta_anc_rho = numeric(1),
beta_anc_alpha = numeric(1),
beta_anc_rho_t2 = numeric(1),
beta_anc_alpha_t2 = numeric(1),
u_rho_x = numeric(ncol(dtmb$Z_rho_x)),
us_rho_x = numeric(ncol(dtmb$Z_rho_x)),
u_rho_xs = numeric(ncol(dtmb$Z_rho_xs)),
us_rho_xs = numeric(ncol(dtmb$Z_rho_xs)),
u_rho_a = numeric(ncol(dtmb$Z_rho_a)),
u_rho_as = numeric(ncol(dtmb$Z_rho_as)),
u_rho_xa = numeric(ncol(dtmb$Z_rho_xa)),
ui_asfr_x = numeric(ncol(dtmb$Z_asfr_x)),
ui_anc_rho_x = numeric(ncol(dtmb$Z_ancrho_x)),
ui_anc_alpha_x = numeric(ncol(dtmb$Z_ancalpha_x)),
ui_anc_rho_xt = numeric(ncol(dtmb$Z_ancrho_x)),
ui_anc_alpha_xt = numeric(ncol(dtmb$Z_ancalpha_x)),
##
u_alpha_x = numeric(ncol(dtmb$Z_alpha_x)),
us_alpha_x = numeric(ncol(dtmb$Z_alpha_x)),
u_alpha_xs = numeric(ncol(dtmb$Z_alpha_xs)),
us_alpha_xs = numeric(ncol(dtmb$Z_alpha_xs)),
u_alpha_a = numeric(ncol(dtmb$Z_alpha_a)),
u_alpha_as = numeric(ncol(dtmb$Z_alpha_as)),
u_alpha_xt = numeric(ncol(dtmb$Z_alpha_xt)),
u_alpha_xa = numeric(ncol(dtmb$Z_alpha_xa)),
u_alpha_xat = numeric(ncol(dtmb$Z_alpha_xat)),
u_alpha_xst = numeric(ncol(dtmb$Z_alpha_xst)),
##
log_sigma_lambda_x = log(1.0),
ui_lambda_x = numeric(ncol(dtmb$Z_lambda_x)),
##
logit_phi_rho_a = 0,
log_sigma_rho_a = log(2.5),
logit_phi_rho_as = 2.582,
log_sigma_rho_as = log(2.5),
logit_phi_rho_x = 0,
log_sigma_rho_x = log(2.5),
logit_phi_rho_xs = 0,
log_sigma_rho_xs = log(2.5),
log_sigma_rho_xa = log(0.5),
##
logit_phi_alpha_a = 0,
log_sigma_alpha_a = log(2.5),
logit_phi_alpha_as = 2.582,
log_sigma_alpha_as = log(2.5),
logit_phi_alpha_x = 0,
log_sigma_alpha_x = log(2.5),
logit_phi_alpha_xs = 0,
log_sigma_alpha_xs = log(2.5),
log_sigma_alpha_xt = log(2.5),
log_sigma_alpha_xa = log(2.5),
log_sigma_alpha_xat = log(2.5),
log_sigma_alpha_xst = log(2.5),
##
OmegaT_raw = 0,
log_betaT = 0,
logit_nu_raw = 0,
##
log_sigma_asfr_x = log(0.5),
log_sigma_ancrho_x = log(2.5),
log_sigma_ancalpha_x = log(2.5),
log_sigma_ancrho_xt = log(2.5),
log_sigma_ancalpha_xt = log(2.5),
##
log_or_gamma = numeric(ncol(dtmb$Xgamma)),
log_sigma_or_gamma = log(2.5),
log_or_gamma_t1t2 = numeric(ncol(dtmb$Xgamma_t2)),
log_sigma_or_gamma_t1t2 = log(2.5)
)
v <- list(data = dtmb,
par_init = ptmb)
class(v) <- "naomi_tmb_input"
v
}
sparse_model_matrix <- function(formula, data, binary_interaction = 1,
drop_zero_cols = FALSE) {
if(is.character(binary_interaction))
binary_interaction <- data[[binary_interaction]]
stopifnot(length(binary_interaction) %in% c(1, nrow(data)))
mm <- Matrix::sparse.model.matrix(formula, data)
mm <- mm * binary_interaction
mm <- Matrix::drop0(mm)
if(drop_zero_cols)
mm <- mm[ , apply(mm, 2, Matrix::nnzero) > 0]
mm
}
make_tmb_obj <- function(data, par, calc_outputs = 1L, inner_verbose = FALSE,
progress = NULL) {
data$calc_outputs <- as.integer(calc_outputs)
obj <- TMB::MakeADFun(data = data,
parameters = par,
DLL = "naomi",
silent = !inner_verbose,
random = c("beta_rho",
"beta_alpha", "beta_alpha_t2",
"beta_lambda",
"beta_asfr",
"beta_anc_rho", "beta_anc_alpha",
"beta_anc_rho_t2", "beta_anc_alpha_t2",
"u_rho_x", "us_rho_x",
"u_rho_xs", "us_rho_xs",
"u_rho_a", "u_rho_as",
"u_rho_xa",
##
"u_alpha_x", "us_alpha_x",
"u_alpha_xs", "us_alpha_xs",
"u_alpha_a", "u_alpha_as",
"u_alpha_xt",
"u_alpha_xa", "u_alpha_xat", "u_alpha_xst",
##
"ui_lambda_x",
"logit_nu_raw",
##
"ui_asfr_x",
"ui_anc_rho_x", "ui_anc_alpha_x",
"ui_anc_rho_xt", "ui_anc_alpha_xt",
##
"log_or_gamma", "log_or_gamma_t1t2"))
if (!is.null(progress)) {
obj$fn <- report_progress(obj$fn, progress)
}
obj
}
report_progress <- function(fun, progress) {
fun <- match.fun(fun)
function(...) {
progress$iterate_fit()
fun(...)
}
}
#' Fit TMB model
#'
#' @param tmb_input Model input data
#' @param outer_verbose If TRUE print function and parameters every iteration
#' @param inner_verbose If TRUE then disable tracing information from TMB
#' @param max_iter maximum number of iterations
#' @param progress Progress printer, if null no progress printed
#'
#' @return Fit model.
#' @export
fit_tmb <- function(tmb_input,
outer_verbose = TRUE,
inner_verbose = FALSE,
max_iter = 250,
progress = NULL
) {
stopifnot(inherits(tmb_input, "naomi_tmb_input"))
obj <- make_tmb_obj(tmb_input$data, tmb_input$par_init, calc_outputs = 0L,
inner_verbose, progress)
trace <- if(outer_verbose) 1 else 0
f <- withCallingHandlers(
stats::nlminb(obj$par, obj$fn, obj$gr,
control = list(trace = trace,
iter.max = max_iter)),
warning = function(w) {
if(grepl("NA/NaN function evaluation", w$message))
invokeRestart("muffleWarning")
}
)
if(f$convergence != 0)
warning(paste("convergence error:", f$message))
if(outer_verbose)
message(paste("converged:", f$message))
f$par.fixed <- f$par
f$par.full <- obj$env$last.par
objout <- make_tmb_obj(tmb_input$data, tmb_input$par_init, calc_outputs = 1L, inner_verbose)
f$mode <- objout$report(f$par.full)
val <- c(f, obj = list(objout))
class(val) <- "naomi_fit"
val
}
#' Calculate Posterior Mean and Uncertainty Via TMB `sdreport()`
#'
#' @param naomi_fit Fitted TMB model.
#'
#' @export
report_tmb <- function(naomi_fit) {
stopifnot(methods::is(fit, "naomi_fit"))
naomi_fit$sdreport <- TMB::sdreport(naomi_fit$obj, naomi_fit$par,
getReportCovariance = FALSE,
bias.correct = TRUE)
naomi_fit
}
#' Sample TMB fit
#'
#' @param fit The TMB fit
#' @param nsample Number of samples
#' @param rng_seed seed passed to set.seed.
#' @param random_only Random only
#' @param verbose If TRUE prints additional information.
#'
#' @return Sampled fit.
#' @export
sample_tmb <- function(fit, nsample = 1000, rng_seed = NULL,
random_only = TRUE, verbose = FALSE) {
set.seed(rng_seed)
stopifnot(methods::is(fit, "naomi_fit"))
stopifnot(nsample > 1)
to_tape <- TMB:::isNullPointer(fit$obj$env$ADFun$ptr)
if (to_tape)
fit$obj$retape(FALSE)
if(!random_only) {
if(verbose) print("Calculating joint precision")
hess <- sdreport_joint_precision(fit$obj, fit$par.fixed)
if(verbose) print("Inverting precision for joint covariance")
cov <- solve(hess)
if(verbose) print("Drawing sample")
## TODO: write a version of rmvnorm that uses precision instead of covariance
smp <- mvtnorm::rmvnorm(nsample, fit$par.full, cov)
} else {
r <- fit$obj$env$random
par_f <- fit$par.full[-r]
par_r <- fit$par.full[r]
hess_r <- fit$obj$env$spHess(fit$par.full, random = TRUE)
smp_r <- rmvnorm_sparseprec(nsample, par_r, hess_r)
smp <- matrix(0, nsample, length(fit$par.full))
smp[ , r] <- smp_r
smp[ ,-r] <- matrix(par_f, nsample, length(par_f), byrow = TRUE)
colnames(smp)[r] <- colnames(smp_r)
colnames(smp)[-r] <- names(par_f)
}
if(verbose) print("Simulating outputs")
sim <- apply(smp, 1, fit$obj$report)
r <- fit$obj$report()
if(verbose) print("Returning sample")
fit$sample <- Map(vapply, list(sim), "[[", lapply(lengths(r), numeric), names(r))
is_vector <- vapply(fit$sample, inherits, logical(1), "numeric")
fit$sample[is_vector] <- lapply(fit$sample[is_vector], matrix, nrow = 1)
names(fit$sample) <- names(r)
fit
}
rmvnorm_sparseprec <- function(
n,
mean = rep(0, nrow(prec)),
prec = diag(length(mean))
) {
z = matrix(stats::rnorm(n * length(mean)), ncol = n)
L_inv = Matrix::Cholesky(prec)
v <- mean + Matrix::solve(as(L_inv, "pMatrix"), Matrix::solve(Matrix::t(as(L_inv, "Matrix")), z))
as.matrix(Matrix::t(v))
}
create_artattend_Amat <- function(artnum_df, age_groups, sexes, area_aggregation,
df_art_attend, by_residence = FALSE, by_survey = FALSE) {
## If by_residence = TRUE, merge by reside_area_id, else aggregate over all
## reside_area_id
by_vars <- c("attend_area_id", "sex", "age_group")
if (by_residence) {
by_vars <- c(by_vars, "reside_area_id")
}
id_vars <- by_vars
if (by_survey) {
id_vars <- c(id_vars, "survey_id")
}
if(!("artnum_idx" %in% colnames(artnum_df))) {
artnum_df$artnum_idx <- seq_len(nrow(artnum_df))
}
A_artnum <- artnum_df %>%
dplyr::select(tidyselect::all_of(id_vars), artnum_idx) %>%
dplyr::rename(artdat_age_group = age_group,
artdat_sex = sex) %>%
dplyr::left_join(
get_age_groups() %>%
dplyr::transmute(
artdat_age_group = age_group,
artdat_age_start = age_group_start,
artdat_age_end = age_group_start + age_group_span
),
by = "artdat_age_group"
) %>%
## Note: this would be much faster with tree data structure for age rather than crossing...
tidyr::crossing(
get_age_groups() %>%
dplyr::filter(age_group %in% age_groups)
) %>%
dplyr::filter(
artdat_age_start <= age_group_start,
age_group_start + age_group_span <= artdat_age_end
) %>%
dplyr::left_join(
data.frame(artdat_sex = c("male", "female", "both", "both", "both"),
sex = c("male", "female", "male", "female", "both"),
stringsAsFactors = FALSE) %>%
dplyr::filter(sex %in% sexes),
by = "artdat_sex",
multiple = "all"
)
## Map artattend_area_id to model_area_id
A_artnum <- A_artnum %>%
dplyr::left_join(
area_aggregation,
by = c("attend_area_id" = "area_id"),
multiple = "all"
) %>%
dplyr::mutate(attend_area_id = model_area_id,
model_area_id = NULL)
## Check no areas with duplicated reporting
art_duplicated_check <- A_artnum %>%
dplyr::group_by_at(id_vars) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::filter(n > 1)
if (nrow(art_duplicated_check)) {
stop(paste("ART or ANC data multiply reported for some age/sex strata in areas:",
paste(unique(art_duplicated_check$attend_area_id), collapse = ", ")))
}
## Merge to ART attendance data frame
df_art_attend <- df_art_attend %>%
dplyr::select(tidyselect::all_of(by_vars)) %>%
dplyr::mutate(
Aidx = dplyr::row_number(),
value = 1
)
A_artnum <- dplyr::left_join(A_artnum, df_art_attend, by = by_vars, multiple = "all")
A_artnum <- A_artnum %>%
{
Matrix::spMatrix(nrow(artnum_df),
nrow(df_art_attend),
.$artnum_idx,
.$Aidx,
.$value)
}
A_artnum
}
|
########################################################
## Power function, compute the stratified power-realted quantities
##
## Return values:
## TD: number of True Discoveries in each stratum
## FD: number of False Discoveries in each stratum
## power: within strata, proportion of TD out of total DE
## alpha.nomial: cutoff of alpha on raw p-values
## alpha: empirical p-value in each stratum
## alpha.marginal: overall empirical p-value
########################################################
PowerEst = function(fdr, alpha, Zg, Zg2, xgr){
## p is input nominal p-value or FDR.
## alpha is cutoff of p
## !Zg is indicator for TN, Zg2 is indicators for TP,
## xgr is grouping in covariate
ix.D = fdr <= alpha
N = sum(ix.D) ## this is the total discovery
N.stratified = tapply(ix.D, xgr, sum)
## TD (called DE genes)
id.TP = Zg2==1
TD = tapply(fdr[id.TP] <= alpha, xgr[id.TP], sum)
TD[is.na(TD)] = 0
## FD
id.TN = Zg==0
FD = tapply(fdr[id.TN] <= alpha, xgr[id.TN], sum)
FD[is.na(FD)] = 0
## type I error
alpha = as.vector(FD/table(xgr[id.TN]))
alpha.marginal = sum(FD)/sum(id.TN)
## power & precision
power=as.vector(TD/table(xgr[id.TP]))
power.marginal=sum(TD,na.rm=TRUE)/sum(id.TP)
## FDR
FDR = FD / N.stratified
FDR.marginal = sum(FD, na.rm=TRUE) / N
## conditional truths (true DE genes)
CT = table(xgr[id.TP])
list(CD=TD,FD=FD,TD=CT,alpha.nominal=alpha,
alpha=alpha, alpha.marginal=alpha.marginal,
power=power, power.marginal=power.marginal,
FDR=FDR, FDR.marginal=FDR.marginal)
}
#' Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes.
