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 |
|---|---|---|---|---|---|---|---|---|---|
### ###
### script parameters correlation
### ###
no_cores = 12
optimisation.table <- read.table(paste(path.list$optimisation.results, "otimisation_fit.csv", sep = "/" ),
header = TRUE,
sep = ",")
optimisation.initiate <- InitiateOptimisation(
path.list = path.list)
parameters.conditions <- optimisation.initiate$parameters.conditions
parameters.conditions$variable <- paste("p",
1:nrow(parameters.conditions),
sep ="")
registerDoParallel(no_cores)
parameters.estimated.list <-
foreach( i = 1:length(optimisation.table$par.id)) %dopar% {
par_id <- optimisation.table[i,]$par.id
parameters.tmp <- read.table(
file = paste(path.list$optimisation.data, par_id, "parameters.csv", sep = "/"),
header = TRUE,
sep = ","
)
parameters.tmp <- parameters.tmp %>%
dplyr::mutate(data.id = id) %>%
dplyr::mutate(par.id = par_id) %>%
dplyr::select(-id)
}
stopImplicitCluster()
parameters.estimated <-
do.call(what = rbind,
args = parameters.estimated.list)
#install.packages("PerformanceAnalytics")
library("PerformanceAnalytics")
parameters.estimated <- parameters.estimated %>%
dplyr::filter(likelihood < -50000)
parameters.estimated.2 <- parameters.estimated %>%
#dplyr::mutate(p1p6 = p11*p21*p1/(p6+p19*p11))
dplyr::mutate(p1p6 = p1/p6) %>%
dplyr::arrange(likelihood) %>%
dplyr::filter(likelihood < -54278.41) %>%
dplyr::summarise(p1p6 = mean(p1p6))
parameters.estimated.2$p1p6[1]
df <- parameters.estimated[, c(paste("p",c(2,3,8,9,10), sep = ""), "p1p6")]
M <- cor(df)
#M <- M[-c(4,5,7),-c(4,5,7)]
do.call(pdf,
args = append(plot.args.ggsave,
list(file =
paste(path.list$optimisation.results,
"parameters_correlations.pdf", sep ="/"))))
chart.Correlation(log(df))
corrplot(M, method = "color")
corrplot(M, method = "number")
dev.off()
parameters.estimated <- parameters.estimated %>%
dplyr::mutate(p1p6 = p1/p6) %>%
dplyr::mutate(p9p10 = p9/p10) %>%
dplyr::mutate(p8p9 = p8*p9*p10) %>%
dplyr::mutate(p2p8 = -42237642*p2+ 3.752908*p8) %>%
dplyr::mutate(p9p10 = -0.183*p9+ 259*p10)
parameters.estimated.melt <- parameters.estimated %>%
melt(id.vars = c("likelihood", "par.id", "data.id")) %>%
data.table()
parameters.estimated.melt <- parameters.estimated.melt %>% dplyr::mutate(variable = as.character(variable))
par <- "p9p10"
q <- quantile(parameters.estimated.melt$likelihood, probs = c(0.75))[[1]]
cols <- c(colnames(df), "p2p8")
gplot.list <- list()
for(par in colnames(df)){
gplot.list[[par]] <- ggplot(
data = parameters.estimated.melt %>%
dplyr::filter(variable %in% c(par),
likelihood < q),
aes(
x = log(value),
y = log(-likelihood))
) +
geom_point() +
#xlim(c(-16, -15)) +
xlab(paste("log(",par,")")) +
ggtitle(paste("Dependence of likelihood on parameters (",par,")"))
}
do.call(what = ggsave,
args = append(plot.args.ggsave,
list(filename = paste(path.list$optimisation.results,
"parameters_dependence.pdf", sep ="/"),
plot = marrangeGrob(grobs = gplot.list, ncol = 1, nrow = 1))))
#### canoncial correlations ####
df.params <- as.matrix(parameters.estimated[, c(paste("p",c(2,3,8,9,10), sep = ""), "p1p6")])
df.likelihood <- matrix(parameters.estimated[, "likelihood"],ncol = 1)
cov(x = df.params, y = df.likelihood)
# install.packages("CCA")
# require("CCA")acepack
ccor <- cc(X = df.params[,c(4,5)], Y = df.likelihood)
ccor$cor
?cc
est
| /R/computations/computations_summary_parameters.R | no_license | stork119/OSigA | R | false | false | 3,785 | r | ### ###
### script parameters correlation
### ###
no_cores = 12
optimisation.table <- read.table(paste(path.list$optimisation.results, "otimisation_fit.csv", sep = "/" ),
header = TRUE,
sep = ",")
optimisation.initiate <- InitiateOptimisation(
path.list = path.list)
parameters.conditions <- optimisation.initiate$parameters.conditions
parameters.conditions$variable <- paste("p",
1:nrow(parameters.conditions),
sep ="")
registerDoParallel(no_cores)
parameters.estimated.list <-
foreach( i = 1:length(optimisation.table$par.id)) %dopar% {
par_id <- optimisation.table[i,]$par.id
parameters.tmp <- read.table(
file = paste(path.list$optimisation.data, par_id, "parameters.csv", sep = "/"),
header = TRUE,
sep = ","
)
parameters.tmp <- parameters.tmp %>%
dplyr::mutate(data.id = id) %>%
dplyr::mutate(par.id = par_id) %>%
dplyr::select(-id)
}
stopImplicitCluster()
parameters.estimated <-
do.call(what = rbind,
args = parameters.estimated.list)
#install.packages("PerformanceAnalytics")
library("PerformanceAnalytics")
parameters.estimated <- parameters.estimated %>%
dplyr::filter(likelihood < -50000)
parameters.estimated.2 <- parameters.estimated %>%
#dplyr::mutate(p1p6 = p11*p21*p1/(p6+p19*p11))
dplyr::mutate(p1p6 = p1/p6) %>%
dplyr::arrange(likelihood) %>%
dplyr::filter(likelihood < -54278.41) %>%
dplyr::summarise(p1p6 = mean(p1p6))
parameters.estimated.2$p1p6[1]
df <- parameters.estimated[, c(paste("p",c(2,3,8,9,10), sep = ""), "p1p6")]
M <- cor(df)
#M <- M[-c(4,5,7),-c(4,5,7)]
do.call(pdf,
args = append(plot.args.ggsave,
list(file =
paste(path.list$optimisation.results,
"parameters_correlations.pdf", sep ="/"))))
chart.Correlation(log(df))
corrplot(M, method = "color")
corrplot(M, method = "number")
dev.off()
parameters.estimated <- parameters.estimated %>%
dplyr::mutate(p1p6 = p1/p6) %>%
dplyr::mutate(p9p10 = p9/p10) %>%
dplyr::mutate(p8p9 = p8*p9*p10) %>%
dplyr::mutate(p2p8 = -42237642*p2+ 3.752908*p8) %>%
dplyr::mutate(p9p10 = -0.183*p9+ 259*p10)
parameters.estimated.melt <- parameters.estimated %>%
melt(id.vars = c("likelihood", "par.id", "data.id")) %>%
data.table()
parameters.estimated.melt <- parameters.estimated.melt %>% dplyr::mutate(variable = as.character(variable))
par <- "p9p10"
q <- quantile(parameters.estimated.melt$likelihood, probs = c(0.75))[[1]]
cols <- c(colnames(df), "p2p8")
gplot.list <- list()
for(par in colnames(df)){
gplot.list[[par]] <- ggplot(
data = parameters.estimated.melt %>%
dplyr::filter(variable %in% c(par),
likelihood < q),
aes(
x = log(value),
y = log(-likelihood))
) +
geom_point() +
#xlim(c(-16, -15)) +
xlab(paste("log(",par,")")) +
ggtitle(paste("Dependence of likelihood on parameters (",par,")"))
}
do.call(what = ggsave,
args = append(plot.args.ggsave,
list(filename = paste(path.list$optimisation.results,
"parameters_dependence.pdf", sep ="/"),
plot = marrangeGrob(grobs = gplot.list, ncol = 1, nrow = 1))))
#### canoncial correlations ####
df.params <- as.matrix(parameters.estimated[, c(paste("p",c(2,3,8,9,10), sep = ""), "p1p6")])
df.likelihood <- matrix(parameters.estimated[, "likelihood"],ncol = 1)
cov(x = df.params, y = df.likelihood)
# install.packages("CCA")
# require("CCA")acepack
ccor <- cc(X = df.params[,c(4,5)], Y = df.likelihood)
ccor$cor
?cc
est
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fns.R
\name{cholsolve}
\alias{cholsolve}
\title{Solve the equation Qx = y}
\usage{
cholsolve(Q, y, perm = F, cholQ = matrix(1, 0, 0), cholQp = matrix(1, 0,
0), P = NA)
}
\arguments{
\item{Q}{matrix (sparse or dense), the Cholesky factor of which needs to be found}
\item{y}{matrix with the same number of rows as Q}
\item{perm}{if F no permutation is carried out, if T permuted Cholesky factors are used}
\item{cholQ}{the Cholesky factor of Q (if known already)}
\item{cholQp}{the permuted Cholesky factor of Q (if known already)}
\item{P}{the pivot matrix (if known already)}
}
\value{
x solution to Qx = y
}
\description{
This function is similar to \code{solve(Q,y)} but with the added benefit that it allows for permuted matrices. This function does the job in order to minimise
user error when attempting to re-permute the matrices prior or after solving. The user also has an option for the permuted Cholesky factorisation of Q to be carried out
internally.
}
\examples{
require(Matrix)
Q = sparseMatrix(i=c(1,1,2,2),j=c(1,2,1,2),x=c(0.1,0.2,0.2,1))
y = matrix(c(1,2),2,1)
cholsolve(Q,y)
}
\references{
Havard Rue and Leonhard Held (2005). Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC Press
}
\keyword{Cholesky}
\keyword{factor,}
\keyword{linear}
\keyword{solve}
| /man/cholsolve.Rd | no_license | jeffwong-nflx/sparseinv | R | false | true | 1,384 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fns.R
\name{cholsolve}
\alias{cholsolve}
\title{Solve the equation Qx = y}
\usage{
cholsolve(Q, y, perm = F, cholQ = matrix(1, 0, 0), cholQp = matrix(1, 0,
0), P = NA)
}
\arguments{
\item{Q}{matrix (sparse or dense), the Cholesky factor of which needs to be found}
\item{y}{matrix with the same number of rows as Q}
\item{perm}{if F no permutation is carried out, if T permuted Cholesky factors are used}
\item{cholQ}{the Cholesky factor of Q (if known already)}
\item{cholQp}{the permuted Cholesky factor of Q (if known already)}
\item{P}{the pivot matrix (if known already)}
}
\value{
x solution to Qx = y
}
\description{
This function is similar to \code{solve(Q,y)} but with the added benefit that it allows for permuted matrices. This function does the job in order to minimise
user error when attempting to re-permute the matrices prior or after solving. The user also has an option for the permuted Cholesky factorisation of Q to be carried out
internally.
}
\examples{
require(Matrix)
Q = sparseMatrix(i=c(1,1,2,2),j=c(1,2,1,2),x=c(0.1,0.2,0.2,1))
y = matrix(c(1,2),2,1)
cholsolve(Q,y)
}
\references{
Havard Rue and Leonhard Held (2005). Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC Press
}
\keyword{Cholesky}
\keyword{factor,}
\keyword{linear}
\keyword{solve}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/area_of_sq.R
\name{sq_a}
\alias{sq_a}
\title{Square Area}
\usage{
sq_a(l, w)
}
\arguments{
\item{l}{the length of rectangular}
\item{w}{the width of rectangular}
}
\description{
Calculates area of a rectangular
}
\author{
M Usman Mirza
}
| /man/sq_a.Rd | no_license | muhusmanmirza/myfirstpackage | R | false | true | 317 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/area_of_sq.R
\name{sq_a}
\alias{sq_a}
\title{Square Area}
\usage{
sq_a(l, w)
}
\arguments{
\item{l}{the length of rectangular}
\item{w}{the width of rectangular}
}
\description{
Calculates area of a rectangular
}
\author{
M Usman Mirza
}
|
#' Access files in the current app
#'
#' @param ... Character vector specifying directory and or file to
#' point to inside the current package.
#'
#' @noRd
app_sys <- function(...){
system.file(..., package = "sporecounter")
}
#' Read App Config
#'
#' @param value Value to retrieve from the config file.
#' @param config R_CONFIG_ACTIVE value.
#' @param use_parent Logical, scan the parent directory for config file.
#'
#' @importFrom config get
#'
#' @noRd
get_golem_config <- function(
value,
config = Sys.getenv("R_CONFIG_ACTIVE", "default"),
use_parent = TRUE
){
config::get(
value = value,
config = config,
# Modify this if your config file is somewhere else:
file = app_sys("golem-config.yml"),
use_parent = use_parent
)
}
| /R/app_config.R | permissive | astrzalka/sporecounter | R | false | false | 786 | r | #' Access files in the current app
#'
#' @param ... Character vector specifying directory and or file to
#' point to inside the current package.
#'
#' @noRd
app_sys <- function(...){
system.file(..., package = "sporecounter")
}
#' Read App Config
#'
#' @param value Value to retrieve from the config file.
#' @param config R_CONFIG_ACTIVE value.
#' @param use_parent Logical, scan the parent directory for config file.
#'
#' @importFrom config get
#'
#' @noRd
get_golem_config <- function(
value,
config = Sys.getenv("R_CONFIG_ACTIVE", "default"),
use_parent = TRUE
){
config::get(
value = value,
config = config,
# Modify this if your config file is somewhere else:
file = app_sys("golem-config.yml"),
use_parent = use_parent
)
}
|
#############################################################################################
# Analyze Time Series ratio for each year
#
#############################################################################################
#remove old objects for safety resons
rm(list=ls(all=TRUE))
#set seed to make analysis reproducible if any pseudo random number generator is used by any function
set.seed(123)
#utility function to glue together text without separator
glue<-function(...){paste(...,sep="")}
#read the local paths to different directories from an external file
source("workingDir.R")
#change to the data directory
setwd(dataDir)
d<-read.table("timeSeries.txt", header=TRUE,sep=";")
names(d)[1]<-"year"
with(d, plot(year,num.ips))
setwd(plotDir)
jpeg("ObservationsIPs.jpeg")
with(d,plot(year,num.obs/num.ips,
pch="+",type="b",
xlab="Year",
ylab="Number of observations / IP"))
dev.off()
jpeg("BadExitProportion.jpeg")
with(d,plot(year,num.ips.flag.bad/num.ips,
pch="+",type="b",
xlab="Year",
ylab="Ratio of nodes with \"BadExit\" flag"))
dev.off()
jpeg("NumSybils.jpeg")
with(d,plot(year,num.sybills,
pch="+",type="b",
xlab="Year",
ylab="Number of sybils"))
dev.off()
#Extraordinary peak of IPs with flag "BadExit" in 2012
with(d, plot(year,num.obs.fingerprint/num.ips))
with(d, plot(year,num.obs.fingerprint))
#Why are there so few IPs sampled in the files from which we extracted the fingerprints?
with(d, plot(year, num.obs))
#The number of observations per increases over time
with(d, plot(year, num.obs/num.ips))
#The number of observations /IP increases
with(d, plot(year, num.obs.fingerprint/num.obs))
with(d, plot(year, num.sybills))
with(d, plot(year,num.obs.fingerprint))
#scale to view all the different values in one plot
ds<-as.data.frame(scale(d[,-1]))
ds<-cbind(year=d$year,ds)
#we will use melt from reshape2 to create a better plot
require(ggplot2)
require(reshape2)
ds.melt<-melt(ds,id="year")
#function to add new values to the dataframe
calculate.value<-function(dataframe,operation){
temp.dataframe<-NULL
temp.dataframe$year<-dataframe$year
temp.dataframe$variable<-as.factor(rep(deparse(substitute(operation)),nrow(dataframe)))
temp.dataframe$value<-eval(parse(text=glue("with(",deparse(substitute(dataframe)),",",deparse(substitute(operation)),")")))#with(dataframe,operation)
return(as.data.frame(temp.dataframe))
}
#finally we row join the values
ds.melt<-rbind(ds.melt,
calculate.value(ds,num.ips.flag.bad/num.ips),
calculate.value(ds,num.obs/num.ips),
calculate.value(ds,num.obs.fingerprint/num.obs))
#drop values that are not needed in plot
ds.reduced<-(droplevels( ds.melt[-which(ds.melt$variable %in% c("num.ips.flag.bad","num.obs","num.ips","num.obs.known.fingerprint")), ] ))
#and the plot is grouped by variable
ggplot(ds.reduced, aes(x=year, y=value, color=variable)) +
geom_line(aes(linetype=variable), size=1) +
geom_point(aes(shape=variable, size=4)) +
scale_linetype_manual(values =sample(1:10,nlevels(ds.reduced$variable),replace=T)) +
scale_shape_manual(values=sample(1:10,nlevels(ds.reduced$variable),replace=T))
levels(ds.melt$variable)
#drop values that are not needed in plot
ds.reduced<-(droplevels( ds.melt[-which(ds.melt$variable %in% c("num.obs.fingerprint/num.obs","num.ips.flag.bad","num.obs","num.ips","num.obs.known.fingerprint")), ] ))
#and the plot is grouped by variable
ggplot(ds.reduced, aes(x=year, y=value, color=variable)) +
geom_line(aes(linetype=variable), size=1) +
geom_point(aes(shape=variable, size=4)) +
scale_linetype_manual(values =sample(1:10,nlevels(ds.reduced$variable),replace=T)) +
scale_shape_manual(values=sample(1:10,nlevels(ds.reduced$variable),replace=T))
cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
jpeg("BadExitProportion.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.ips.flag.bad/num.ips))+
geom_line(size=0.9,color=cbPalette[1])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Ratio of nodes with \"BadExit\" flag",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
jpeg("NumSybils.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.sybills))+
geom_line(size=0.9,color=cbPalette[5])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Number of sybils",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
jpeg("ObservationsIPs.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.obs/num.ips))+
geom_line(size=0.9,color=cbPalette[3])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Number of observations / IP",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
# Cross information of IP from counted fingerprints and each year
setwd(dataDir)
lines<-readLines("CountFingerprints.txt")
trim.leading <- function (x) sub("^\\s+", "", x)
getFirstColumn<-function(x){
unlist(trim.leading(substr(trim.leading(x), 1, 2)))
}
getSecondColumn<-function(x){
unlist(substr(trim.leading(x), 3, 16))
}
count <- as.numeric(sapply(lines,getFirstColumn))
IP <- as.character(sapply(lines,getSecondColumn))
d.fp<-data.frame(count,IP)
d.fpy<-NULL
# Get all ips by year
for(year in seq(2008,2017)){
setwd(dataDir)
fileName<-glue("AggregatedDataSet",year,".txt")
d<-read.table(fileName, header=FALSE, sep=" ",
stringsAsFactors=FALSE,comment.char="")
names(d)<-header
d<-d[,-1]
d$IP<- as.factor(d$IP)
d<-cbind.data.frame(IP=d$IP)
d.aux<-merge(d,d.fp,by="IP")
d.aux<-cbind(d.aux,year)
d.fpy<-rbind(d.fpy,d.aux)
# outputFileName<-glue("IPCountFingerPrintByYear.txt")
# setwd(dataDir)
# write.table(d.aux, file=outputFileName,append=TRUE,col.names=FALSE, row.names = F)
}
library(dplyr)
set.seed(1)
d.count.fpy<-d.fpy %>% group_by(year) %>% summarise("IP Count" = length(year))
setwd(plotDir)
jpeg("ObservationsFingerprintsYear.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d.count.fpy, aes(x=d.count.fpy$year, y=d.count.fpy$`IP Count`))+
geom_line(size=0.9,color=cbPalette[3])+
geom_point(aes(shape=as.factor(d.count.fpy$year), size=4,color=as.factor(d.count.fpy$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d.count.fpy$year))))+
labs(x="Year", y="Number of observations changed fingerprints",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d.count.fpy$year)
dev.off()
| /analyzeTimeSeries.R | no_license | svana1/RottenOnions | R | false | false | 7,303 | r | #############################################################################################
# Analyze Time Series ratio for each year
#
#############################################################################################
#remove old objects for safety resons
rm(list=ls(all=TRUE))
#set seed to make analysis reproducible if any pseudo random number generator is used by any function
set.seed(123)
#utility function to glue together text without separator
glue<-function(...){paste(...,sep="")}
#read the local paths to different directories from an external file
source("workingDir.R")
#change to the data directory
setwd(dataDir)
d<-read.table("timeSeries.txt", header=TRUE,sep=";")
names(d)[1]<-"year"
with(d, plot(year,num.ips))
setwd(plotDir)
jpeg("ObservationsIPs.jpeg")
with(d,plot(year,num.obs/num.ips,
pch="+",type="b",
xlab="Year",
ylab="Number of observations / IP"))
dev.off()
jpeg("BadExitProportion.jpeg")
with(d,plot(year,num.ips.flag.bad/num.ips,
pch="+",type="b",
xlab="Year",
ylab="Ratio of nodes with \"BadExit\" flag"))
dev.off()
jpeg("NumSybils.jpeg")
with(d,plot(year,num.sybills,
pch="+",type="b",
xlab="Year",
ylab="Number of sybils"))
dev.off()
#Extraordinary peak of IPs with flag "BadExit" in 2012
with(d, plot(year,num.obs.fingerprint/num.ips))
with(d, plot(year,num.obs.fingerprint))
#Why are there so few IPs sampled in the files from which we extracted the fingerprints?
with(d, plot(year, num.obs))
#The number of observations per increases over time
with(d, plot(year, num.obs/num.ips))
#The number of observations /IP increases
with(d, plot(year, num.obs.fingerprint/num.obs))
with(d, plot(year, num.sybills))
with(d, plot(year,num.obs.fingerprint))
#scale to view all the different values in one plot
ds<-as.data.frame(scale(d[,-1]))
ds<-cbind(year=d$year,ds)
#we will use melt from reshape2 to create a better plot
require(ggplot2)
require(reshape2)
ds.melt<-melt(ds,id="year")
#function to add new values to the dataframe
calculate.value<-function(dataframe,operation){
temp.dataframe<-NULL
temp.dataframe$year<-dataframe$year
temp.dataframe$variable<-as.factor(rep(deparse(substitute(operation)),nrow(dataframe)))
temp.dataframe$value<-eval(parse(text=glue("with(",deparse(substitute(dataframe)),",",deparse(substitute(operation)),")")))#with(dataframe,operation)
return(as.data.frame(temp.dataframe))
}
#finally we row join the values
ds.melt<-rbind(ds.melt,
calculate.value(ds,num.ips.flag.bad/num.ips),
calculate.value(ds,num.obs/num.ips),
calculate.value(ds,num.obs.fingerprint/num.obs))
#drop values that are not needed in plot
ds.reduced<-(droplevels( ds.melt[-which(ds.melt$variable %in% c("num.ips.flag.bad","num.obs","num.ips","num.obs.known.fingerprint")), ] ))
#and the plot is grouped by variable
ggplot(ds.reduced, aes(x=year, y=value, color=variable)) +
geom_line(aes(linetype=variable), size=1) +
geom_point(aes(shape=variable, size=4)) +
scale_linetype_manual(values =sample(1:10,nlevels(ds.reduced$variable),replace=T)) +
scale_shape_manual(values=sample(1:10,nlevels(ds.reduced$variable),replace=T))
levels(ds.melt$variable)
#drop values that are not needed in plot
ds.reduced<-(droplevels( ds.melt[-which(ds.melt$variable %in% c("num.obs.fingerprint/num.obs","num.ips.flag.bad","num.obs","num.ips","num.obs.known.fingerprint")), ] ))
#and the plot is grouped by variable
ggplot(ds.reduced, aes(x=year, y=value, color=variable)) +
geom_line(aes(linetype=variable), size=1) +
geom_point(aes(shape=variable, size=4)) +
scale_linetype_manual(values =sample(1:10,nlevels(ds.reduced$variable),replace=T)) +
scale_shape_manual(values=sample(1:10,nlevels(ds.reduced$variable),replace=T))
cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
jpeg("BadExitProportion.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.ips.flag.bad/num.ips))+
geom_line(size=0.9,color=cbPalette[1])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Ratio of nodes with \"BadExit\" flag",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
jpeg("NumSybils.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.sybills))+
geom_line(size=0.9,color=cbPalette[5])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Number of sybils",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
jpeg("ObservationsIPs.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d, aes(x=year, y=num.obs/num.ips))+
geom_line(size=0.9,color=cbPalette[3])+
geom_point(aes(shape=as.factor(d$year), size=4,color=as.factor(d$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d$year))))+
labs(x="Year", y="Number of observations / IP",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d$year)
dev.off()
# Cross information of IP from counted fingerprints and each year
setwd(dataDir)
lines<-readLines("CountFingerprints.txt")
trim.leading <- function (x) sub("^\\s+", "", x)
getFirstColumn<-function(x){
unlist(trim.leading(substr(trim.leading(x), 1, 2)))
}
getSecondColumn<-function(x){
unlist(substr(trim.leading(x), 3, 16))
}
count <- as.numeric(sapply(lines,getFirstColumn))
IP <- as.character(sapply(lines,getSecondColumn))
d.fp<-data.frame(count,IP)
d.fpy<-NULL
# Get all ips by year
for(year in seq(2008,2017)){
setwd(dataDir)
fileName<-glue("AggregatedDataSet",year,".txt")
d<-read.table(fileName, header=FALSE, sep=" ",
stringsAsFactors=FALSE,comment.char="")
names(d)<-header
d<-d[,-1]
d$IP<- as.factor(d$IP)
d<-cbind.data.frame(IP=d$IP)
d.aux<-merge(d,d.fp,by="IP")
d.aux<-cbind(d.aux,year)
d.fpy<-rbind(d.fpy,d.aux)
# outputFileName<-glue("IPCountFingerPrintByYear.txt")
# setwd(dataDir)
# write.table(d.aux, file=outputFileName,append=TRUE,col.names=FALSE, row.names = F)
}
library(dplyr)
set.seed(1)
d.count.fpy<-d.fpy %>% group_by(year) %>% summarise("IP Count" = length(year))
setwd(plotDir)
jpeg("ObservationsFingerprintsYear.jpeg",width = 900,height = 600,units = "px",res = 150)
ggplot(d.count.fpy, aes(x=d.count.fpy$year, y=d.count.fpy$`IP Count`))+
geom_line(size=0.9,color=cbPalette[3])+
geom_point(aes(shape=as.factor(d.count.fpy$year), size=4,color=as.factor(d.count.fpy$year))) +
scale_shape_manual(values=sample(1:10,nlevels(as.factor(d.count.fpy$year))))+
labs(x="Year", y="Number of observations changed fingerprints",shape="Year", colour="Year") +
theme(axis.text=element_text(size=10), axis.title=element_text(size=12,face="bold"))+
scale_x_continuous(breaks=d.count.fpy$year)
dev.off()
|
setGeneric(
name = "directionCluster",
def = function(track, minD, minT , tolerance)
{
.loadPackages()
standardGeneric("directionCluster")
}
)
setMethod(
f = "directionCluster",
signature = c("Track","numeric", "numeric", "numeric"),
definition = function(track, minD, minT , tolerance)
{
if (is.null(track)|| length(track) < 2){
return (0)}
cl<-list()
clusterId = 1
clusterOpen = FALSE
tracksize <- length(track@connections$direction)
tol = tolerance
clusterini = 0
lastindex=1
for(n in 1:(tracksize-1)){
dirc = track@connections$direction[n]-track@connections$direction[n+1]
if(dirc<0){
dirc = dirc*(-1)
}
if(dirc >= minD){
cl[n]<-clusterId
if(!clusterOpen){
clusterini = n
}
clusterOpen = TRUE
}
else{
if(clusterOpen){
i=1
tol=tolerance
while(tol >0 && (n+i)<(tracksize+1)){
dirc = track@connections$direction[n+i]-track@connections$direction[n+i+1]
if(dirc<0){
dirc = dirc*(-1)
}
if(dirc>=minD){
i = i+1
lastindex = n+i
break
}
else{
tol = tol-1
if(tol==0){
lastindex = n+i
}
}
}
}
if(lastindex<(n+tolerance)){
for(j in n:lastindex){
cl[j]<-clusterId
}
n=lastindex
}
else{
ctime=0
for (j in clusterini:n){
ctime = ctime + track@connections$duration[j]
}
if(ctime>minT){
n=lastindex
for (j in clusterini:n){
cl[j]<-clusterId
}
clusterId=clusterId+1
}
else{
for (j in clusterini:n){
# print("j e clusterId")
print(j)
print(clusterId)
cl[j]<--1
}
}
clusterOpen = FALSE
}
}
}
return (cl)
}
)
| /R/DirectionCluster.R | no_license | dvm1607/TrajDataMining | R | false | false | 2,135 | r | setGeneric(
name = "directionCluster",
def = function(track, minD, minT , tolerance)
{
.loadPackages()
standardGeneric("directionCluster")
}
)
setMethod(
f = "directionCluster",
signature = c("Track","numeric", "numeric", "numeric"),
definition = function(track, minD, minT , tolerance)
{
if (is.null(track)|| length(track) < 2){
return (0)}
cl<-list()
clusterId = 1
clusterOpen = FALSE
tracksize <- length(track@connections$direction)
tol = tolerance
clusterini = 0
lastindex=1
for(n in 1:(tracksize-1)){
dirc = track@connections$direction[n]-track@connections$direction[n+1]
if(dirc<0){
dirc = dirc*(-1)
}
if(dirc >= minD){
cl[n]<-clusterId
if(!clusterOpen){
clusterini = n
}
clusterOpen = TRUE
}
else{
if(clusterOpen){
i=1
tol=tolerance
while(tol >0 && (n+i)<(tracksize+1)){
dirc = track@connections$direction[n+i]-track@connections$direction[n+i+1]
if(dirc<0){
dirc = dirc*(-1)
}
if(dirc>=minD){
i = i+1
lastindex = n+i
break
}
else{
tol = tol-1
if(tol==0){
lastindex = n+i
}
}
}
}
if(lastindex<(n+tolerance)){
for(j in n:lastindex){
cl[j]<-clusterId
}
n=lastindex
}
else{
ctime=0
for (j in clusterini:n){
ctime = ctime + track@connections$duration[j]
}
if(ctime>minT){
n=lastindex
for (j in clusterini:n){
cl[j]<-clusterId
}
clusterId=clusterId+1
}
else{
for (j in clusterini:n){
# print("j e clusterId")
print(j)
print(clusterId)
cl[j]<--1
}
}
clusterOpen = FALSE
}
}
}
return (cl)
}
)
|
% Generated by roxygen2 (4.0.2): do not edit by hand
\docType{data}
\name{napkins}
\alias{napkins}
\title{Napkin Use}
\format{A data frame with 86 observations on the following 2 variables.