#' These parameters include four gene-wise parameters and two cell-wise parameters.
#'
#' @param DErslt is from the DE analysis by MAST
#' @param simData is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
#' @param alpha is the cutoff for the fdr which can be modified
#' @param delta or the lfc is the cutoff (=0.5) used to determined the high DE genes for Form II
#' @param strata can be modified by the user. By default, it is (0, 10], (10, 20], (20, 40], (40, 80], (80, Inf]
#' @return a list of metrics for power analysis such as: stratified targeted power and marginal power.
#' @export Power_Cont
## Continous case corresponding to the Phase II DE, delta means lfc
Power_Cont = function(DErslt, simData, alpha = 0.1, delta = 0.5, strata = c(0,10,2^(1:4)*10,Inf)){
fdrvec = DErslt$cont$fdr
lfc = simData$lfc
ngenes = nrow(simData$sce)
DEid = simData$ix.DE2
Zg = Zg2 = rep(0, ngenes)
Zg[DEid] = 1
ix = which(abs(lfc) > delta)
Zg2[DEid[ix]] = 1
sce = simData$sce
Y = round(assays(sce)[[1]])
# sizeF = colSums(Y)
# sizeF = sizeF/median(sizeF)
# X.bar = rowMeans(sweep(Y,2,sizeF,FUN="/"))
X.bar = rowMeans(Y)
ix.keep = which(X.bar>0)
xgr = cut(X.bar[ix.keep], strata)
# lev = levels(xgr)
# ix.keep = ix.keep[!(xgr %in% lev[strata.filtered])]
# ## recut
# xgr = cut(X.bar[ix.keep], strata[-strata.filtered])
# Interested genes
Zg = Zg[ix.keep]; Zg2 = Zg2[ix.keep]
fdrvec = fdrvec[ix.keep]
pow = PowerEst(fdrvec, alpha, Zg, Zg2, xgr=xgr)
return(pow)
}
#' Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes.
#' These parameters include four gene-wise parameters and two cell-wise parameters.
#'
#' @param DErslt is from the DE analysis by MAST
#' @param simData is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
#' @param alpha is the cutoff for the fdr which can be modified
#' @param delta or the zero ratio change is the cutoff (=0.1) used to determined the high DE genes for Form II
#' @param strata can be modified by the user. By default, it is (0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1]
#' @return a list of metrics for power analysis such as: stratified targeted power and marginal power.
#' @export Power_Disc
## Discreate case corresponding to the Phase I DE, delta means pi.df
Power_Disc = function(DErslt, simData, alpha = 0.1, delta = 0.1, strata = seq(0, 1, by = 0.2)){
fdrvec = DErslt$disc$fdr
pi.df = simData$pi.df
ngenes = nrow(simData$sce)
DEid = simData$ix.DE1
Zg = Zg2 = rep(0, ngenes)
Zg[DEid] = 1
ix = which(abs(pi.df) > delta)
Zg2[DEid[ix]] = 1
sce = simData$sce
ntotal = ncol(sce)
Y = round(assays(sce)[[1]])
rate0 = rowMeans(Y==0)
ix.keep = intersect(which(rate0 < 0.99), which(rate0 > 0.01)) # too small none 0 rate cannot detect
xgr = cut(rate0[ix.keep], strata)
# lev = levels(xgr)
# ix.keep = ix.keep[!(xgr %in% lev[strata.filtered])]
# ## recut
# xgr = cut(X.bar[ix.keep], strata[-strata.filtered])
# Interested genes
Zg = Zg[ix.keep]; Zg2 = Zg2[ix.keep]
fdrvec = fdrvec[ix.keep]
pow = PowerEst(fdrvec, alpha, Zg, Zg2, xgr=xgr)
return(pow)
}
| /R/estPower.R | no_license | alaminzju/POWSC | R | false | false | 5,070 | r | ########################################################
## Power function, compute the stratified power-realted quantities
##
## Return values:
## TD: number of True Discoveries in each stratum
## FD: number of False Discoveries in each stratum
## power: within strata, proportion of TD out of total DE
## alpha.nomial: cutoff of alpha on raw p-values
## alpha: empirical p-value in each stratum
## alpha.marginal: overall empirical p-value
########################################################
PowerEst = function(fdr, alpha, Zg, Zg2, xgr){
## p is input nominal p-value or FDR.
## alpha is cutoff of p
## !Zg is indicator for TN, Zg2 is indicators for TP,
## xgr is grouping in covariate
ix.D = fdr <= alpha
N = sum(ix.D) ## this is the total discovery
N.stratified = tapply(ix.D, xgr, sum)
## TD (called DE genes)
id.TP = Zg2==1
TD = tapply(fdr[id.TP] <= alpha, xgr[id.TP], sum)
TD[is.na(TD)] = 0
## FD
id.TN = Zg==0
FD = tapply(fdr[id.TN] <= alpha, xgr[id.TN], sum)
FD[is.na(FD)] = 0
## type I error
alpha = as.vector(FD/table(xgr[id.TN]))
alpha.marginal = sum(FD)/sum(id.TN)
## power & precision
power=as.vector(TD/table(xgr[id.TP]))
power.marginal=sum(TD,na.rm=TRUE)/sum(id.TP)
## FDR
FDR = FD / N.stratified
FDR.marginal = sum(FD, na.rm=TRUE) / N
## conditional truths (true DE genes)
CT = table(xgr[id.TP])
list(CD=TD,FD=FD,TD=CT,alpha.nominal=alpha,
alpha=alpha, alpha.marginal=alpha.marginal,
power=power, power.marginal=power.marginal,
FDR=FDR, FDR.marginal=FDR.marginal)
}
#' Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes.
#' These parameters include four gene-wise parameters and two cell-wise parameters.
#'
#' @param DErslt is from the DE analysis by MAST
#' @param simData is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
#' @param alpha is the cutoff for the fdr which can be modified
#' @param delta or the lfc is the cutoff (=0.5) used to determined the high DE genes for Form II
#' @param strata can be modified by the user. By default, it is (0, 10], (10, 20], (20, 40], (40, 80], (80, Inf]
#' @return a list of metrics for power analysis such as: stratified targeted power and marginal power.
#' @export Power_Cont
## Continous case corresponding to the Phase II DE, delta means lfc
Power_Cont = function(DErslt, simData, alpha = 0.1, delta = 0.5, strata = c(0,10,2^(1:4)*10,Inf)){
fdrvec = DErslt$cont$fdr
lfc = simData$lfc
ngenes = nrow(simData$sce)
DEid = simData$ix.DE2
Zg = Zg2 = rep(0, ngenes)
Zg[DEid] = 1
ix = which(abs(lfc) > delta)
Zg2[DEid[ix]] = 1
sce = simData$sce
Y = round(assays(sce)[[1]])
# sizeF = colSums(Y)
# sizeF = sizeF/median(sizeF)
# X.bar = rowMeans(sweep(Y,2,sizeF,FUN="/"))
X.bar = rowMeans(Y)
ix.keep = which(X.bar>0)
xgr = cut(X.bar[ix.keep], strata)
# lev = levels(xgr)
# ix.keep = ix.keep[!(xgr %in% lev[strata.filtered])]
# ## recut
# xgr = cut(X.bar[ix.keep], strata[-strata.filtered])
# Interested genes
Zg = Zg[ix.keep]; Zg2 = Zg2[ix.keep]
fdrvec = fdrvec[ix.keep]
pow = PowerEst(fdrvec, alpha, Zg, Zg2, xgr=xgr)
return(pow)
}
#' Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes.
#' These parameters include four gene-wise parameters and two cell-wise parameters.
#'
#' @param DErslt is from the DE analysis by MAST
#' @param simData is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
#' @param alpha is the cutoff for the fdr which can be modified
#' @param delta or the zero ratio change is the cutoff (=0.1) used to determined the high DE genes for Form II
#' @param strata can be modified by the user. By default, it is (0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1]
#' @return a list of metrics for power analysis such as: stratified targeted power and marginal power.
#' @export Power_Disc
## Discreate case corresponding to the Phase I DE, delta means pi.df
Power_Disc = function(DErslt, simData, alpha = 0.1, delta = 0.1, strata = seq(0, 1, by = 0.2)){
fdrvec = DErslt$disc$fdr
pi.df = simData$pi.df
ngenes = nrow(simData$sce)
DEid = simData$ix.DE1
Zg = Zg2 = rep(0, ngenes)
Zg[DEid] = 1
ix = which(abs(pi.df) > delta)
Zg2[DEid[ix]] = 1
sce = simData$sce
ntotal = ncol(sce)
Y = round(assays(sce)[[1]])
rate0 = rowMeans(Y==0)
ix.keep = intersect(which(rate0 < 0.99), which(rate0 > 0.01)) # too small none 0 rate cannot detect
xgr = cut(rate0[ix.keep], strata)
# lev = levels(xgr)
# ix.keep = ix.keep[!(xgr %in% lev[strata.filtered])]
# ## recut
# xgr = cut(X.bar[ix.keep], strata[-strata.filtered])
# Interested genes
Zg = Zg[ix.keep]; Zg2 = Zg2[ix.keep]
fdrvec = fdrvec[ix.keep]
pow = PowerEst(fdrvec, alpha, Zg, Zg2, xgr=xgr)
return(pow)
}
|
### R programming: Programming assignment 2 - Catching the inverse of a matrix###
## As a way to make the inversion of a matrix more efficient, the next couple of functions
## store the result of this computation directly in the cache for subsequent fast recovery.
## makeCacheMatrix creates a list containing four elements is: the first element sets the
## values of a square invertible matrix, the next one gets these values, the third one sets
## the inverse of the matrix, while the last one stores this inverse matrix.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y = matrix){ ## The operator <<- assigns y to x, which means
x <<- y ## that if this variable is found in the parent
m <<- NULL ## environment it will be assigned; otherwise,
} ## the assigment takes place in the global environment.
get <- function() x
setInverse <- function(solve) m <<- solve
getInverse <- function() m
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## The cacheSolve function verifies whether the inverse matrix of the matrix created with
## makeCacheMatrix has been calculated, in which case it will return a message indicating
## that is was recovered from the cache along with the value, without the need of doing it
## again. If this is not the case, the function will calculate the inverse with the 'solve'
## function and it will set the value in the cache for further usage.
cacheSolve <- function(x, ...) {
m <- x$getInverse()
if(!is.null(m)){ ## The condition will look if there is already a value.
message("Getting cached data")
return(m)
}
data <- x$get() ## Otherwise, it will return a matrix that is the inverse of x.
m <- solve(data, ...)
x$setInverse(m)
m
}
| /cachematrix.R | no_license | mruizvel/ProgrammingAssignment2 | R | false | false | 1,797 | r | ### R programming: Programming assignment 2 - Catching the inverse of a matrix###
## As a way to make the inversion of a matrix more efficient, the next couple of functions
## store the result of this computation directly in the cache for subsequent fast recovery.
## makeCacheMatrix creates a list containing four elements is: the first element sets the
## values of a square invertible matrix, the next one gets these values, the third one sets
## the inverse of the matrix, while the last one stores this inverse matrix.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y = matrix){ ## The operator <<- assigns y to x, which means
x <<- y ## that if this variable is found in the parent
m <<- NULL ## environment it will be assigned; otherwise,
} ## the assigment takes place in the global environment.
get <- function() x
setInverse <- function(solve) m <<- solve
getInverse <- function() m
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## The cacheSolve function verifies whether the inverse matrix of the matrix created with
## makeCacheMatrix has been calculated, in which case it will return a message indicating
## that is was recovered from the cache along with the value, without the need of doing it
## again. If this is not the case, the function will calculate the inverse with the 'solve'
## function and it will set the value in the cache for further usage.
cacheSolve <- function(x, ...) {
m <- x$getInverse()
if(!is.null(m)){ ## The condition will look if there is already a value.
message("Getting cached data")
return(m)
}
data <- x$get() ## Otherwise, it will return a matrix that is the inverse of x.
m <- solve(data, ...)
x$setInverse(m)
m
}
|
#' @name example5
#' @title Example 5: Transformation of treatment levels to improve model fit
#' @description
#' Mead (1988, p. 323) describes an experiment on spacing effects with turnips,
#' which was laid out in three complete blocks. Five different seed rates
#' (0.5, 2, 8, 20, 32 lb/acre) were tested in combination with four different row widths
#' (4, 8, 16, 32 inches), giving rise to a total of 20 treatments.
#' @details
#' Transformation of the dependent variable will often stabilize the variance of the observations
#' whereas transformation of the regressor variables will often simplify the fitted model. In this
#' example, the fit of a regression model based on the original seed rate and row width variables is compared
#' with the fit of a regression model based on the log transformed seed rates and log transformed row widths.
#' In each case, the model lack-of-fit is examined by assessing the extra variability explained when the
#' Density and Spacing treatment factors and their interactions are added to the quadratic regression models.
#' All yields are logarithmically transformed to stabilize the variance.
#'
#' The first analysis fits a quadratic regression model of log yields on the untransformed seed rates and row
#' widths (Table 16) while the second analysis fits a quadratic regression model of log yields on the log
#' transformed seed rates and log transformed row widths (Table 17). The analysis of variance of the first model
#' shows that significant extra variability is explained by the Density and
#' Spacing factors and this shows that a quadratic regression model is inadequate for the untransformed regressor
#' variables. The analysis of variance of the second model, however, shows no significant extra variability
#' explained by the Density and Spacing factors and this shows that the quadratic regression model with the log
#' transformed regressor variables gives a good fit to the data and therefore is the preferred model for the
#' observed data.
#'
#' The superiority of the model with the log transformed regressor variables is confirmed by comparing the fit of the
#' quadratic regression model for the untransformed regressor variables (Figs 8 and 9) versus the fit of the
#' quadratic regression model for the log transformed regressor variables (Figs 10 and 11).
#'
#' Fig 12a shows diagnostic plots for the fit of a quadratic model with untransformed regressor variables
#' while Fig 12b shows corresponding diagnostic plots for the fit of a quadratic model with
#' loge transformed regressor variables. Each of the four types of diagnostic plots in the two figures
#' shows an improvement in fit for the transformed versus the untransformed regressor variables.
#'
#' \code{\link[agriTutorial]{agriTutorial}}: return to home page if you want to select a different example \cr
#'
#' @references
#' Mead, R. (1988). The design of experiments. Statistical principles for practical application.
#' Cambridge: Cambridge University Press.
#'
#' Piepho, H. P, and Edmondson. R. N. (2018). A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative
#' treatment factor levels. Journal of Agronomy and Crop Science. DOI: 10.1111/jac.12267.
#' \href{http://dx.doi.org/10.1111/jac.12267}{View}
#'
#' @examples
#'
#' ## *************************************************************************************
#' ## How to run the code
#' ## *************************************************************************************
#'
#' ## Either type example("example5") to run ALL the examples succesively
#' ## or copy and paste examples sucessively, as required
#'
#' ## *************************************************************************************
#' ## Options and required packages
#' ## *************************************************************************************
#'
#' options(contrasts = c('contr.treatment', 'contr.poly'))
#' require(lattice)
#'
#' ## *************************************************************************************
#' ## Quadratic regression models with and without transformation of regressor variables
#' ## *************************************************************************************
#'
#' RowSpacing = poly(turnip$rowspacing, 3, raw = TRUE)
#' colnames(RowSpacing) = c("linSpacing", "quadSpacing", "cubSpacing")
#' Density = poly(turnip$density, 4, raw = TRUE)
#' colnames(Density) = c("linDensity", "quadDensity", "cubDensity", "quartDensity")
#' turnip = cbind(turnip, Density, RowSpacing)
#'
#' ## Log transformed row spacing and density polynomials
#' logRowSpacing = poly(log(turnip$rowspacing), 3, raw = TRUE)
#' colnames(logRowSpacing) = c("linlogSpacing", "quadlogSpacing", "cublogSpacing")
#' logDensity = poly(log(turnip$density), 4, raw = TRUE)
#' colnames(logDensity) = c("linlogDensity", "quadlogDensity", "cublogDensity", "quartlogDensity")
#' turnip = cbind(turnip, logDensity, logRowSpacing)
#'
#' ## Table 16 Quadratic response surface for untransformed planting density by row spacing model
#' quad.mod = lm(log_yield ~ Replicate + linDensity * linSpacing + quadDensity + quadSpacing +
#' Density * Spacing, turnip)
#' anova(quad.mod)
#'
#' ## Table 17 Quadratic response surface for transformed log planting density by log row spacing
#' log.quad.mod = lm(log_yield ~ Replicate + linlogDensity * linlogSpacing +
#' quadlogDensity + quadlogSpacing + Density * Spacing, turnip)
#' anova(log.quad.mod)
#'
#' ## *************************************************************************************
#' ## Quadratic regression model plots with and without transformations
#' ## Averaged over replicate blocks to give mean of block effects
#' ## *************************************************************************************
#'
#' ## Quadratic response surface for untransformed planting density by row spacing model
#' quad.mod = lm(log_yield ~ linDensity * linSpacing + quadDensity + quadSpacing , turnip)
#' quad.mod$coefficients
#'
#' ## Fig 8 Plot of loge yield (lb/plot) versus row width
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' SeedDensity = c(0.5,2,8,20,32)[panel.number()]
#' panel.curve(1.1146900855 + 0.0284788787 * x -0.0007748656 * x * x + 0.1564753713 *SeedDensity -
#' 0.0033192569 * SeedDensity* SeedDensity -0.0006749985 * x * SeedDensity,
#' from = 4, to = 32.0, type = "l", lwd = 2)
#' }
#' Seed_Rate=factor(turnip$linDensity)
#' xyplot(log_yield ~ linSpacing|Seed_Rate, data = turnip,
#' scales = list(x = list(at = c(10,20,30), labels = c(10,20,30))),
#' main = "Fig 8: loge yield versus row width",
#' xlab = " Row Width ", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("0.5", "2", "8", "20", "32")),
#' panel = panel.plot)
#'
#' ## Fig 9 Plot of loge yield (lb/plot) versus seed rate
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' RowWidth = c(4, 8, 16, 32)[panel.number()]
#' panel.curve(1.1146900855 + 0.1564753713 * x - 0.0033192569 * x * x + 0.0284788787 * RowWidth -
#' 0.0007748656* RowWidth * RowWidth -0.0006749985 * x * RowWidth,
#' from = 0.5, to = 32.0, type = "l", lwd = 2)
#' }
#' Row_Width=factor(turnip$linSpacing)
#' xyplot(log_yield ~ linDensity|Row_Width, data = turnip,
#' scales = list(x = list(at = c(0,10,20,30), labels = c(0,10,20,30))),
#' main = "Fig 9: loge yield versus seed rate",
#' xlab = " Seed Rate", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("4", "8", "16", "32")),
#' panel = panel.plot)
#'
#' ## Quadratic response surface for log transformed planting density by log row spacing model
#' log.quad.mod = lm(log_yield ~ linlogDensity * linlogSpacing + quadlogDensity + quadlogSpacing,
#' turnip)
#' log.quad.mod$coefficients
#' ## Fig 10 Plot of loge yield (lb/plot) versus log row width
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' LogSeedDensity = c(-0.6931472,0.6931472,2.0794415,2.9957323,3.4657359)[panel.number()]
#' panel.curve( 0.18414803 + 1.09137389 * x - 0.20987137 * x * x + 0.94207543 *LogSeedDensity -
#' 0.10875560 * LogSeedDensity* LogSeedDensity -0.09440938 * x * LogSeedDensity,
#' from = 1.35, to =3.50, type = "l", lwd = 2)
#' }
#' xyplot(log_yield ~ linlogSpacing|Seed_Rate, data = turnip,
#' scales = list(x = list(at = c(1.5,2.0,2.5,3.0,3.5), labels = c(1.5,2.0,2.5,3.0,3.5))),
#' main = "Fig 10: loge yield versus loge row width",
#' xlab = " Loge Row Width ", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("0.5", "2", "8", "20", "32")),
#' panel = panel.plot)
#'
#' ## Fig 11 Plot of loge yield (lb/plot) versus log seed rate
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' LogRowWidth = c(1.386294, 2.079442, 2.772589,3.465736)[panel.number()]
#' panel.curve(0.18414803 + 0.94207543 * x -0.10875560 * x * x + 1.09137389* LogRowWidth -
#' 0.20987137* LogRowWidth * LogRowWidth -0.09440938 * x * LogRowWidth,
#' from = -0.7 , to = 3.5, type = "l", lwd = 2)
#' }
#' xyplot(log_yield ~ linlogDensity|Row_Width, data = turnip,
#' scales = list(x = list(at = c(0,1,2,3),labels = c(0,1,2,3))),
#' main = "Fig 11: loge yield versus loge seed rate",
#' xlab = " Loge Seed Rate", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("4", "8", "16", "32")),
#' panel = panel.plot)
#'
#' ## *************************************************************************************
#' ## Quadratic regression model diagnostic plots with and without transformations
#' ## *************************************************************************************
#'
#' ## graphical plots of untransformed data
#' par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
#' fit.quad.mod = lm(log_yield ~ linDensity * linSpacing + quadDensity + quadSpacing,
#' turnip)
#' plot(fit.quad.mod, sub.caption = NA)
#' title(main = "Fig 12a Diagnostics for untransformed sowing density and row spacing", outer = TRUE)
#'
#' ## graphical plots of log transformed data
#' par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
#' fit.log.quad.mod = lm(log_yield ~ linlogDensity * linlogSpacing + quadlogDensity +
#' quadlogSpacing, turnip)
#' plot(fit.log.quad.mod, sub.caption = NA)
#' title(main = "Fig 12b Diagnostics for log transformed sowing density and row spacing", outer = TRUE)
#'
NULL
| /R/example5.R | no_license | cran/agriTutorial | R | false | false | 10,553 | r | #' @name example5
#' @title Example 5: Transformation of treatment levels to improve model fit
#' @description
#' Mead (1988, p. 323) describes an experiment on spacing effects with turnips,
#' which was laid out in three complete blocks. Five different seed rates
#' (0.5, 2, 8, 20, 32 lb/acre) were tested in combination with four different row widths
#' (4, 8, 16, 32 inches), giving rise to a total of 20 treatments.
#' @details
#' Transformation of the dependent variable will often stabilize the variance of the observations
#' whereas transformation of the regressor variables will often simplify the fitted model. In this
#' example, the fit of a regression model based on the original seed rate and row width variables is compared
#' with the fit of a regression model based on the log transformed seed rates and log transformed row widths.
#' In each case, the model lack-of-fit is examined by assessing the extra variability explained when the
#' Density and Spacing treatment factors and their interactions are added to the quadratic regression models.
#' All yields are logarithmically transformed to stabilize the variance.
#'
#' The first analysis fits a quadratic regression model of log yields on the untransformed seed rates and row
#' widths (Table 16) while the second analysis fits a quadratic regression model of log yields on the log
#' transformed seed rates and log transformed row widths (Table 17). The analysis of variance of the first model
#' shows that significant extra variability is explained by the Density and
#' Spacing factors and this shows that a quadratic regression model is inadequate for the untransformed regressor
#' variables. The analysis of variance of the second model, however, shows no significant extra variability
#' explained by the Density and Spacing factors and this shows that the quadratic regression model with the log
#' transformed regressor variables gives a good fit to the data and therefore is the preferred model for the
#' observed data.
#'
#' The superiority of the model with the log transformed regressor variables is confirmed by comparing the fit of the
#' quadratic regression model for the untransformed regressor variables (Figs 8 and 9) versus the fit of the
#' quadratic regression model for the log transformed regressor variables (Figs 10 and 11).
#'
#' Fig 12a shows diagnostic plots for the fit of a quadratic model with untransformed regressor variables
#' while Fig 12b shows corresponding diagnostic plots for the fit of a quadratic model with
#' loge transformed regressor variables. Each of the four types of diagnostic plots in the two figures
#' shows an improvement in fit for the transformed versus the untransformed regressor variables.
#'
#' \code{\link[agriTutorial]{agriTutorial}}: return to home page if you want to select a different example \cr
#'
#' @references
#' Mead, R. (1988). The design of experiments. Statistical principles for practical application.
#' Cambridge: Cambridge University Press.
#'
#' Piepho, H. P, and Edmondson. R. N. (2018). A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative
#' treatment factor levels. Journal of Agronomy and Crop Science. DOI: 10.1111/jac.12267.