\describe{ \item{napkins}{number of napkins used
by the subject during the meal.} \item{sex}{a factor with levels
\code{female} \code{male} Sex of the person being observed}}}
\source{
MAT 111 at Georgetown College
}
\description{
Students at GC observed their fellow students in the Cafe at lunch.
}
\keyword{datasets}
| /man/napkins.Rd | no_license | arturochian/tigerstats | R | false | false | 511 | rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\docType{data}
\name{napkins}
\alias{napkins}
\title{Napkin Use}
\format{A data frame with 86 observations on the following 2 variables.
\describe{ \item{napkins}{number of napkins used
by the subject during the meal.} \item{sex}{a factor with levels
\code{female} \code{male} Sex of the person being observed}}}
\source{
MAT 111 at Georgetown College
}
\description{
Students at GC observed their fellow students in the Cafe at lunch.
}
\keyword{datasets}
|
/Exercícios_R/Features_scalling.R | no_license | olimpiojunior/Estudos_R | R | false | false | 275 | r | ||
source(file='code/packages.R')
load(file='data_clean/cluster_ConHum.Rdata')
load(file='data_clean/cluster_FactInf.Rdata')
load(file='data_clean/data_clean.Rdata')
clustersConHum2 <- read.csv(file='data_clean/cluster_ConHum.csv')
clustersFactInf2 <- read.csv(file='data_clean/cluster_FactInf.csv')
names(index_all)[1]<- "Formulario"
all <- clustersConHum2 %>%
full_join(ConHum , by="Formulario") %>%
full_join(clustersFactInf2 , by="Formulario") %>%
left_join(FactInf, by="Formulario") %>%
left_join(vars, by="Formulario") %>%
left_join(index_all, by="Formulario") %>%
select(Formulario:groups5.x, groups5.y, BuenEstado,IV, CI, HI, VI,
asp,tipo_hum,nat_art,Perimetro_m,Area_Has)
names(all) <- c("ID","Name","cCH5", "cFI5", "BE", "IV","CI", "HI", "VI","asp","tipo_hum",
"nat_art","Perimetro_m","Area_Has")
head(all)
all %>%
group_by(cCH5, cFI5)%>%
summarise(mean=mean(VI)) %>%
spread(cCH5, mean)%>%
kable()
all%>%
group_by(cCH5, cFI5)%>%
summarise(n=n())%>%
spread(cCH5, n)%>%
kable()
all %>%
filter(cCH5 ==1) %>%
group_by(cFI5, BE)%>%
summarise(n = n()) %>%
spread(cFI5,n)%>%
kable()
dataset <- ConHum %>% select(Formulario, BuenEstado) %>%
full_join(clustersConHum2, by="Formulario") %>%
full_join(clustersFactInf2, by="Formulario") %>%
left_join(FactInf, by="Formulario") %>%
left_join(vars, by="Formulario") %>%
left_join(index_all, by="Formulario") %>%
select(Formulario, BuenEstado, groups5.x, groups5.y, IV, CI, HI, VI, asp,
tipo_hum,nat_art,Perimetro_m,Area_Has,
Bosques, Gan_Extens, Sabanas,Pesca,
Acuacult,Moluscos,Turism_Com)
names(dataset) <- c("ID","IBE","cCH5", "cFI5", "IV","CI", "HI", "VI","asp","tipo_hum",
"nat_art","Perimetro_m","Area_Has","Bosques","Gan_Extens",
"Sabanas","Pesca","Acuacult","Moluscos" , "Turism_Com")
mod1 <- lm(VI ~ as.factor(cCH5) + as.factor(cFI5),data = dataset)
summary(mod1)
plot(mod1)
extract_eq(mod1)
aa<-aov(mod1)
plot(TukeyHSD(aa, "as.factor(cCH5)"))
plot(TukeyHSD(aa, "as.factor(cFI5)"))
mod2 <- lm(IV ~ IBE,data = dataset)
summary(mod2)
mod3 <- lm(IV ~ scale(Area_Has),data = dataset)
summary(mod3)
mod4 <- lm(IV ~ scale(Perimetro_m),data = dataset)
summary(mod4)
mod5 <- glm(IBE ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod6 <- glm(IBE ~ Bosques + Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod7 <- glm(IBE ~ Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod8 <- glm(IBE ~ Sabanas + Pesca,
data = dataset, family = "binomial")
summary(mod5)$aic;summary(mod6)$aic;summary(mod7)$aic;summary(mod8)$aic
confint(mod5)
exp(coef(mod5))
exp(cbind(OR = coef(mod5), confint(mod5)))
#Now we can say that having forest (influenciado por) a wetland,
#increases the odds of a wetland area being in good condition
#(versus not being in good condition) by a factor of 5.99.
#Sabana's factor is 2.54 and fishing 4.31.The area, and
# extensive cattle raising decrease that same odds.
mod9 <- glm(VI ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
mod10 <- glm(VI ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ nat_art,
data = dataset, family = "gaussian")
mod11 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
mod12 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ nat_art,
data = dataset, family = "gaussian")
summary(mod9)$aic;summary(mod10)$aic;summary(mod11)$aic;summary(mod12)$aic
### changes missing in here: set up as a baseline: Inside protected area,
### palustrine and natural
dataset <- dataset %>%
mutate(tipo_hum = factor(tipo_hum, levels=c("Palustre","Lacustre","Estuarino"),
labels=c("0Palustre","1Lacustre","2Estuarino")),
nat_art = factor(nat_art, levels=c("Natural", "Artificial"),
labels=c("0Natural", "1Artificial")))
mod11 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
summary(mod11)
plot(mod11)
| /code/analysis.R | no_license | malfaro2/humedales | R | false | false | 4,388 | r | source(file='code/packages.R')
load(file='data_clean/cluster_ConHum.Rdata')
load(file='data_clean/cluster_FactInf.Rdata')
load(file='data_clean/data_clean.Rdata')
clustersConHum2 <- read.csv(file='data_clean/cluster_ConHum.csv')
clustersFactInf2 <- read.csv(file='data_clean/cluster_FactInf.csv')
names(index_all)[1]<- "Formulario"
all <- clustersConHum2 %>%
full_join(ConHum , by="Formulario") %>%
full_join(clustersFactInf2 , by="Formulario") %>%
left_join(FactInf, by="Formulario") %>%
left_join(vars, by="Formulario") %>%
left_join(index_all, by="Formulario") %>%
select(Formulario:groups5.x, groups5.y, BuenEstado,IV, CI, HI, VI,
asp,tipo_hum,nat_art,Perimetro_m,Area_Has)
names(all) <- c("ID","Name","cCH5", "cFI5", "BE", "IV","CI", "HI", "VI","asp","tipo_hum",
"nat_art","Perimetro_m","Area_Has")
head(all)
all %>%
group_by(cCH5, cFI5)%>%
summarise(mean=mean(VI)) %>%
spread(cCH5, mean)%>%
kable()
all%>%
group_by(cCH5, cFI5)%>%
summarise(n=n())%>%
spread(cCH5, n)%>%
kable()
all %>%
filter(cCH5 ==1) %>%
group_by(cFI5, BE)%>%
summarise(n = n()) %>%
spread(cFI5,n)%>%
kable()
dataset <- ConHum %>% select(Formulario, BuenEstado) %>%
full_join(clustersConHum2, by="Formulario") %>%
full_join(clustersFactInf2, by="Formulario") %>%
left_join(FactInf, by="Formulario") %>%
left_join(vars, by="Formulario") %>%
left_join(index_all, by="Formulario") %>%
select(Formulario, BuenEstado, groups5.x, groups5.y, IV, CI, HI, VI, asp,
tipo_hum,nat_art,Perimetro_m,Area_Has,
Bosques, Gan_Extens, Sabanas,Pesca,
Acuacult,Moluscos,Turism_Com)
names(dataset) <- c("ID","IBE","cCH5", "cFI5", "IV","CI", "HI", "VI","asp","tipo_hum",
"nat_art","Perimetro_m","Area_Has","Bosques","Gan_Extens",
"Sabanas","Pesca","Acuacult","Moluscos" , "Turism_Com")
mod1 <- lm(VI ~ as.factor(cCH5) + as.factor(cFI5),data = dataset)
summary(mod1)
plot(mod1)
extract_eq(mod1)
aa<-aov(mod1)
plot(TukeyHSD(aa, "as.factor(cCH5)"))
plot(TukeyHSD(aa, "as.factor(cFI5)"))
mod2 <- lm(IV ~ IBE,data = dataset)
summary(mod2)
mod3 <- lm(IV ~ scale(Area_Has),data = dataset)
summary(mod3)
mod4 <- lm(IV ~ scale(Perimetro_m),data = dataset)
summary(mod4)
mod5 <- glm(IBE ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod6 <- glm(IBE ~ Bosques + Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod7 <- glm(IBE ~ Gan_Extens + Sabanas + Pesca,
data = dataset, family = "binomial")
mod8 <- glm(IBE ~ Sabanas + Pesca,
data = dataset, family = "binomial")
summary(mod5)$aic;summary(mod6)$aic;summary(mod7)$aic;summary(mod8)$aic
confint(mod5)
exp(coef(mod5))
exp(cbind(OR = coef(mod5), confint(mod5)))
#Now we can say that having forest (influenciado por) a wetland,
#increases the odds of a wetland area being in good condition
#(versus not being in good condition) by a factor of 5.99.
#Sabana's factor is 2.54 and fishing 4.31.The area, and
# extensive cattle raising decrease that same odds.
mod9 <- glm(VI ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
mod10 <- glm(VI ~ scale(Area_Has)+ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ nat_art,
data = dataset, family = "gaussian")
mod11 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
mod12 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ nat_art,
data = dataset, family = "gaussian")
summary(mod9)$aic;summary(mod10)$aic;summary(mod11)$aic;summary(mod12)$aic
### changes missing in here: set up as a baseline: Inside protected area,
### palustrine and natural
dataset <- dataset %>%
mutate(tipo_hum = factor(tipo_hum, levels=c("Palustre","Lacustre","Estuarino"),
labels=c("0Palustre","1Lacustre","2Estuarino")),
nat_art = factor(nat_art, levels=c("Natural", "Artificial"),
labels=c("0Natural", "1Artificial")))
mod11 <- glm(VI ~ Bosques + Gan_Extens + Sabanas + Pesca +
asp+ tipo_hum + nat_art,
data = dataset, family = "gaussian")
summary(mod11)
plot(mod11)
|
# install course dependencies
devtools::install_version("tibble", "1.2")
devtools::install_version("dplyr", "0.5.0")
devtools::install_version("data.table", "1.10.0")
devtools::install_version("hflights", "0.1")
devtools::install_version("DBI", "0.5-1")
devtools::install_version("RMySQL", "0.10.9")
devtools::install_version("ggplot2", "2.2.1")
| /requirements.r | no_license | ramnathv/test-course-r2 | R | false | false | 346 | r | # install course dependencies
devtools::install_version("tibble", "1.2")
devtools::install_version("dplyr", "0.5.0")
devtools::install_version("data.table", "1.10.0")
devtools::install_version("hflights", "0.1")
devtools::install_version("DBI", "0.5-1")
devtools::install_version("RMySQL", "0.10.9")
devtools::install_version("ggplot2", "2.2.1")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{model_gam_ex}
\alias{model_gam_ex}
\title{Model output tibble from the \code{\link{model_gam}} function}
\format{
A data frame with 84 rows and 17 variables:
\describe{
\item{\code{id}}{Numerical IDs for the IND~press combinations.}
\item{\code{ind}}{Indicator names.}
\item{\code{press}}{Pressure names.}
\item{\code{model_type}}{Specification of the model type; at this stage containing only
"gam" (Generalized Additive Model).}
\item{\code{corrstruc}}{Specification of the correlation structure; at this stage
containing only "none".}
\item{\code{aic}}{AIC of the fitted models}
\item{\code{edf}}{Estimated degrees of freedom for the model terms.}
\item{\code{p_val}}{The p values for the smoothing term (the pressure).}
\item{\code{signif_code}}{The significance codes for the p-values.}
\item{\code{r_sq}}{The adjusted r-squared for the models. Defined as the proportion
of variance explained, where original variance and residual variance are
both estimated using unbiased estimators. This quantity can be negative
if your model is worse than a one parameter constant model, and can be
higher for the smaller of two nested models.}
\item{\code{expl_dev}}{The proportion of the null deviance explained by the models.}
\item{\code{nrmse}}{Absolute values of the root mean square error normalized by the
standard deviation (NRMSE) using no back-transformation.}
\item{\code{ks_test}}{The p-values from a Kolmogorov-Smirnov Test applied on the model
residuals to test for normal distribution. P-values > 0.05 indicate
normally distributed residuals.}
\item{\code{tac}}{logical; indicates whether temporal autocorrelation (TAC) was detected
in the residuals. TRUE if model residuals show TAC.}
\item{\code{pres_outlier}}{A list-column with outliers identified for each model (i.e.
Cook`s distance > 1). The indices present the position in
the training data, including NAs.}
\item{\code{excl_outlier}}{A list-column listing all outliers per model that have been
excluded in the GAM fitting}
\item{\code{model}}{A list-column of IND~press-specific gam objects.}
}
}
\usage{
model_gam_ex
}
\description{
This is an example output tibble from the \code{model_gam} function applied
on the Central Baltic Sea food web indicator demonstration data.
}
\keyword{datasets}
| /man/model_gam_ex.Rd | no_license | saskiaotto/INDperform | R | false | true | 2,584 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{model_gam_ex}
\alias{model_gam_ex}
\title{Model output tibble from the \code{\link{model_gam}} function}
\format{
A data frame with 84 rows and 17 variables:
\describe{
\item{\code{id}}{Numerical IDs for the IND~press combinations.}
\item{\code{ind}}{Indicator names.}
\item{\code{press}}{Pressure names.}
\item{\code{model_type}}{Specification of the model type; at this stage containing only
"gam" (Generalized Additive Model).}
\item{\code{corrstruc}}{Specification of the correlation structure; at this stage
containing only "none".}
\item{\code{aic}}{AIC of the fitted models}
\item{\code{edf}}{Estimated degrees of freedom for the model terms.}
\item{\code{p_val}}{The p values for the smoothing term (the pressure).}
\item{\code{signif_code}}{The significance codes for the p-values.}
\item{\code{r_sq}}{The adjusted r-squared for the models. Defined as the proportion
of variance explained, where original variance and residual variance are
both estimated using unbiased estimators. This quantity can be negative
if your model is worse than a one parameter constant model, and can be
higher for the smaller of two nested models.}
\item{\code{expl_dev}}{The proportion of the null deviance explained by the models.}
\item{\code{nrmse}}{Absolute values of the root mean square error normalized by the
standard deviation (NRMSE) using no back-transformation.}
\item{\code{ks_test}}{The p-values from a Kolmogorov-Smirnov Test applied on the model
residuals to test for normal distribution. P-values > 0.05 indicate
normally distributed residuals.}
\item{\code{tac}}{logical; indicates whether temporal autocorrelation (TAC) was detected
in the residuals. TRUE if model residuals show TAC.}
\item{\code{pres_outlier}}{A list-column with outliers identified for each model (i.e.
Cook`s distance > 1). The indices present the position in
the training data, including NAs.}
\item{\code{excl_outlier}}{A list-column listing all outliers per model that have been
excluded in the GAM fitting}
\item{\code{model}}{A list-column of IND~press-specific gam objects.}
}
}
\usage{
model_gam_ex
}
\description{
This is an example output tibble from the \code{model_gam} function applied
on the Central Baltic Sea food web indicator demonstration data.
}
\keyword{datasets}
|
chi_emps <- read.csv("data/ChicagoEmployees.csv", stringsAsFactors = FALSE)
## Alternatively, we can use read_csv from the readr library
library(readr)
chi_emps <- read_csv("data/ChicagoEmployees.csv")
## summary to give summary statistics
summary(chi_emps)
## info about the data types
str(chi_emps)
### get counts of values using the table command
table(chi_emps$Dept)
### get the counts in order using sort
sort(table(chi_emps$Dept))
sort(table(chi_emps$Dept), decreasing = TRUE)
police <- chi_emps[chi_emps$Dept == "POLICE", ]
hist(police$AnnualSalary, xlab = "Annual Salary",
ylab = "Count", main = "Salaries of Chicago Police Officers",
col = "royalblue", breaks = 50)
big2 <- c("POLICE", "FIRE")
chi_emps$Dept2 <- ifelse(chi_emps$Dept %in% big2, chi_emps$Dept, "OTHER")
table(chi_emps$Dept2, chi_emps$SalHour)
boxplot(AnnualSalary~Dept2, data = chi_emps)
#### Get a dataframe of all individuals who make more than $150k
highly_paid <- chi_emps[chi_emps$AnnualSalary > 150000 &
!is.na(chi_emps$AnnualSalary), ]
| /.Rproj.user/9D0D6351/sources/per/t/49D5B43A-contents | no_license | thisisdaryn/saturday | R | false | false | 1,075 |
chi_emps <- read.csv("data/ChicagoEmployees.csv", stringsAsFactors = FALSE)
## Alternatively, we can use read_csv from the readr library
library(readr)
chi_emps <- read_csv("data/ChicagoEmployees.csv")
## summary to give summary statistics
summary(chi_emps)
## info about the data types
str(chi_emps)
### get counts of values using the table command
table(chi_emps$Dept)
### get the counts in order using sort
sort(table(chi_emps$Dept))
sort(table(chi_emps$Dept), decreasing = TRUE)
police <- chi_emps[chi_emps$Dept == "POLICE", ]
hist(police$AnnualSalary, xlab = "Annual Salary",
ylab = "Count", main = "Salaries of Chicago Police Officers",
col = "royalblue", breaks = 50)
big2 <- c("POLICE", "FIRE")
chi_emps$Dept2 <- ifelse(chi_emps$Dept %in% big2, chi_emps$Dept, "OTHER")
table(chi_emps$Dept2, chi_emps$SalHour)
boxplot(AnnualSalary~Dept2, data = chi_emps)
#### Get a dataframe of all individuals who make more than $150k
highly_paid <- chi_emps[chi_emps$AnnualSalary > 150000 &
!is.na(chi_emps$AnnualSalary), ]
| |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/robCov.R
\name{robCov}
\alias{robCov}
\title{Robust covariance matrix estimation}
\usage{
robCov(sY, alpha = 2, beta = 1.25)
}
\arguments{
\item{sY}{A matrix, where each column is a replicate observation on a multivariate r.v.}
\item{alpha}{tuning parameter, see details.}
\item{beta}{tuning parameter, see details.}
}
\value{
A list where:
\itemize{
\item{\code{COV}}{ The estimated covariance matrix.}
\item{\code{E}}{ A square root of the inverse covariance matrix. i.e. the inverse cov
matrix is \code{t(E)\%*\%E};}
\item{\code{half.ldet.V}}{ Half the log of the determinant of the covariance matrix;}
\item{\code{mY}}{ The estimated mean;}
\item{\code{sd}}{ The estimated standard deviations of each variable.}
\item{\code{weights}}{ This is \code{w1/sum(w1)*ncol(sY)}, where \code{w1} are the weights of Campbell (1980).}
\item{\code{lowVar}}{ The indexes of the columns of \code{sY} whose variance is zero (if any). These
variable were removed and excluded from the covariance matrix. }
}
}
\description{
Obtains a robust estimate of the covariance matrix of a sample of multivariate data,
using Campbell's (1980) method as described on p231-235 of Krzanowski (1988).
}
\details{
Campbell (1980) suggests an estimator of the covariance matrix which downweights observations
at more than some Mahalanobis distance \code{d.0} from the mean.
\code{d.0} is \code{sqrt(nrow(sY))+alpha/sqrt(2)}. Weights are one for observations
with Mahalanobis distance, \code{d}, less than \code{d.0}. Otherwise weights are
\code{d.0*exp(-.5*(d-d.0)^2/beta^2)/d}. The defaults are as recommended by Campbell.
This routine also uses pre-conditioning to ensure good scaling and stable
numerical calculations. If some of the columns of \code{sY} has zero variance, these
are removed.
}
\examples{
p <- 5;n <- 100
Y <- matrix(runif(p*n),p,n)
robCov(Y)
}
\references{
Krzanowski, W.J. (1988) Principles of Multivariate Analysis. Oxford.
Campbell, N.A. (1980) Robust procedures in multivariate analysis I: robust covariance estimation. JRSSC 29, 231-237.
}
\author{
Simon N. Wood, maintained by Matteo Fasiolo <matteo.fasiolo@gmail.com>.
}
| /fuzzedpackages/esaddle/man/robCov.Rd | no_license | akhikolla/testpackages | R | false | true | 2,536 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/robCov.R
\name{robCov}
\alias{robCov}
\title{Robust covariance matrix estimation}
\usage{
robCov(sY, alpha = 2, beta = 1.25)
}
\arguments{
\item{sY}{A matrix, where each column is a replicate observation on a multivariate r.v.}
\item{alpha}{tuning parameter, see details.}
\item{beta}{tuning parameter, see details.}
}
\value{
A list where:
\itemize{
\item{\code{COV}}{ The estimated covariance matrix.}
\item{\code{E}}{ A square root of the inverse covariance matrix. i.e. the inverse cov
matrix is \code{t(E)\%*\%E};}
\item{\code{half.ldet.V}}{ Half the log of the determinant of the covariance matrix;}
\item{\code{mY}}{ The estimated mean;}
\item{\code{sd}}{ The estimated standard deviations of each variable.}
\item{\code{weights}}{ This is \code{w1/sum(w1)*ncol(sY)}, where \code{w1} are the weights of Campbell (1980).}
\item{\code{lowVar}}{ The indexes of the columns of \code{sY} whose variance is zero (if any). These
variable were removed and excluded from the covariance matrix. }
}
}
\description{
Obtains a robust estimate of the covariance matrix of a sample of multivariate data,
using Campbell's (1980) method as described on p231-235 of Krzanowski (1988).
}
\details{
Campbell (1980) suggests an estimator of the covariance matrix which downweights observations
at more than some Mahalanobis distance \code{d.0} from the mean.
\code{d.0} is \code{sqrt(nrow(sY))+alpha/sqrt(2)}. Weights are one for observations
with Mahalanobis distance, \code{d}, less than \code{d.0}. Otherwise weights are
\code{d.0*exp(-.5*(d-d.0)^2/beta^2)/d}. The defaults are as recommended by Campbell.
This routine also uses pre-conditioning to ensure good scaling and stable
numerical calculations. If some of the columns of \code{sY} has zero variance, these
are removed.
}
\examples{
p <- 5;n <- 100
Y <- matrix(runif(p*n),p,n)
robCov(Y)
}
\references{
Krzanowski, W.J. (1988) Principles of Multivariate Analysis. Oxford.
Campbell, N.A. (1980) Robust procedures in multivariate analysis I: robust covariance estimation. JRSSC 29, 231-237.
}
\author{
Simon N. Wood, maintained by Matteo Fasiolo <matteo.fasiolo@gmail.com>.
}
|
# Class specification and constructors for mizer base parameters class
# Class has members to store parameters of size based model
# Copyright 2012 Finlay Scott and Julia Blanchard.
# Copyright 2018 Gustav Delius and Richard Southwell.
# Development has received funding from the European Commission's Horizon 2020
# Research and Innovation Programme under Grant Agreement No. 634495
# for the project MINOUW (http://minouw-project.eu/).
# Distributed under the GPL 3 or later
# Maintainer: Gustav Delius, University of York, <gustav.delius@york.ac.uk>
# Naming conventions:
# S4 classes and constructors: AClass
# Functions: aFunction
# Variables: a_variable
# Validity function ---------------------------------------------------------
# Not documented as removed later on
validMizerParams <- function(object) {
errors <- character()
# grab some dims
no_w <- length(object@w)
no_w_full <- length(object@w_full)
w_idx <- (no_w_full - no_w + 1):no_w_full
# Check weight grids ----
# Check dw and dw_full are correct length
if (length(object@dw) != no_w) {
msg <- paste("dw is length ", length(object@dw),
" and w is length ", no_w,
". These should be the same length", sep = "")
errors <- c(errors, msg)
}
if (length(object@dw_full) != no_w_full) {
msg <- paste("dw_full is length ", length(object@dw_full),
" and w_full is length ", no_w_full,
". These should be the same length", sep = "")
errors <- c(errors, msg)
}
# Check that the last entries of w_full and dw_full agree with w and dw
if (any(object@w[] != object@w_full[w_idx])) {
msg <- "The later entries of w_full should be equal to those of w."
errors <- c(errors, msg)
}
if (any(object@dw[] != object@dw_full[w_idx])) {
msg <- "The later entries of dw_full should be equal to those of dw."
errors <- c(errors, msg)
}
# Check the array dimensions are good ----
# 2D arrays
if (!all(c(length(dim(object@psi)),
length(dim(object@intake_max)),
length(dim(object@search_vol)),
length(dim(object@metab)),
length(dim(object@mu_b)),
length(dim(object@interaction)),
length(dim(object@catchability))) == 2)) {
msg <- "psi, intake_max, search_vol, metab, mu_b, interaction and catchability must all be two dimensions"
errors <- c(errors, msg)
}
# 3D arrays
if (length(dim(object@selectivity)) != 3) {
msg <- "selectivity must be three dimensions"
errors <- c(errors, msg)
}
# Check number of species is equal across relevant slots
if (!all(c(
dim(object@psi)[1],
dim(object@intake_max)[1],
dim(object@search_vol)[1],
dim(object@metab)[1],
dim(object@mu_b)[1],
dim(object@selectivity)[2],
dim(object@catchability)[2],
dim(object@interaction)[1],
dim(object@interaction)[2]) ==
dim(object@species_params)[1])) {
msg <- "The number of species in the model must be consistent across the species_params, psi, intake_max, search_vol, mu_b, interaction (dim 1), selectivity, catchability and interaction (dim 2) slots"
errors <- c(errors, msg)
}
# Check number of size groups
if (!all(c(
dim(object@psi)[2],
dim(object@intake_max)[2],
dim(object@search_vol)[2],
dim(object@metab)[2],
dim(object@selectivity)[3]) ==
no_w)) {
msg <- "The number of size bins in the model must be consistent across the w, psi, intake_max, search_vol, and selectivity (dim 3) slots"
errors <- c(errors, msg)
}
# Check numbe of gears
if (!isTRUE(all.equal(dim(object@selectivity)[1], dim(object@catchability)[1]))) {
msg <- "The number of fishing gears must be consistent across the catchability and selectivity (dim 1) slots"
errors <- c(errors, msg)
}
# Check names of dimnames of arrays ----
# sp dimension
if (!all(c(
names(dimnames(object@psi))[1],
names(dimnames(object@intake_max))[1],
names(dimnames(object@search_vol))[1],
names(dimnames(object@metab))[1],
names(dimnames(object@mu_b))[1],
names(dimnames(object@selectivity))[2],
names(dimnames(object@catchability))[2]) == "sp")) {
msg <- "Name of first dimension of psi, intake_max, search_vol, metab, mu_b, and the second dimension of selectivity and catchability must be 'sp'"
errors <- c(errors, msg)
}
#interaction dimension names
if (names(dimnames(object@interaction))[1] != "predator") {
msg <- "The first dimension of interaction must be called 'predator'"
errors <- c(errors, msg)
}
if (names(dimnames(object@interaction))[2] != "prey") {
msg <- "The first dimension of interaction must be called 'prey'"
errors <- c(errors, msg)
}
# w dimension
if (!all(c(
names(dimnames(object@psi))[2],
names(dimnames(object@intake_max))[2],
names(dimnames(object@search_vol))[2],
names(dimnames(object@metab))[2],
names(dimnames(object@selectivity))[3]) == "w")) {
msg <- "Name of second dimension of psi, intake_max, search_vol, metab and third dimension of selectivity must be 'w'"
errors <- c(errors, msg)
}
if (!all(c(
names(dimnames(object@selectivity))[1],
names(dimnames(object@catchability))[1]) == "gear")) {
msg <- "Name of first dimension of selectivity and catchability must be 'gear'"
errors <- c(errors, msg)
}
# Check dimnames of species are identical
# Bit tricky this one as I don't know of a way to compare lots of vectors
# at the same time. Just use == and the recycling rule
if (!all(c(
dimnames(object@psi)[[1]],
dimnames(object@intake_max)[[1]],
dimnames(object@search_vol)[[1]],
dimnames(object@metab)[[1]],
dimnames(object@mu_b)[[1]],
dimnames(object@selectivity)[[2]],
dimnames(object@catchability)[[2]],
dimnames(object@interaction)[[1]],
dimnames(object@interaction)[[2]]) ==
object@species_params$species)) {
msg <- "The species names of species_params, psi, intake_max, search_vol, metab, mu_b, selectivity, catchability and interaction must all be the same"
errors <- c(errors, msg)
}
# Check dimnames of w
if (!all(c(
dimnames(object@psi)[[2]],
dimnames(object@intake_max)[[2]],
dimnames(object@search_vol)[[2]],
dimnames(object@metab)[[2]]) ==
dimnames(object@selectivity)[[3]])) {
msg <- "The size names of psi, intake_max, search_vol, metab and selectivity must all be the same"
errors <- c(errors, msg)
}
# Check dimnames of gear
if (!isTRUE(all.equal(
dimnames(object@catchability)[[1]],
dimnames(object@selectivity)[[1]]))) {
msg <- "The gear names of selectivity and catchability must all be the same"
errors <- c(errors, msg)
}
# Check the vector slots ----
if (length(object@rr_pp) != length(object@w_full)) {
msg <- "rr_pp must be the same length as w_full"
errors <- c(errors, msg)
}
if (length(object@cc_pp) != length(object@w_full)) {
msg <- "cc_pp must be the same length as w_full"
errors <- c(errors, msg)
}
# TODO: Rewrite the following into a test of the @rates_funcs slot ----
# SRR
# if (!is.string(object@srr)) {
# msg <- "srr needs to be specified as a string giving the name of the function"
# errors <- c(errors, msg)
# } else {
# if (!exists(object@srr)) {
# msg <- paste0("The stock-recruitment function ",
# object@srr,
# "does not exist.")
# errors <- c(errors, msg)
# } else {
# srr <- get(object@srr)
# if (!is.function(srr)) {
# msg <- "The specified srr is not a function."
# errors <- c(errors, msg)
# } else {
# # Must have two arguments: rdi amd species_params
# if (!isTRUE(all.equal(names(formals(srr)), c("rdi", "species_params")))) {
# msg <- "Arguments of srr function must be 'rdi' and 'species_params'"
# errors <- c(errors, msg)
# }
# }
# }
# }
# Should not have legacy r_max column (has been renamed to R_max)
if ("r_max" %in% names(object@species_params)) {
msg <- "The 'r_max' column in species_params should be called 'R_max'. You can use 'upgradeParams()' to upgrade your params object."
errors <- c(errors, msg)
}
# # species_params data.frame must have columns:
# # species, z0, alpha, eRepro
# species_params_cols <- c("species","z0","alpha","erepro")
# if (!all(species_params_cols %in% names(object@species_params))) {
# msg <- "species_params data.frame must have 'species', 'z0', 'alpha' and 'erepro' columms"
# errors <- c(errors,msg)
# }
# must also have SRR params but not sorted out yet
# species_params
# Column check done in constructor
# If everything is OK
if (length(errors) == 0) TRUE else errors
}
#### Class definition ####
#' A class to hold the parameters for a size based model.