#' \href{http://dx.doi.org/10.1111/jac.12267}{View}
#'
#' @examples
#'
#' ## *************************************************************************************
#' ## How to run the code
#' ## *************************************************************************************
#'
#' ## Either type example("example5") to run ALL the examples succesively
#' ## or copy and paste examples sucessively, as required
#'
#' ## *************************************************************************************
#' ## Options and required packages
#' ## *************************************************************************************
#'
#' options(contrasts = c('contr.treatment', 'contr.poly'))
#' require(lattice)
#'
#' ## *************************************************************************************
#' ## Quadratic regression models with and without transformation of regressor variables
#' ## *************************************************************************************
#'
#' RowSpacing = poly(turnip$rowspacing, 3, raw = TRUE)
#' colnames(RowSpacing) = c("linSpacing", "quadSpacing", "cubSpacing")
#' Density = poly(turnip$density, 4, raw = TRUE)
#' colnames(Density) = c("linDensity", "quadDensity", "cubDensity", "quartDensity")
#' turnip = cbind(turnip, Density, RowSpacing)
#'
#' ## Log transformed row spacing and density polynomials
#' logRowSpacing = poly(log(turnip$rowspacing), 3, raw = TRUE)
#' colnames(logRowSpacing) = c("linlogSpacing", "quadlogSpacing", "cublogSpacing")
#' logDensity = poly(log(turnip$density), 4, raw = TRUE)
#' colnames(logDensity) = c("linlogDensity", "quadlogDensity", "cublogDensity", "quartlogDensity")
#' turnip = cbind(turnip, logDensity, logRowSpacing)
#'
#' ## Table 16 Quadratic response surface for untransformed planting density by row spacing model
#' quad.mod = lm(log_yield ~ Replicate + linDensity * linSpacing + quadDensity + quadSpacing +
#' Density * Spacing, turnip)
#' anova(quad.mod)
#'
#' ## Table 17 Quadratic response surface for transformed log planting density by log row spacing
#' log.quad.mod = lm(log_yield ~ Replicate + linlogDensity * linlogSpacing +
#' quadlogDensity + quadlogSpacing + Density * Spacing, turnip)
#' anova(log.quad.mod)
#'
#' ## *************************************************************************************
#' ## Quadratic regression model plots with and without transformations
#' ## Averaged over replicate blocks to give mean of block effects
#' ## *************************************************************************************
#'
#' ## Quadratic response surface for untransformed planting density by row spacing model
#' quad.mod = lm(log_yield ~ linDensity * linSpacing + quadDensity + quadSpacing , turnip)
#' quad.mod$coefficients
#'
#' ## Fig 8 Plot of loge yield (lb/plot) versus row width
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' SeedDensity = c(0.5,2,8,20,32)[panel.number()]
#' panel.curve(1.1146900855 + 0.0284788787 * x -0.0007748656 * x * x + 0.1564753713 *SeedDensity -
#' 0.0033192569 * SeedDensity* SeedDensity -0.0006749985 * x * SeedDensity,
#' from = 4, to = 32.0, type = "l", lwd = 2)
#' }
#' Seed_Rate=factor(turnip$linDensity)
#' xyplot(log_yield ~ linSpacing|Seed_Rate, data = turnip,
#' scales = list(x = list(at = c(10,20,30), labels = c(10,20,30))),
#' main = "Fig 8: loge yield versus row width",
#' xlab = " Row Width ", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("0.5", "2", "8", "20", "32")),
#' panel = panel.plot)
#'
#' ## Fig 9 Plot of loge yield (lb/plot) versus seed rate
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' RowWidth = c(4, 8, 16, 32)[panel.number()]
#' panel.curve(1.1146900855 + 0.1564753713 * x - 0.0033192569 * x * x + 0.0284788787 * RowWidth -
#' 0.0007748656* RowWidth * RowWidth -0.0006749985 * x * RowWidth,
#' from = 0.5, to = 32.0, type = "l", lwd = 2)
#' }
#' Row_Width=factor(turnip$linSpacing)
#' xyplot(log_yield ~ linDensity|Row_Width, data = turnip,
#' scales = list(x = list(at = c(0,10,20,30), labels = c(0,10,20,30))),
#' main = "Fig 9: loge yield versus seed rate",
#' xlab = " Seed Rate", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("4", "8", "16", "32")),
#' panel = panel.plot)
#'
#' ## Quadratic response surface for log transformed planting density by log row spacing model
#' log.quad.mod = lm(log_yield ~ linlogDensity * linlogSpacing + quadlogDensity + quadlogSpacing,
#' turnip)
#' log.quad.mod$coefficients
#' ## Fig 10 Plot of loge yield (lb/plot) versus log row width
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' LogSeedDensity = c(-0.6931472,0.6931472,2.0794415,2.9957323,3.4657359)[panel.number()]
#' panel.curve( 0.18414803 + 1.09137389 * x - 0.20987137 * x * x + 0.94207543 *LogSeedDensity -
#' 0.10875560 * LogSeedDensity* LogSeedDensity -0.09440938 * x * LogSeedDensity,
#' from = 1.35, to =3.50, type = "l", lwd = 2)
#' }
#' xyplot(log_yield ~ linlogSpacing|Seed_Rate, data = turnip,
#' scales = list(x = list(at = c(1.5,2.0,2.5,3.0,3.5), labels = c(1.5,2.0,2.5,3.0,3.5))),
#' main = "Fig 10: loge yield versus loge row width",
#' xlab = " Loge Row Width ", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("0.5", "2", "8", "20", "32")),
#' panel = panel.plot)
#'
#' ## Fig 11 Plot of loge yield (lb/plot) versus log seed rate
#' panel.plot = function(x, y) {
#' panel.xyplot(x, y) # lattice plot shows observed points
#' LogRowWidth = c(1.386294, 2.079442, 2.772589,3.465736)[panel.number()]
#' panel.curve(0.18414803 + 0.94207543 * x -0.10875560 * x * x + 1.09137389* LogRowWidth -
#' 0.20987137* LogRowWidth * LogRowWidth -0.09440938 * x * LogRowWidth,
#' from = -0.7 , to = 3.5, type = "l", lwd = 2)
#' }
#' xyplot(log_yield ~ linlogDensity|Row_Width, data = turnip,
#' scales = list(x = list(at = c(0,1,2,3),labels = c(0,1,2,3))),
#' main = "Fig 11: loge yield versus loge seed rate",
#' xlab = " Loge Seed Rate", ylab = "Loge yield ",
#' strip = strip.custom(strip.names = TRUE,
#' factor.levels = c("4", "8", "16", "32")),
#' panel = panel.plot)
#'
#' ## *************************************************************************************
#' ## Quadratic regression model diagnostic plots with and without transformations
#' ## *************************************************************************************
#'
#' ## graphical plots of untransformed data
#' par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
#' fit.quad.mod = lm(log_yield ~ linDensity * linSpacing + quadDensity + quadSpacing,
#' turnip)
#' plot(fit.quad.mod, sub.caption = NA)
#' title(main = "Fig 12a Diagnostics for untransformed sowing density and row spacing", outer = TRUE)
#'
#' ## graphical plots of log transformed data
#' par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
#' fit.log.quad.mod = lm(log_yield ~ linlogDensity * linlogSpacing + quadlogDensity +
#' quadlogSpacing, turnip)
#' plot(fit.log.quad.mod, sub.caption = NA)
#' title(main = "Fig 12b Diagnostics for log transformed sowing density and row spacing", outer = TRUE)
#'
NULL
|
# ############################################################################################################
# ############################################# Dados simulados ##############################################
# ############################################################################################################
# carregando dependencias
source("dependencies.R",encoding = "UTF-8")
# Amostrador de Gibbs para modelo linear bayesiano --------------------------
############################
# Gerando a amostra
############################
set.seed(22-03-2018)
x = c(8, 15, 22, 29, 36)
xbarra = mean(x)
x = x - xbarra
n = 30
t = 5
alpha_c = 20
beta_c = 2
alpha = beta = NULL
taualpha = 1/0.2
taubeta = 1/0.2
y = matrix(NA,n,t)
tau = 1
e = matrix(rnorm(n*t,0,sqrt(1/tau)),n,t)
for(i in 1:n){
alpha[i] = rnorm(1,alpha_c,sqrt(1/taualpha))
beta[i] = rnorm(1,beta_c,sqrt(1/taubeta))
for(j in 1:t){
y[i,j] = alpha[i] + beta[i]*x[j] + e[i,j]
}}
hist(y)
############################
# Parametros a Priori
############################
m.alpha = 0
V.alpha =1/0.0001
m.beta =0
V.beta =1/0.0001
a.tau =0.001
b.tau =0.001
a.alpha =0.001
b.alpha =0.001
a.beta =0.001
b.beta =0.001
############################
# Valores da cadeia
############################
nsim = 150000
burnin = nsim/2
#Criando os objetos:
cadeia.alpha.i = matrix(NA,nsim,n)
cadeia.beta.i = matrix(NA,nsim,n)
cadeia.tau.c = matrix(NA,nsim,1)
cadeia.alpha.c = matrix(NA,nsim,1)
cadeia.beta.c = matrix(NA,nsim,1)
cadeia.tau.alpha = matrix(NA,nsim,1)
cadeia.tau.beta = matrix(NA,nsim,1)
#Chutes iniciais:
cadeia.alpha.i[1,] = 0
cadeia.beta.i[1,] = 0
cadeia.tau.c[1,] = 1
cadeia.alpha.c[1,] = 0
cadeia.beta.c[1,] = 0
cadeia.tau.alpha[1,] = 1
cadeia.tau.beta[1,] = 1
# Obs.: Indice da cadeia sera k
# ----------------------------------------------------------------------------------
############################
# Algoritimo da cadeia
############################
# Sementes:
set.seed(1)
#Criar uma barra de processo e acompanhar o carregamento:
pb <- txtProgressBar(min = 0, max = nsim, style = 3);antes = Sys.time()
for(k in 2:nsim){
soma = 0
#Cadeia alpha.i e beta.i
for(i in 1:n){
dccp.tau.alpha = 1 / ((t*cadeia.tau.c[k-1]) + cadeia.tau.alpha[k-1])
dccp.alpha.c = ( cadeia.tau.c[k-1]*sum( y[i,] - cadeia.beta.i[k-1,i]*x ) +
cadeia.tau.alpha[k-1]*cadeia.alpha.c[k-1] ) * dccp.tau.alpha
cadeia.alpha.i[k,i] = rnorm(1,dccp.alpha.c,sqrt(dccp.tau.alpha))
dccp.tau.beta = 1 / ((cadeia.tau.c[k-1]*sum(x^2)) + cadeia.tau.beta[k-1])
u = (y[i,]-cadeia.alpha.i[k,i])*x
dccp.beta.c = (cadeia.tau.c[k-1]*sum(u) + (cadeia.tau.beta[k-1]*cadeia.beta.c[k-1])) * dccp.tau.beta
cadeia.beta.i[k,i] = rnorm(1,dccp.beta.c,sqrt(dccp.tau.beta))
for(j in 1:t){soma = soma + (y[i,j] - cadeia.alpha.i[k,i]-cadeia.beta.i[k,i]*x[j])^2}
}
# cadeia de tau
dccp.a = ((n*t)/2) + a.tau
dccp.beta = b.tau + 0.5 * soma
cadeia.tau.c[k] = rgamma(1,dccp.a,dccp.beta)
#cadeia.tau.c[k] = tau
#cadeia de tau.alpha
dccp.a.alpha = (n/2) + a.alpha
dccp.b.alpha = b.alpha + 0.5 * sum((cadeia.alpha.i[k,]-cadeia.alpha.c[k-1,])^2)
cadeia.tau.alpha[k] = rgamma(1,dccp.a.alpha,dccp.b.alpha)
#cadeia.tau.alpha[k] = taualpha
#cadeia de tau.beta
dccp.a.beta = (n/2) + a.beta
dccp.b.beta = b.beta + 0.5 * sum((cadeia.beta.i[k,]-cadeia.beta.c[k-1,])^2)
cadeia.tau.beta[k] = rgamma(1,dccp.a.beta,dccp.b.beta)
#cadeia.tau.beta[k] = taubeta
#Cadeia de alpha.c
dccp.V.alpha = 1 / (n*cadeia.tau.alpha[k] + 1/V.alpha)
dccp.m.alpha = (cadeia.tau.alpha[k] * sum(cadeia.alpha.i[k,]) + m.alpha/V.alpha) * dccp.V.alpha
cadeia.alpha.c[k] = rnorm(1,dccp.m.alpha,sqrt(dccp.V.alpha))
#cadeia.alpha.c[k] = alpha_c
#Cadeia de beta.c
dccp.V.beta = 1 / (n*cadeia.tau.beta[k] + 1/V.beta)
dccp.m.beta = (cadeia.tau.beta[k] * sum(cadeia.beta.i[k,]) + m.beta/V.beta) * dccp.V.beta
cadeia.beta.c[k] = rnorm(1,dccp.m.beta,sqrt(dccp.V.beta))
#cadeia.beta.c[k] = beta_c
# update barra de processo
setTxtProgressBar(pb, k)
}; close(pb); depois = Sys.time() - antes; depois #Encerrando barra de processo e tempo da cadeia
############################
# Resultados da cadeia
############################
# Juntando resultados:
inds = seq(burnin,nsim,50)
results <- cbind(cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta) %>% as.data.frame() %>% .[inds,]
real <- c(alpha_c,beta_c,tau,taualpha,taubeta)
name <- c(expression(alpha[c]), expression(beta[c]), expression(tau[c]), expression(tau[alpha]), expression(tau[beta]))
# Cadeia
png("imagens/cadeia_hierarquica_sim.png",1100,1000)
cadeia(results,name,real)
dev.off()
# Histograma e densidade
png("imagens/hist_hierarquica_sim.png",830,610)
g1 <- hist_den(results[,1],name = name[1], p = real[1])
g2 <- hist_den(results[,2],name = name[2], p = real[2])
g3 <- hist_den(results[,3],name = name[3], p = real[3])
g4 <- hist_den(results[,4],name = name[4], p = real[4])
g5 <- hist_den(results[,5],name = name[5], p = real[5])
grid.arrange(g1,g2,g3,g4,g5,ncol=1)
dev.off()
# ACF
png("imagens/acf_hierarquica_sim.png",1000,610)
FAC(results)
dev.off()
# Coeficientes:
coeficientes <- coeficientes_hierarquico(cadeia.alpha.i[inds,],alpha,
cadeia.beta.i[inds,], beta,
cadeia.alpha.c, alpha_c,
cadeia.beta.c, beta_c,
cadeia.tau.c,tau,
cadeia.tau.alpha, taualpha,
cadeia.tau.beta, taubeta,
ncol(results)+60
)
# sumario:
sumario <- coeficientes[[1]];sumario
# credibilidade:
tab <- coeficientes[[2]];tab
tab %>% tail(5) %>% .[,c(1,3,4)] %>% cbind(sd = results %>% apply(2,sd) %>% round(4)) %>% xtable()
# Com GGPLOT para o texto -------------------------------------------------
##############
# Para alpha #
##############
png("imagens/caterplot_a_hierarquica_sim.png",800,600)
cbind(alpha=1:30,tab[1:30,])%>%
as.data.frame%>%
ggplot( aes(x=alpha, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=alpha,y=`Param. Pop.`),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de alpha") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[1:30,1]),max(tab[1:30,4])),breaks=seq(min(tab[1:30,1]),max(tab[1:30,4]),0.2)) + # Set tick every 4
theme_bw()+
geom_hline(yintercept = alpha_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
##############
# Para beta #
##############
png("imagens/caterplot_b_hierarquica_sim.png",800,600)
cbind(beta=31:60,tab[31:60,])%>%
as.data.frame%>%
ggplot( aes(x=beta, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
geom_point(aes(x=beta,y=`Param. Pop.`),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
# geom_line(position=pd) +
xlab("Cadeia de beta") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[31:60,1]),max(tab[31:60,4])),breaks=seq(min(tab[31:60,1]),max(tab[31:60,4]),0.2)) + # Set tick every 4
theme_bw()+
geom_hline(yintercept = beta_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
# ############################################################################################################
# ############################################# Dados reais ##############################################
# ############################################################################################################
# Amostrador de Gibbs para modelo linear bayesiano --------------------------
############################
# Amostra utilizada
############################
rats = read.table("rats.txt",header=T)
y=rats
x=c(8, 15, 22, 29, 36)
xbarra=mean(x)
x=x-xbarra
n=nrow(y)
t=ncol(y)
############################
# Parametros a Priori
############################
m.alpha = 0
V.alpha =1/0.0001
m.beta =0
V.beta =1/0.0001
a.tau =0.001
b.tau =0.001
a.alpha =0.001
b.alpha =0.001
a.beta =0.001
b.beta =0.001
############################
# Valores da cadeia
############################
nsim = 50000
burnin = nsim/3
#Criando os objetos:
cadeia.alpha.i = matrix(NA,nsim,n)
cadeia.beta.i = matrix(NA,nsim,n)
cadeia.tau.c = matrix(NA,nsim,1)
cadeia.alpha.c = matrix(NA,nsim,1)
cadeia.beta.c = matrix(NA,nsim,1)
cadeia.tau.alpha = matrix(NA,nsim,1)
cadeia.tau.beta = matrix(NA,nsim,1)
#Chutes iniciais:
cadeia.alpha.i[1,] = 0
cadeia.beta.i[1,] = 0
cadeia.tau.c[1,] = 1
cadeia.alpha.c[1,] = 0
cadeia.beta.c[1,] = 0
cadeia.tau.alpha[1,] = 1
cadeia.tau.beta[1,] = 1
# Obs.: Indice da cadeia sera k
# ----------------------------------------------------------------------------------
############################
# Algoritimo da cadeia
############################
# Sementes:
set.seed(1)
#Criar uma barra de processo e acompanhar o carregamento:
pb <- txtProgressBar(min = 0, max = nsim, style = 3);antes = Sys.time()
for(k in 2:nsim){
soma = 0
#Cadeia alpha.i e beta.i
for(i in 1:n){
dccp.tau.alpha = 1 / ((t*cadeia.tau.c[k-1]) + cadeia.tau.alpha[k-1])
dccp.alpha.c = ( cadeia.tau.c[k-1]*sum( y[i,] - cadeia.beta.i[k-1,i]*x ) +
cadeia.tau.alpha[k-1]*cadeia.alpha.c[k-1] ) * dccp.tau.alpha
cadeia.alpha.i[k,i] = rnorm(1,dccp.alpha.c,sqrt(dccp.tau.alpha))
dccp.tau.beta = 1 / ((cadeia.tau.c[k-1]*sum(x^2)) + cadeia.tau.beta[k-1])
u = (y[i,]-cadeia.alpha.i[k,i])*x
dccp.beta.c = (cadeia.tau.c[k-1]*sum(u) + (cadeia.tau.beta[k-1]*cadeia.beta.c[k-1])) * dccp.tau.beta
cadeia.beta.i[k,i] = rnorm(1,dccp.beta.c,sqrt(dccp.tau.beta))
for(j in 1:t){soma = soma + (y[i,j] - cadeia.alpha.i[k,i]-cadeia.beta.i[k,i]*x[j])^2}
}
# cadeia de tau
dccp.a = ((n*t)/2) + a.tau
dccp.beta = b.tau + 0.5 * soma
cadeia.tau.c[k] = rgamma(1,dccp.a,dccp.beta)
#cadeia.tau.c[k] = tau
#cadeia de tau.alpha
dccp.a.alpha = (n/2) + a.alpha
dccp.b.alpha = b.alpha + 0.5 * sum((cadeia.alpha.i[k,]-cadeia.alpha.c[k-1,])^2)
cadeia.tau.alpha[k] = rgamma(1,dccp.a.alpha,dccp.b.alpha)
#cadeia.tau.alpha[k] = taualpha
#cadeia de tau.beta
dccp.a.beta = (n/2) + a.beta
dccp.b.beta = b.beta + 0.5 * sum((cadeia.beta.i[k,]-cadeia.beta.c[k-1,])^2)
cadeia.tau.beta[k] = rgamma(1,dccp.a.beta,dccp.b.beta)
#cadeia.tau.beta[k] = taubeta
#Cadeia de alpha.c
dccp.V.alpha = 1 / (n*cadeia.tau.alpha[k] + 1/V.alpha)
dccp.m.alpha = (cadeia.tau.alpha[k] * sum(cadeia.alpha.i[k,]) + m.alpha/V.alpha) * dccp.V.alpha
cadeia.alpha.c[k] = rnorm(1,dccp.m.alpha,sqrt(dccp.V.alpha))
#cadeia.alpha.c[k] = alpha_c
#Cadeia de beta.c
dccp.V.beta = 1 / (n*cadeia.tau.beta[k] + 1/V.beta)
dccp.m.beta = (cadeia.tau.beta[k] * sum(cadeia.beta.i[k,]) + m.beta/V.beta) * dccp.V.beta
cadeia.beta.c[k] = rnorm(1,dccp.m.beta,sqrt(dccp.V.beta))
#cadeia.beta.c[k] = beta_c
# update barra de processo
setTxtProgressBar(pb, k)
}; close(pb); depois = Sys.time() - antes; depois #Encerrando barra de processo e tempo da cadeia
############################
# Resultados da cadeia
############################
# Juntando resultados:
inds = seq(burnin,nsim,50)
results <- cbind(cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta) %>% as.data.frame() %>% .[inds,]
name <- c(expression(alpha[c]), expression(beta[c]), expression(tau[c]), expression(tau[alpha]), expression(tau[beta]))
# Cadeia
png("imagens/cadeia_hierarquica_rats.png",830,610)
cadeia(results,name)
dev.off()
# Histograma e densidade
png("imagens/hist_hierarquica_rats.png",830,610)
g1 <- hist_den(results[,1],name = name[1])
g2 <- hist_den(results[,2],name = name[2])
g3 <- hist_den(results[,3],name = name[3])
g4 <- hist_den(results[,4],name = name[4])
g5 <- hist_den(results[,5],name = name[5])
grid.arrange(g1,g2,g3,g4,g5,ncol=1)
dev.off()
# ACF
png("imagens/acf_hierarquica_rats.png",1000,610)
FAC(results)
dev.off()
###################
# continuar aqui
##################
# Coeficientes:
coeficientes <- coeficientes_hierarquico2(cadeia.alpha.i[inds,],
cadeia.beta.i[inds,],
cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta,
ncol(results)+60
)
# sumario:
sumario <- coeficientes[[1]];sumario
# credibilidade:
tab <- coeficientes[[2]];tab
tab %>% tail(5) %>% cbind(sd = results %>% apply(2,sd) %>% round(4)) %>% xtable()
# Com GGPLOT para o texto -------------------------------------------------
##############
# Para alpha #
##############
png("imagens/caterplot_a_hierarquica_rats.png",800,600)
cbind(alpha=1:30,tab[1:30,])%>%
as.data.frame%>%
ggplot( aes(x=alpha, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=alpha,y=Média),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de alpha") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[1:30,1]),max(tab[1:30,3]))) + # Set tick every 4
scale_x_continuous(breaks = seq(1,30,1)) + # Set tick every 4
theme_bw()
# +
# geom_hline(yintercept = alpha_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
# axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
##############
# Para beta #
##############
png("imagens/caterplot_b_hierarquica_rats.png",800,600)
cbind(beta=1:30,tab[31:60,])%>%
as.data.frame%>%
ggplot( aes(x=beta, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=beta,y=Média),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de beta") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[31:60,1]),max(tab[31:60,3]))) + # Set tick every 4
scale_x_continuous(breaks = seq(1,30,1)) + # Set tick every 4
theme_bw()
# +
# geom_hline(yintercept = beta_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
# axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
tab
| /modelo linear hierarquico bayesiano com dados simulados e rats.R | no_license | gomesfellipe/projeto_modelos_hierarquicos_bayesianos | R | false | false | 18,743 | r | # ############################################################################################################
# ############################################# Dados simulados ##############################################
# ############################################################################################################
# carregando dependencias
source("dependencies.R",encoding = "UTF-8")
# Amostrador de Gibbs para modelo linear bayesiano --------------------------
############################
# Gerando a amostra
############################
set.seed(22-03-2018)
x = c(8, 15, 22, 29, 36)
xbarra = mean(x)
x = x - xbarra
n = 30
t = 5
alpha_c = 20
beta_c = 2
alpha = beta = NULL
taualpha = 1/0.2
taubeta = 1/0.2
y = matrix(NA,n,t)
tau = 1
e = matrix(rnorm(n*t,0,sqrt(1/tau)),n,t)
for(i in 1:n){
alpha[i] = rnorm(1,alpha_c,sqrt(1/taualpha))
beta[i] = rnorm(1,beta_c,sqrt(1/taubeta))
for(j in 1:t){
y[i,j] = alpha[i] + beta[i]*x[j] + e[i,j]
}}
hist(y)
############################
# Parametros a Priori
############################
m.alpha = 0
V.alpha =1/0.0001
m.beta =0
V.beta =1/0.0001
a.tau =0.001
b.tau =0.001
a.alpha =0.001
b.alpha =0.001
a.beta =0.001
b.beta =0.001
############################
# Valores da cadeia
############################
nsim = 150000
burnin = nsim/2
#Criando os objetos:
cadeia.alpha.i = matrix(NA,nsim,n)
cadeia.beta.i = matrix(NA,nsim,n)
cadeia.tau.c = matrix(NA,nsim,1)
cadeia.alpha.c = matrix(NA,nsim,1)
cadeia.beta.c = matrix(NA,nsim,1)
cadeia.tau.alpha = matrix(NA,nsim,1)
cadeia.tau.beta = matrix(NA,nsim,1)
#Chutes iniciais:
cadeia.alpha.i[1,] = 0
cadeia.beta.i[1,] = 0
cadeia.tau.c[1,] = 1
cadeia.alpha.c[1,] = 0