#'
#' Although it is possible to build a `MizerParams` object by hand it is
#' not recommended and several constructors are available. Dynamic simulations
#' are performed using [project()] function on objects of this class. As a
#' user you should never need to access the slots inside a `MizerParams` object
#' directly.
#'
#' @slot w The size grid for the fish part of the spectrum. An increasing
#' vector of weights (in grams) running from the smallest egg size to the
#' largest asymptotic size.
#' @slot dw The widths (in grams) of the size bins
#' @slot w_full The size grid for the full size range including the resource
#' spectrum. An increasing vector of weights (in grams) running from the
#' smallest resource size to the largest asymptotic size of fish. The
#' last entries of the vector have to be equal to the content of the w slot.
#' @slot dw_full The width of the size bins for the full spectrum. The last
#' entries have to be equal to the content of the dw slot.
#' @slot w_min_idx A vector holding the index of the weight of the egg size
#' of each species
#' @slot maturity An array (species x size) that holds the proportion of
#' individuals of each species at size that are mature. This enters in the
#' calculation of the spawning stock biomass with [getSSB()]. Set
#' with [setReproduction()].
#' @slot psi An array (species x size) that holds the allocation to reproduction
#' for each species at size, \eqn{\psi_i(w)}. Changed with
#' [setReproduction()].
#' @slot intake_max An array (species x size) that holds the maximum intake for
#' each species at size. Changed with [setMaxIntakeRate()].
#' @slot search_vol An array (species x size) that holds the search volume for
#' each species at size. Changed with [setSearchVolume()].
#' @slot metab An array (species x size) that holds the metabolism
#' for each species at size. Changed with [setMetabolicRate()].
#' @slot mu_b An array (species x size) that holds the external mortality rate
#' \eqn{\mu_{b.i}(w)}. Changed with [setExtMort()].
#' @slot pred_kernel An array (species x predator size x prey size) that holds
#' the predation coefficient of each predator at size on each prey size. If
#' this is NA then the following two slots will be used. Changed with
#' [setPredKernel()].
#' @slot ft_pred_kernel_e An array (species x log of predator/prey size ratio)
#' that holds the Fourier transform of the feeding kernel in a form
#' appropriate for evaluating the encounter rate integral. If this is NA
#' then the `pred_kernel` will be used to calculate the available
#' energy integral. Changed with [setPredKernel()].
#' @slot ft_pred_kernel_p An array (species x log of predator/prey size ratio)
#' that holds the Fourier transform of the feeding kernel in a form
#' appropriate for evaluating the predation mortality integral. If this is NA
#' then the `pred_kernel` will be used to calculate the integral.
#' Changed with [setPredKernel()].
#' @slot rr_pp A vector the same length as the w_full slot. The size specific
#' growth rate of the resource spectrum. Changed with [setResource()].
#' @slot cc_pp A vector the same length as the w_full slot. The size specific
#' carrying capacity of the resource spectrum. Changed with
#' [setResource()].
#' @slot resource_dynamics Name of the function for projecting the resource abundance
#' density by one timestep. The default is
#' [resource_semichemostat()].
#' Changed with [setResource()].
#' @slot other_dynamics A named list of functions for projecting the
#' values of other dynamical components of the ecosystem that may be modelled
#' by a mizer extensions you have installed. The names of the list entries
#' are the names of those components.
#' @slot other_encounter A named list of functions for calculating the
#' contribution to the encounter rate from each other dynamical component.
#' @slot other_mort A named list of functions for calculating the
#' contribution to the mortality rate from each other dynamical components.
#' @slot other_params A list containing the parameters needed by any mizer
#' extensions you may have installed to model other dynamical components of
#' the ecosystem.
#' @slot rates_funcs A named list with the names of the functions that should be
#' used to calculate the rates needed by `project()`. By default this will be
#' set to the names of the built-in rate functions.
#' @slot sc The community abundance of the scaling community
#' @slot species_params A data.frame to hold the species specific parameters.
#' See [newMultispeciesParams()] for details.
#' @slot gear_params Data frame with parameters for gear selectivity. See
#' [setFishing()] for details.
#' @slot interaction The species specific interaction matrix, \eqn{\theta_{ij}}.
#' Changed with [setInteraction()].
#' @slot selectivity An array (gear x species x w) that holds the selectivity of
#' each gear for species and size, \eqn{S_{g,i,w}}. Changed with
#' [setFishing()].
#' @slot catchability An array (gear x species) that holds the catchability of
#' each species by each gear, \eqn{Q_{g,i}}. Changed with
#' [setFishing()].
#' @slot initial_effort A vector containing the initial fishing effort for each
#' gear. Changed with [setFishing()].
#' @slot initial_n An array (species x size) that holds the initial abundance of
#' each species at each weight.
#' @slot initial_n_pp A vector the same length as the w_full slot that describes
#' the initial resource abundance at each weight.
#' @slot initial_n_other A list with the initial abundances of all other
#' ecosystem components. Has length zero if there are no other components.
#' @slot resource_params List with parameters for resource. See [setResource()].
#' @slot A Abundance multipliers.
#' @slot linecolour A named vector of colour values, named by species.
#' Used to give consistent colours in plots.
#' @slot linetype A named vector of linetypes, named by species.
#' Used to give consistent line types in plots.
#' @slot ft_mask An array (species x w_full) with zeros for weights larger than
#' the asymptotic weight of each species. Used to efficiently minimize
#' wrap-around errors in Fourier transform calculations.
#'
#' The \linkS4class{MizerParams} class is fairly complex with a large number of
#' slots, many of which are multidimensional arrays. The dimensions of these
#' arrays is strictly enforced so that `MizerParams` objects are consistent
#' in terms of number of species and number of size classes.
#'
#' The `MizerParams` class does not hold any dynamic information, e.g.
#' abundances or harvest effort through time. These are held in
#' \linkS4class{MizerSim} objects.
#'
#' @seealso [project()] [MizerSim()]
#' [emptyParams()] [newMultispeciesParams()]
#' [newCommunityParams()]
#' [newTraitParams()]
#' @export
setClass(
"MizerParams",
slots = c(
w = "numeric",
dw = "numeric",
w_full = "numeric",
dw_full = "numeric",
w_min_idx = "numeric",
maturity = "array",
psi = "array",
initial_n = "array",
intake_max = "array",
search_vol = "array",
metab = "array",
pred_kernel = "array",
ft_pred_kernel_e = "array",
ft_pred_kernel_p = "array",
mu_b = "array",
rr_pp = "numeric",
cc_pp = "numeric",
resource_dynamics = "character",
resource_params = "list",
other_dynamics = "list",
other_params = "list",
other_encounter = "list",
other_mort = "list",
rates_funcs = "list",
sc = "numeric",
initial_n_pp = "numeric",
initial_n_other = "list",
species_params = "data.frame",
interaction = "array",
gear_params = "data.frame",
selectivity = "array",
catchability = "array",
initial_effort = "numeric",
A = "numeric",
linecolour = "character",
linetype = "character",
ft_mask = "array"
),
)
setValidity("MizerParams", validMizerParams)
remove(validMizerParams)
#' Create empty MizerParams object of the right size
#'
#' An internal function.
#' Sets up a valid \linkS4class{MizerParams} object with all the slots
#' initialised and given dimension names, but with some slots left empty. This
#' function is to be used by other functions to set up full parameter objects.
#'
#' @section Size grid:
# Some code is commented out that would allow the user to
# specify a grid with a non-constant log spacing. But we comment this out
# for now because of the fft.
# #' When the `w_full` argument is not given, then
#' A size grid is created so that
#' the log-sizes are equally spaced. The spacing is chosen so that there will be
#' `no_w` fish size bins, with the smallest starting at `min_w` and the largest
#' starting at `max_w`. For `w_full` additional size bins are added below
#' `min_w`, with the same log size. The number of extra bins is such that
#' `min_w_pp` comes to lie within the smallest bin.
#'
#' @section Changes to species params:
#' The `species_params` slot of the returned MizerParams object may differ
#' slightly from the data frame supplied as argument to this function in the
#' following ways:
#' \itemize{
#' \item Default values are set for \code{w_min, w_inf, alpha, gear, interaction_p}.
#' \item The egg sizes in `w_min` are rounded down to lie on a grid point.
#' }
#' Note that the other characteristic sizes of the species, like `w_mat` and
#' `w_inf`, are not modified to lie on grid points.
#'
#' @param species_params A data frame of species-specific parameter values.
#' @param gear_params A data frame with gear-specific parameter values.
#' @param no_w The number of size bins in the consumer spectrum.
#' @param min_w Sets the size of the eggs of all species for which this is not
#' given in the `w_min` column of the `species_params` dataframe.
# #' @param w_full Increasing vector of weights giving the boundaries of size
# #' classes. Must include the value min_w. Has one more entry than the number
# #' of size bins. The last entry is the upper end of the largest size class. It
# #' be used to calculate the sizes of the size bins but will not be stored in
# #' the w_full slot of the returned MizerParams object. If this argument is not
# #' provided then size classes are set by the other arguments as described in
# #' the Details.
#' @param max_w The largest size of the consumer spectrum. By default this is
#' set to the largest `w_inf` specified in the `species_params` data
#' frame.
#' @param min_w_pp The smallest size of the resource spectrum.
# #' Ignored if w_full is specified.
#'
#' @return An empty but valid MizerParams object
#' @seealso See [newMultispeciesParams()] for a function that fills
#' the slots left empty by this function.
#' @export
emptyParams <- function(species_params,
gear_params = data.frame(),
no_w = 100,
min_w = 0.001,
# w_full = NA,
max_w = NA,
min_w_pp = 1e-12) {
assert_that(is.data.frame(species_params),
is.data.frame(gear_params),
no_w > 10)
## Set defaults ----
if (is.na(min_w_pp)) min_w_pp <- 1e-12
species_params <- set_species_param_default(species_params, "w_min", min_w)
min_w <- min(species_params$w_min)
species_params <- validSpeciesParams(species_params)
gear_params <- validGearParams(gear_params, species_params)
if (is.na(max_w)) {
max_w <- max(species_params$w_inf)
} else {
if (max(species_params$w_inf) > max_w * (1 + 1e-9)) { # The fudge factor
# is there to avoid false alerts due to rounding errors.
too_large <- species_params$species[max_w < species_params$w_inf]
stop("Some of your species have an maximum size larger than max_w: ",
toString(too_large))
}
}
# Set up grids ----
# The following code anticipates that in future we might allow the user to
# specify a grid with a non-constant log spacing. But we comment this out
# for now because of the fft.
# if (missing(w_full)) {
# set up logarithmic grids
dx <- log10(max_w / min_w) / (no_w - 1)
# Community grid
w <- 10^(seq(from = log10(min_w), by = dx, length.out = no_w))
# dw[i] = w[i+1] - w[i]. Following formula works also for last entry dw[no_w]
dw <- (10^dx - 1) * w
# To avoid issues due to numerical imprecision
min_w <- w[1]
# For fft methods we need a constant log bin size throughout.
# Therefore we use as many steps as are necessary so that the first size
# class includes min_w_pp.
x_pp <- rev(seq(from = log10(min_w),
to = log10(min_w_pp),
by = -dx)) - dx
w_full <- c(10^x_pp, w)
# If min_w_pp happened to lie exactly on a grid point, we now added
# one grid point too much which we need to remove again
if (w_full[2] == min_w_pp) {
w_full <- w_full[2:length(w_full)]
}
no_w_full <- length(w_full)
dw_full <- (10^dx - 1) * w_full
# } else {
# # use supplied w_full
# no_w_full <- length(w_full) - 1
# dw_full <- diff(w_full)
# w_full <- w_full[seq_along(dw_full)]
# # Check that sizes are increasing
# if (any(dw_full <= 0)) {
# stop("w_full must be increasing.")
# }
# w_min_idx <- match(min_w, w_full)
# if (is.na(w_min_idx)) {
# stop("w_min must be contained in w_full.")
# }
# w <- w_full[w_min_idx:no_w_full]
# dw <- dw_full[w_min_idx:no_w_full]
# no_w <- length(w)
# min_w_pp <- w_full[1]
# }
# Basic arrays for templates ----
no_sp <- nrow(species_params)
species_names <- as.character(species_params$species)
gear_names <- unique(gear_params$gear)
mat1 <- array(NA, dim = c(no_sp, no_w),
dimnames = list(sp = species_names, w = signif(w,3)))
ft_pred_kernel <- array(NA, dim = c(no_sp, no_w_full),
dimnames = list(sp = species_names, k = 1:no_w_full))
ft_mask <- plyr::aaply(species_params$w_inf, 1,
function(x) w_full < x, .drop = FALSE)
selectivity <- array(0, dim = c(length(gear_names), no_sp, no_w),
dimnames = list(gear = gear_names, sp = species_names,
w = signif(w, 3)))
catchability <- array(0, dim = c(length(gear_names), no_sp),
dimnames = list(gear = gear_names, sp = species_names))
initial_effort <- rep(0, length(gear_names))
names(initial_effort) <- gear_names
interaction <- array(1, dim = c(no_sp, no_sp),
dimnames = list(predator = species_names,
prey = species_names))
vec1 <- as.numeric(rep(NA, no_w_full))
names(vec1) <- signif(w_full, 3)
# Round down w_min to lie on grid points and store the indices of these
# grid points in w_min_idx
w_min_idx <- as.vector(suppressWarnings(
tapply(species_params$w_min, 1:no_sp,
function(w_min, wx) max(which(wx <= w_min)), wx = w)))
# Due to rounding errors this might happen:
w_min_idx[w_min_idx == -Inf] <- 1
names(w_min_idx) = species_names
species_params$w_min <- w[w_min_idx]
# Colour and linetype scales ----
# for use in plots
# Colour-blind-friendly palettes
# From http://dr-k-lo.blogspot.co.uk/2013/07/a-color-blind-friendly-palette-for-r.html
# cbbPalette <- c("#000000", "#009E73", "#e79f00", "#9ad0f3", "#0072B2", "#D55E00",
# "#CC79A7", "#F0E442")
# From http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
# cbbPalette <- c("#E69F00", "#56B4E9", "#009E73",
# "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Random palette gemerated pm https://medialab.github.io/iwanthue/
colour_palette <- c("#815f00",
"#6237e2",
"#8da600",
"#de53ff",
"#0e4300",
"#430079",
"#6caa72",
"#ee0053",
"#007957",
"#b42979",
"#142300",
"#a08dfb",
"#644500",
"#04004c",
"#b79955",
"#0060a8",
"#dc8852",
"#007ca9",
"#ab003c",
"#9796d9",
"#472c00",
"#b492b0",
"#140000",
"#dc8488",
"#005c67",
"#5c585a")
# type_palette <- c("solid", "dashed", "dotdash", "longdash",
# "twodash")
type_palette <- c("solid")
if ("linecolour" %in% names(species_params)) {
linecolour <- species_params$linecolour
# If any NA's first fill them with unused colours
linecolour[is.na(linecolour)] <-
setdiff(colour_palette, linecolour)[1:sum(is.na(linecolour))]
# if there are still NAs, start from beginning of palette again
linecolour[is.na(linecolour)] <-
colour_palette[1:sum(is.na(linecolour))]
} else {
linecolour <- rep(colour_palette, length.out = no_sp)
}
names(linecolour) <- as.character(species_names)
linecolour <- c(linecolour, "Total" = "black", "Resource" = "green",
"Background" = "grey", "Fishing" = "red")
if ("linetype" %in% names(species_params)) {
linetype <- species_params$linetype
linetype[is.na(linetype)] <- "solid"
} else {
linetype <- rep(type_palette, length.out = no_sp)
}
names(linetype) <- as.character(species_names)
linetype <- c(linetype, "Total" = "solid", "Resource" = "solid",
"Background" = "solid", "Fishing" = "solid")
# Make object ----
# Should Z0, rrPP and ccPP have names (species names etc)?
params <- new(
"MizerParams",
w = w,
dw = dw,
w_full = w_full,
dw_full = dw_full,
w_min_idx = w_min_idx,
maturity = mat1,
psi = mat1,
initial_n = mat1,
intake_max = mat1,
search_vol = mat1,
metab = mat1,
mu_b = mat1,
ft_pred_kernel_e = ft_pred_kernel,
ft_pred_kernel_p = ft_pred_kernel,
pred_kernel = array(),
gear_params = gear_params,
selectivity = selectivity,
catchability = catchability,
initial_effort = initial_effort,
rr_pp = vec1,
cc_pp = vec1,
sc = w,
initial_n_pp = vec1,
species_params = species_params,
interaction = interaction,
other_dynamics = list(),
other_encounter = list(),
other_mort = list(),
rates_funcs = list(
Rates = "mizerRates",
Encounter = "mizerEncounter",
FeedingLevel = "mizerFeedingLevel",
EReproAndGrowth = "mizerEReproAndGrowth",
PredRate = "mizerPredRate",
PredMort = "mizerPredMort",
FMort = "mizerFMort",
Mort = "mizerMort",
ERepro = "mizerERepro",
EGrowth = "mizerEGrowth",
ResourceMort = "mizerResourceMort",
RDI = "mizerRDI",
RDD = "BevertonHoltRDD"),
resource_dynamics = "resource_semichemostat",
other_params = list(),
initial_n_other = list(),
A = as.numeric(rep(NA, no_sp)),
linecolour = linecolour,
linetype = linetype,
ft_mask = ft_mask
)
return(params)
}
#' Set line colours to be used in mizer plots
#'
#' @param params A MizerParams object
#' @param colours A named list or named vector of line colours.
#'
#' @return The MizerParams object with updated line colours
#' @export
#' @examples
#' params <- NS_params
#' params <- setColours(params, list("Cod" = "red", "Haddock" = "#00ff00"))
#' plotSpectra(params)
#' getColours(params)
setColours <- function(params, colours) {
assert_that(is(params, "MizerParams"),
all(validColour(colours)))
params@linecolour <- unlist(
modifyList(as.list(params@linecolour), as.list(colours)))
params
}
#' @rdname setColours
#' @export
getColours <- function(params) {
params@linecolour
}
validColour <- function(colour) {
sapply(colour, function(X) {
tryCatch(is.matrix(col2rgb(X)),
error = function(e) FALSE)
})
}
#' Set linetypes to be used in mizer plots
#'
#' @param params A MizerParams object
#' @param linetypes A named list or named vector of linetypes.
#'
#' @return The MizerParams object with updated linetypes
#' @export
#' @examples
#' params <- NS_params
#' params <- setLinetypes(params, list("Cod" = "solid"))
#' plotSpectra(params)
#' getLinetypes(params)
setLinetypes <- function(params, linetypes) {
assert_that(is(params, "MizerParams"))
params@linetype <- unlist(
modifyList(as.list(params@linetype), as.list(linetypes)))
params
}
#' @rdname setLinetypes
#' @export
getLinetypes <- function(params) {
as.list(params@linetype)
}
#' Size bins
#'
#' This is a good place to explain how mizer discretises the size
#'
#' @param params A MizerParams object
#'
#' @export
w <- function(params) {
params@w
}
#' @rdname w
#' @export
w_full <- function(params) {
params@w_full
}
#' @rdname w
#' @export
dw <- function(params) {
params@dw
}
#' @rdname w
#' @export
dw_full <- function(params) {
params@dw_full
}
| /R/MizerParams-class.R | no_license | ReneevanDorst/mizer | R | false | false | 31,828 | r | # Class specification and constructors for mizer base parameters class
# Class has members to store parameters of size based model
# Copyright 2012 Finlay Scott and Julia Blanchard.
# Copyright 2018 Gustav Delius and Richard Southwell.
# Development has received funding from the European Commission's Horizon 2020
# Research and Innovation Programme under Grant Agreement No. 634495
# for the project MINOUW (http://minouw-project.eu/).
# Distributed under the GPL 3 or later
# Maintainer: Gustav Delius, University of York, <gustav.delius@york.ac.uk>
# Naming conventions:
# S4 classes and constructors: AClass
# Functions: aFunction
# Variables: a_variable
# Validity function ---------------------------------------------------------
# Not documented as removed later on
validMizerParams <- function(object) {
errors <- character()
# grab some dims
no_w <- length(object@w)
no_w_full <- length(object@w_full)
w_idx <- (no_w_full - no_w + 1):no_w_full
# Check weight grids ----
# Check dw and dw_full are correct length
if (length(object@dw) != no_w) {
msg <- paste("dw is length ", length(object@dw),
" and w is length ", no_w,
". These should be the same length", sep = "")
errors <- c(errors, msg)
}
if (length(object@dw_full) != no_w_full) {
msg <- paste("dw_full is length ", length(object@dw_full),
" and w_full is length ", no_w_full,
". These should be the same length", sep = "")
errors <- c(errors, msg)
}
# Check that the last entries of w_full and dw_full agree with w and dw
if (any(object@w[] != object@w_full[w_idx])) {
msg <- "The later entries of w_full should be equal to those of w."
errors <- c(errors, msg)
}
if (any(object@dw[] != object@dw_full[w_idx])) {
msg <- "The later entries of dw_full should be equal to those of dw."
errors <- c(errors, msg)
}
# Check the array dimensions are good ----
# 2D arrays
if (!all(c(length(dim(object@psi)),
length(dim(object@intake_max)),
length(dim(object@search_vol)),
length(dim(object@metab)),
length(dim(object@mu_b)),
length(dim(object@interaction)),
length(dim(object@catchability))) == 2)) {
msg <- "psi, intake_max, search_vol, metab, mu_b, interaction and catchability must all be two dimensions"
errors <- c(errors, msg)
}
# 3D arrays
if (length(dim(object@selectivity)) != 3) {
msg <- "selectivity must be three dimensions"
errors <- c(errors, msg)
}
# Check number of species is equal across relevant slots
if (!all(c(
dim(object@psi)[1],
dim(object@intake_max)[1],
dim(object@search_vol)[1],
dim(object@metab)[1],
dim(object@mu_b)[1],
dim(object@selectivity)[2],
dim(object@catchability)[2],
dim(object@interaction)[1],
dim(object@interaction)[2]) ==
dim(object@species_params)[1])) {
msg <- "The number of species in the model must be consistent across the species_params, psi, intake_max, search_vol, mu_b, interaction (dim 1), selectivity, catchability and interaction (dim 2) slots"
errors <- c(errors, msg)
}
# Check number of size groups
if (!all(c(
dim(object@psi)[2],
dim(object@intake_max)[2],
dim(object@search_vol)[2],
dim(object@metab)[2],
dim(object@selectivity)[3]) ==
no_w)) {
msg <- "The number of size bins in the model must be consistent across the w, psi, intake_max, search_vol, and selectivity (dim 3) slots"
errors <- c(errors, msg)
}
# Check numbe of gears
if (!isTRUE(all.equal(dim(object@selectivity)[1], dim(object@catchability)[1]))) {
msg <- "The number of fishing gears must be consistent across the catchability and selectivity (dim 1) slots"
errors <- c(errors, msg)
}
# Check names of dimnames of arrays ----
# sp dimension
if (!all(c(
names(dimnames(object@psi))[1],
names(dimnames(object@intake_max))[1],
names(dimnames(object@search_vol))[1],
names(dimnames(object@metab))[1],
names(dimnames(object@mu_b))[1],
names(dimnames(object@selectivity))[2],
names(dimnames(object@catchability))[2]) == "sp")) {
msg <- "Name of first dimension of psi, intake_max, search_vol, metab, mu_b, and the second dimension of selectivity and catchability must be 'sp'"
errors <- c(errors, msg)
}
#interaction dimension names
if (names(dimnames(object@interaction))[1] != "predator") {
msg <- "The first dimension of interaction must be called 'predator'"
errors <- c(errors, msg)
}
if (names(dimnames(object@interaction))[2] != "prey") {
msg <- "The first dimension of interaction must be called 'prey'"
errors <- c(errors, msg)
}
# w dimension
if (!all(c(
names(dimnames(object@psi))[2],
names(dimnames(object@intake_max))[2],
names(dimnames(object@search_vol))[2],
names(dimnames(object@metab))[2],
names(dimnames(object@selectivity))[3]) == "w")) {
msg <- "Name of second dimension of psi, intake_max, search_vol, metab and third dimension of selectivity must be 'w'"
errors <- c(errors, msg)
}
if (!all(c(
names(dimnames(object@selectivity))[1],
names(dimnames(object@catchability))[1]) == "gear")) {
msg <- "Name of first dimension of selectivity and catchability must be 'gear'"
errors <- c(errors, msg)
}
# Check dimnames of species are identical
# Bit tricky this one as I don't know of a way to compare lots of vectors
# at the same time. Just use == and the recycling rule
if (!all(c(
dimnames(object@psi)[[1]],
dimnames(object@intake_max)[[1]],
dimnames(object@search_vol)[[1]],
dimnames(object@metab)[[1]],
dimnames(object@mu_b)[[1]],
dimnames(object@selectivity)[[2]],
dimnames(object@catchability)[[2]],
dimnames(object@interaction)[[1]],
dimnames(object@interaction)[[2]]) ==
object@species_params$species)) {
msg <- "The species names of species_params, psi, intake_max, search_vol, metab, mu_b, selectivity, catchability and interaction must all be the same"
errors <- c(errors, msg)
}
# Check dimnames of w
if (!all(c(
dimnames(object@psi)[[2]],
dimnames(object@intake_max)[[2]],
dimnames(object@search_vol)[[2]],
dimnames(object@metab)[[2]]) ==
dimnames(object@selectivity)[[3]])) {
msg <- "The size names of psi, intake_max, search_vol, metab and selectivity must all be the same"
errors <- c(errors, msg)
}
# Check dimnames of gear
if (!isTRUE(all.equal(
dimnames(object@catchability)[[1]],
dimnames(object@selectivity)[[1]]))) {
msg <- "The gear names of selectivity and catchability must all be the same"
errors <- c(errors, msg)
}
# Check the vector slots ----
if (length(object@rr_pp) != length(object@w_full)) {
msg <- "rr_pp must be the same length as w_full"
errors <- c(errors, msg)
}
if (length(object@cc_pp) != length(object@w_full)) {
msg <- "cc_pp must be the same length as w_full"
errors <- c(errors, msg)
}
# TODO: Rewrite the following into a test of the @rates_funcs slot ----
# SRR
# if (!is.string(object@srr)) {
# msg <- "srr needs to be specified as a string giving the name of the function"
# errors <- c(errors, msg)
# } else {
# if (!exists(object@srr)) {
# msg <- paste0("The stock-recruitment function ",
# object@srr,
# "does not exist.")
# errors <- c(errors, msg)
# } else {
# srr <- get(object@srr)
# if (!is.function(srr)) {
# msg <- "The specified srr is not a function."
# errors <- c(errors, msg)
# } else {
# # Must have two arguments: rdi amd species_params
# if (!isTRUE(all.equal(names(formals(srr)), c("rdi", "species_params")))) {
# msg <- "Arguments of srr function must be 'rdi' and 'species_params'"
# errors <- c(errors, msg)
# }
# }
# }
# }
# Should not have legacy r_max column (has been renamed to R_max)
if ("r_max" %in% names(object@species_params)) {
msg <- "The 'r_max' column in species_params should be called 'R_max'. You can use 'upgradeParams()' to upgrade your params object."
errors <- c(errors, msg)
}
# # species_params data.frame must have columns:
# # species, z0, alpha, eRepro
# species_params_cols <- c("species","z0","alpha","erepro")
# if (!all(species_params_cols %in% names(object@species_params))) {
# msg <- "species_params data.frame must have 'species', 'z0', 'alpha' and 'erepro' columms"
# errors <- c(errors,msg)
# }
# must also have SRR params but not sorted out yet
# species_params
# Column check done in constructor
# If everything is OK
if (length(errors) == 0) TRUE else errors
}
#### Class definition ####
#' A class to hold the parameters for a size based model.
#'
#' Although it is possible to build a `MizerParams` object by hand it is
#' not recommended and several constructors are available. Dynamic simulations
#' are performed using [project()] function on objects of this class. As a
#' user you should never need to access the slots inside a `MizerParams` object
#' directly.
#'
#' @slot w The size grid for the fish part of the spectrum. An increasing
#' vector of weights (in grams) running from the smallest egg size to the
#' largest asymptotic size.
#' @slot dw The widths (in grams) of the size bins
#' @slot w_full The size grid for the full size range including the resource
#' spectrum. An increasing vector of weights (in grams) running from the
#' smallest resource size to the largest asymptotic size of fish. The
#' last entries of the vector have to be equal to the content of the w slot.
#' @slot dw_full The width of the size bins for the full spectrum. The last
#' entries have to be equal to the content of the dw slot.
#' @slot w_min_idx A vector holding the index of the weight of the egg size
#' of each species
#' @slot maturity An array (species x size) that holds the proportion of
#' individuals of each species at size that are mature. This enters in the
#' calculation of the spawning stock biomass with [getSSB()]. Set
#' with [setReproduction()].
#' @slot psi An array (species x size) that holds the allocation to reproduction
#' for each species at size, \eqn{\psi_i(w)}. Changed with
#' [setReproduction()].
#' @slot intake_max An array (species x size) that holds the maximum intake for
#' each species at size. Changed with [setMaxIntakeRate()].
#' @slot search_vol An array (species x size) that holds the search volume for
#' each species at size. Changed with [setSearchVolume()].
#' @slot metab An array (species x size) that holds the metabolism
#' for each species at size. Changed with [setMetabolicRate()].
#' @slot mu_b An array (species x size) that holds the external mortality rate
#' \eqn{\mu_{b.i}(w)}. Changed with [setExtMort()].
#' @slot pred_kernel An array (species x predator size x prey size) that holds
#' the predation coefficient of each predator at size on each prey size. If
#' this is NA then the following two slots will be used. Changed with
#' [setPredKernel()].
#' @slot ft_pred_kernel_e An array (species x log of predator/prey size ratio)
#' that holds the Fourier transform of the feeding kernel in a form
#' appropriate for evaluating the encounter rate integral. If this is NA
#' then the `pred_kernel` will be used to calculate the available
#' energy integral. Changed with [setPredKernel()].
#' @slot ft_pred_kernel_p An array (species x log of predator/prey size ratio)
#' that holds the Fourier transform of the feeding kernel in a form
#' appropriate for evaluating the predation mortality integral. If this is NA
#' then the `pred_kernel` will be used to calculate the integral.
#' Changed with [setPredKernel()].
#' @slot rr_pp A vector the same length as the w_full slot. The size specific
#' growth rate of the resource spectrum. Changed with [setResource()].
#' @slot cc_pp A vector the same length as the w_full slot. The size specific
#' carrying capacity of the resource spectrum. Changed with
#' [setResource()].
#' @slot resource_dynamics Name of the function for projecting the resource abundance
#' density by one timestep. The default is
#' [resource_semichemostat()].
#' Changed with [setResource()].
#' @slot other_dynamics A named list of functions for projecting the
#' values of other dynamical components of the ecosystem that may be modelled
#' by a mizer extensions you have installed. The names of the list entries
#' are the names of those components.
#' @slot other_encounter A named list of functions for calculating the
#' contribution to the encounter rate from each other dynamical component.
#' @slot other_mort A named list of functions for calculating the
#' contribution to the mortality rate from each other dynamical components.
#' @slot other_params A list containing the parameters needed by any mizer
#' extensions you may have installed to model other dynamical components of
#' the ecosystem.
#' @slot rates_funcs A named list with the names of the functions that should be
#' used to calculate the rates needed by `project()`. By default this will be
#' set to the names of the built-in rate functions.
#' @slot sc The community abundance of the scaling community
#' @slot species_params A data.frame to hold the species specific parameters.
#' See [newMultispeciesParams()] for details.
#' @slot gear_params Data frame with parameters for gear selectivity. See
#' [setFishing()] for details.
#' @slot interaction The species specific interaction matrix, \eqn{\theta_{ij}}.
#' Changed with [setInteraction()].
#' @slot selectivity An array (gear x species x w) that holds the selectivity of
#' each gear for species and size, \eqn{S_{g,i,w}}. Changed with
#' [setFishing()].
#' @slot catchability An array (gear x species) that holds the catchability of
#' each species by each gear, \eqn{Q_{g,i}}. Changed with
#' [setFishing()].
#' @slot initial_effort A vector containing the initial fishing effort for each
#' gear. Changed with [setFishing()].
#' @slot initial_n An array (species x size) that holds the initial abundance of
#' each species at each weight.
#' @slot initial_n_pp A vector the same length as the w_full slot that describes
#' the initial resource abundance at each weight.
#' @slot initial_n_other A list with the initial abundances of all other
#' ecosystem components. Has length zero if there are no other components.
#' @slot resource_params List with parameters for resource. See [setResource()].
#' @slot A Abundance multipliers.
#' @slot linecolour A named vector of colour values, named by species.
#' Used to give consistent colours in plots.
#' @slot linetype A named vector of linetypes, named by species.
#' Used to give consistent line types in plots.
#' @slot ft_mask An array (species x w_full) with zeros for weights larger than
#' the asymptotic weight of each species. Used to efficiently minimize
#' wrap-around errors in Fourier transform calculations.
#'
#' The \linkS4class{MizerParams} class is fairly complex with a large number of
#' slots, many of which are multidimensional arrays. The dimensions of these
#' arrays is strictly enforced so that `MizerParams` objects are consistent
#' in terms of number of species and number of size classes.
#'
#' The `MizerParams` class does not hold any dynamic information, e.g.
#' abundances or harvest effort through time. These are held in
#' \linkS4class{MizerSim} objects.
#'
#' @seealso [project()] [MizerSim()]
#' [emptyParams()] [newMultispeciesParams()]
#' [newCommunityParams()]
#' [newTraitParams()]
#' @export
setClass(
"MizerParams",
slots = c(
w = "numeric",
dw = "numeric",
w_full = "numeric",
dw_full = "numeric",
w_min_idx = "numeric",
maturity = "array",
psi = "array",
initial_n = "array",
intake_max = "array",
search_vol = "array",
metab = "array",
pred_kernel = "array",
ft_pred_kernel_e = "array",
ft_pred_kernel_p = "array",
mu_b = "array",
rr_pp = "numeric",
cc_pp = "numeric",
resource_dynamics = "character",
resource_params = "list",
other_dynamics = "list",
other_params = "list",
other_encounter = "list",
other_mort = "list",
rates_funcs = "list",
sc = "numeric",
initial_n_pp = "numeric",
initial_n_other = "list",
species_params = "data.frame",
interaction = "array",
gear_params = "data.frame",
selectivity = "array",
catchability = "array",
initial_effort = "numeric",
A = "numeric",
linecolour = "character",
linetype = "character",
ft_mask = "array"
),
)
setValidity("MizerParams", validMizerParams)
remove(validMizerParams)
#' Create empty MizerParams object of the right size
#'
#' An internal function.
#' Sets up a valid \linkS4class{MizerParams} object with all the slots
#' initialised and given dimension names, but with some slots left empty. This
#' function is to be used by other functions to set up full parameter objects.
#'
#' @section Size grid:
# Some code is commented out that would allow the user to
# specify a grid with a non-constant log spacing. But we comment this out
# for now because of the fft.
# #' When the `w_full` argument is not given, then
#' A size grid is created so that
#' the log-sizes are equally spaced. The spacing is chosen so that there will be
#' `no_w` fish size bins, with the smallest starting at `min_w` and the largest
#' starting at `max_w`. For `w_full` additional size bins are added below
#' `min_w`, with the same log size. The number of extra bins is such that
#' `min_w_pp` comes to lie within the smallest bin.
#'
#' @section Changes to species params:
#' The `species_params` slot of the returned MizerParams object may differ
#' slightly from the data frame supplied as argument to this function in the
#' following ways:
#' \itemize{
#' \item Default values are set for \code{w_min, w_inf, alpha, gear, interaction_p}.
#' \item The egg sizes in `w_min` are rounded down to lie on a grid point.
#' }
#' Note that the other characteristic sizes of the species, like `w_mat` and
#' `w_inf`, are not modified to lie on grid points.
#'
#' @param species_params A data frame of species-specific parameter values.
#' @param gear_params A data frame with gear-specific parameter values.
#' @param no_w The number of size bins in the consumer spectrum.
#' @param min_w Sets the size of the eggs of all species for which this is not
#' given in the `w_min` column of the `species_params` dataframe.
# #' @param w_full Increasing vector of weights giving the boundaries of size
# #' classes. Must include the value min_w. Has one more entry than the number
# #' of size bins. The last entry is the upper end of the largest size class. It
# #' be used to calculate the sizes of the size bins but will not be stored in
# #' the w_full slot of the returned MizerParams object. If this argument is not
# #' provided then size classes are set by the other arguments as described in
# #' the Details.
#' @param max_w The largest size of the consumer spectrum. By default this is
#' set to the largest `w_inf` specified in the `species_params` data
#' frame.
#' @param min_w_pp The smallest size of the resource spectrum.
# #' Ignored if w_full is specified.
#'
#' @return An empty but valid MizerParams object
#' @seealso See [newMultispeciesParams()] for a function that fills
#' the slots left empty by this function.
#' @export
emptyParams <- function(species_params,
gear_params = data.frame(),
no_w = 100,
min_w = 0.001,
# w_full = NA,
max_w = NA,
min_w_pp = 1e-12) {
assert_that(is.data.frame(species_params),
is.data.frame(gear_params),
no_w > 10)
## Set defaults ----
if (is.na(min_w_pp)) min_w_pp <- 1e-12
species_params <- set_species_param_default(species_params, "w_min", min_w)
min_w <- min(species_params$w_min)
species_params <- validSpeciesParams(species_params)
gear_params <- validGearParams(gear_params, species_params)
if (is.na(max_w)) {
max_w <- max(species_params$w_inf)
} else {
if (max(species_params$w_inf) > max_w * (1 + 1e-9)) { # The fudge factor
# is there to avoid false alerts due to rounding errors.
too_large <- species_params$species[max_w < species_params$w_inf]
stop("Some of your species have an maximum size larger than max_w: ",
toString(too_large))
}
}
# Set up grids ----
# The following code anticipates that in future we might allow the user to
# specify a grid with a non-constant log spacing. But we comment this out
# for now because of the fft.
# if (missing(w_full)) {
# set up logarithmic grids
dx <- log10(max_w / min_w) / (no_w - 1)
# Community grid
w <- 10^(seq(from = log10(min_w), by = dx, length.out = no_w))
# dw[i] = w[i+1] - w[i]. Following formula works also for last entry dw[no_w]
dw <- (10^dx - 1) * w
# To avoid issues due to numerical imprecision
min_w <- w[1]
# For fft methods we need a constant log bin size throughout.
# Therefore we use as many steps as are necessary so that the first size
# class includes min_w_pp.
x_pp <- rev(seq(from = log10(min_w),
to = log10(min_w_pp),
by = -dx)) - dx
w_full <- c(10^x_pp, w)
# If min_w_pp happened to lie exactly on a grid point, we now added
# one grid point too much which we need to remove again
if (w_full[2] == min_w_pp) {
w_full <- w_full[2:length(w_full)]
}
no_w_full <- length(w_full)
dw_full <- (10^dx - 1) * w_full
# } else {
# # use supplied w_full
# no_w_full <- length(w_full) - 1
# dw_full <- diff(w_full)
# w_full <- w_full[seq_along(dw_full)]
# # Check that sizes are increasing
# if (any(dw_full <= 0)) {
# stop("w_full must be increasing.")
# }
# w_min_idx <- match(min_w, w_full)
# if (is.na(w_min_idx)) {
# stop("w_min must be contained in w_full.")
# }
# w <- w_full[w_min_idx:no_w_full]
# dw <- dw_full[w_min_idx:no_w_full]
# no_w <- length(w)
# min_w_pp <- w_full[1]
# }
# Basic arrays for templates ----
no_sp <- nrow(species_params)
species_names <- as.character(species_params$species)
gear_names <- unique(gear_params$gear)
mat1 <- array(NA, dim = c(no_sp, no_w),
dimnames = list(sp = species_names, w = signif(w,3)))
ft_pred_kernel <- array(NA, dim = c(no_sp, no_w_full),
dimnames = list(sp = species_names, k = 1:no_w_full))
ft_mask <- plyr::aaply(species_params$w_inf, 1,
function(x) w_full < x, .drop = FALSE)
selectivity <- array(0, dim = c(length(gear_names), no_sp, no_w),
dimnames = list(gear = gear_names, sp = species_names,
w = signif(w, 3)))
catchability <- array(0, dim = c(length(gear_names), no_sp),
dimnames = list(gear = gear_names, sp = species_names))
initial_effort <- rep(0, length(gear_names))
names(initial_effort) <- gear_names
interaction <- array(1, dim = c(no_sp, no_sp),
dimnames = list(predator = species_names,
prey = species_names))
vec1 <- as.numeric(rep(NA, no_w_full))
names(vec1) <- signif(w_full, 3)
# Round down w_min to lie on grid points and store the indices of these
# grid points in w_min_idx
w_min_idx <- as.vector(suppressWarnings(
tapply(species_params$w_min, 1:no_sp,
function(w_min, wx) max(which(wx <= w_min)), wx = w)))
# Due to rounding errors this might happen:
w_min_idx[w_min_idx == -Inf] <- 1
names(w_min_idx) = species_names
species_params$w_min <- w[w_min_idx]
# Colour and linetype scales ----
# for use in plots
# Colour-blind-friendly palettes
# From http://dr-k-lo.blogspot.co.uk/2013/07/a-color-blind-friendly-palette-for-r.html
# cbbPalette <- c("#000000", "#009E73", "#e79f00", "#9ad0f3", "#0072B2", "#D55E00",
# "#CC79A7", "#F0E442")
# From http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
# cbbPalette <- c("#E69F00", "#56B4E9", "#009E73",
# "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Random palette gemerated pm https://medialab.github.io/iwanthue/
colour_palette <- c("#815f00",
"#6237e2",
"#8da600",
"#de53ff",
"#0e4300",
"#430079",
"#6caa72",
"#ee0053",
"#007957",
"#b42979",
"#142300",
"#a08dfb",
"#644500",
"#04004c",
"#b79955",
"#0060a8",
"#dc8852",
"#007ca9",
"#ab003c",
"#9796d9",
"#472c00",
"#b492b0",
"#140000",
"#dc8488",
"#005c67",
"#5c585a")
# type_palette <- c("solid", "dashed", "dotdash", "longdash",
# "twodash")
type_palette <- c("solid")
if ("linecolour" %in% names(species_params)) {
linecolour <- species_params$linecolour
# If any NA's first fill them with unused colours
linecolour[is.na(linecolour)] <-
setdiff(colour_palette, linecolour)[1:sum(is.na(linecolour))]
# if there are still NAs, start from beginning of palette again
linecolour[is.na(linecolour)] <-
colour_palette[1:sum(is.na(linecolour))]
} else {
linecolour <- rep(colour_palette, length.out = no_sp)
}
names(linecolour) <- as.character(species_names)
linecolour <- c(linecolour, "Total" = "black", "Resource" = "green",
"Background" = "grey", "Fishing" = "red")
if ("linetype" %in% names(species_params)) {
linetype <- species_params$linetype
linetype[is.na(linetype)] <- "solid"
} else {
linetype <- rep(type_palette, length.out = no_sp)
}
names(linetype) <- as.character(species_names)
linetype <- c(linetype, "Total" = "solid", "Resource" = "solid",
"Background" = "solid", "Fishing" = "solid")
# Make object ----
# Should Z0, rrPP and ccPP have names (species names etc)?
params <- new(
"MizerParams",
w = w,
dw = dw,
w_full = w_full,
dw_full = dw_full,
w_min_idx = w_min_idx,
maturity = mat1,
psi = mat1,
initial_n = mat1,
intake_max = mat1,
search_vol = mat1,
metab = mat1,
mu_b = mat1,
ft_pred_kernel_e = ft_pred_kernel,
ft_pred_kernel_p = ft_pred_kernel,
pred_kernel = array(),
gear_params = gear_params,
selectivity = selectivity,
catchability = catchability,
initial_effort = initial_effort,
rr_pp = vec1,
cc_pp = vec1,
sc = w,
initial_n_pp = vec1,
species_params = species_params,
interaction = interaction,
other_dynamics = list(),
other_encounter = list(),
other_mort = list(),
rates_funcs = list(
Rates = "mizerRates",
Encounter = "mizerEncounter",
FeedingLevel = "mizerFeedingLevel",
EReproAndGrowth = "mizerEReproAndGrowth",
PredRate = "mizerPredRate",
PredMort = "mizerPredMort",
FMort = "mizerFMort",
Mort = "mizerMort",
ERepro = "mizerERepro",
EGrowth = "mizerEGrowth",
ResourceMort = "mizerResourceMort",
RDI = "mizerRDI",
RDD = "BevertonHoltRDD"),
resource_dynamics = "resource_semichemostat",
other_params = list(),
initial_n_other = list(),
A = as.numeric(rep(NA, no_sp)),
linecolour = linecolour,
linetype = linetype,
ft_mask = ft_mask
)
return(params)
}
#' Set line colours to be used in mizer plots
#'
#' @param params A MizerParams object
#' @param colours A named list or named vector of line colours.
#'
#' @return The MizerParams object with updated line colours
#' @export
#' @examples
#' params <- NS_params
#' params <- setColours(params, list("Cod" = "red", "Haddock" = "#00ff00"))
#' plotSpectra(params)
#' getColours(params)
setColours <- function(params, colours) {
assert_that(is(params, "MizerParams"),
all(validColour(colours)))
params@linecolour <- unlist(
modifyList(as.list(params@linecolour), as.list(colours)))
params
}
#' @rdname setColours
#' @export
getColours <- function(params) {
params@linecolour
}
validColour <- function(colour) {
sapply(colour, function(X) {
tryCatch(is.matrix(col2rgb(X)),
error = function(e) FALSE)
})
}
#' Set linetypes to be used in mizer plots
#'
#' @param params A MizerParams object
#' @param linetypes A named list or named vector of linetypes.
#'
#' @return The MizerParams object with updated linetypes
#' @export
#' @examples
#' params <- NS_params
#' params <- setLinetypes(params, list("Cod" = "solid"))
#' plotSpectra(params)
#' getLinetypes(params)
setLinetypes <- function(params, linetypes) {
assert_that(is(params, "MizerParams"))
params@linetype <- unlist(
modifyList(as.list(params@linetype), as.list(linetypes)))
params
}
#' @rdname setLinetypes
#' @export
getLinetypes <- function(params) {
as.list(params@linetype)
}
#' Size bins
#'
#' This is a good place to explain how mizer discretises the size
#'
#' @param params A MizerParams object
#'
#' @export
w <- function(params) {
params@w
}
#' @rdname w
#' @export
w_full <- function(params) {
params@w_full
}
#' @rdname w
#' @export
dw <- function(params) {
params@dw
}
#' @rdname w
#' @export
dw_full <- function(params) {
params@dw_full
}
|
library(shiny)
library(shinydashboard)
library(reshape2)
library(dplyr)
library(plotly)
library(shinythemes)
#Load Data
adult <- read.csv("AdultCare_StatisticalReport.csv")
# Avoid plotly issues ----------------------------------------------
pdf(NULL)
# Application header & title ----------------------------------------------
header <- dashboardHeader(title = "Adult Care Facility Statistical Report: 2013-2018", titleWidth = 450)
# Dashboard Sidebar ----------------------------------------------
sidebar <- dashboardSidebar(
sidebarMenu(
id = "tabs",
# Menu Items ----------------------------------------------
menuItem("Gender Stats", icon = icon("bar-chart"), tabName = "genders"),
menuItem("Count Stats", icon = icon("bar-chart"), tabName = "counts"),
menuItem("Organization Type Stats", icon = icon("bar-chart"), tabName = "organizations"),
menuItem("Table", icon = icon("th"), tabName = "table", badgeLabel = "new", badgeColor = "green"),
# Inputs: select county to plot ----------------------------------------------
selectInput(inputId = "county",
label = "Select County",
choices = sort(unique(adult$County)),
selected = "Albany"),
# Inputs: select quarter to plot ----------------------------------------------
checkboxGroupInput(inputId = "type",
label = "Select Organziation Type",
choices = sort(unique(adult$Certified.Type)),
selected = c("S","F","E","N")),
# Reporting year Selection ----------------------------------------------
sliderInput(inputId = "year",
label = "Year Range",
min = min(adult$Reporting.Year),
max = max(adult$Reporting.Year),
value = c(min(adult$Reporting.Year), max(adult$Reporting.Year)),
step = 1,
sep = "")
)
)
# Dashboard body ----------------------------------------------
body <- dashboardBody(tabItems(
# Quarter Stats page ----------------------------------------------
tabItem("genders",
# Value Boxes ----------------------------------------------
fluidRow(
infoBoxOutput("female"),
infoBoxOutput("male")
),
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 12,
tabPanel("Female Resident Distribution By Reporting Years", plotlyOutput("plot_gender")),
tabPanel("Male Resident Distribution By Reporting Years", plotlyOutput("plot_gender2")))
)),
# Quarter Stats page ----------------------------------------------
tabItem("counts",
# Input Boxes ----------------------------------------------
fluidRow(
valueBoxOutput("total"),
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 8,
tabPanel("Overall Distribution by the Number of Resident", plotlyOutput("plot_count")))
))),
# Organization Stats page ----------------------------------------------
tabItem("organizations",
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 8,
tabPanel("Resident Distribution by Organzaition Type", plotlyOutput("plot_type")))
)),
# Data Table Page ----------------------------------------------
tabItem("table",
fluidPage(
box(title = "Statisical Report for Your Selection", DT::dataTableOutput("table"), width = 12)))
)
)
ui <- dashboardPage(header, sidebar, body, skin = "purple")
# Define server function required to create plots and value boxes -----
server <- function(input, output) {
# Reactive data function -------------------------------------------
adultInput <- reactive({
adult <- adult %>%
#Year Filter
filter(Reporting.Year >= input$year[1] & Reporting.Year <= input$year[2])
#County Filter
adult <- subset(adult, County %in% input$county)
#Quarter Filter
adult <- subset(adult, Certified.Type %in% input$type)
# Return dataframe ----------------------------------------------
return(adult)
})
# Plots showing the Gender stats -----------------------------------
# Female
output$plot_gender <- renderPlotly({
ggplot(data = adultInput(),
aes(Female.Census)) +
geom_histogram(binwidth = 20) +
facet_wrap(~Reporting.Year) +
labs(x = "The Number of Female Residents", y = "Count")+
theme_classic()
})
#Male
output$plot_gender2 <- renderPlotly({
ggplot(data = adultInput(),
aes (Male.Census)) +
geom_histogram(binwidth = 20) +
facet_wrap(~Reporting.Year) +
labs(x = "The Number of Male Residents", y = "Count") +
theme_classic()
})
# A plot showing the Resident count stats -----------------------------------
output$plot_count <- renderPlotly({
ggplot(data = adultInput(),
aes(x = End.Census)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white")+
geom_density(color = "darkblue", fill = "lightblue", alpha = 0.4) +
labs (x = "Number of Residents per Organization") +
theme_classic()
})
# A plot showing the Organization Type stats -----------------------------------
output$plot_type <- renderPlotly({
ggplot(data = adultInput(),
aes(x = Certified.Type,
y = End.Census)) +
geom_boxplot(aes(fill = Certified.Type)) +
labs(fill="Type" , x = "Organization Type", y = "The number of residents") +
scale_fill_brewer(palette = "Blues") +
theme_classic()
})
# Data table of characters ----------------------------------------------
output$table <- DT::renderDataTable({
ad <-subset(adultInput(), select = c(Reporting.Year, Reporting.Quarter, Reporting.Organization, Certified.Type, County, Certified.Capacity,
End.Census, Male.Census, Female.Census))
DT::datatable(ad, options = list(scrollX = TRUE))
})
# Male Average box ----------------------------------------------
output$male <- renderValueBox({
ai <- adultInput()
infoBox("Avg Male Count:", value = round(mean(ai$Male.Census), 0), color = "blue", width = 6)
})
# Female Average box ----------------------------------------------
output$female <- renderValueBox({
ai <- adultInput()
infoBox("Avg Female Count:", value = round(mean(ai$Female.Census), 0), color = "maroon", width = 6)
})
# Total Census box ----------------------------------------------
output$total <- renderValueBox({
ai <- adultInput()
valueBox("Total Number of Residents", value = sum(ai$End.Census), color = "olive", width = 4)
})
}
# Run the application
shinyApp(ui = ui, server = server) | /app.R | no_license | rforoperations2019/project1_jiaying2 | R | false | false | 6,998 | r | library(shiny)
library(shinydashboard)
library(reshape2)
library(dplyr)
library(plotly)
library(shinythemes)
#Load Data
adult <- read.csv("AdultCare_StatisticalReport.csv")
# Avoid plotly issues ----------------------------------------------
pdf(NULL)
# Application header & title ----------------------------------------------
header <- dashboardHeader(title = "Adult Care Facility Statistical Report: 2013-2018", titleWidth = 450)
# Dashboard Sidebar ----------------------------------------------
sidebar <- dashboardSidebar(
sidebarMenu(
id = "tabs",
# Menu Items ----------------------------------------------
menuItem("Gender Stats", icon = icon("bar-chart"), tabName = "genders"),
menuItem("Count Stats", icon = icon("bar-chart"), tabName = "counts"),
menuItem("Organization Type Stats", icon = icon("bar-chart"), tabName = "organizations"),
menuItem("Table", icon = icon("th"), tabName = "table", badgeLabel = "new", badgeColor = "green"),
# Inputs: select county to plot ----------------------------------------------
selectInput(inputId = "county",
label = "Select County",
choices = sort(unique(adult$County)),
selected = "Albany"),
# Inputs: select quarter to plot ----------------------------------------------
checkboxGroupInput(inputId = "type",
label = "Select Organziation Type",
choices = sort(unique(adult$Certified.Type)),
selected = c("S","F","E","N")),
# Reporting year Selection ----------------------------------------------
sliderInput(inputId = "year",
label = "Year Range",
min = min(adult$Reporting.Year),
max = max(adult$Reporting.Year),
value = c(min(adult$Reporting.Year), max(adult$Reporting.Year)),
step = 1,
sep = "")
)
)
# Dashboard body ----------------------------------------------
body <- dashboardBody(tabItems(
# Quarter Stats page ----------------------------------------------
tabItem("genders",
# Value Boxes ----------------------------------------------
fluidRow(
infoBoxOutput("female"),
infoBoxOutput("male")
),
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 12,
tabPanel("Female Resident Distribution By Reporting Years", plotlyOutput("plot_gender")),
tabPanel("Male Resident Distribution By Reporting Years", plotlyOutput("plot_gender2")))
)),
# Quarter Stats page ----------------------------------------------
tabItem("counts",
# Input Boxes ----------------------------------------------
fluidRow(
valueBoxOutput("total"),
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 8,
tabPanel("Overall Distribution by the Number of Resident", plotlyOutput("plot_count")))
))),
# Organization Stats page ----------------------------------------------
tabItem("organizations",
# Plot ----------------------------------------------
fluidRow(
tabBox(width = 8,
tabPanel("Resident Distribution by Organzaition Type", plotlyOutput("plot_type")))
)),
# Data Table Page ----------------------------------------------
tabItem("table",
fluidPage(
box(title = "Statisical Report for Your Selection", DT::dataTableOutput("table"), width = 12)))
)
)
ui <- dashboardPage(header, sidebar, body, skin = "purple")
# Define server function required to create plots and value boxes -----
server <- function(input, output) {
# Reactive data function -------------------------------------------
adultInput <- reactive({
adult <- adult %>%
#Year Filter
filter(Reporting.Year >= input$year[1] & Reporting.Year <= input$year[2])
#County Filter
adult <- subset(adult, County %in% input$county)
#Quarter Filter
adult <- subset(adult, Certified.Type %in% input$type)
# Return dataframe ----------------------------------------------
return(adult)
})
# Plots showing the Gender stats -----------------------------------
# Female
output$plot_gender <- renderPlotly({
ggplot(data = adultInput(),
aes(Female.Census)) +
geom_histogram(binwidth = 20) +
facet_wrap(~Reporting.Year) +
labs(x = "The Number of Female Residents", y = "Count")+
theme_classic()
})
#Male
output$plot_gender2 <- renderPlotly({
ggplot(data = adultInput(),
aes (Male.Census)) +
geom_histogram(binwidth = 20) +
facet_wrap(~Reporting.Year) +
labs(x = "The Number of Male Residents", y = "Count") +
theme_classic()
})
# A plot showing the Resident count stats -----------------------------------
output$plot_count <- renderPlotly({
ggplot(data = adultInput(),
aes(x = End.Census)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white")+
geom_density(color = "darkblue", fill = "lightblue", alpha = 0.4) +
labs (x = "Number of Residents per Organization") +
theme_classic()
})
# A plot showing the Organization Type stats -----------------------------------
output$plot_type <- renderPlotly({
ggplot(data = adultInput(),
aes(x = Certified.Type,
y = End.Census)) +
geom_boxplot(aes(fill = Certified.Type)) +
labs(fill="Type" , x = "Organization Type", y = "The number of residents") +
scale_fill_brewer(palette = "Blues") +
theme_classic()
})
# Data table of characters ----------------------------------------------
output$table <- DT::renderDataTable({
ad <-subset(adultInput(), select = c(Reporting.Year, Reporting.Quarter, Reporting.Organization, Certified.Type, County, Certified.Capacity,
End.Census, Male.Census, Female.Census))
DT::datatable(ad, options = list(scrollX = TRUE))
})
# Male Average box ----------------------------------------------
output$male <- renderValueBox({
ai <- adultInput()
infoBox("Avg Male Count:", value = round(mean(ai$Male.Census), 0), color = "blue", width = 6)
})
# Female Average box ----------------------------------------------
output$female <- renderValueBox({
ai <- adultInput()
infoBox("Avg Female Count:", value = round(mean(ai$Female.Census), 0), color = "maroon", width = 6)
})
# Total Census box ----------------------------------------------
output$total <- renderValueBox({
ai <- adultInput()
valueBox("Total Number of Residents", value = sum(ai$End.Census), color = "olive", width = 4)
})
}
# Run the application
shinyApp(ui = ui, server = server) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trajectory.R
\name{TrajScale}
\alias{TrajScale}
\title{Scale a trajectory}
\usage{
TrajScale(trj, scale, units, yScale = scale)
}
\arguments{
\item{trj}{The trajectory to be scaled.}
\item{scale}{Scaling factor to be applied to the trajectory coordinates.}
\item{units}{Character specifying the spatial units after scaling, e.g. "m"
or "metres"}
\item{yScale}{Optional scaling factor to be applied to the y-axis, which may
be specified if the original coordinates are not square. Defaults to
\code{scale}.}
}
\value{
new scaled trajectory.