cadeia.beta.c[1,] = 0
cadeia.tau.alpha[1,] = 1
cadeia.tau.beta[1,] = 1
# Obs.: Indice da cadeia sera k
# ----------------------------------------------------------------------------------
############################
# Algoritimo da cadeia
############################
# Sementes:
set.seed(1)
#Criar uma barra de processo e acompanhar o carregamento:
pb <- txtProgressBar(min = 0, max = nsim, style = 3);antes = Sys.time()
for(k in 2:nsim){
soma = 0
#Cadeia alpha.i e beta.i
for(i in 1:n){
dccp.tau.alpha = 1 / ((t*cadeia.tau.c[k-1]) + cadeia.tau.alpha[k-1])
dccp.alpha.c = ( cadeia.tau.c[k-1]*sum( y[i,] - cadeia.beta.i[k-1,i]*x ) +
cadeia.tau.alpha[k-1]*cadeia.alpha.c[k-1] ) * dccp.tau.alpha
cadeia.alpha.i[k,i] = rnorm(1,dccp.alpha.c,sqrt(dccp.tau.alpha))
dccp.tau.beta = 1 / ((cadeia.tau.c[k-1]*sum(x^2)) + cadeia.tau.beta[k-1])
u = (y[i,]-cadeia.alpha.i[k,i])*x
dccp.beta.c = (cadeia.tau.c[k-1]*sum(u) + (cadeia.tau.beta[k-1]*cadeia.beta.c[k-1])) * dccp.tau.beta
cadeia.beta.i[k,i] = rnorm(1,dccp.beta.c,sqrt(dccp.tau.beta))
for(j in 1:t){soma = soma + (y[i,j] - cadeia.alpha.i[k,i]-cadeia.beta.i[k,i]*x[j])^2}
}
# cadeia de tau
dccp.a = ((n*t)/2) + a.tau
dccp.beta = b.tau + 0.5 * soma
cadeia.tau.c[k] = rgamma(1,dccp.a,dccp.beta)
#cadeia.tau.c[k] = tau
#cadeia de tau.alpha
dccp.a.alpha = (n/2) + a.alpha
dccp.b.alpha = b.alpha + 0.5 * sum((cadeia.alpha.i[k,]-cadeia.alpha.c[k-1,])^2)
cadeia.tau.alpha[k] = rgamma(1,dccp.a.alpha,dccp.b.alpha)
#cadeia.tau.alpha[k] = taualpha
#cadeia de tau.beta
dccp.a.beta = (n/2) + a.beta
dccp.b.beta = b.beta + 0.5 * sum((cadeia.beta.i[k,]-cadeia.beta.c[k-1,])^2)
cadeia.tau.beta[k] = rgamma(1,dccp.a.beta,dccp.b.beta)
#cadeia.tau.beta[k] = taubeta
#Cadeia de alpha.c
dccp.V.alpha = 1 / (n*cadeia.tau.alpha[k] + 1/V.alpha)
dccp.m.alpha = (cadeia.tau.alpha[k] * sum(cadeia.alpha.i[k,]) + m.alpha/V.alpha) * dccp.V.alpha
cadeia.alpha.c[k] = rnorm(1,dccp.m.alpha,sqrt(dccp.V.alpha))
#cadeia.alpha.c[k] = alpha_c
#Cadeia de beta.c
dccp.V.beta = 1 / (n*cadeia.tau.beta[k] + 1/V.beta)
dccp.m.beta = (cadeia.tau.beta[k] * sum(cadeia.beta.i[k,]) + m.beta/V.beta) * dccp.V.beta
cadeia.beta.c[k] = rnorm(1,dccp.m.beta,sqrt(dccp.V.beta))
#cadeia.beta.c[k] = beta_c
# update barra de processo
setTxtProgressBar(pb, k)
}; close(pb); depois = Sys.time() - antes; depois #Encerrando barra de processo e tempo da cadeia
############################
# Resultados da cadeia
############################
# Juntando resultados:
inds = seq(burnin,nsim,50)
results <- cbind(cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta) %>% as.data.frame() %>% .[inds,]
real <- c(alpha_c,beta_c,tau,taualpha,taubeta)
name <- c(expression(alpha[c]), expression(beta[c]), expression(tau[c]), expression(tau[alpha]), expression(tau[beta]))
# Cadeia
png("imagens/cadeia_hierarquica_sim.png",1100,1000)
cadeia(results,name,real)
dev.off()
# Histograma e densidade
png("imagens/hist_hierarquica_sim.png",830,610)
g1 <- hist_den(results[,1],name = name[1], p = real[1])
g2 <- hist_den(results[,2],name = name[2], p = real[2])
g3 <- hist_den(results[,3],name = name[3], p = real[3])
g4 <- hist_den(results[,4],name = name[4], p = real[4])
g5 <- hist_den(results[,5],name = name[5], p = real[5])
grid.arrange(g1,g2,g3,g4,g5,ncol=1)
dev.off()
# ACF
png("imagens/acf_hierarquica_sim.png",1000,610)
FAC(results)
dev.off()
# Coeficientes:
coeficientes <- coeficientes_hierarquico(cadeia.alpha.i[inds,],alpha,
cadeia.beta.i[inds,], beta,
cadeia.alpha.c, alpha_c,
cadeia.beta.c, beta_c,
cadeia.tau.c,tau,
cadeia.tau.alpha, taualpha,
cadeia.tau.beta, taubeta,
ncol(results)+60
)
# sumario:
sumario <- coeficientes[[1]];sumario
# credibilidade:
tab <- coeficientes[[2]];tab
tab %>% tail(5) %>% .[,c(1,3,4)] %>% cbind(sd = results %>% apply(2,sd) %>% round(4)) %>% xtable()
# Com GGPLOT para o texto -------------------------------------------------
##############
# Para alpha #
##############
png("imagens/caterplot_a_hierarquica_sim.png",800,600)
cbind(alpha=1:30,tab[1:30,])%>%
as.data.frame%>%
ggplot( aes(x=alpha, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=alpha,y=`Param. Pop.`),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de alpha") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[1:30,1]),max(tab[1:30,4])),breaks=seq(min(tab[1:30,1]),max(tab[1:30,4]),0.2)) + # Set tick every 4
theme_bw()+
geom_hline(yintercept = alpha_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
##############
# Para beta #
##############
png("imagens/caterplot_b_hierarquica_sim.png",800,600)
cbind(beta=31:60,tab[31:60,])%>%
as.data.frame%>%
ggplot( aes(x=beta, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
geom_point(aes(x=beta,y=`Param. Pop.`),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
# geom_line(position=pd) +
xlab("Cadeia de beta") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[31:60,1]),max(tab[31:60,4])),breaks=seq(min(tab[31:60,1]),max(tab[31:60,4]),0.2)) + # Set tick every 4
theme_bw()+
geom_hline(yintercept = beta_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
# ############################################################################################################
# ############################################# Dados reais ##############################################
# ############################################################################################################
# Amostrador de Gibbs para modelo linear bayesiano --------------------------
############################
# Amostra utilizada
############################
rats = read.table("rats.txt",header=T)
y=rats
x=c(8, 15, 22, 29, 36)
xbarra=mean(x)
x=x-xbarra
n=nrow(y)
t=ncol(y)
############################
# Parametros a Priori
############################
m.alpha = 0
V.alpha =1/0.0001
m.beta =0
V.beta =1/0.0001
a.tau =0.001
b.tau =0.001
a.alpha =0.001
b.alpha =0.001
a.beta =0.001
b.beta =0.001
############################
# Valores da cadeia
############################
nsim = 50000
burnin = nsim/3
#Criando os objetos:
cadeia.alpha.i = matrix(NA,nsim,n)
cadeia.beta.i = matrix(NA,nsim,n)
cadeia.tau.c = matrix(NA,nsim,1)
cadeia.alpha.c = matrix(NA,nsim,1)
cadeia.beta.c = matrix(NA,nsim,1)
cadeia.tau.alpha = matrix(NA,nsim,1)
cadeia.tau.beta = matrix(NA,nsim,1)
#Chutes iniciais:
cadeia.alpha.i[1,] = 0
cadeia.beta.i[1,] = 0
cadeia.tau.c[1,] = 1
cadeia.alpha.c[1,] = 0
cadeia.beta.c[1,] = 0
cadeia.tau.alpha[1,] = 1
cadeia.tau.beta[1,] = 1
# Obs.: Indice da cadeia sera k
# ----------------------------------------------------------------------------------
############################
# Algoritimo da cadeia
############################
# Sementes:
set.seed(1)
#Criar uma barra de processo e acompanhar o carregamento:
pb <- txtProgressBar(min = 0, max = nsim, style = 3);antes = Sys.time()
for(k in 2:nsim){
soma = 0
#Cadeia alpha.i e beta.i
for(i in 1:n){
dccp.tau.alpha = 1 / ((t*cadeia.tau.c[k-1]) + cadeia.tau.alpha[k-1])
dccp.alpha.c = ( cadeia.tau.c[k-1]*sum( y[i,] - cadeia.beta.i[k-1,i]*x ) +
cadeia.tau.alpha[k-1]*cadeia.alpha.c[k-1] ) * dccp.tau.alpha
cadeia.alpha.i[k,i] = rnorm(1,dccp.alpha.c,sqrt(dccp.tau.alpha))
dccp.tau.beta = 1 / ((cadeia.tau.c[k-1]*sum(x^2)) + cadeia.tau.beta[k-1])
u = (y[i,]-cadeia.alpha.i[k,i])*x
dccp.beta.c = (cadeia.tau.c[k-1]*sum(u) + (cadeia.tau.beta[k-1]*cadeia.beta.c[k-1])) * dccp.tau.beta
cadeia.beta.i[k,i] = rnorm(1,dccp.beta.c,sqrt(dccp.tau.beta))
for(j in 1:t){soma = soma + (y[i,j] - cadeia.alpha.i[k,i]-cadeia.beta.i[k,i]*x[j])^2}
}
# cadeia de tau
dccp.a = ((n*t)/2) + a.tau
dccp.beta = b.tau + 0.5 * soma
cadeia.tau.c[k] = rgamma(1,dccp.a,dccp.beta)
#cadeia.tau.c[k] = tau
#cadeia de tau.alpha
dccp.a.alpha = (n/2) + a.alpha
dccp.b.alpha = b.alpha + 0.5 * sum((cadeia.alpha.i[k,]-cadeia.alpha.c[k-1,])^2)
cadeia.tau.alpha[k] = rgamma(1,dccp.a.alpha,dccp.b.alpha)
#cadeia.tau.alpha[k] = taualpha
#cadeia de tau.beta
dccp.a.beta = (n/2) + a.beta
dccp.b.beta = b.beta + 0.5 * sum((cadeia.beta.i[k,]-cadeia.beta.c[k-1,])^2)
cadeia.tau.beta[k] = rgamma(1,dccp.a.beta,dccp.b.beta)
#cadeia.tau.beta[k] = taubeta
#Cadeia de alpha.c
dccp.V.alpha = 1 / (n*cadeia.tau.alpha[k] + 1/V.alpha)
dccp.m.alpha = (cadeia.tau.alpha[k] * sum(cadeia.alpha.i[k,]) + m.alpha/V.alpha) * dccp.V.alpha
cadeia.alpha.c[k] = rnorm(1,dccp.m.alpha,sqrt(dccp.V.alpha))
#cadeia.alpha.c[k] = alpha_c
#Cadeia de beta.c
dccp.V.beta = 1 / (n*cadeia.tau.beta[k] + 1/V.beta)
dccp.m.beta = (cadeia.tau.beta[k] * sum(cadeia.beta.i[k,]) + m.beta/V.beta) * dccp.V.beta
cadeia.beta.c[k] = rnorm(1,dccp.m.beta,sqrt(dccp.V.beta))
#cadeia.beta.c[k] = beta_c
# update barra de processo
setTxtProgressBar(pb, k)
}; close(pb); depois = Sys.time() - antes; depois #Encerrando barra de processo e tempo da cadeia
############################
# Resultados da cadeia
############################
# Juntando resultados:
inds = seq(burnin,nsim,50)
results <- cbind(cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta) %>% as.data.frame() %>% .[inds,]
name <- c(expression(alpha[c]), expression(beta[c]), expression(tau[c]), expression(tau[alpha]), expression(tau[beta]))
# Cadeia
png("imagens/cadeia_hierarquica_rats.png",830,610)
cadeia(results,name)
dev.off()
# Histograma e densidade
png("imagens/hist_hierarquica_rats.png",830,610)
g1 <- hist_den(results[,1],name = name[1])
g2 <- hist_den(results[,2],name = name[2])
g3 <- hist_den(results[,3],name = name[3])
g4 <- hist_den(results[,4],name = name[4])
g5 <- hist_den(results[,5],name = name[5])
grid.arrange(g1,g2,g3,g4,g5,ncol=1)
dev.off()
# ACF
png("imagens/acf_hierarquica_rats.png",1000,610)
FAC(results)
dev.off()
###################
# continuar aqui
##################
# Coeficientes:
coeficientes <- coeficientes_hierarquico2(cadeia.alpha.i[inds,],
cadeia.beta.i[inds,],
cadeia.alpha.c,
cadeia.beta.c,
cadeia.tau.c,
cadeia.tau.alpha,
cadeia.tau.beta,
ncol(results)+60
)
# sumario:
sumario <- coeficientes[[1]];sumario
# credibilidade:
tab <- coeficientes[[2]];tab
tab %>% tail(5) %>% cbind(sd = results %>% apply(2,sd) %>% round(4)) %>% xtable()
# Com GGPLOT para o texto -------------------------------------------------
##############
# Para alpha #
##############
png("imagens/caterplot_a_hierarquica_rats.png",800,600)
cbind(alpha=1:30,tab[1:30,])%>%
as.data.frame%>%
ggplot( aes(x=alpha, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=alpha,y=Média),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de alpha") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[1:30,1]),max(tab[1:30,3]))) + # Set tick every 4
scale_x_continuous(breaks = seq(1,30,1)) + # Set tick every 4
theme_bw()
# +
# geom_hline(yintercept = alpha_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
# axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
##############
# Para beta #
##############
png("imagens/caterplot_b_hierarquica_rats.png",800,600)
cbind(beta=1:30,tab[31:60,])%>%
as.data.frame%>%
ggplot( aes(x=beta, y=Média)) + #, colour=supp, group=supp <- separar por cadeia?
geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), colour="black", width=.1, position=position_dodge(0.1)) +
# geom_line(position=pd) +
geom_point(aes(x=beta,y=Média),position=position_dodge(0.1), size=2, shape=21, fill="blue") + # 21 is filled circle
geom_point(position=position_dodge(0.1), size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Cadeia de beta") +
ylab("Intervalo de credibilidade") +
# scale_colour_hue(name="Supplement type", # Legend label, use darker colors
# breaks=c("OJ", "VC"),
# labels=c("Orange juice", "Ascorbic acid"),
# l=40) + # Use darker colors, lightness=40
ggtitle(" ") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(limits=c(min(tab[31:60,1]),max(tab[31:60,3]))) + # Set tick every 4
scale_x_continuous(breaks = seq(1,30,1)) + # Set tick every 4
theme_bw()
# +
# geom_hline(yintercept = beta_c, linetype="dashed", color = "red",size=1.3)+theme(axis.text=element_text(size=12),
# axis.title=element_text(size=14,face="bold"))
# + # Para incluir legenda
# theme(legend.justification=c(1,0),
# legend.position=c(1,0)) # Position legend in bottom right
dev.off()
tab
|
f <- function(n, x=1) for(i in 1:n) x <- 1/(1+x)
g <- function(n, x=1) for(i in 1:n) x <- (1/(1+x))
h <- function(n, x=1) for(i in 1:n) x <- (1+x)^(-1)
j <- function(n, x=1) for(i in 1:n) x <- {1/{1+x}}
k <- function(n, x=1) for(i in 1:n) x <- 1/{1+x}
library(rbenchmark)
N <- 1e5
benchmark(f(N,1),g(N,1),h(N,1),j(N,1),k(N,1))[,1:4]
library(Rcpp)
cppFunction("
double m(int n, double x=1) {
for (int i=0; i<n; i++)
x = 1 / (1+x);
return x;
}")
benchmark(f(N,1),g(N,1),h(N,1),j(N,1),k(N,1),m(N,1),order="relative")[,1:4]
| /uzuerich-2015-06/straightorcurly.R | no_license | eddelbuettel/samplecode | R | false | false | 563 | r |
f <- function(n, x=1) for(i in 1:n) x <- 1/(1+x)
g <- function(n, x=1) for(i in 1:n) x <- (1/(1+x))
h <- function(n, x=1) for(i in 1:n) x <- (1+x)^(-1)
j <- function(n, x=1) for(i in 1:n) x <- {1/{1+x}}
k <- function(n, x=1) for(i in 1:n) x <- 1/{1+x}
library(rbenchmark)
N <- 1e5
benchmark(f(N,1),g(N,1),h(N,1),j(N,1),k(N,1))[,1:4]
library(Rcpp)
cppFunction("
double m(int n, double x=1) {
for (int i=0; i<n; i++)
x = 1 / (1+x);
return x;
}")
benchmark(f(N,1),g(N,1),h(N,1),j(N,1),k(N,1),m(N,1),order="relative")[,1:4]
|
## This file takes an annotSnpStats object and removes all SNPs that are not in basis
library(data.table)
# basis reference file
ref_af_file<-'/rds/user/ob219/hpc-work/as_basis/support_tab/as_basis_snp_support_feb_2018_w_ms.tab'
m_file<-'/home/ob219/git/as_basis/manifest/as_manifest_mar_2018.csv'
## dir where all preprocessed gwas files are.
## we expect variants to be reflected in ref_af_file, have there OR aligned and be defined in the manifest file
gwas_data_dir <- '/rds/user/ob219/hpc-work/as_basis/gwas_stats/filter_feb_2018_w_ms/aligned'
bsnps <- fread(ref_af_file)
bsnps[,c('chr','pos'):=tstrsplit(pid,':')]
out.dir <- '/rds/user/ob219/hpc-work/as_basis/JIA_basis_annotSnpStats'
jfil <- list.files(path="/home/ob219/rds/rds-cew54-wallace-share/Data/GWAS/JIA-2017-data/",pattern="*.RData",full.names=TRUE)
library(annotSnpStats)
PF <- function(fn){
G <- get(load(fn))
#chr <- gsub("annotsnpstats-([^.]+)\\.RData","\\1",basename(fn))
sdt <- data.table(snps(G))
keep <- which(paste(sdt$chromosome,sdt$position,sep=':') %in% bsnps$pid)
missing <- nrow(bsnps[chr==sdt$chromosome[1]]) - length(keep)
if(missing!=0)
message(sprintf("Not matching missing %d",missing))
G <- G[,keep]
sdt <- data.table(snps(G))[,.(position,a0=allele.1,a1=allele.2,rs_id=ID,chromosome)]
samps <- data.table(samples(G))
## we don't have any control data so cannot compute RAF for them
#controls <- which(samps$phenotype==0)
sdt[,af.wrt.a2:=col.summary(G)[,"RAF"]]
#G <- G[-controls,]
snps(G)<-as.data.frame(sdt)
alleles(G)<-c('a0','a1')
save(G,file=file.path(out.dir,basename(fn)))
}
for(f in jfil){
message(sprintf("Processing %s",f))
PF(f)
}
| /R/JIA_IND_MAY/cut_down_annoSnpStats_JIA.R | no_license | ollyburren/as_basis | R | false | false | 1,675 | r | ## This file takes an annotSnpStats object and removes all SNPs that are not in basis
library(data.table)
# basis reference file
ref_af_file<-'/rds/user/ob219/hpc-work/as_basis/support_tab/as_basis_snp_support_feb_2018_w_ms.tab'
m_file<-'/home/ob219/git/as_basis/manifest/as_manifest_mar_2018.csv'
## dir where all preprocessed gwas files are.
## we expect variants to be reflected in ref_af_file, have there OR aligned and be defined in the manifest file
gwas_data_dir <- '/rds/user/ob219/hpc-work/as_basis/gwas_stats/filter_feb_2018_w_ms/aligned'
bsnps <- fread(ref_af_file)
bsnps[,c('chr','pos'):=tstrsplit(pid,':')]
out.dir <- '/rds/user/ob219/hpc-work/as_basis/JIA_basis_annotSnpStats'
jfil <- list.files(path="/home/ob219/rds/rds-cew54-wallace-share/Data/GWAS/JIA-2017-data/",pattern="*.RData",full.names=TRUE)
library(annotSnpStats)
PF <- function(fn){
G <- get(load(fn))
#chr <- gsub("annotsnpstats-([^.]+)\\.RData","\\1",basename(fn))
sdt <- data.table(snps(G))
keep <- which(paste(sdt$chromosome,sdt$position,sep=':') %in% bsnps$pid)
missing <- nrow(bsnps[chr==sdt$chromosome[1]]) - length(keep)
if(missing!=0)
message(sprintf("Not matching missing %d",missing))
G <- G[,keep]
sdt <- data.table(snps(G))[,.(position,a0=allele.1,a1=allele.2,rs_id=ID,chromosome)]
samps <- data.table(samples(G))
## we don't have any control data so cannot compute RAF for them
#controls <- which(samps$phenotype==0)
sdt[,af.wrt.a2:=col.summary(G)[,"RAF"]]
#G <- G[-controls,]
snps(G)<-as.data.frame(sdt)
alleles(G)<-c('a0','a1')
save(G,file=file.path(out.dir,basename(fn)))
}
for(f in jfil){
message(sprintf("Processing %s",f))
PF(f)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dictionary.functions.R
\name{mapHugo}
\alias{mapHugo}
\title{Convert from hugo gene names to entrez ids}
\usage{
mapHugo(hugo.ids)
}
\arguments{
\item{hugo.ids}{: vector of hugo gene names, requires hugo2entrez to be loaded}
}
\value{
: vector of entrez ids
}
\description{
Convert from hugo gene names to entrez ids
}
\examples{
mapHugo(c("A1CF","PTEN"))
}
\seealso{
\code{\link[MOMA]{mapEntrez}}
}
| /man/mapHugo.Rd | no_license | califano-lab/MOMA | R | false | true | 478 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dictionary.functions.R
\name{mapHugo}
\alias{mapHugo}
\title{Convert from hugo gene names to entrez ids}
\usage{
mapHugo(hugo.ids)
}
\arguments{
\item{hugo.ids}{: vector of hugo gene names, requires hugo2entrez to be loaded}
}
\value{
: vector of entrez ids
}
\description{
Convert from hugo gene names to entrez ids
}
\examples{
mapHugo(c("A1CF","PTEN"))
}
\seealso{
\code{\link[MOMA]{mapEntrez}}
}
|
library(readxl)
library(dplyr)
library(tidyr)
#reading BP data
CDE <- read_excel("bp-stats-review-2020-all-data.xlsx", sheet = "Carbon Dioxide Emissions", na = "n/a")
CDE <- as.data.frame(CDE, stringsAsFactors = False)
#fixing few names
CDE[111,1] = 'OECD'
CDE[113,1] = 'European Union'
CDE[2,1] = 'Country' #changing JEDNOSTKA to Country will help us later
#first variable without NAs
List_of_observations <- na.exclude(CDE[,1])
CDE <- CDE[CDE[,1] %in% List_of_observations,]
#creating function which negates %in%
'%!in%' = Negate('%in%')
#creating two variables to contain names of Organisations and Totals
Totals <- c("Total North America","Total S. & Cent. America", "Total Asia Pacific", "Total Europe", "Total CIS", "Total Africa", "Total World")
Organizations <- c("of which: OECD" = "OECD", "Non-OECD", "European Union")
#deleting totals' rows
CDE <- CDE[CDE[,1] %!in% Totals,]
#deleting organistions' rows
CDE <- CDE[CDE[,1] %!in% Organizations,]
#adding column's names
colnames(CDE) <- CDE[1,]
#deleting comments in the last seven rows and the last one
CDE <- head(CDE, -7)
#deleting few last columns and first row
CDE <- CDE[2:94,1:56]
#unPivoting columns
CDE2 <- gather(CDE, key = 'Year', value = 'Million tonnes of carbon dioxide', -'Country' )
| /Data import and cleaning/Carbon_Dioxide_Emissions.R | no_license | sbrylka/Statistical_Review_of_World_Energy | R | false | false | 1,276 | r | library(readxl)
library(dplyr)
library(tidyr)
#reading BP data
CDE <- read_excel("bp-stats-review-2020-all-data.xlsx", sheet = "Carbon Dioxide Emissions", na = "n/a")
CDE <- as.data.frame(CDE, stringsAsFactors = False)
#fixing few names
CDE[111,1] = 'OECD'
CDE[113,1] = 'European Union'
CDE[2,1] = 'Country' #changing JEDNOSTKA to Country will help us later
#first variable without NAs
List_of_observations <- na.exclude(CDE[,1])
CDE <- CDE[CDE[,1] %in% List_of_observations,]
#creating function which negates %in%
'%!in%' = Negate('%in%')
#creating two variables to contain names of Organisations and Totals
Totals <- c("Total North America","Total S. & Cent. America", "Total Asia Pacific", "Total Europe", "Total CIS", "Total Africa", "Total World")
Organizations <- c("of which: OECD" = "OECD", "Non-OECD", "European Union")
#deleting totals' rows
CDE <- CDE[CDE[,1] %!in% Totals,]
#deleting organistions' rows
CDE <- CDE[CDE[,1] %!in% Organizations,]
#adding column's names
colnames(CDE) <- CDE[1,]
#deleting comments in the last seven rows and the last one
CDE <- head(CDE, -7)
#deleting few last columns and first row
CDE <- CDE[2:94,1:56]
#unPivoting columns
CDE2 <- gather(CDE, key = 'Year', value = 'Million tonnes of carbon dioxide', -'Country' )
|
suppressPackageStartupMessages(library(okcpuid, quietly=TRUE))
suppressPackageStartupMessages(library(pbdMPI, quietly=TRUE))
if (interactive())
comm.stop("This benchmark may not be run interactively.")
suppressPackageStartupMessages(library(pbdDMAT, quietly=TRUE))
init.grid()
N <- 4500
bldim <- 64
# comm.set.seed(diff=TRUE)
if (length(bldim) == 1)
bldim <- rep(bldim, 2)
A <- ddmatrix("rnorm", N, N)
B <- ddmatrix("rnorm", N, 1)
peak <- linpack(A=A, B=B)
comm.print(peak)
finalize()
| /demo/pbdlinpack.r | permissive | shinra-dev/okcpuid | R | false | false | 501 | r | suppressPackageStartupMessages(library(okcpuid, quietly=TRUE))
suppressPackageStartupMessages(library(pbdMPI, quietly=TRUE))
if (interactive())
comm.stop("This benchmark may not be run interactively.")
suppressPackageStartupMessages(library(pbdDMAT, quietly=TRUE))
init.grid()
N <- 4500
bldim <- 64
# comm.set.seed(diff=TRUE)
if (length(bldim) == 1)
bldim <- rep(bldim, 2)
A <- ddmatrix("rnorm", N, N)
B <- ddmatrix("rnorm", N, 1)
peak <- linpack(A=A, B=B)
comm.print(peak)
finalize()
|
##' key drawing function
##'
##'
##' @name draw_key
##' @param data A single row data frame containing the scaled aesthetics to display in this key
##' @param params A list of additional parameters supplied to the geom.
##' @param size Width and height of key in mm
##' @return A grid grob
NULL
ggname <- getFromNamespace("ggname", "ggplot2")
##' @rdname draw_key
##' @importFrom grid rectGrob
##' @importFrom grid pointsGrob
##' @importFrom grid gpar
##' @importFrom scales alpha
##' @export
draw_key_image <- function(data, params, size) {
kt <- getOption("ggimage.keytype")
if (is.null(kt)) {
kt <- "point"
}
if (kt == "point") {
keyGrob <- pointsGrob(
0.5, 0.5,
pch = 19,
gp = gpar (
col = alpha(data$colour, data$alpha),
fill = alpha(data$colour, data$alpha),
fontsize = 3 * ggplot2::.pt,
lwd = 0.94
)
)
} else if (kt == "rect") {
keyGrob <- rectGrob(gp = gpar(
col = NA,
fill = alpha(data$colour, data$alpha)
))
} else if (kt == "image") {
img <- image_read(system.file("extdata/Rlogo.png", package="ggimage"))
grobs <- lapply(seq_along(data$colour), function(i) {
img <- color_image(img, data$colour[i], data$alpha[i])
rasterGrob(
0.5, 0.5,
image = img,
width = 1,
height = 1
)
})
class(grobs) <- "gList"
keyGrob <- ggname("image_key",
gTree(children = grobs))
}
return(keyGrob)
}
| /R/draw_key.R | no_license | cran/ggimage | R | false | false | 1,772 | r |
##' key drawing function
##'
##'
##' @name draw_key
##' @param data A single row data frame containing the scaled aesthetics to display in this key
##' @param params A list of additional parameters supplied to the geom.