}
\description{
Scales the cartesian coordinates in a trajectory, for example, to convert
units from pixels to metres.
}
\examples{
set.seed(42)
trj <- TrajGenerate()
# original trajectory units are pixels, measured as having
# 47 pixels in 10 mm, so to convert to metres, scale the
# trajectory by the approriate factor, i.e. (size in metres) / (size in pixels).
scale <- .01 / 47
scaled <- TrajScale(trj, scale, "m")
}
| /man/TrajScale.Rd | no_license | cran/trajr | R | false | true | 1,080 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trajectory.R
\name{TrajScale}
\alias{TrajScale}
\title{Scale a trajectory}
\usage{
TrajScale(trj, scale, units, yScale = scale)
}
\arguments{
\item{trj}{The trajectory to be scaled.}
\item{scale}{Scaling factor to be applied to the trajectory coordinates.}
\item{units}{Character specifying the spatial units after scaling, e.g. "m"
or "metres"}
\item{yScale}{Optional scaling factor to be applied to the y-axis, which may
be specified if the original coordinates are not square. Defaults to
\code{scale}.}
}
\value{
new scaled trajectory.
}
\description{
Scales the cartesian coordinates in a trajectory, for example, to convert
units from pixels to metres.
}
\examples{
set.seed(42)
trj <- TrajGenerate()
# original trajectory units are pixels, measured as having
# 47 pixels in 10 mm, so to convert to metres, scale the
# trajectory by the approriate factor, i.e. (size in metres) / (size in pixels).
scale <- .01 / 47
scaled <- TrajScale(trj, scale, "m")
}
|
# PhenoCombinedBrassicaDec13data.R
# R version 3.3.1 (2016-06-21)
# January 12, 2017. Mallory B. Lai.
# Reviewed by: TODO (Mallory B. Lai) : Find reviewer to proofread
# Combining Brassica phenotype data for analysis
# This "data clean-up" is meant to combine separate files and get rid
# of columns and data that are not needed for analysis.
#-----------------------------------------------------------------------
library(data.table)
#-----------------------------------------------------------------------
# Read in file 1.
Starch <-fread(file = "4Mall_NSC_Starchdec2013_edit.csv")
Starch$NSC <- as.numeric(Starch$NSC)
Starch$Starch <- as.numeric(Starch$Starch)
# Read in file 2.
Soil <-fread(file = "4Mall_Soil_Moisture_dec_2013_edit.csv")
Soil$Timepoint <- as.numeric(Soil$Timepoint)
# Read in file 3.
Photo <-fread(file = "4Mall_PhotoFv'Fm'gs_dec_2013_edit.csv")
# File1 doesn't have replicate values. Since we're only interested
# in leaf tissue so we'll want to remove any readings taken from the
# roots.
Starch <- Starch[Tissue == "leaves"]
# For each file, remove columns that aren't "Treatment",
# "Timepoint", and output values.
Starch <- Starch[, c("Treatment", "Timepoint", "NSC", "Starch")]
Soil <- Soil[, c("Treatment", "Timepoint", "SM(%)")]
Photo <- Photo[, c("Treatment", "Timepoint", "Photo", "gs", "Fv'Fm'")]
# Examine the number of replicates at each Timepoint.
replicates <- data.table(Photo = Photo[ , .N, by = Timepoint],
Soil = Soil[ , .N, by = Timepoint],
Starch = Starch[ , .N, by = Timepoint])
# Note, the mismatched values have been recycled.
# We can see that Photo is missing one timepoint and Soil and Starch
# are missing two.
# Photo is missing eight replicates for the first three timepoints.
# Starch has an extra replicate for timepoint 4 and a missing replicate
# for timepoint 12.
# Build dataframe for combined data.
# Start with timepoint 1, treatment "WW".
Pheno <- Photo[Timepoint == 1 & Treatment == "WW"]
Pheno <- cbind(Pheno,
Starch[Timepoint == 1 & Treatment == "WW", NSC, Starch],
Soil[Timepoint == 1 & Treatment == "WW", "SM(%)"])
# For now, missing replicates for Photo will be recycled.
# Timepoint 1, Dry.
tp1dry <- Photo[Timepoint == 1 & Treatment == "Dry"]
tp1dry <- cbind(tp1dry,
Starch[Timepoint == 1 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 1 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp1dry)
rm(tp1dry)
# Timepoint 2, WW.
tp2ww <- Photo[Timepoint == 2 & Treatment == "WW"]
tp2ww <- cbind(tp2ww,
Starch[Timepoint == 2 & Treatment == "WW", NSC, Starch],
Soil[Timepoint == 2 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp2ww)
rm(tp2ww)
# Timepoint 2, Dry.
tp2dry <- Photo[Timepoint == 2 & Treatment == "Dry"]
tp2dry <- cbind(tp2dry,
Starch[Timepoint == 2 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 2 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp2dry)
rm(tp2dry)
# Timepoint 3, WW. Fill in missing starch timepoint with NA's.
tp3ww <- Photo[Timepoint == 3 & Treatment == "WW"]
tp3ww <- cbind(tp3ww,
Starch = NA, NSC = NA,
Soil[Timepoint == 3 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp3ww)
rm(tp3ww)
# Timepoint 3, Dry.
tp3dry <- Photo[Timepoint == 3 & Treatment == "Dry"]
tp3dry <- cbind(tp3dry,
Starch = NA, NSC = NA,
Soil[Timepoint == 3 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp3dry)
rm(tp3dry)
# Timepoint 4, WW. Remove extra starch value.
# The repeated value in the first row of "WW" looks like a human error.
starch24 <- Starch[Timepoint == 4 & Treatment == "WW", NSC, Starch]
starch24 <- starch24[-1]
tp4ww <- Photo[Timepoint == 4 & Treatment == "WW"]
tp4ww <- cbind(tp4ww,
starch24,
Soil[Timepoint == 4 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp4ww)
rm(tp4ww)
rm(starch24)
# Timepoint 4, Dry.
tp4dry <- Photo[Timepoint == 4 & Treatment == "Dry"]
tp4dry <- cbind(tp4dry,
Starch[Timepoint == 4 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 4 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp4dry)
rm(tp4dry)
# Timepoint 5. Fill in missing soil values with NA's.
tp5 <- Photo[Timepoint == 5]
tp5 <- cbind(tp5,
Starch[Timepoint == 5, NSC, Starch],
"SM(%)" = NA)
Pheno <- rbind(Pheno, tp5)
rm(tp5)
# Timepoint 6. Fill in missing soil and Photo values with NA's.
tp6 <- Starch[Timepoint == 6, Timepoint, Treatment]
tp6 <- cbind(tp6, Photo = NA, gs = NA, "Fv'Fm'" = 0,
Starch[Timepoint == 6, NSC, Starch],
"SM(%)" = NA)
Pheno <- rbind(Pheno, tp6)
rm(tp6)
# Reorder Soil moisture columns to match Photo and Starch.
Soil <- Soil[order(Soil[, 2], -Soil[, 1]), ]
# Timepoint 7.
tp7 <- Photo[Timepoint == 7]
tp7 <- cbind(tp7,
Starch[Timepoint == 7, NSC, Starch],
Soil[Timepoint == 7, "SM(%)"])
Pheno <- rbind(Pheno, tp7)
rm(tp7)
# Timepoint 8.
tp8 <- Photo[Timepoint == 8]
tp8 <- cbind(tp8,
Starch[Timepoint == 8, NSC, Starch],
Soil[Timepoint == 8, "SM(%)"])
Pheno <- rbind(Pheno, tp8)
rm(tp8)
# Timepoint 9. Fill in missing starch timepoint with NA's.
tp9 <- Photo[Timepoint == 9]
tp9 <- cbind(tp9,
Starch = NA, NSC = NA,
Soil[Timepoint == 9, "SM(%)"])
Pheno <- rbind(Pheno, tp9)
rm(tp9)
# Timepoint 10.
tp10 <- Photo[Timepoint == 10]
tp10 <- cbind(tp10,
Starch[Timepoint == 10, NSC, Starch],
Soil[Timepoint == 10, "SM(%)"])
Pheno <- rbind(Pheno, tp10)
rm(tp10)
# Timepoint 11.
tp11 <- Photo[Timepoint == 11]
tp11 <- cbind(tp11,
Starch[Timepoint == 11, NSC, Starch],
Soil[Timepoint == 11, "SM(%)"])
Pheno <- rbind(Pheno, tp11)
rm(tp11)
# Timepoint 12.
tp12 <- Photo[Timepoint == 12]
tp12 <- cbind(tp12,
Starch[Timepoint == 12, NSC, Starch],
Soil[Timepoint == 12, "SM(%)"])
Pheno <- rbind(Pheno, tp12)
rm(tp12)
# Convert to data.frame.
Pheno <- as.data.frame(Pheno)
# Remove data points containing errors.
# Starch should never have a negative value.
sum(Pheno$Starch < 0, na.rm = T)
# Only one value is negative. Replace negative value with NA.
Pheno[which(Pheno$Starch < 0), "Starch"] <- NA
# NSC should also never be negative.
sum(Pheno$NSC < 0, na.rm = T)
# Only one value is negative. Replace negative value with NA.
Pheno[which(Pheno$NSC < 0), "NSC"] <- NA
# The Soil Moisture value of zero also seems to be an error.
Pheno[which(Pheno$`SM(%)` == 0), "SM(%)"] <- NA
# Check that Photo is negative at timepoints 5, 6, 11, & 12.
Pheno[which(Pheno$Photo > 0), "Timepoint" == 5]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 6]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 11]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 12]
# Check that Fv'Fm' is zero at timepoints 5, 6, 11, 12.
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 5]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 6]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 11]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 12]
# Check that Fv'Fm' is positive at all timepoints.
sum(Pheno$`Fv'Fm'` < 0)
# Write file to csv.
write.csv(Pheno, file = "PhenotypeBrassica.csv")
| /DataWrangling/PhenoCombinedBrassicaDec13data.R | no_license | mblai35/BrassicaDBN_Neo4j | R | false | false | 7,392 | r | # PhenoCombinedBrassicaDec13data.R
# R version 3.3.1 (2016-06-21)
# January 12, 2017. Mallory B. Lai.
# Reviewed by: TODO (Mallory B. Lai) : Find reviewer to proofread
# Combining Brassica phenotype data for analysis
# This "data clean-up" is meant to combine separate files and get rid
# of columns and data that are not needed for analysis.
#-----------------------------------------------------------------------
library(data.table)
#-----------------------------------------------------------------------
# Read in file 1.
Starch <-fread(file = "4Mall_NSC_Starchdec2013_edit.csv")
Starch$NSC <- as.numeric(Starch$NSC)
Starch$Starch <- as.numeric(Starch$Starch)
# Read in file 2.
Soil <-fread(file = "4Mall_Soil_Moisture_dec_2013_edit.csv")
Soil$Timepoint <- as.numeric(Soil$Timepoint)
# Read in file 3.
Photo <-fread(file = "4Mall_PhotoFv'Fm'gs_dec_2013_edit.csv")
# File1 doesn't have replicate values. Since we're only interested
# in leaf tissue so we'll want to remove any readings taken from the
# roots.
Starch <- Starch[Tissue == "leaves"]
# For each file, remove columns that aren't "Treatment",
# "Timepoint", and output values.
Starch <- Starch[, c("Treatment", "Timepoint", "NSC", "Starch")]
Soil <- Soil[, c("Treatment", "Timepoint", "SM(%)")]
Photo <- Photo[, c("Treatment", "Timepoint", "Photo", "gs", "Fv'Fm'")]
# Examine the number of replicates at each Timepoint.
replicates <- data.table(Photo = Photo[ , .N, by = Timepoint],
Soil = Soil[ , .N, by = Timepoint],
Starch = Starch[ , .N, by = Timepoint])
# Note, the mismatched values have been recycled.
# We can see that Photo is missing one timepoint and Soil and Starch
# are missing two.
# Photo is missing eight replicates for the first three timepoints.
# Starch has an extra replicate for timepoint 4 and a missing replicate
# for timepoint 12.
# Build dataframe for combined data.
# Start with timepoint 1, treatment "WW".
Pheno <- Photo[Timepoint == 1 & Treatment == "WW"]
Pheno <- cbind(Pheno,
Starch[Timepoint == 1 & Treatment == "WW", NSC, Starch],
Soil[Timepoint == 1 & Treatment == "WW", "SM(%)"])
# For now, missing replicates for Photo will be recycled.
# Timepoint 1, Dry.
tp1dry <- Photo[Timepoint == 1 & Treatment == "Dry"]
tp1dry <- cbind(tp1dry,
Starch[Timepoint == 1 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 1 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp1dry)
rm(tp1dry)
# Timepoint 2, WW.
tp2ww <- Photo[Timepoint == 2 & Treatment == "WW"]
tp2ww <- cbind(tp2ww,
Starch[Timepoint == 2 & Treatment == "WW", NSC, Starch],
Soil[Timepoint == 2 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp2ww)
rm(tp2ww)
# Timepoint 2, Dry.
tp2dry <- Photo[Timepoint == 2 & Treatment == "Dry"]
tp2dry <- cbind(tp2dry,
Starch[Timepoint == 2 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 2 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp2dry)
rm(tp2dry)
# Timepoint 3, WW. Fill in missing starch timepoint with NA's.
tp3ww <- Photo[Timepoint == 3 & Treatment == "WW"]
tp3ww <- cbind(tp3ww,
Starch = NA, NSC = NA,
Soil[Timepoint == 3 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp3ww)
rm(tp3ww)
# Timepoint 3, Dry.
tp3dry <- Photo[Timepoint == 3 & Treatment == "Dry"]
tp3dry <- cbind(tp3dry,
Starch = NA, NSC = NA,
Soil[Timepoint == 3 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp3dry)
rm(tp3dry)
# Timepoint 4, WW. Remove extra starch value.
# The repeated value in the first row of "WW" looks like a human error.
starch24 <- Starch[Timepoint == 4 & Treatment == "WW", NSC, Starch]
starch24 <- starch24[-1]
tp4ww <- Photo[Timepoint == 4 & Treatment == "WW"]
tp4ww <- cbind(tp4ww,
starch24,
Soil[Timepoint == 4 & Treatment == "WW", "SM(%)"])
Pheno <- rbind(Pheno, tp4ww)
rm(tp4ww)
rm(starch24)
# Timepoint 4, Dry.
tp4dry <- Photo[Timepoint == 4 & Treatment == "Dry"]
tp4dry <- cbind(tp4dry,
Starch[Timepoint == 4 & Treatment == "Dry", NSC, Starch],
Soil[Timepoint == 4 & Treatment == "Dry", "SM(%)"])
Pheno <- rbind(Pheno, tp4dry)
rm(tp4dry)
# Timepoint 5. Fill in missing soil values with NA's.
tp5 <- Photo[Timepoint == 5]
tp5 <- cbind(tp5,
Starch[Timepoint == 5, NSC, Starch],
"SM(%)" = NA)
Pheno <- rbind(Pheno, tp5)
rm(tp5)
# Timepoint 6. Fill in missing soil and Photo values with NA's.
tp6 <- Starch[Timepoint == 6, Timepoint, Treatment]
tp6 <- cbind(tp6, Photo = NA, gs = NA, "Fv'Fm'" = 0,
Starch[Timepoint == 6, NSC, Starch],
"SM(%)" = NA)
Pheno <- rbind(Pheno, tp6)
rm(tp6)
# Reorder Soil moisture columns to match Photo and Starch.
Soil <- Soil[order(Soil[, 2], -Soil[, 1]), ]
# Timepoint 7.
tp7 <- Photo[Timepoint == 7]
tp7 <- cbind(tp7,
Starch[Timepoint == 7, NSC, Starch],
Soil[Timepoint == 7, "SM(%)"])
Pheno <- rbind(Pheno, tp7)
rm(tp7)
# Timepoint 8.
tp8 <- Photo[Timepoint == 8]
tp8 <- cbind(tp8,
Starch[Timepoint == 8, NSC, Starch],
Soil[Timepoint == 8, "SM(%)"])
Pheno <- rbind(Pheno, tp8)
rm(tp8)
# Timepoint 9. Fill in missing starch timepoint with NA's.
tp9 <- Photo[Timepoint == 9]
tp9 <- cbind(tp9,
Starch = NA, NSC = NA,
Soil[Timepoint == 9, "SM(%)"])
Pheno <- rbind(Pheno, tp9)
rm(tp9)
# Timepoint 10.
tp10 <- Photo[Timepoint == 10]
tp10 <- cbind(tp10,
Starch[Timepoint == 10, NSC, Starch],
Soil[Timepoint == 10, "SM(%)"])
Pheno <- rbind(Pheno, tp10)
rm(tp10)
# Timepoint 11.
tp11 <- Photo[Timepoint == 11]
tp11 <- cbind(tp11,
Starch[Timepoint == 11, NSC, Starch],
Soil[Timepoint == 11, "SM(%)"])
Pheno <- rbind(Pheno, tp11)
rm(tp11)
# Timepoint 12.
tp12 <- Photo[Timepoint == 12]
tp12 <- cbind(tp12,
Starch[Timepoint == 12, NSC, Starch],
Soil[Timepoint == 12, "SM(%)"])
Pheno <- rbind(Pheno, tp12)
rm(tp12)
# Convert to data.frame.
Pheno <- as.data.frame(Pheno)
# Remove data points containing errors.
# Starch should never have a negative value.
sum(Pheno$Starch < 0, na.rm = T)
# Only one value is negative. Replace negative value with NA.
Pheno[which(Pheno$Starch < 0), "Starch"] <- NA
# NSC should also never be negative.
sum(Pheno$NSC < 0, na.rm = T)
# Only one value is negative. Replace negative value with NA.
Pheno[which(Pheno$NSC < 0), "NSC"] <- NA
# The Soil Moisture value of zero also seems to be an error.
Pheno[which(Pheno$`SM(%)` == 0), "SM(%)"] <- NA
# Check that Photo is negative at timepoints 5, 6, 11, & 12.
Pheno[which(Pheno$Photo > 0), "Timepoint" == 5]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 6]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 11]
Pheno[which(Pheno$Photo > 0), "Timepoint" == 12]
# Check that Fv'Fm' is zero at timepoints 5, 6, 11, 12.
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 5]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 6]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 11]
Pheno[which(Pheno$`Fv'Fm'` != 0), "Timepoint" == 12]
# Check that Fv'Fm' is positive at all timepoints.
sum(Pheno$`Fv'Fm'` < 0)
# Write file to csv.
write.csv(Pheno, file = "PhenotypeBrassica.csv")
|
# import library
library(PopGenome)
# import data
gen <- (readVCF("vcf/sub3.vcf.gz", include.unknown = TRUE, frompos = 1,
topos = 10049, tid = "1", numcols = 10000))
# set populations
bhhz <- as.character(read.table("populations/bhhz.txt", sep = ",")[[1]])
hznearmofo3003 <- as.character(read.table("populations/hznearmofo3003.txt", sep = ",")[[1]])
hznearmost005 <- as.character(read.table("populations/hznearmost005.txt", sep = ",")[[1]])
mofo3001nn <- as.character(read.table("populations/mofo3001nn.txt", sep = ",")[[1]])
mofo3002 <- as.character(read.table("populations/mofo3002.txt", sep = ",")[[1]])
mofo3003 <- as.character(read.table("populations/mofo3005.txt", sep = ",")[[1]])
mofo3009 <- as.character(read.table("populations/mofo3009.txt", sep = ",")[[1]])
lfo <- as.character(read.table("populations/lfo.txt", sep = ",")[[1]])
mosh <- as.character(read.table("populations/mosh.txt", sep = ",")[[1]])
most001 <- as.character(read.table("populations/most001.txt", sep = ",")[[1]])
most003 <- as.character(read.table("populations/most003.txt", sep = ",")[[1]])
most004 <- as.character(read.table("populations/most004.txt", sep = ",")[[1]])
most005 <- as.character(read.table("populations/most005.txt", sep = ",")[[1]])
rhhz <- as.character(read.table("populations/rhhz.txt", sep = ",")[[1]])
poplist <- list(bhhz, hznearmofo3003, hznearmost005, mofo3001nn, mofo3002,
mofo3003, mofo3009, lfo, mosh, most001, most003, most004, most005,
rhhz)
gen <- set.populations(gen, poplist, diploid=TRUE)
# get stats
gen <- diversity.stats(gen, pi = TRUE)
gen <- diversity.stats.between(gen)
# get per site stats
Pi.per.site <- gen@Pi / gen@n.sites
Pi.per.site <- data.frame(Pi.per.site)
dxy.per.site <- gen@nuc.diversity.between / gen@n.sites
dxy.per.site <- data.frame(dxy.per.site)
# print stats
gen@Pi
gen@nuc.diversity.between
Pi.per.site
dxy.per.site
# write to file
write.table(dxy.per.site, file = "dxy.csv", sep = ",")
write.table(Pi.per.site, file = "pi.csv", sep = ",") | /monardella_pop_genome.R | no_license | brettasmi/biology | R | false | false | 2,034 | r | # import library
library(PopGenome)
# import data
gen <- (readVCF("vcf/sub3.vcf.gz", include.unknown = TRUE, frompos = 1,
topos = 10049, tid = "1", numcols = 10000))
# set populations
bhhz <- as.character(read.table("populations/bhhz.txt", sep = ",")[[1]])
hznearmofo3003 <- as.character(read.table("populations/hznearmofo3003.txt", sep = ",")[[1]])
hznearmost005 <- as.character(read.table("populations/hznearmost005.txt", sep = ",")[[1]])
mofo3001nn <- as.character(read.table("populations/mofo3001nn.txt", sep = ",")[[1]])
mofo3002 <- as.character(read.table("populations/mofo3002.txt", sep = ",")[[1]])
mofo3003 <- as.character(read.table("populations/mofo3005.txt", sep = ",")[[1]])
mofo3009 <- as.character(read.table("populations/mofo3009.txt", sep = ",")[[1]])
lfo <- as.character(read.table("populations/lfo.txt", sep = ",")[[1]])
mosh <- as.character(read.table("populations/mosh.txt", sep = ",")[[1]])
most001 <- as.character(read.table("populations/most001.txt", sep = ",")[[1]])
most003 <- as.character(read.table("populations/most003.txt", sep = ",")[[1]])
most004 <- as.character(read.table("populations/most004.txt", sep = ",")[[1]])
most005 <- as.character(read.table("populations/most005.txt", sep = ",")[[1]])
rhhz <- as.character(read.table("populations/rhhz.txt", sep = ",")[[1]])
poplist <- list(bhhz, hznearmofo3003, hznearmost005, mofo3001nn, mofo3002,
mofo3003, mofo3009, lfo, mosh, most001, most003, most004, most005,
rhhz)
gen <- set.populations(gen, poplist, diploid=TRUE)
# get stats
gen <- diversity.stats(gen, pi = TRUE)
gen <- diversity.stats.between(gen)
# get per site stats
Pi.per.site <- gen@Pi / gen@n.sites
Pi.per.site <- data.frame(Pi.per.site)
dxy.per.site <- gen@nuc.diversity.between / gen@n.sites
dxy.per.site <- data.frame(dxy.per.site)
# print stats
gen@Pi
gen@nuc.diversity.between
Pi.per.site
dxy.per.site
# write to file
write.table(dxy.per.site, file = "dxy.csv", sep = ",")
write.table(Pi.per.site, file = "pi.csv", sep = ",") |
## load the dataset
## set global variables
setglobalVariable <- function(){
activity<<-NULL
## setting global variables to null
}
readFile<- function(fname){
## read the csv file that contains headers
## any blanks as NA, turn off interpretation of comments
## do i need to specify data types for date and interval columns?
## , colClasses=c("integer", "character", "character")
x<-read.csv(fname, header = TRUE)
x$date<-ymd(x$date)
activity<<-x
}
getfile<- function(){
## zipped file in repository
## unzip and send to readFile function
temp <- "repdata-data-activity.zip"
## Create a temp file to hold the Zipped file
temp<-unzip(temp,files="activity.csv")
## unzip the temp file to activity.csv
readFile(temp)
## read temp
unlink(temp)
}
## check if global variable is null, if it is retrieve file
if(exists("activity", mode = "any")){
if(is.null(activity)){
getfile()
}
} else {
setglobalVariable()
getfile()
}
## Make a histogram of the total number of steps taken each day
y<- ddply(activity, c("date"), summarize, steps=sum(steps))
png(filename = "plot1.png", width = 480, height = 480, units = "px", bg = "transparent")
hist(y$steps, xlab="Steps", ylab="daily", main="Steps per day", col="red")
## What is mean total number of steps taken per day?
mean(y$steps, na.rm=TRUE)
## Median total number of steps taken per day
median(y$steps, na.rm=TRUE)
## What is the average daily activity pattern?
## Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
z<-ddply(activity, c("interval"), summarize, steps=sum(steps, na.rm=TRUE))
plot(z$interval, z$steps, type='l', xlab="Interval", ylab="steps")
## Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
subset(z,z$steps==max(z$steps), select = interval)
## Imputing missing values
## Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
## Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
count(activity[!complete.cases(activity),])
## Devise a strategy for filling in all of the missing values in the dataset.
## The strategy does not need to be sophisticated. For example, you could use
## the mean/median for that day, or the mean for that 5-minute interval, etc.
## remove the missing values from the dataset
## x$steps[is.na(x$steps)] <- 0
## Create a new dataset that is equal to the original dataset but with the missing data filled in.
## Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
## Are there differences in activity patterns between weekdays and weekends?
| /getData.R | no_license | Sasha299/RepData_PeerAssessment1 | R | false | false | 3,387 | r | ## load the dataset
## set global variables
setglobalVariable <- function(){
activity<<-NULL
## setting global variables to null
}
readFile<- function(fname){
## read the csv file that contains headers
## any blanks as NA, turn off interpretation of comments
## do i need to specify data types for date and interval columns?
## , colClasses=c("integer", "character", "character")
x<-read.csv(fname, header = TRUE)
x$date<-ymd(x$date)
activity<<-x
}
getfile<- function(){
## zipped file in repository
## unzip and send to readFile function
temp <- "repdata-data-activity.zip"
## Create a temp file to hold the Zipped file
temp<-unzip(temp,files="activity.csv")
## unzip the temp file to activity.csv
readFile(temp)
## read temp
unlink(temp)
}
## check if global variable is null, if it is retrieve file
if(exists("activity", mode = "any")){
if(is.null(activity)){
getfile()
}
} else {
setglobalVariable()
getfile()
}
## Make a histogram of the total number of steps taken each day
y<- ddply(activity, c("date"), summarize, steps=sum(steps))
png(filename = "plot1.png", width = 480, height = 480, units = "px", bg = "transparent")
hist(y$steps, xlab="Steps", ylab="daily", main="Steps per day", col="red")
## What is mean total number of steps taken per day?
mean(y$steps, na.rm=TRUE)
## Median total number of steps taken per day
median(y$steps, na.rm=TRUE)
## What is the average daily activity pattern?
## Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
z<-ddply(activity, c("interval"), summarize, steps=sum(steps, na.rm=TRUE))
plot(z$interval, z$steps, type='l', xlab="Interval", ylab="steps")
## Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
subset(z,z$steps==max(z$steps), select = interval)
## Imputing missing values
## Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
## Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
count(activity[!complete.cases(activity),])
## Devise a strategy for filling in all of the missing values in the dataset.
## The strategy does not need to be sophisticated. For example, you could use
## the mean/median for that day, or the mean for that 5-minute interval, etc.
## remove the missing values from the dataset
## x$steps[is.na(x$steps)] <- 0
## Create a new dataset that is equal to the original dataset but with the missing data filled in.
## Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
## Are there differences in activity patterns between weekdays and weekends?
|
## rm(list=ls())
## load("chemical.r")
for (ichemical in chemical_name)
{
fname = paste("chemistry/",gsub("/", "_", ichemical),".jpg",sep="")
sample_date = as.Date(data[which(data[,"STD_CON_LONG_NAME"]==ichemical),"SAMP_DATE_TIME"])
jpeg(fname,units="in",width=6,height=5,quality=100,res=600)
hist(sample_date,#breaks="years",
breaks = seq(as.Date("1970-01-01"),
as.Date("2020-01-01"),
by = "5 years"
),
freq=TRUE,
ylim=c(0,2000),
main = ichemical,
xlab="year",format="%Y")
dev.off()
}
count_data= rep(NA,length(chemical_name))
names(count_data) = chemical_name
for (ichemical in 1:length(chemical_name))
{
count_data[ichemical] = length(which(data[,"STD_CON_LONG_NAME"]==chemical_name[ichemical]))
}
fname = paste("chemistry.jpg",sep="")
jpeg(fname,units="in",width=10,height=15,quality=100,res=600)
par(mar = c(5,15,1,2))
barplot(sort(count_data),horiz=T,las=2,xlab="Number of samples")
dev.off()
total_sample_date = c()
for (ichemical in chemical_name)
{
sample_date = as.Date(data[which(data[,"STD_CON_LONG_NAME"]==ichemical),"SAMP_DATE_TIME"])
total_sample_date = c(total_sample_date,as.character(sample_date))
}
total_sample_date = as.Date(total_sample_date)
fname = paste("chemistry2.jpg",sep="")
jpeg(fname,units="in",width=6,height=5,quality=100,res=600)
hist(total_sample_date,#breaks="years",
breaks = seq(as.Date("1970-01-01"),
as.Date("2020-01-01"),
by = "5 years"
),
freq=TRUE,
ylim=c(0,70000),
main = "All Major Chemicals",
xlab="year",format="%Y")
dev.off()
| /homework_for_2016_ESS_PI_meeting/plot_chemist.R | no_license | mrubayet/archived_codes_for_sfa_modeling | R | false | false | 1,689 | r | ## rm(list=ls())
## load("chemical.r")
for (ichemical in chemical_name)
{
fname = paste("chemistry/",gsub("/", "_", ichemical),".jpg",sep="")
sample_date = as.Date(data[which(data[,"STD_CON_LONG_NAME"]==ichemical),"SAMP_DATE_TIME"])
jpeg(fname,units="in",width=6,height=5,quality=100,res=600)
hist(sample_date,#breaks="years",
breaks = seq(as.Date("1970-01-01"),
as.Date("2020-01-01"),
by = "5 years"
),
freq=TRUE,
ylim=c(0,2000),
main = ichemical,
xlab="year",format="%Y")
dev.off()
}
count_data= rep(NA,length(chemical_name))
names(count_data) = chemical_name
for (ichemical in 1:length(chemical_name))
{
count_data[ichemical] = length(which(data[,"STD_CON_LONG_NAME"]==chemical_name[ichemical]))
}
fname = paste("chemistry.jpg",sep="")
jpeg(fname,units="in",width=10,height=15,quality=100,res=600)
par(mar = c(5,15,1,2))
barplot(sort(count_data),horiz=T,las=2,xlab="Number of samples")
dev.off()
total_sample_date = c()
for (ichemical in chemical_name)
{
sample_date = as.Date(data[which(data[,"STD_CON_LONG_NAME"]==ichemical),"SAMP_DATE_TIME"])
total_sample_date = c(total_sample_date,as.character(sample_date))
}
total_sample_date = as.Date(total_sample_date)
fname = paste("chemistry2.jpg",sep="")
jpeg(fname,units="in",width=6,height=5,quality=100,res=600)
hist(total_sample_date,#breaks="years",
breaks = seq(as.Date("1970-01-01"),
as.Date("2020-01-01"),
by = "5 years"
),
freq=TRUE,
ylim=c(0,70000),
main = "All Major Chemicals",
xlab="year",format="%Y")
dev.off()
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 53006
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 53006
c
c Input Parameter (command line, file):
c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query48_query44_1344n.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 9935
c no.of clauses 53006
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 53006
c
c QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query48_query44_1344n.qdimacs 9935 53006 E1 [] 0 180 9755 53006 NONE
| /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query48_query44_1344n/query48_query44_1344n.R | no_license | arey0pushpa/dcnf-autarky | R | false | false | 720 | r | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 53006
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 53006
c
c Input Parameter (command line, file):
c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query48_query44_1344n.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 9935
c no.of clauses 53006
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 53006
c
c QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query48_query44_1344n.qdimacs 9935 53006 E1 [] 0 180 9755 53006 NONE
|
library(DiagrammeR)
### Name: colorize_edge_attrs
### Title: Apply colors based on edge attribute values
### Aliases: colorize_edge_attrs
### ** Examples
# Create a graph with 5
# nodes and 4 edges
graph <-
create_graph() %>%
add_path(n = 5) %>%
set_edge_attrs(
edge_attr = weight,
values = c(3.7, 6.3, 9.2, 1.6))
# We can bucketize values in
# the edge `weight` attribute using
# `cut_points` and, by doing so,
# assign colors to each of the
# bucketed ranges (for values not
# part of any bucket, a gray color
# is assigned by default)
graph <-
graph %>%
colorize_edge_attrs(
edge_attr_from = weight,
edge_attr_to = color,
cut_points = c(0, 2, 4, 6, 8, 10),
palette = "RdYlGn")
# Now there will be a `color`
# edge attribute with distinct
# colors (from the RColorBrewer
# Red-Yellow-Green palette)
graph %>%
get_edge_df()
| /data/genthat_extracted_code/DiagrammeR/examples/colorize_edge_attrs.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 870 | r | library(DiagrammeR)
### Name: colorize_edge_attrs
### Title: Apply colors based on edge attribute values
### Aliases: colorize_edge_attrs
### ** Examples
# Create a graph with 5
# nodes and 4 edges
graph <-
create_graph() %>%
add_path(n = 5) %>%
set_edge_attrs(
edge_attr = weight,
values = c(3.7, 6.3, 9.2, 1.6))
# We can bucketize values in
# the edge `weight` attribute using
# `cut_points` and, by doing so,
# assign colors to each of the
# bucketed ranges (for values not
# part of any bucket, a gray color
# is assigned by default)
graph <-
graph %>%
colorize_edge_attrs(
edge_attr_from = weight,
edge_attr_to = color,
cut_points = c(0, 2, 4, 6, 8, 10),
palette = "RdYlGn")
# Now there will be a `color`
# edge attribute with distinct
# colors (from the RColorBrewer
# Red-Yellow-Green palette)
graph %>%
get_edge_df()
|
pkgs <- c(
"Seurat", "SeuratWrappers", "ggplot2", "batchelor",
"dplyr", "optparse", "reshape2", "data.table", "magrittr",
"patchwork", "scales", "GSVA", "RColorBrewer", "ggridges",
"clusterProfiler", "survminer", "survminer", "monocle",
"psych", "ggrepel", "pheatmap", "escape", "multcomp", "agricolae"
)
lapply(pkgs, function(x) require(package = x, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)) # nolint
# lymphocyte--9
yr <- c(
"#fafac0", "#f5eca6", "#fee391", "#fec44f",
"#fe9929", "#ec7014", "#cc4c02", "#8c2d04",
"#611f03"
)
# endothelium--5
gr <- c(
"#b9e9e0", "#7dc9b7", "#59b898", "#41ae76",
"#16d355", "#238b45", "#116d37", "#025826",
"#003516"
)
# fibroblast--9
bl <- c(
"#82cbf5", "#7ba7e0", "#5199cc", "#488dbe",
"#3690c0", "#0570b0", "#0d71aa", "#045a8d",
"#023858"
)
# myeloid--7
pur <- c(
"#8c97c6", "#a28abd", "#997abd", "#9362cc",
"#88419d", "#810f7c", "#4d004b"
)
# plasma--6
bro <- c(
"#8c510a", "#995401", "#be7816", "#be9430",
"#ad8d36", "#a07540"
)
color1 <- c(bl[1:8], yr, pur, gr[c(3, 5, 6, 7, 9)], bro, "#A9A9A9")
color2 <- c(
"#E0D39B", "#D05146", "#748EAE", "#567161",
"#574F84", "#967447"
)
data <- readRDS("final_input1.Rds")
## Tissue and sample number analysis
tissue_sample_number <- read.table("number.txt", header = T, sep = "\t")
tissue_sample_number <- melt(tissue_sample_number)
tissue_sample_number$variable <- factor(
tissue_sample_number$variable,
levels = c("Normal", "Adjacent", "Tumor_1", "Tumor_2")
)
pdf("F2B.pdf", width = 6, height = 3)
ggplot(tissue_sample_number, aes(x = tissue, y = value, fill = variable)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929", "#fcbf47")) +
theme_bw() +
labs(x = "", y = "Number of samples") +
theme(
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, color = "black", size = 10), # nolint
axis.text.y = element_text(color = "black", size = 10),
axis.title = element_text(color = "black", size = 12)
)
dev.off()
## The Umap of clusters and celltype
mat <- data.frame(data@reductions$umap@cell.embeddings, group = data$celltype)
mt <- mat[order(mat$group != "Epithelium"), ]
pdf("umap_subtype.pdf", width = 10, height = 9)
ggplot(mt, aes(x = UMAP_1, y = UMAP_2, color = group)) +
geom_point(size = 1e-5) +
theme_classic() +
scale_color_manual(values = color2) +
theme(
legend.title = element_blank(),
legend.text = element_text(size = 20, color = "black"),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
) +
guides(colour = guide_legend(override.aes = list(size = 8)))
dev.off()
pdf("umap_subcluster.pdf", width = 10, height = 10)
DimPlot(data, label = F) +
NoLegend() +
scale_color_manual(values = color1) +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
dev.off()
## Proportion chart of cells from different sources in each cluster
percent_result <- read.table("percent_result.txt", header = T, sep = "\t")
percent_result <- melt(percent_result)
percent_result$variable <- factor(
percent_result$variable,
levels = c("Normal", "Adjacent", "Tumor")
)
pdf("Percent_types.pdf", width = 4, height = 3)
ggplot(percent_result, aes(x = tissue, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)") +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
dev.off()
percent_subtypes <- read.table("percent_subtype.txt", header = T, sep = "\t")
percent_subtypes <- melt(percent_subtypes)
percent_subtypes$variable <- factor(
percent_subtypes$variable,
levels = c("Normal", "Adjacent", "Tumor")
)
percent_subtypes$cluster <- factor(
percent_subtypes$cluster,
levels = c(paste0("c", 34:1), "total")
)
plot_list <- foreach::foreach(gp = unique(percent_subtypes$group)) %do% {
plot_dat <- percent_subtypes[percent_subtypes$group == gp, ]
ggplot(plot_dat, aes(x = cluster, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)", title = i) +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
}
final <- wrap_plots(plot_list, ncol = length(plot_list), guides = "collect")
ggsave("percent_subtype.pdf", final, width = 10, height = 3)
## FeaturePlot or each tissue
subtype <- c(
"Bladder", "Breast", "Colorectal", "Gastric", "Intrahepatic duct",
"Lung", "Ovarian", "Pancreas", "Prostate", "Thyroid"
)
plot_tissue <- foreach::foreach(ts = unique(data$tissue)) %do% {
data$co <- ifelse(
data$tissue != ts | data$clusters == 34, "Others", subtype[k]
)
mat <- data.frame(data@reductions$umap@cell.embeddings, group = data$co)
mt <- rbind(mat[which(mat$group == "Others"), ], mat[which(mat$group == subtype[k]), ]) # nolint
mt$group <- factor(mt$group, levels = c("Others", subtype[k]))
ggplot(mt, aes(x = cluster, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)", title = i) +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
}
final_plot_tissue <- wrap_plots(plot_tissue, ncol = length(plot_tissue) / 2, guides = "collect") # nolint
ggsave("featureplot_each_cluster.pdf", final_plot_tissue, width = 30, height = 13) # nolint
## Subcluster analysis for DC
dc_clusters <- subset(data, ident = 13)
dc_clusters %<>% NormalizeData(object = ., normalization.method = "LogNormalize") %>% # nolint
FindVariableFeatures(selection.method = "vst")
all.genes <- rownames(dc_clusters)
dc_clusters %<>% ScaleData(object = ., features = all.genes) %>%
ScaleData(object = ., vars.to.regress = "percent.mt")
dc_clusters %<>% RunPCA(object = ., features = VariableFeatures(object = .)) %>%
RunFastMNN(object = ., object.list = SplitObject(., split.by = "SampleID"))
dc_clusters %<>% FindNeighbors(reduction = "mnn", dims = 1:30) %>%
FindClusters(resolution = 0.7) %<>%
RunTSNE(reduction = "mnn", dims = 1:30) %>%
RunUMAP(reduction = "mnn", dims = 1:30)
dc_clusters <- RenameIdents(dc_clusters,
"0" = "1", "1" = "1", "2" = "1", "3" = "1",
"8" = "1", "7" = "5", "5" = "2", "4" = "4",
"6" = "3", "10" = "3", "9" = "6"
)
dc_clusters$seurat_clusters <- dc_clusters@active.ident
dc_clusters$seurat_clusters <- factor(dc_clusters$seurat_clusters, levels = 1:6)
saveRDS(dc_clusters, "DC_sub-clusters.Rds")
pdf("DC_sub-clusters_VlnPlot.pdf", width = 3, height = 6)
VlnPlot(dc_clusters,
features = c("CD3D", "CD3E"),
pt.size = 0, cols = pal_npg()(6), ncol = 1
) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
dot_plot_genes <- c(
"CLEC10A", "FCGR2B", "FCER1G", "FCGR2A", "S100B", "LTB",
"CD1A", "CD1E", "STMN1", "TUBB", "TYMS", "TOP2A", "CLEC9A",
"LGALS2", "CPVL", "XCR1", "CCL22", "BIRC3", "CCL19", "CCR7",
"TRAC", "CD3D", "CD3E", "CD2"
)
dc_clusters@active.ident <- factor(dc_clusters@active.ident, levels = 6:1)
pdf("DC_sub-clusters_DotPlot.pdf", width = 8, height = 3.5)
DotPlot(dc_clusters,
features = dot_plot_genes,
cols = c(
"#FAFCFA", "#4D9157"
)
) +
theme_bw() +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, color = "black"),
axis.text.y = element_text(color = "black"),
panel.grid.major = element_blank()
)
dev.off()
## Subcluster analysis for Endothelium
endothelium_clusters <- subset(data, ident = c(25, 26, 28))
endothelium_clusters$div <- ifelse(endothelium_clusters@active.ident == 25, "TEC", "NEC") # nolint
endothelium_clusters@active.ident <- factor(endothelium_clusters$div, levels = c("NEC", "TEC")) # nolint
pdf("endothelium_cluster_vln_plot.pdf", width = 4.5, height = 7)
VlnPlot(endothelium_clusters, features = c(
"IGFBP2", "IGFBP4", "INSR",
"SPRY1", "CD320", "IGHG4"
), pt.size = 0, cols = pal_npg()(6), ncol = 2) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
TEC_NEC_DEG_Marker <- FindAllMarkers(endothelium_clusters, min.pct = 0.25, only.pos = T) # nolint
TEC_NEC_DEG_Marker$fc <- ifelse(
TEC_NEC_DEG_Marker$cluster == "NEC",
-1 * TEC_NEC_DEG_Marker$avg_log2FC, TEC_NEC_DEG_Marker$avg_log2FC
) # nolint
TEC_NEC_DEG_Marker$q_value <- -log(TEC_NEC_DEG_Marker$p_val_adj, 10)
TEC_NEC_DEG_Marker$group <- ifelse(
TEC_NEC_DEG_Marker$fc > 0.5 & TEC_NEC_DEG_Marker$p_val_adj < 0.05,
"sig up",
ifelse(TEC_NEC_DEG_Marker$fc < -0.5 & TEC_NEC_DEG_Marker$p_val_adj < 0.05,
"sig down", "not sig"
)
)
select_genes <- c(
TEC_NEC_DEG_Marker[which(TEC_NEC_DEG_Marker$group == "sig up"), "gene"][1:10], # nolint
TEC_NEC_DEG_Marker[which(TEC_NEC_DEG_Marker$group == "sig down"), "gene"][1:10] # nolint
)
TEC_NEC_DEG_Marker$label <- ifelse(
TEC_NEC_DEG_Marker$gene %in% select_genes,
TEC_NEC_DEG_Marker$gene, NA
)
p <- ggplot(TEC_NEC_DEG_Marker, aes(x = fc, y = q_value)) +
geom_point(aes(color = group), size = 1) +
scale_color_manual(values = c("gray", "blue", "red")) +
labs(x = "log2 fold change", y = "-log10 padj", title = "CAMR vs stable") +
geom_text_repel(aes(label = label), size = 2) +
geom_hline(yintercept = 1.30103, linetype = "dotted") +
geom_vline(xintercept = c(-0.5, 0.5), linetype = "dotted") +
theme(plot.title = element_text(hjust = 0.5))
ggsave("TEC_NEC_DEG_Volcano_Plot.pdf", p, width = 6.5, height = 6)
## Cluster analysis of tissue origin specificity
Percent_Calculated <- read.table(
"Percent_Calculated.txt",
header = T, sep = "\t"
)
Percent_Calculated <- melt(Percent_Calculated)
Percent_Calculated$variable <- factor(
Percent_Calculated$variable,
levels = c("c21", "c17", "c6", "c5", "c4")
)
npg_color <- pal_npg(alpha = 0.8)(10)
color_for_use <- rev(
c(
"#9269A2", "#7dc9b7", colo[3], "#5199cc", "#6479cc",
npg_color[c(10, 1, 8, 2, 6, 9)],
"#EEC877", npg_color[5]
)
)
p <- ggplot(Percent_Calculated, aes(x = variable, y = value, fill = cluster)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)") +
scale_fill_manual(values = color_for_use) + # nolint
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 10
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
ggsave(
"Tissue_Origin_Specificity_Percent_Calculated.pdf", p,
width = 5, height = 3.8
)
## Analysis of the distribution of cells of tumor, adjacent and normal tissue origin, respectively
plot_list <- foreach::foreach(gp = c("Normal", "Adjacent", "Tumor")) %do% {
select_subsets <- data
select_subsets$group_for_color <- ifelse(
select_subsets$clusters != 35 & select_subsets$group == gp, gp, "Others"
)
plot_data <- data.frame(
select_subsets@reductions$umap@cell.embeddings,
group = select_subsets$group_for_color
)
plot_data$group <- factor(plot_data$group, levels = c("Others", gp))
plot_data <- plot_data[order(plot_data$group), ]
ggplot(tt, aes(x = UMAP_1, y = UMAP_2, color = group)) +
geom_point(size = 0.1) +
scale_color_manual(values = c("#D9D9D9", "#9362cc")) +
labs(title = gp) +
theme_classic() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
plot.title = element_text(hjust = 0.5, size = 25, color = "black"),
legend.position = "none"
)
}
final <- wrap_plots(plot_list, ncol = length(plot_list), guides = "collect")
ggsave("Subtype_Umap_Plot", final, width = 24, height = 8)
## Subcluster analysis for C24
C24_cluster <- subset(data, ident = 24)
C24_cluster_clusters %<>% NormalizeData(object = ., normalization.method = "LogNormalize") %>% # nolint
FindVariableFeatures(selection.method = "vst")
all.genes <- rownames(C24_cluster)
C24_cluster %<>% ScaleData(object = ., features = all.genes) %>%
ScaleData(object = ., vars.to.regress = "percent.mt")
C24_cluster %<>% RunPCA(object = ., features = VariableFeatures(object = .)) %>%
RunFastMNN(object = ., object.list = SplitObject(., split.by = "SampleID"))
C24_cluster %<>% FindNeighbors(reduction = "mnn", dims = 1:30) %>%
FindClusters(resolution = 0.7) %<>%
RunTSNE(reduction = "mnn", dims = 1:30) %>%
RunUMAP(reduction = "mnn", dims = 1:30)
pdf("C24_Sub_Cluster_Umap.pdf", width = 4, height = 3.5)
DimPlot(C24_cluster, label = F) +
scale_color_manual(values = pal_npg()(6))
dev.off()
pdf("C24_Sub_Cluster_Vln_Plot.pdf", width = 3, height = 4.5)
VlnPlot(C24_cluster,
features = "RGS13",
pt.size = 0, cols = pal_npg()(6), ncol = 1
) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
## Analysis of FABP4+ macrophage (c20) proportion
Validation_Results <- readRDS("Validation_Results/input.Rds") # nolint
Validation_Matrix <- table(Validation_Results@meta.data[, c("sampleid", "seurat_clusters")]) # nolint
Validation_Matrix_Sum <- 100 * rowSums(Validation_Matrix[, c(4, 32)]) / as.vector(table(Validation_Results$sampleid)) # nolint
Validation_Matrix_Value <- data.frame(name = names(Validation_Matrix_Sum), value = as.numeric(Validation_Matrix_Sum)) # nolint
Reference <- unique(Validation_Results@meta.data[, c("sampleid", "group")]) # nolint
Validation_Matrix_Draw <- merge(Validation_Matrix_Value, Reference, by.x = "name", by.y = "sampleid") # nolint
Validation_Matrix_Draw <- Validation_Matrix_Draw[
which(Validation_Matrix_Draw$group %in% c("Lung_N", "Lung_T", "tLB", "mBrain")), # nolint
]
Validation_Matrix_Draw$group <- factor(
Validation_Matrix_Draw$group,
levels = c("Lung_N", "Lung_T", "tLB", "mBrain")
)
model <- aov(value ~ group, data = tt)
rht <- glht(model, linfct = mcp(group = "Dunnett"), alternative = "two.side")
summary(rht)
p <- ggplot(tt, aes(x = group, y = value)) +
geom_boxplot(
size = 0.8, fill = "white", outlier.fill = NA,
outlier.color = NA, outlier.size = 0
) +
geom_point(aes(fill = group, color = group), shape = 21, size = 3) +
scale_color_manual(values = pal_npg()(4)) +
scale_fill_manual(values = pal_npg()(4)) +
theme_classic() +
labs(y = "Proportion (%)") +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(size = 12, color = "black"),
axis.text.x = element_text(
size = 10, color = "black", angle = 45, hjust = 1
),
legend.title = element_blank(),
legend.position = "none",
axis.text.y = element_text(size = 10, color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
ggsave("Validation_FABP4_Macrophage_Proportion.pdf", width = 2.3, height = 3)
| /Main_Part1.R | no_license | Xiaxy-XuLab/PanCAF | R | false | false | 17,831 | r | pkgs <- c(
"Seurat", "SeuratWrappers", "ggplot2", "batchelor",
"dplyr", "optparse", "reshape2", "data.table", "magrittr",
"patchwork", "scales", "GSVA", "RColorBrewer", "ggridges",
"clusterProfiler", "survminer", "survminer", "monocle",
"psych", "ggrepel", "pheatmap", "escape", "multcomp", "agricolae"
)
lapply(pkgs, function(x) require(package = x, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)) # nolint
# lymphocyte--9
yr <- c(
"#fafac0", "#f5eca6", "#fee391", "#fec44f",
"#fe9929", "#ec7014", "#cc4c02", "#8c2d04",
"#611f03"
)
# endothelium--5
gr <- c(
"#b9e9e0", "#7dc9b7", "#59b898", "#41ae76",
"#16d355", "#238b45", "#116d37", "#025826",
"#003516"
)
# fibroblast--9
bl <- c(
"#82cbf5", "#7ba7e0", "#5199cc", "#488dbe",
"#3690c0", "#0570b0", "#0d71aa", "#045a8d",
"#023858"
)
# myeloid--7
pur <- c(
"#8c97c6", "#a28abd", "#997abd", "#9362cc",
"#88419d", "#810f7c", "#4d004b"
)
# plasma--6
bro <- c(
"#8c510a", "#995401", "#be7816", "#be9430",
"#ad8d36", "#a07540"
)
color1 <- c(bl[1:8], yr, pur, gr[c(3, 5, 6, 7, 9)], bro, "#A9A9A9")
color2 <- c(
"#E0D39B", "#D05146", "#748EAE", "#567161",
"#574F84", "#967447"
)
data <- readRDS("final_input1.Rds")
## Tissue and sample number analysis
tissue_sample_number <- read.table("number.txt", header = T, sep = "\t")
tissue_sample_number <- melt(tissue_sample_number)
tissue_sample_number$variable <- factor(
tissue_sample_number$variable,
levels = c("Normal", "Adjacent", "Tumor_1", "Tumor_2")
)
pdf("F2B.pdf", width = 6, height = 3)
ggplot(tissue_sample_number, aes(x = tissue, y = value, fill = variable)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929", "#fcbf47")) +
theme_bw() +
labs(x = "", y = "Number of samples") +
theme(
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, color = "black", size = 10), # nolint
axis.text.y = element_text(color = "black", size = 10),
axis.title = element_text(color = "black", size = 12)
)
dev.off()
## The Umap of clusters and celltype
mat <- data.frame(data@reductions$umap@cell.embeddings, group = data$celltype)
mt <- mat[order(mat$group != "Epithelium"), ]