##' @param size Width and height of key in mm
##' @return A grid grob
NULL
ggname <- getFromNamespace("ggname", "ggplot2")
##' @rdname draw_key
##' @importFrom grid rectGrob
##' @importFrom grid pointsGrob
##' @importFrom grid gpar
##' @importFrom scales alpha
##' @export
draw_key_image <- function(data, params, size) {
kt <- getOption("ggimage.keytype")
if (is.null(kt)) {
kt <- "point"
}
if (kt == "point") {
keyGrob <- pointsGrob(
0.5, 0.5,
pch = 19,
gp = gpar (
col = alpha(data$colour, data$alpha),
fill = alpha(data$colour, data$alpha),
fontsize = 3 * ggplot2::.pt,
lwd = 0.94
)
)
} else if (kt == "rect") {
keyGrob <- rectGrob(gp = gpar(
col = NA,
fill = alpha(data$colour, data$alpha)
))
} else if (kt == "image") {
img <- image_read(system.file("extdata/Rlogo.png", package="ggimage"))
grobs <- lapply(seq_along(data$colour), function(i) {
img <- color_image(img, data$colour[i], data$alpha[i])
rasterGrob(
0.5, 0.5,
image = img,
width = 1,
height = 1
)
})
class(grobs) <- "gList"
keyGrob <- ggname("image_key",
gTree(children = grobs))
}
return(keyGrob)
}
|
testlist <- list(data = structure(c(4.77773545311322e-299, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 7L)), q = 0)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610555225-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 174 | r | testlist <- list(data = structure(c(4.77773545311322e-299, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 7L)), q = 0)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multilevel_qp.R
\name{create_q_vector_multi}
\alias{create_q_vector_multi}
\title{Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x}
\usage{
create_q_vector_multi(Xz, trtz)
}
\arguments{
\item{Xz}{list of J n x d matrices of covariates split by group}
\item{target}{Vector of population means to re-weight to}
\item{aux_dim}{Dimension of auxiliary weights}
}
\value{
q vector
}
\description{
Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x
}
| /man/create_q_vector_multi.Rd | no_license | ebenmichael/balancer | R | false | true | 554 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multilevel_qp.R
\name{create_q_vector_multi}
\alias{create_q_vector_multi}
\title{Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x}
\usage{
create_q_vector_multi(Xz, trtz)
}
\arguments{
\item{Xz}{list of J n x d matrices of covariates split by group}
\item{target}{Vector of population means to re-weight to}
\item{aux_dim}{Dimension of auxiliary weights}
}
\value{
q vector
}
\description{
Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x
}
|
packages = c("shiny", "markdown", "shinythemes", "dplyr", "ggplot2",
"plotly", "tidyr", "kableExtra", "reshape2", "quantmod",
"lubridate", "shinyWidgets", "Hmisc", "fGarch", "parallel",
"shinycssloaders", "colourpicker")
invisible(lapply(packages, library, character.only = TRUE))
shinythemes::themeSelector()
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
)
tagList(
tags$head(tags$style(HTML("
.navbar-nav {
float: none !important;
}
.navbar-nav > li:nth-child(4) {
float: right;
}
.fa { font-size: 20px; }
"))),
navbarPage("Praedicere", theme = shinytheme("darkly"),
navbarMenu("Stock Overview",
tabPanel("Daily",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "stockSymbol", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "overviewDateRan", label = "Start and End Date:", start = Sys.Date() %m-% months(1), end = Sys.Date())),
column(4, align="center", selectInput(inputId = "overviewGraphic", label = "Graph:", choices = c("Candlestick" = "cs", "Time Series" = "ts")))),
hr(style = "border-color: #ffffff;"),
tags$br(),
withSpinner(plotly::plotlyOutput("overviewPlot")),
tags$br(),
tags$br(),
tags$br(),
hr(style = "border-color: #ffffff;"),
tags$br(),
tags$br(),
tags$br(),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("overviewTable")),
tags$br(),
tags$br(),
tags$br()
)),
"----",
tabPanel("Intraday",
fluidPage(
fluidRow(align = "center", column(4, align="center", textInput(inputId = "intra.ov.stockSymbol", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "intra.ov.overviewDateRan", label = "Start and End Date:", start = Sys.Date(), end = Sys.Date()), selectInput("intra.ov.intraday.step", label = "Choose Intraday Step:", choices = c("1 min." = "1min", "5 min." = "5min", "15 min." = "15min", "30 min." = "30min", "60 min." = "60min"))),
column(4, align="center", selectInput(inputId = "intra.ov.overviewGraphic", label = "Graph:", choices = c("Candlestick" = "cs", "Time Series" = "ts")))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("intra.ov.overviewPlot")),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
DT::dataTableOutput("intra.ov.overviewTable")
))),
tabPanel("Daily Forecast",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "stockSymbolForecast", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "forecastDateRan", label = "Start and End Date:", start = Sys.Date() %m-% months(1), end = Sys.Date())),
column(4, align="center", numericInput(inputId = "numForecast", label = "Number of Forecasts:", value = 1))),
fluidRow(column(4, align="center", actionButton(inputId = "forecastButton", label = "Auto GARCH", width = '75%')),
column(4, align="center", actionButton(inputId = "model.performance", label = "Model Performance", width = '75%')),
column(4, align="center", actionButton(inputId = "man.forecast.button", label = "Manual GARCH", width = '75%'))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("forecastPlot")),
hr(style = "border-color: #ffffff;"),
fluidRow(column(8, offset = 2, align="center", selectInput(inputId = "forecastDisplay.button", label = "Display", choices = c("Actuals and Forecast", "Only Forecast")))),
fluidRow(column(4, offset = 4, align="center", downloadButton(outputId = "forecast.download", label = "Download Forecast .csv"))),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("forecastTable")),
hr(style = "border-color: #ffffff;")
)),
tabPanel("Intraday Forecast",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "intra.stockSymbolForecast", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "intra.forecastDateRan", label = "Start and End Date:", start = Sys.Date(), end = Sys.Date()), selectInput("intra.intraday.step", label = "Choose Intraday Step:", choices = c("5 min." = "5min", "15 min." = "15min", "30 min." = "30min", "60 min." = "60min"), selected = "5 min.")),
column(4, align="center", numericInput(inputId = "intra.numForecast", label = "Number of Forecasts:", value = 1))),
fluidRow(column(4, align="center", actionButton(inputId = "intra.forecastButton", label = "Auto GARCH", width = '75%')),
column(4, align="center", actionButton(inputId = "intra.model.performance", label = "Model Performance", width = '75%')),
column(4, align="center", actionButton(inputId = "intra.man.forecast.button", label = "Manual GARCH", width = '75%'))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("intra.forecastPlot")),
hr(style = "border-color: #ffffff;"),
fluidRow(column(8, offset = 2, align="center", selectInput(inputId = "intra.forecastDisplay.button", label = "Display", choices = c("Actuals and Forecast", "Only Forecast")))),
fluidRow(column(4, offset = 4, align="center", downloadButton(outputId = "intra.forecast.download", label = "Download Forecast .csv"))),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("intra.forecastTable")),
hr(style = "border-color: #ffffff;")
)),
tabPanel("", icon = icon("cog"),
fluidPage())
))
| /Application/OLD/Pres/ui.R | no_license | jordanmwheeler/UNO-MasterProject | R | false | false | 9,100 | r | packages = c("shiny", "markdown", "shinythemes", "dplyr", "ggplot2",
"plotly", "tidyr", "kableExtra", "reshape2", "quantmod",
"lubridate", "shinyWidgets", "Hmisc", "fGarch", "parallel",
"shinycssloaders", "colourpicker")
invisible(lapply(packages, library, character.only = TRUE))
shinythemes::themeSelector()
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
)
tagList(
tags$head(tags$style(HTML("
.navbar-nav {
float: none !important;
}
.navbar-nav > li:nth-child(4) {
float: right;
}
.fa { font-size: 20px; }
"))),
navbarPage("Praedicere", theme = shinytheme("darkly"),
navbarMenu("Stock Overview",
tabPanel("Daily",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "stockSymbol", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "overviewDateRan", label = "Start and End Date:", start = Sys.Date() %m-% months(1), end = Sys.Date())),
column(4, align="center", selectInput(inputId = "overviewGraphic", label = "Graph:", choices = c("Candlestick" = "cs", "Time Series" = "ts")))),
hr(style = "border-color: #ffffff;"),
tags$br(),
withSpinner(plotly::plotlyOutput("overviewPlot")),
tags$br(),
tags$br(),
tags$br(),
hr(style = "border-color: #ffffff;"),
tags$br(),
tags$br(),
tags$br(),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("overviewTable")),
tags$br(),
tags$br(),
tags$br()
)),
"----",
tabPanel("Intraday",
fluidPage(
fluidRow(align = "center", column(4, align="center", textInput(inputId = "intra.ov.stockSymbol", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "intra.ov.overviewDateRan", label = "Start and End Date:", start = Sys.Date(), end = Sys.Date()), selectInput("intra.ov.intraday.step", label = "Choose Intraday Step:", choices = c("1 min." = "1min", "5 min." = "5min", "15 min." = "15min", "30 min." = "30min", "60 min." = "60min"))),
column(4, align="center", selectInput(inputId = "intra.ov.overviewGraphic", label = "Graph:", choices = c("Candlestick" = "cs", "Time Series" = "ts")))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("intra.ov.overviewPlot")),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
DT::dataTableOutput("intra.ov.overviewTable")
))),
tabPanel("Daily Forecast",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "stockSymbolForecast", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "forecastDateRan", label = "Start and End Date:", start = Sys.Date() %m-% months(1), end = Sys.Date())),
column(4, align="center", numericInput(inputId = "numForecast", label = "Number of Forecasts:", value = 1))),
fluidRow(column(4, align="center", actionButton(inputId = "forecastButton", label = "Auto GARCH", width = '75%')),
column(4, align="center", actionButton(inputId = "model.performance", label = "Model Performance", width = '75%')),
column(4, align="center", actionButton(inputId = "man.forecast.button", label = "Manual GARCH", width = '75%'))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("forecastPlot")),
hr(style = "border-color: #ffffff;"),
fluidRow(column(8, offset = 2, align="center", selectInput(inputId = "forecastDisplay.button", label = "Display", choices = c("Actuals and Forecast", "Only Forecast")))),
fluidRow(column(4, offset = 4, align="center", downloadButton(outputId = "forecast.download", label = "Download Forecast .csv"))),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("forecastTable")),
hr(style = "border-color: #ffffff;")
)),
tabPanel("Intraday Forecast",
fluidPage(
fluidRow(column(4, align="center", textInput(inputId = "intra.stockSymbolForecast", label = "Stock Symbol:", value = "AAPL")),
column(4, align="center", dateRangeInput(inputId = "intra.forecastDateRan", label = "Start and End Date:", start = Sys.Date(), end = Sys.Date()), selectInput("intra.intraday.step", label = "Choose Intraday Step:", choices = c("5 min." = "5min", "15 min." = "15min", "30 min." = "30min", "60 min." = "60min"), selected = "5 min.")),
column(4, align="center", numericInput(inputId = "intra.numForecast", label = "Number of Forecasts:", value = 1))),
fluidRow(column(4, align="center", actionButton(inputId = "intra.forecastButton", label = "Auto GARCH", width = '75%')),
column(4, align="center", actionButton(inputId = "intra.model.performance", label = "Model Performance", width = '75%')),
column(4, align="center", actionButton(inputId = "intra.man.forecast.button", label = "Manual GARCH", width = '75%'))),
hr(style = "border-color: #ffffff;"),
withSpinner(plotly::plotlyOutput("intra.forecastPlot")),
hr(style = "border-color: #ffffff;"),
fluidRow(column(8, offset = 2, align="center", selectInput(inputId = "intra.forecastDisplay.button", label = "Display", choices = c("Actuals and Forecast", "Only Forecast")))),
fluidRow(column(4, offset = 4, align="center", downloadButton(outputId = "intra.forecast.download", label = "Download Forecast .csv"))),
hr(style = "border-color: #ffffff;"),
tags$style(HTML(".dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate .paginate_button, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled {
color: #ffffff !important;}")),
withSpinner(DT::dataTableOutput("intra.forecastTable")),
hr(style = "border-color: #ffffff;")
)),
tabPanel("", icon = icon("cog"),
fluidPage())
))
|
test_that("dat_generate_unrestricted properly generates date in desired format", {
schema <- read_schema("schema-date.yml")
col_def <- schema$public$tables$patients$columns$birth
options <- default_faker_opts
faked_col <- dat_generate_unrestricted(2, col_def, schema, options)
expect_true(all(!is.na(as.Date(faked_col, format = col_def$format))))
})
test_that("dat_generate_restricted properly generates date in desired range", {
schema <- read_schema("schema-date.yml")
col_def <- schema$public$tables$patients$columns$treatment
options <- default_faker_opts
faked_col <- dat_generate_restricted(2, col_def, schema, options)
sim_dates <- as.Date(faked_col)
min_date <- as.Date(col_def$range[1])
max_date <- as.Date(col_def$range[2])
expect_true(all(min_date <= sim_dates & sim_dates < max_date))
})
| /tests/testthat/test-simulate_dat_col.R | no_license | LenaNoel/DataFakeR | R | false | false | 830 | r | test_that("dat_generate_unrestricted properly generates date in desired format", {
schema <- read_schema("schema-date.yml")
col_def <- schema$public$tables$patients$columns$birth
options <- default_faker_opts
faked_col <- dat_generate_unrestricted(2, col_def, schema, options)
expect_true(all(!is.na(as.Date(faked_col, format = col_def$format))))
})
test_that("dat_generate_restricted properly generates date in desired range", {
schema <- read_schema("schema-date.yml")
col_def <- schema$public$tables$patients$columns$treatment
options <- default_faker_opts
faked_col <- dat_generate_restricted(2, col_def, schema, options)
sim_dates <- as.Date(faked_col)
min_date <- as.Date(col_def$range[1])
max_date <- as.Date(col_def$range[2])
expect_true(all(min_date <= sim_dates & sim_dates < max_date))
})
|
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