pdf("umap_subtype.pdf", width = 10, height = 9)
ggplot(mt, aes(x = UMAP_1, y = UMAP_2, color = group)) +
geom_point(size = 1e-5) +
theme_classic() +
scale_color_manual(values = color2) +
theme(
legend.title = element_blank(),
legend.text = element_text(size = 20, color = "black"),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
) +
guides(colour = guide_legend(override.aes = list(size = 8)))
dev.off()
pdf("umap_subcluster.pdf", width = 10, height = 10)
DimPlot(data, label = F) +
NoLegend() +
scale_color_manual(values = color1) +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
dev.off()
## Proportion chart of cells from different sources in each cluster
percent_result <- read.table("percent_result.txt", header = T, sep = "\t")
percent_result <- melt(percent_result)
percent_result$variable <- factor(
percent_result$variable,
levels = c("Normal", "Adjacent", "Tumor")
)
pdf("Percent_types.pdf", width = 4, height = 3)
ggplot(percent_result, aes(x = tissue, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)") +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
dev.off()
percent_subtypes <- read.table("percent_subtype.txt", header = T, sep = "\t")
percent_subtypes <- melt(percent_subtypes)
percent_subtypes$variable <- factor(
percent_subtypes$variable,
levels = c("Normal", "Adjacent", "Tumor")
)
percent_subtypes$cluster <- factor(
percent_subtypes$cluster,
levels = c(paste0("c", 34:1), "total")
)
plot_list <- foreach::foreach(gp = unique(percent_subtypes$group)) %do% {
plot_dat <- percent_subtypes[percent_subtypes$group == gp, ]
ggplot(plot_dat, aes(x = cluster, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)", title = i) +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
}
final <- wrap_plots(plot_list, ncol = length(plot_list), guides = "collect")
ggsave("percent_subtype.pdf", final, width = 10, height = 3)
## FeaturePlot or each tissue
subtype <- c(
"Bladder", "Breast", "Colorectal", "Gastric", "Intrahepatic duct",
"Lung", "Ovarian", "Pancreas", "Prostate", "Thyroid"
)
plot_tissue <- foreach::foreach(ts = unique(data$tissue)) %do% {
data$co <- ifelse(
data$tissue != ts | data$clusters == 34, "Others", subtype[k]
)
mat <- data.frame(data@reductions$umap@cell.embeddings, group = data$co)
mt <- rbind(mat[which(mat$group == "Others"), ], mat[which(mat$group == subtype[k]), ]) # nolint
mt$group <- factor(mt$group, levels = c("Others", subtype[k]))
ggplot(mt, aes(x = cluster, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)", title = i) +
scale_fill_manual(values = c("#9362cc", "#5199cc", "#fe9929")) +
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 7
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
}
final_plot_tissue <- wrap_plots(plot_tissue, ncol = length(plot_tissue) / 2, guides = "collect") # nolint
ggsave("featureplot_each_cluster.pdf", final_plot_tissue, width = 30, height = 13) # nolint
## Subcluster analysis for DC
dc_clusters <- subset(data, ident = 13)
dc_clusters %<>% NormalizeData(object = ., normalization.method = "LogNormalize") %>% # nolint
FindVariableFeatures(selection.method = "vst")
all.genes <- rownames(dc_clusters)
dc_clusters %<>% ScaleData(object = ., features = all.genes) %>%
ScaleData(object = ., vars.to.regress = "percent.mt")
dc_clusters %<>% RunPCA(object = ., features = VariableFeatures(object = .)) %>%
RunFastMNN(object = ., object.list = SplitObject(., split.by = "SampleID"))
dc_clusters %<>% FindNeighbors(reduction = "mnn", dims = 1:30) %>%
FindClusters(resolution = 0.7) %<>%
RunTSNE(reduction = "mnn", dims = 1:30) %>%
RunUMAP(reduction = "mnn", dims = 1:30)
dc_clusters <- RenameIdents(dc_clusters,
"0" = "1", "1" = "1", "2" = "1", "3" = "1",
"8" = "1", "7" = "5", "5" = "2", "4" = "4",
"6" = "3", "10" = "3", "9" = "6"
)
dc_clusters$seurat_clusters <- dc_clusters@active.ident
dc_clusters$seurat_clusters <- factor(dc_clusters$seurat_clusters, levels = 1:6)
saveRDS(dc_clusters, "DC_sub-clusters.Rds")
pdf("DC_sub-clusters_VlnPlot.pdf", width = 3, height = 6)
VlnPlot(dc_clusters,
features = c("CD3D", "CD3E"),
pt.size = 0, cols = pal_npg()(6), ncol = 1
) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
dot_plot_genes <- c(
"CLEC10A", "FCGR2B", "FCER1G", "FCGR2A", "S100B", "LTB",
"CD1A", "CD1E", "STMN1", "TUBB", "TYMS", "TOP2A", "CLEC9A",
"LGALS2", "CPVL", "XCR1", "CCL22", "BIRC3", "CCL19", "CCR7",
"TRAC", "CD3D", "CD3E", "CD2"
)
dc_clusters@active.ident <- factor(dc_clusters@active.ident, levels = 6:1)
pdf("DC_sub-clusters_DotPlot.pdf", width = 8, height = 3.5)
DotPlot(dc_clusters,
features = dot_plot_genes,
cols = c(
"#FAFCFA", "#4D9157"
)
) +
theme_bw() +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, color = "black"),
axis.text.y = element_text(color = "black"),
panel.grid.major = element_blank()
)
dev.off()
## Subcluster analysis for Endothelium
endothelium_clusters <- subset(data, ident = c(25, 26, 28))
endothelium_clusters$div <- ifelse(endothelium_clusters@active.ident == 25, "TEC", "NEC") # nolint
endothelium_clusters@active.ident <- factor(endothelium_clusters$div, levels = c("NEC", "TEC")) # nolint
pdf("endothelium_cluster_vln_plot.pdf", width = 4.5, height = 7)
VlnPlot(endothelium_clusters, features = c(
"IGFBP2", "IGFBP4", "INSR",
"SPRY1", "CD320", "IGHG4"
), pt.size = 0, cols = pal_npg()(6), ncol = 2) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
TEC_NEC_DEG_Marker <- FindAllMarkers(endothelium_clusters, min.pct = 0.25, only.pos = T) # nolint
TEC_NEC_DEG_Marker$fc <- ifelse(
TEC_NEC_DEG_Marker$cluster == "NEC",
-1 * TEC_NEC_DEG_Marker$avg_log2FC, TEC_NEC_DEG_Marker$avg_log2FC
) # nolint
TEC_NEC_DEG_Marker$q_value <- -log(TEC_NEC_DEG_Marker$p_val_adj, 10)
TEC_NEC_DEG_Marker$group <- ifelse(
TEC_NEC_DEG_Marker$fc > 0.5 & TEC_NEC_DEG_Marker$p_val_adj < 0.05,
"sig up",
ifelse(TEC_NEC_DEG_Marker$fc < -0.5 & TEC_NEC_DEG_Marker$p_val_adj < 0.05,
"sig down", "not sig"
)
)
select_genes <- c(
TEC_NEC_DEG_Marker[which(TEC_NEC_DEG_Marker$group == "sig up"), "gene"][1:10], # nolint
TEC_NEC_DEG_Marker[which(TEC_NEC_DEG_Marker$group == "sig down"), "gene"][1:10] # nolint
)
TEC_NEC_DEG_Marker$label <- ifelse(
TEC_NEC_DEG_Marker$gene %in% select_genes,
TEC_NEC_DEG_Marker$gene, NA
)
p <- ggplot(TEC_NEC_DEG_Marker, aes(x = fc, y = q_value)) +
geom_point(aes(color = group), size = 1) +
scale_color_manual(values = c("gray", "blue", "red")) +
labs(x = "log2 fold change", y = "-log10 padj", title = "CAMR vs stable") +
geom_text_repel(aes(label = label), size = 2) +
geom_hline(yintercept = 1.30103, linetype = "dotted") +
geom_vline(xintercept = c(-0.5, 0.5), linetype = "dotted") +
theme(plot.title = element_text(hjust = 0.5))
ggsave("TEC_NEC_DEG_Volcano_Plot.pdf", p, width = 6.5, height = 6)
## Cluster analysis of tissue origin specificity
Percent_Calculated <- read.table(
"Percent_Calculated.txt",
header = T, sep = "\t"
)
Percent_Calculated <- melt(Percent_Calculated)
Percent_Calculated$variable <- factor(
Percent_Calculated$variable,
levels = c("c21", "c17", "c6", "c5", "c4")
)
npg_color <- pal_npg(alpha = 0.8)(10)
color_for_use <- rev(
c(
"#9269A2", "#7dc9b7", colo[3], "#5199cc", "#6479cc",
npg_color[c(10, 1, 8, 2, 6, 9)],
"#EEC877", npg_color[5]
)
)
p <- ggplot(Percent_Calculated, aes(x = variable, y = value, fill = cluster)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion (%)") +
scale_fill_manual(values = color_for_use) + # nolint
coord_flip() +
theme(
axis.line = element_blank(),
legend.title = element_blank(),
panel.grid = element_blank(),
legend.text = element_text(size = 12),
axis.text.y = element_text(size = 10, color = "black"),
axis.title.x = element_text(size = 12),
axis.title.y = element_blank(),
legend.key.size = unit(0.6, "cm"),
axis.text.x = element_text(
angle = 45, hjust = 1,
color = "black", size = 10
),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(expand = c(0, 0.01), labels = percent)
ggsave(
"Tissue_Origin_Specificity_Percent_Calculated.pdf", p,
width = 5, height = 3.8
)
## Analysis of the distribution of cells of tumor, adjacent and normal tissue origin, respectively
plot_list <- foreach::foreach(gp = c("Normal", "Adjacent", "Tumor")) %do% {
select_subsets <- data
select_subsets$group_for_color <- ifelse(
select_subsets$clusters != 35 & select_subsets$group == gp, gp, "Others"
)
plot_data <- data.frame(
select_subsets@reductions$umap@cell.embeddings,
group = select_subsets$group_for_color
)
plot_data$group <- factor(plot_data$group, levels = c("Others", gp))
plot_data <- plot_data[order(plot_data$group), ]
ggplot(tt, aes(x = UMAP_1, y = UMAP_2, color = group)) +
geom_point(size = 0.1) +
scale_color_manual(values = c("#D9D9D9", "#9362cc")) +
labs(title = gp) +
theme_classic() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
plot.title = element_text(hjust = 0.5, size = 25, color = "black"),
legend.position = "none"
)
}
final <- wrap_plots(plot_list, ncol = length(plot_list), guides = "collect")
ggsave("Subtype_Umap_Plot", final, width = 24, height = 8)
## Subcluster analysis for C24
C24_cluster <- subset(data, ident = 24)
C24_cluster_clusters %<>% NormalizeData(object = ., normalization.method = "LogNormalize") %>% # nolint
FindVariableFeatures(selection.method = "vst")
all.genes <- rownames(C24_cluster)
C24_cluster %<>% ScaleData(object = ., features = all.genes) %>%
ScaleData(object = ., vars.to.regress = "percent.mt")
C24_cluster %<>% RunPCA(object = ., features = VariableFeatures(object = .)) %>%
RunFastMNN(object = ., object.list = SplitObject(., split.by = "SampleID"))
C24_cluster %<>% FindNeighbors(reduction = "mnn", dims = 1:30) %>%
FindClusters(resolution = 0.7) %<>%
RunTSNE(reduction = "mnn", dims = 1:30) %>%
RunUMAP(reduction = "mnn", dims = 1:30)
pdf("C24_Sub_Cluster_Umap.pdf", width = 4, height = 3.5)
DimPlot(C24_cluster, label = F) +
scale_color_manual(values = pal_npg()(6))
dev.off()
pdf("C24_Sub_Cluster_Vln_Plot.pdf", width = 3, height = 4.5)
VlnPlot(C24_cluster,
features = "RGS13",
pt.size = 0, cols = pal_npg()(6), ncol = 1
) &
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, color = "black"),
axis.title.x = element_blank()
)
dev.off()
## Analysis of FABP4+ macrophage (c20) proportion
Validation_Results <- readRDS("Validation_Results/input.Rds") # nolint
Validation_Matrix <- table(Validation_Results@meta.data[, c("sampleid", "seurat_clusters")]) # nolint
Validation_Matrix_Sum <- 100 * rowSums(Validation_Matrix[, c(4, 32)]) / as.vector(table(Validation_Results$sampleid)) # nolint
Validation_Matrix_Value <- data.frame(name = names(Validation_Matrix_Sum), value = as.numeric(Validation_Matrix_Sum)) # nolint
Reference <- unique(Validation_Results@meta.data[, c("sampleid", "group")]) # nolint
Validation_Matrix_Draw <- merge(Validation_Matrix_Value, Reference, by.x = "name", by.y = "sampleid") # nolint
Validation_Matrix_Draw <- Validation_Matrix_Draw[
which(Validation_Matrix_Draw$group %in% c("Lung_N", "Lung_T", "tLB", "mBrain")), # nolint
]
Validation_Matrix_Draw$group <- factor(
Validation_Matrix_Draw$group,
levels = c("Lung_N", "Lung_T", "tLB", "mBrain")
)
model <- aov(value ~ group, data = tt)
rht <- glht(model, linfct = mcp(group = "Dunnett"), alternative = "two.side")
summary(rht)
p <- ggplot(tt, aes(x = group, y = value)) +
geom_boxplot(
size = 0.8, fill = "white", outlier.fill = NA,
outlier.color = NA, outlier.size = 0
) +
geom_point(aes(fill = group, color = group), shape = 21, size = 3) +
scale_color_manual(values = pal_npg()(4)) +
scale_fill_manual(values = pal_npg()(4)) +
theme_classic() +
labs(y = "Proportion (%)") +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(size = 12, color = "black"),
axis.text.x = element_text(
size = 10, color = "black", angle = 45, hjust = 1
),
legend.title = element_blank(),
legend.position = "none",
axis.text.y = element_text(size = 10, color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
ggsave("Validation_FABP4_Macrophage_Proportion.pdf", width = 2.3, height = 3)
|
## Functions Caching the Inverse of a Matrix
## Code written for R 3.1.0 - Platform: x86_64-w64-mingw32/x64 (64-bit)
## makeCacheMatrix function creates a special "matrix"
## object that can cache its inverse.
##
## Args:
## myMatrix: The square Matrix which has to be inverted.
##
## Returns:
## a list containing
## set: function to "store" the original matrix
## get: function to "retrieve" the original matrix
## setInverse: function to "store" the inverse matrix
## getInverse: function to "retrieve" the inverse matrix
makeCacheMatrix <- function(myMatrix = matrix()) {
invMatrix <- NULL
set <- function(y) {
myMatrix <<- y
invMatrix <<- NULL
}
get <- function() myMatrix
setInverse <- function(solve) invMatrix <<- solve
getInverse <- function() invMatrix
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## cacheSolve function computes the inverse of the special "matrix"
## returned by makeCacheMatrix above.
## If already calculated & no change in matrix, retrieves it from cache.
##
## Args:
## x: The special "Matrix" returned by makeCacheMatrix function
## containing list of special functions (get, set, setInverse, getInverse)
##
## Returns:
## a matrix that is the inverse of original matrix
cacheSolve <- function(x, ...) {
invMatrix <- x$getInverse()
if(!is.null(invMatrix)) {
message("getting cached data")
return(invMatrix)
}
data <- x$get()
invMatrix <- solve(data, ...)
x$setInverse(invMatrix)
invMatrix
} | /cachematrix.R | no_license | andreataglioni/ProgrammingAssignment2 | R | false | false | 1,703 | r | ## Functions Caching the Inverse of a Matrix
## Code written for R 3.1.0 - Platform: x86_64-w64-mingw32/x64 (64-bit)
## makeCacheMatrix function creates a special "matrix"
## object that can cache its inverse.
##
## Args:
## myMatrix: The square Matrix which has to be inverted.
##
## Returns:
## a list containing
## set: function to "store" the original matrix
## get: function to "retrieve" the original matrix
## setInverse: function to "store" the inverse matrix
## getInverse: function to "retrieve" the inverse matrix
makeCacheMatrix <- function(myMatrix = matrix()) {
invMatrix <- NULL
set <- function(y) {
myMatrix <<- y
invMatrix <<- NULL
}
get <- function() myMatrix
setInverse <- function(solve) invMatrix <<- solve
getInverse <- function() invMatrix
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## cacheSolve function computes the inverse of the special "matrix"
## returned by makeCacheMatrix above.
## If already calculated & no change in matrix, retrieves it from cache.
##
## Args:
## x: The special "Matrix" returned by makeCacheMatrix function
## containing list of special functions (get, set, setInverse, getInverse)
##
## Returns:
## a matrix that is the inverse of original matrix
cacheSolve <- function(x, ...) {
invMatrix <- x$getInverse()
if(!is.null(invMatrix)) {
message("getting cached data")
return(invMatrix)
}
data <- x$get()
invMatrix <- solve(data, ...)
x$setInverse(invMatrix)
invMatrix
} |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{setcolor}
\alias{setcolor}
\title{return colors with given a vector}
\usage{
setcolor(x)
}
\arguments{
\item{x}{Number of color}
}
\value{
color vector
}
\description{
Setcolor will provide a list of color vectors based on
the number used as an input.
}
\examples{
mycol <- setcolor(10)
mycol
}
\author{
Kai Guo
}
| /man/setcolor.Rd | no_license | sridhar0605/VennDetail | R | false | true | 405 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{setcolor}
\alias{setcolor}
\title{return colors with given a vector}
\usage{
setcolor(x)
}
\arguments{
\item{x}{Number of color}
}
\value{
color vector
}
\description{
Setcolor will provide a list of color vectors based on
the number used as an input.
}
\examples{
mycol <- setcolor(10)
mycol
}
\author{
Kai Guo
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{ori_et_al_complex_ppis}
\alias{ori_et_al_complex_ppis}
\title{Data frame of eukaryotic protein-protein
interactions inferred from annotated protein
complexes by Ori et al. and StringDB interations
with a combined score of at least 900}
\format{
data frame with columns complex_name, x, y,
pair (unique pair id)
}
\usage{
data("ori_et_al_complex_ppis")
}
\description{
data frame assigning proteins to (in)directly
interacting proteins within protein complexes
}
\examples{
data("ori_et_al_complex_ppis")
}
\references{
Ori et al. (2016), Genome Biology, 17, 47;
Jensen et al. (2009), Nucleic Acids Research,
37, D412–D416
}
\keyword{datasets}
| /man/ori_et_al_complex_ppis.Rd | no_license | nkurzaw/Rtpca | R | false | true | 755 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{ori_et_al_complex_ppis}
\alias{ori_et_al_complex_ppis}
\title{Data frame of eukaryotic protein-protein
interactions inferred from annotated protein
complexes by Ori et al. and StringDB interations
with a combined score of at least 900}
\format{
data frame with columns complex_name, x, y,
pair (unique pair id)
}
\usage{
data("ori_et_al_complex_ppis")
}
\description{
data frame assigning proteins to (in)directly
interacting proteins within protein complexes
}
\examples{
data("ori_et_al_complex_ppis")
}
\references{
Ori et al. (2016), Genome Biology, 17, 47;
Jensen et al. (2009), Nucleic Acids Research,
37, D412–D416
}
\keyword{datasets}
|
power <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE)
power_subset <- power[power$Date %in% c("1/2/2007","2/2/2007"),]
date <- strptime(paste(power_subset$Date, power_subset$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
esm1 <- as.numeric(power_subset$Sub_metering_1)
esm2 <- as.numeric(power_subset$Sub_metering_2)
esm3 <- as.numeric(power_subset$Sub_metering_3)
png("plot3.png", width=480, height=480)
with(power_subset, {
plot(date,esm1, type="S",
ylab="Energy Sub Metering", xlab="")
lines(date,esm2,col='Red')
lines(date,esm3,col='Blue')
})
#plot(date,esm1, col = "black", type= "1", ylab ="Energy Sub Meeting", xlab= "")
#plot(date,esm2, col = "red", type= "S")
#plot(date,esm3, col = "blue", type= "S")
legend ("topright", col=c("black","red","blue") , lty=1, lwd=2,c("Sub_metering_1","Sub_metering_2","Sub_metering_3") )
| /Explonatory Analysis/Assignment 1/plot3.R | no_license | Kamaldass/datasciencecoursera | R | false | false | 908 | r | power <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE)
power_subset <- power[power$Date %in% c("1/2/2007","2/2/2007"),]
date <- strptime(paste(power_subset$Date, power_subset$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
esm1 <- as.numeric(power_subset$Sub_metering_1)
esm2 <- as.numeric(power_subset$Sub_metering_2)
esm3 <- as.numeric(power_subset$Sub_metering_3)
png("plot3.png", width=480, height=480)
with(power_subset, {
plot(date,esm1, type="S",
ylab="Energy Sub Metering", xlab="")
lines(date,esm2,col='Red')
lines(date,esm3,col='Blue')
})
#plot(date,esm1, col = "black", type= "1", ylab ="Energy Sub Meeting", xlab= "")
#plot(date,esm2, col = "red", type= "S")
#plot(date,esm3, col = "blue", type= "S")
legend ("topright", col=c("black","red","blue") , lty=1, lwd=2,c("Sub_metering_1","Sub_metering_2","Sub_metering_3") )
|
testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 1.22810536289265e+146, 4.12396251261199e-221, 0, 0, 0), .Dim = c(1L, 7L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613111875-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 257 | r | testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 1.22810536289265e+146, 4.12396251261199e-221, 0, 0, 0), .Dim = c(1L, 7L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) |
#' @method ggplot summary.margarita.sim.rl
#' @export
#' @importFrom scales comma
ggplot.summary.margarita.sim.rl <- function(data=NULL, trans="log10", labels=comma,
xlab="Return level", ylab="", main=NULL,
xbreaks = waiver(),
ptcol="blue", linecol=c("blue", "blue"),
ptsize=4, linesize=c(.5, 1.5),
ncol=1, as.table=TRUE,
...){
data <- as.data.frame(data)
data$M <- factor(data$M, levels=unique(data$M))
ng <- length(unique(data$groups))
if (ng == 1) data$groups <- data$M # <------------------ Redundant now???
nint <- ncol(data)/2 - .5 # Number of intervals
names(data)[(ncol(data)-2)/2 + .5] <- "median" # Middle column (could be mean or median or something else)
# data$group <- factor(rownames(data), levels=rownames(data))
seg <- getSegmentData(data)
seg[[1]]$M <- seg[[2]]$M <- data$M
if (ng > 1){
p <- ggplot(data=data, aes(median, groups)) +
geom_point(size=ptsize, color=ptcol) +
facet_wrap(~M, ncol=ncol, as.table=as.table) +
scale_x_continuous(xlab, trans=trans, labels=labels, breaks=xbreaks) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
} # Close if
} # Close if ng > 1
else{
p <- ggplot(data=data, aes(median, groups)) +
geom_point(size=ptsize, color=ptcol) +
scale_x_continuous(xlab, trans=trans, labels=labels, breaks=xbreaks) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
}
}
p
}
#' @method ggplot summary.margarita.sim.prob
#' @export
ggplot.summary.margarita.sim.prob <- function(data=NULL, ptcol="blue",
linecol=c("blue", "blue"),
ptsize=4, linesize=c(.5, 1.5),
scales="free", ncol=NULL, as.table=TRUE,
xlab="P( > RL)", ylab="", M, main=NULL,
...){
g <- names(data)
data <- unclass(data)
nM <- nrow(data[[1]])
g <- rep(g, each=nM)
# Add M to each data.frame
if (missing(M))
M <- factor(rownames(data[[1]]), levels=rownames(data[[1]]))
data <- lapply(1:length(data), function(x, data, M) {
data <- as.data.frame(data[[x]])
data$M <- M
data }, data=data, M=M)
# Make groups to trellis on
data <- do.call("rbind", data)
data$groups <- factor(g, levels=unique(g))
if (ncol(data) == 7){
names(data)[3] <- "mid"
}
else if (ncol(data) == 5){
names(data)[2] <- "mid"
}
else {
stop("data object has wrong number of columns")
}
seg <- getSegmentData(data)
seg <- lapply(seg, function(x, M){ if (!is.null(x)){ x$M <- M }; x }, M=data$M)
p <- ggplot(data, aes(mid, groups)) +
geom_point(size=ptsize, color=ptcol) +
facet_wrap(~M, scales=scales, ncol=ncol, as.table=as.table) +
scale_x_continuous(xlab) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
}
p
}
#' @method ggplot margarita.sim.prob
#' @export
ggplot.margarita.sim.prob <- function(data=NULL, mapping = aes(), ...,
environment = parent.frame()){
stop("No method available. You need to call 'summary' on the simulated margarita object first.")
}
#' @method ggplot margarita.sim.rl
#' @export
ggplot.margarita.sim.rl <- ggplot.margarita.sim.prob
| /R/ggplot.sim.R | no_license | harrysouthworth/margarita | R | false | false | 4,820 | r | #' @method ggplot summary.margarita.sim.rl
#' @export
#' @importFrom scales comma
ggplot.summary.margarita.sim.rl <- function(data=NULL, trans="log10", labels=comma,
xlab="Return level", ylab="", main=NULL,
xbreaks = waiver(),
ptcol="blue", linecol=c("blue", "blue"),
ptsize=4, linesize=c(.5, 1.5),
ncol=1, as.table=TRUE,
...){
data <- as.data.frame(data)
data$M <- factor(data$M, levels=unique(data$M))
ng <- length(unique(data$groups))
if (ng == 1) data$groups <- data$M # <------------------ Redundant now???
nint <- ncol(data)/2 - .5 # Number of intervals
names(data)[(ncol(data)-2)/2 + .5] <- "median" # Middle column (could be mean or median or something else)
# data$group <- factor(rownames(data), levels=rownames(data))
seg <- getSegmentData(data)
seg[[1]]$M <- seg[[2]]$M <- data$M
if (ng > 1){
p <- ggplot(data=data, aes(median, groups)) +
geom_point(size=ptsize, color=ptcol) +
facet_wrap(~M, ncol=ncol, as.table=as.table) +
scale_x_continuous(xlab, trans=trans, labels=labels, breaks=xbreaks) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
} # Close if
} # Close if ng > 1
else{
p <- ggplot(data=data, aes(median, groups)) +
geom_point(size=ptsize, color=ptcol) +
scale_x_continuous(xlab, trans=trans, labels=labels, breaks=xbreaks) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
}
}
p
}
#' @method ggplot summary.margarita.sim.prob
#' @export
ggplot.summary.margarita.sim.prob <- function(data=NULL, ptcol="blue",
linecol=c("blue", "blue"),
ptsize=4, linesize=c(.5, 1.5),
scales="free", ncol=NULL, as.table=TRUE,
xlab="P( > RL)", ylab="", M, main=NULL,
...){
g <- names(data)
data <- unclass(data)
nM <- nrow(data[[1]])
g <- rep(g, each=nM)
# Add M to each data.frame
if (missing(M))
M <- factor(rownames(data[[1]]), levels=rownames(data[[1]]))
data <- lapply(1:length(data), function(x, data, M) {
data <- as.data.frame(data[[x]])
data$M <- M
data }, data=data, M=M)
# Make groups to trellis on
data <- do.call("rbind", data)
data$groups <- factor(g, levels=unique(g))
if (ncol(data) == 7){
names(data)[3] <- "mid"
}
else if (ncol(data) == 5){
names(data)[2] <- "mid"
}
else {
stop("data object has wrong number of columns")
}
seg <- getSegmentData(data)
seg <- lapply(seg, function(x, M){ if (!is.null(x)){ x$M <- M }; x }, M=data$M)
p <- ggplot(data, aes(mid, groups)) +
geom_point(size=ptsize, color=ptcol) +
facet_wrap(~M, scales=scales, ncol=ncol, as.table=as.table) +
scale_x_continuous(xlab) +
scale_y_discrete(ylab) +
ggtitle(main) +
geom_segment(data=seg[[1]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[1], color=linecol[1]) +
if (!is.null(seg[[2]])){
geom_segment(data=seg[[2]], aes(x=lo, xend=hi, y=group, yend=group),
size=linesize[2], color=linecol[2])
}
p
}
#' @method ggplot margarita.sim.prob
#' @export
ggplot.margarita.sim.prob <- function(data=NULL, mapping = aes(), ...,
environment = parent.frame()){
stop("No method available. You need to call 'summary' on the simulated margarita object first.")
}
#' @method ggplot margarita.sim.rl
#' @export
ggplot.margarita.sim.rl <- ggplot.margarita.sim.prob
|
get_result_cache <- function () {
if (!iatlas.data:::present(.GlobalEnv$result_cache))
.GlobalEnv$result_cache <- new.env()
.GlobalEnv$result_cache
}
result_cached <- function (key, value) {
result_cache <- get_result_cache()
if (iatlas.data:::present(result_cache[[key]])) result_cache[[key]]
else result_cache[[key]] <- value
}
reset_results_cache <- function () {
if (iatlas.data:::present(.GlobalEnv$result_cache)) {
rm(result_cache, pos = .GlobalEnv)
}
gc()
}
reset_results_cache()
| /R/result_cache.R | no_license | CRI-iAtlas/iatlas-data | R | false | false | 514 | r | get_result_cache <- function () {
if (!iatlas.data:::present(.GlobalEnv$result_cache))
.GlobalEnv$result_cache <- new.env()
.GlobalEnv$result_cache
}
result_cached <- function (key, value) {
result_cache <- get_result_cache()
if (iatlas.data:::present(result_cache[[key]])) result_cache[[key]]
else result_cache[[key]] <- value
}
reset_results_cache <- function () {
if (iatlas.data:::present(.GlobalEnv$result_cache)) {
rm(result_cache, pos = .GlobalEnv)
}
gc()
}
reset_results_cache()
|
%[dont read]
\name{Rfast2-package}
\alias{Rfast2-package}
\docType{package}
\title{
Really fast R functions
}
\description{
A collection of Rfast2 functions for data analysis.
Note 1: The vast majority of the functions accept matrices only, not data.frames.
Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results.
Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.
}
\details{
\tabular{ll}{
Package: \tab Rfast2\cr
Type: \tab Package\cr
Version: \tab 0.0.7 \cr
Date: \tab 2020-10-19\cr
License: \tab GPL-2\cr
}
}
\author{
Manos Papadakis <papadakm95@gmail.com>, Michail Tsagris <mtsagris@yahoo.gr>, Stefanos Fafalios <stefanosfafalios@gmail.com>, Marios Dimitriadis <kmdimitriadis@gmail.com>.
}
\section{Maintainers }{
Manos Papadakis \email{rfastofficial@gmail.com}
}
%\note{
%Acknowledgments:
%}
| /man/Rfast2-package.Rd | no_license | minghao2016/Rfast2 | R | false | false | 1,043 | rd | %[dont read]
\name{Rfast2-package}
\alias{Rfast2-package}
\docType{package}
\title{
Really fast R functions
}
\description{
A collection of Rfast2 functions for data analysis.
Note 1: The vast majority of the functions accept matrices only, not data.frames.
Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results.
Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.
}
\details{
\tabular{ll}{
Package: \tab Rfast2\cr
Type: \tab Package\cr
Version: \tab 0.0.7 \cr
Date: \tab 2020-10-19\cr
License: \tab GPL-2\cr
}
}
\author{
Manos Papadakis <papadakm95@gmail.com>, Michail Tsagris <mtsagris@yahoo.gr>, Stefanos Fafalios <stefanosfafalios@gmail.com>, Marios Dimitriadis <kmdimitriadis@gmail.com>.
}
\section{Maintainers }{
Manos Papadakis \email{rfastofficial@gmail.com}
}
%\note{
%Acknowledgments:
%}
|
#'@import futile.logger
#'@import hashmap
library(utils)
library(hashmap)
#'Read gRNA counts given sample and gene specification files
#'
#'\code{read_counts_from_spec_files()} takes a file containing raw read counts, a replicate to sample specification file, and a gRNA ID to gene specification file, and returns a \code{\link{SummarizedExperiment}}
#'object of normalized log2-scale read counts and standard deviations.
#'
#'@param count_file String path to read count file. See \code{read_count_table_from_spec} for requirements.
#'@param sample_spec_file String path to sample specification file. Has to contain \code{replicate_col} and \code{sample_col} columns.
#'@param replicate_col String Name of the column in \code{sample_spec_file} that contains identifiers for each experimental replicate, as used as the column header for that replicate in the corresponding count file (globally unique identifiers per line, treatment, replicate, etc., not just '1','2', 'A', or 'R1').
#'@param sample_col String Name of the column in \code{sample_spec_file} that contains the sample identifiers. For example, if three columns in the count file are replicate measurements of the same sample, they should all have the same sample id here.
#'@param reference_sample String Sample name of the single reference sample all other samples will be compared against
#'@param gene_spec_file String path to gRNA-gene link specification file. Has to contain \code{grna_col} and \code{gene_col} columns.
#'@param grna_col String Name of the column in \code{gene_spec_file} that contains the gRNA identifiers.
#'@param gene_col String Name of the column in \code{gene_spec_file} that contains the gene identifiers (to identify which gRNAs map to the same gene).
#'@param count_prior Scalar double pseudocounts for each gRNA added to observations. Default: 32.
#'@param normalization String per-sample normalization method. Default: 'median' [sets median log2-scale count to 0]
#'@param window Scalar integer Length of smoothing window used in standard deviation estimation. Default: 800.
#'@return \code{\link{SummarizedExperiment}} object of log2-scale read count changes compared to reference, and estimated standard deviations across replicates
#'@export
read_counts_from_spec_files <- function(count_file, sample_spec_file, replicate_col, sample_col, reference_sample, gene_spec_file, grna_col, gene_col, count_prior=32., normalization='median', window=800){
sample_spec = .read_sample_spec(count_file, sample_spec_file, replicate_col, sample_col)
gene_spec = .read_gene_spec(gene_spec_file, grna_col, gene_col)
logf = read_count_table_from_spec(sample_spec, gene_spec, count_prior=count_prior, normalization=normalization, window=window)
colData(logf)$Condition = "TEST"
colData(logf)$Condition = as.character(colData(logf)$Condition)
colData(logf)$Condition[as.character(colData(logf)$Name) == reference_sample] = "CTRL"
colData(logf)$Condition = as.factor(colData(logf)$Condition)
return(calc_logfc(logf, reference="CTRL", condition="TEST"))
}
.read_sample_spec <- function(count_file, sample_spec_file, replicate_col, sample_col){
sample_spec = utils::read.table(sample_spec_file, header=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
if(!(replicate_col %in% colnames(sample_spec))){
flog.error(paste("Designated replicate column '", replicate_col, "' not found in columns of sample specification file ", sample_spec_file))
} else if (!(sample_col %in% colnames(sample_spec))){
flog.error(paste("Designated sample column '", sample_col, "' not found in columns of sample specification file ", sample_spec_file))
}
sample_spec = sample_spec[,c(which(colnames(sample_spec) == replicate_col), which(colnames(sample_spec) == sample_col))]
sample_spec = cbind(rep(count_file, dim(sample_spec)[1]), sample_spec) # add count file info
colnames(sample_spec) = c("Filename", "Replicate", "Sample")
return(sample_spec)
}
.read_gene_spec <- function(gene_spec_file, grna_col, gene_col){
gene_spec = utils::read.table(gene_spec_file, header=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
if (!(grna_col %in% colnames(gene_spec))){
flog.error(paste("Designated gRNA ID column '", grna_col, "' not found in columns of gRNA gene specification file ", gene_spec_file))
} else if (!(gene_col %in% colnames(gene_spec))){
flog.error(paste("Designated gene column '", gene_col, "' not found in columns of gRNA gene specification file ", gene_spec_file))
}
gene_spec = gene_spec[,c(which(colnames(gene_spec) == grna_col), which(colnames(gene_spec) == gene_col))]
return(gene_spec)
}
#'Read gRNA counts given sample and gene specification
#'
#'\code{read_count_table_from_spec()} takes a sample specification, and returns a \code{\link{SummarizedExperiment}}
#'object of read counts.
#'
#'The sample specification table provides names of count files to read, columns in those files to retain, and the samples these correspond to.
#'For example,
#'\code{}
#'JACKS assumes that each count file has the same number of rows ordered in the same way, corresponding to the same gRNAs.
#'Every count file has one header line, and the rest of the lines give raw gRNA read counts for one gRNA each, with each column being a different measurement.
#'Only columns with headers as given in the "Replicate" (2nd column of the specification) are stored from each count file.
#'Count file columns that map to the same "Sample" (3rd column of the specification) are treated as replicate measurements for that sample.
#'
#'@param sample_spec Data frame of at least three columns: "Filename": input count file, "Replicate": column names in count file, "Sample": corresponding sample ID to allow combining of replicates within samples.
#'@param gene_spec Data frame of at least two columns: gRNA ID (1st col) and corresponding gene (2nd col)
#'@param count_prior Scalar double pseudocounts for each gRNA added to observations. Default: 32.
#'@param normalization String per-sample normalization method. Default: 'median' [sets median log2-scale count to 0]
#'@param window Scalar integer smoothing window used in standard deviation estimation. Default: 800.
#'@return \code{\link{SummarizedExperiment}} object of log-scale sample read counts and estimated standard deviations across replicates
#'@export
read_count_table_from_spec <- function(sample_spec, gene_spec, count_prior=32., normalization='median', window=800){
all_samples = c()
all_counts = c()
genehash = hashmap(as.character(gene_spec[,1]), as.character(gene_spec[,2]))
for(filename in unique(sample_spec[,1])){ # column 1 of sample spec. is filename
flog.debug(paste("Reading samples from", filename))
counts = c()
meta = c()
d = .read_sample_counts(filename, sample_spec)
grnas = rownames(d)
I = rep(T, dim(d)[1]) # gRNAs to use
for(i in 1:dim(d)[1]){ I[i] = genehash$has_key(grnas[i]) } # see which gRNAs can be mapped
meta = cbind(grnas[I], rep(NA, sum(I))) # retain gRNA metadata info - no gene to begin with
for(i in 1:dim(meta)[1]){ meta[i,2] = genehash[[meta[i,1]]] } # and fill in
if(is.null(all_counts)) { # if first file, set result
all_counts = d[I,]
all_samples = .get_sample_ids(colnames(d), sample_spec, filename)
} else { # some results already exist - append
all_counts = cbind(all_counts, d[I,]) # assume all files are aligned
all_samples = c(all_samples, .get_sample_ids(colnames(d), sample_spec, filename))
}
}
return(.generate_logfc_summarized_experiment(meta, all_counts, all_samples, count_prior, normalization, window))
}
.read_sample_counts <- function(filename, sample_spec){
sep = "\t"
if(".csv" %in% filename){
sep = ","
}
d = utils::read.table(filename, sep=sep,header=TRUE, stringsAsFactors=FALSE, row.names=1, check.names=FALSE)
file_samples = unique(sample_spec[filename == sample_spec[,1], 2]) # Specification column 1 = file name, column 2 = samples to retain from this file
I_sample = rep(F, dim(d)[2])
for(j in 1:length(I_sample)){
I_sample[j] = colnames(d)[j] %in% file_samples
}
return(d[,I_sample])
}
.get_sample_ids <- function(colnames, sample_spec, filename){
sample_ids = c()
spec = sample_spec[sample_spec[,1] == filename,] # column 1 is filename
for(i in 1:length(colnames)){
for(j in 1:dim(spec)[1]){
if(colnames[i] == spec[j,2]){ # Spec. column 2 is sample name in input file
sample_ids = c(sample_ids, spec[j,3]) # Spec. column 3 is sample ID
}
}
}
return(sample_ids)
}
.generate_logfc_summarized_experiment <- function(grna_meta, counts, count_samples, count_prior=32., normalization='median', window=800){
# typecast vectors of vectors to matrices, add names
grna_meta = matrix(unlist(grna_meta), ncol=2, dimnames=list(NULL, c("gRNA", "gene")))
counts = matrix(unlist(counts), nrow=dim(grna_meta)[1], dimnames=list(grna_meta[,1], count_samples))
samples = unique(count_samples)
# create new matrix of log-scale centered and counts
meanmat = matrix(ncol=length(samples), nrow=dim(counts)[1], dimnames=list(grna_meta[,1], samples))
sdmat = matrix(ncol=length(samples), nrow=dim(counts)[1], dimnames=list(grna_meta[,1], samples))
for (i in 1:length(samples)){ # for each sample
flog.debug(paste("Calculating variance estimates for", samples[i]))
I = (count_samples == samples[i]) # pick corresponding counts from replicates
d = .calc_mean_sd(counts[,I], count_prior, window) # calculate
meanmat[,i] = as.vector(d$mean) # and store
sdmat[,i] = as.vector(d$sd)
}
# assemble object - metadata of matrices, row, and column entries
colnames(grna_meta) = c("gRNA", "gene")
sample_meta = data.frame(list(Name=samples, Condition=rep("NA", length(samples))), check.names=FALSE)
rownames(sample_meta) = samples
result = SummarizedExperiment(assays=list("logf_mean"=meanmat, "logf_sd"=sdmat), rowData=grna_meta, colData=sample_meta)
validate_logf_table(result)
flog.info(paste("Done condensing replicates; went from", length(count_samples), "experiments down to", length(samples), "samples"))
return(result)
}
.read_precomputed_x <- function(library){
supported = c("avana","gecko2","yusa_v10")
if( library %in% supported ){ ref = url(paste0("https://raw.githubusercontent.com/felicityallen/JACKS/master/reference_grna_efficacies/", library, "_grna_JACKS_results.txt"))}
else{ ref = library }
x = utils::read.table(ref, header=TRUE, sep="\t", check.names=FALSE, stringsAsFactors=FALSE, row.names = 1)
return(x)
}
#'Extract JACKS output for a gene
#'
#'\code{jacks_w_gene()} takes JACKS output and gene name, and returns a \code{data.frame}
#'that has columns for statistics of JACKS inferred posterior for gene essentiality w - mean
#'and standard deviation. Each row is one cell line, name in row.names.
#'
#'@param expt Output of JACKS inference - SummarizedExperiment endowed with posteriors.
#'@param gene Gene name to extract information for.
#'@return \code{data.table} object estimated means and standard deviations of gene essentiality.
#'@export
jacks_w_gene <- function(expt, gene){
data.frame(
row.names = rownames(colData(expt)),
w = metadata(expt)$jacks_w[[gene]],
sd_w = metadata(expt)$jacks_sdw[[gene]],
neg_pval = metadata(expt)$jacks_neg_pval[[gene]],
pos_pval = metadata(expt)$jacks_pos_pval[[gene]],
neg_fdr = metadata(expt)$jacks_neg_fdr[[gene]],
pos_fdr = metadata(expt)$jacks_pos_fdr[[gene]]
)
}
#'Extract JACKS output for a sample
#'
#'\code{jacks_w_sample()} takes JACKS output and sample name, and returns a \code{data.frame}
#'that has columns for statistics of JACKS inferred posterior for gene essentiality w - mean
#'and standard deviation. Each row is one gene (name in row.names).
#'
#'@param expt Output of JACKS inference - SummarizedExperiment endowed with posteriors.
#'@param sample Sample name to extract information for.
#'@return \code{data.table} object estimated means and standard deviations of gene essentiality.
#'@export
jacks_w_sample <- function(expt, sample){
i = which(row.names(colData(expt)) == sample)
m = metadata(expt)
data.frame(
row.names = colnames(m$jacks_w),
w = m$jacks_w[i,],
sd_w = m$jacks_sdw[i,],
neg_pval = m$jacks_neg_pval[i,],
pos_pval = m$jacks_pos_pval[i,],
neg_fdr = m$jacks_neg_fdr[i,],
pos_fdr = m$jacks_pos_fdr[i,]
)
}
#
#write_jacks_output <- function(){
#
#}
#'Pre-computed gRNA efficacy estimates for the Avana library.
#'Two vectors are provided, both sorted according to the gRNA sequence.
#' \itemize{
#' \item x: Estimated gRNA efficacy for each gRNA. x = 1 means the guide works roughly as an average gRNA would. x = 0 means guide is ineffective.
#' \item sdx: Standard deviation of x estimate.
#' }
#'
#' @format Two numeric vectors.
#' @source \url{http://www.diamondse.info/}
#' @name avana
#' @docType data
#' @author Leopold Parts \email{lp2@sanger.ac.uk}
#' @keywords data
NULL
#'Test
#' @name example_repmap
#' @docType data
#' @keywords data
NULL
#'Test
#' @name example_count_data
#' @docType data
#' @keywords data
NULL
#'Test
#' @name avana_head
#' @docType data
#' @keywords data
NULL
#'Test
#' @name data
#' @docType data
#' @keywords data
NULL
#'Test
#' @name data_err
#' @docType data
#' @keywords data
NULL
#'Test
#' @name pyvals
#' @docType data
#' @keywords data
NULL
#'Test
#' @name x
#' @docType data
#' @keywords data
NULL
#'Test
#' @name sdx
#' @docType data
#' @keywords data
NULL
| /rjacks/jacks/R/io.R | permissive | goedel-gang/JACKS | R | false | false | 13,845 | r | #'@import futile.logger
#'@import hashmap
library(utils)
library(hashmap)
#'Read gRNA counts given sample and gene specification files
#'
#'\code{read_counts_from_spec_files()} takes a file containing raw read counts, a replicate to sample specification file, and a gRNA ID to gene specification file, and returns a \code{\link{SummarizedExperiment}}
#'object of normalized log2-scale read counts and standard deviations.
#'
#'@param count_file String path to read count file. See \code{read_count_table_from_spec} for requirements.
#'@param sample_spec_file String path to sample specification file. Has to contain \code{replicate_col} and \code{sample_col} columns.
#'@param replicate_col String Name of the column in \code{sample_spec_file} that contains identifiers for each experimental replicate, as used as the column header for that replicate in the corresponding count file (globally unique identifiers per line, treatment, replicate, etc., not just '1','2', 'A', or 'R1').
#'@param sample_col String Name of the column in \code{sample_spec_file} that contains the sample identifiers. For example, if three columns in the count file are replicate measurements of the same sample, they should all have the same sample id here.
#'@param reference_sample String Sample name of the single reference sample all other samples will be compared against
#'@param gene_spec_file String path to gRNA-gene link specification file. Has to contain \code{grna_col} and \code{gene_col} columns.
#'@param grna_col String Name of the column in \code{gene_spec_file} that contains the gRNA identifiers.
#'@param gene_col String Name of the column in \code{gene_spec_file} that contains the gene identifiers (to identify which gRNAs map to the same gene).
#'@param count_prior Scalar double pseudocounts for each gRNA added to observations. Default: 32.
#'@param normalization String per-sample normalization method. Default: 'median' [sets median log2-scale count to 0]
#'@param window Scalar integer Length of smoothing window used in standard deviation estimation. Default: 800.
#'@return \code{\link{SummarizedExperiment}} object of log2-scale read count changes compared to reference, and estimated standard deviations across replicates
#'@export
read_counts_from_spec_files <- function(count_file, sample_spec_file, replicate_col, sample_col, reference_sample, gene_spec_file, grna_col, gene_col, count_prior=32., normalization='median', window=800){
sample_spec = .read_sample_spec(count_file, sample_spec_file, replicate_col, sample_col)
gene_spec = .read_gene_spec(gene_spec_file, grna_col, gene_col)
logf = read_count_table_from_spec(sample_spec, gene_spec, count_prior=count_prior, normalization=normalization, window=window)
colData(logf)$Condition = "TEST"
colData(logf)$Condition = as.character(colData(logf)$Condition)
colData(logf)$Condition[as.character(colData(logf)$Name) == reference_sample] = "CTRL"
colData(logf)$Condition = as.factor(colData(logf)$Condition)
return(calc_logfc(logf, reference="CTRL", condition="TEST"))
}
.read_sample_spec <- function(count_file, sample_spec_file, replicate_col, sample_col){
sample_spec = utils::read.table(sample_spec_file, header=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
if(!(replicate_col %in% colnames(sample_spec))){
flog.error(paste("Designated replicate column '", replicate_col, "' not found in columns of sample specification file ", sample_spec_file))
} else if (!(sample_col %in% colnames(sample_spec))){
flog.error(paste("Designated sample column '", sample_col, "' not found in columns of sample specification file ", sample_spec_file))
}
sample_spec = sample_spec[,c(which(colnames(sample_spec) == replicate_col), which(colnames(sample_spec) == sample_col))]
sample_spec = cbind(rep(count_file, dim(sample_spec)[1]), sample_spec) # add count file info
colnames(sample_spec) = c("Filename", "Replicate", "Sample")
return(sample_spec)
}
.read_gene_spec <- function(gene_spec_file, grna_col, gene_col){
gene_spec = utils::read.table(gene_spec_file, header=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
if (!(grna_col %in% colnames(gene_spec))){
flog.error(paste("Designated gRNA ID column '", grna_col, "' not found in columns of gRNA gene specification file ", gene_spec_file))
} else if (!(gene_col %in% colnames(gene_spec))){
flog.error(paste("Designated gene column '", gene_col, "' not found in columns of gRNA gene specification file ", gene_spec_file))
}
gene_spec = gene_spec[,c(which(colnames(gene_spec) == grna_col), which(colnames(gene_spec) == gene_col))]
return(gene_spec)
}
#'Read gRNA counts given sample and gene specification
#'
#'\code{read_count_table_from_spec()} takes a sample specification, and returns a \code{\link{SummarizedExperiment}}
#'object of read counts.
#'
#'The sample specification table provides names of count files to read, columns in those files to retain, and the samples these correspond to.
#'For example,
#'\code{}
#'JACKS assumes that each count file has the same number of rows ordered in the same way, corresponding to the same gRNAs.
#'Every count file has one header line, and the rest of the lines give raw gRNA read counts for one gRNA each, with each column being a different measurement.
#'Only columns with headers as given in the "Replicate" (2nd column of the specification) are stored from each count file.
#'Count file columns that map to the same "Sample" (3rd column of the specification) are treated as replicate measurements for that sample.
#'
#'@param sample_spec Data frame of at least three columns: "Filename": input count file, "Replicate": column names in count file, "Sample": corresponding sample ID to allow combining of replicates within samples.
#'@param gene_spec Data frame of at least two columns: gRNA ID (1st col) and corresponding gene (2nd col)
#'@param count_prior Scalar double pseudocounts for each gRNA added to observations. Default: 32.
#'@param normalization String per-sample normalization method. Default: 'median' [sets median log2-scale count to 0]
#'@param window Scalar integer smoothing window used in standard deviation estimation. Default: 800.
#'@return \code{\link{SummarizedExperiment}} object of log-scale sample read counts and estimated standard deviations across replicates
#'@export
read_count_table_from_spec <- function(sample_spec, gene_spec, count_prior=32., normalization='median', window=800){
all_samples = c()
all_counts = c()
genehash = hashmap(as.character(gene_spec[,1]), as.character(gene_spec[,2]))
for(filename in unique(sample_spec[,1])){ # column 1 of sample spec. is filename
flog.debug(paste("Reading samples from", filename))
counts = c()
meta = c()
d = .read_sample_counts(filename, sample_spec)
grnas = rownames(d)
I = rep(T, dim(d)[1]) # gRNAs to use
for(i in 1:dim(d)[1]){ I[i] = genehash$has_key(grnas[i]) } # see which gRNAs can be mapped
meta = cbind(grnas[I], rep(NA, sum(I))) # retain gRNA metadata info - no gene to begin with
for(i in 1:dim(meta)[1]){ meta[i,2] = genehash[[meta[i,1]]] } # and fill in
if(is.null(all_counts)) { # if first file, set result
all_counts = d[I,]
all_samples = .get_sample_ids(colnames(d), sample_spec, filename)
} else { # some results already exist - append
all_counts = cbind(all_counts, d[I,]) # assume all files are aligned
all_samples = c(all_samples, .get_sample_ids(colnames(d), sample_spec, filename))
}
}
return(.generate_logfc_summarized_experiment(meta, all_counts, all_samples, count_prior, normalization, window))
}
.read_sample_counts <- function(filename, sample_spec){
sep = "\t"
if(".csv" %in% filename){
sep = ","
}
d = utils::read.table(filename, sep=sep,header=TRUE, stringsAsFactors=FALSE, row.names=1, check.names=FALSE)
file_samples = unique(sample_spec[filename == sample_spec[,1], 2]) # Specification column 1 = file name, column 2 = samples to retain from this file
I_sample = rep(F, dim(d)[2])
for(j in 1:length(I_sample)){
I_sample[j] = colnames(d)[j] %in% file_samples
}
return(d[,I_sample])
}
.get_sample_ids <- function(colnames, sample_spec, filename){
sample_ids = c()
spec = sample_spec[sample_spec[,1] == filename,] # column 1 is filename
for(i in 1:length(colnames)){
for(j in 1:dim(spec)[1]){
if(colnames[i] == spec[j,2]){ # Spec. column 2 is sample name in input file
sample_ids = c(sample_ids, spec[j,3]) # Spec. column 3 is sample ID
}
}
}
return(sample_ids)
}
.generate_logfc_summarized_experiment <- function(grna_meta, counts, count_samples, count_prior=32., normalization='median', window=800){
# typecast vectors of vectors to matrices, add names
grna_meta = matrix(unlist(grna_meta), ncol=2, dimnames=list(NULL, c("gRNA", "gene")))
counts = matrix(unlist(counts), nrow=dim(grna_meta)[1], dimnames=list(grna_meta[,1], count_samples))
samples = unique(count_samples)
# create new matrix of log-scale centered and counts
meanmat = matrix(ncol=length(samples), nrow=dim(counts)[1], dimnames=list(grna_meta[,1], samples))
sdmat = matrix(ncol=length(samples), nrow=dim(counts)[1], dimnames=list(grna_meta[,1], samples))
for (i in 1:length(samples)){ # for each sample
flog.debug(paste("Calculating variance estimates for", samples[i]))
I = (count_samples == samples[i]) # pick corresponding counts from replicates
d = .calc_mean_sd(counts[,I], count_prior, window) # calculate
meanmat[,i] = as.vector(d$mean) # and store
sdmat[,i] = as.vector(d$sd)
}
# assemble object - metadata of matrices, row, and column entries
colnames(grna_meta) = c("gRNA", "gene")
sample_meta = data.frame(list(Name=samples, Condition=rep("NA", length(samples))), check.names=FALSE)
rownames(sample_meta) = samples
result = SummarizedExperiment(assays=list("logf_mean"=meanmat, "logf_sd"=sdmat), rowData=grna_meta, colData=sample_meta)
validate_logf_table(result)
flog.info(paste("Done condensing replicates; went from", length(count_samples), "experiments down to", length(samples), "samples"))
return(result)
}
.read_precomputed_x <- function(library){
supported = c("avana","gecko2","yusa_v10")
if( library %in% supported ){ ref = url(paste0("https://raw.githubusercontent.com/felicityallen/JACKS/master/reference_grna_efficacies/", library, "_grna_JACKS_results.txt"))}
else{ ref = library }
x = utils::read.table(ref, header=TRUE, sep="\t", check.names=FALSE, stringsAsFactors=FALSE, row.names = 1)
return(x)
}
#'Extract JACKS output for a gene
#'
#'\code{jacks_w_gene()} takes JACKS output and gene name, and returns a \code{data.frame}
#'that has columns for statistics of JACKS inferred posterior for gene essentiality w - mean
#'and standard deviation. Each row is one cell line, name in row.names.
#'
#'@param expt Output of JACKS inference - SummarizedExperiment endowed with posteriors.
#'@param gene Gene name to extract information for.
#'@return \code{data.table} object estimated means and standard deviations of gene essentiality.
#'@export
jacks_w_gene <- function(expt, gene){
data.frame(
row.names = rownames(colData(expt)),
w = metadata(expt)$jacks_w[[gene]],
sd_w = metadata(expt)$jacks_sdw[[gene]],
neg_pval = metadata(expt)$jacks_neg_pval[[gene]],
pos_pval = metadata(expt)$jacks_pos_pval[[gene]],
neg_fdr = metadata(expt)$jacks_neg_fdr[[gene]],
pos_fdr = metadata(expt)$jacks_pos_fdr[[gene]]
)
}
#'Extract JACKS output for a sample
#'
#'\code{jacks_w_sample()} takes JACKS output and sample name, and returns a \code{data.frame}
#'that has columns for statistics of JACKS inferred posterior for gene essentiality w - mean
#'and standard deviation. Each row is one gene (name in row.names).
#'
#'@param expt Output of JACKS inference - SummarizedExperiment endowed with posteriors.
#'@param sample Sample name to extract information for.
#'@return \code{data.table} object estimated means and standard deviations of gene essentiality.
#'@export
jacks_w_sample <- function(expt, sample){
i = which(row.names(colData(expt)) == sample)
m = metadata(expt)
data.frame(
row.names = colnames(m$jacks_w),
w = m$jacks_w[i,],
sd_w = m$jacks_sdw[i,],
neg_pval = m$jacks_neg_pval[i,],
pos_pval = m$jacks_pos_pval[i,],
neg_fdr = m$jacks_neg_fdr[i,],
pos_fdr = m$jacks_pos_fdr[i,]
)
}
#
#write_jacks_output <- function(){
#
#}
#'Pre-computed gRNA efficacy estimates for the Avana library.
#'Two vectors are provided, both sorted according to the gRNA sequence.
#' \itemize{
#' \item x: Estimated gRNA efficacy for each gRNA. x = 1 means the guide works roughly as an average gRNA would. x = 0 means guide is ineffective.
#' \item sdx: Standard deviation of x estimate.
#' }
#'
#' @format Two numeric vectors.
#' @source \url{http://www.diamondse.info/}
#' @name avana
#' @docType data
#' @author Leopold Parts \email{lp2@sanger.ac.uk}
#' @keywords data
NULL
#'Test
#' @name example_repmap
#' @docType data
#' @keywords data
NULL
#'Test
#' @name example_count_data
#' @docType data
#' @keywords data
NULL
#'Test
#' @name avana_head
#' @docType data
#' @keywords data
NULL
#'Test
#' @name data
#' @docType data
#' @keywords data
NULL
#'Test
#' @name data_err
#' @docType data
#' @keywords data
NULL
#'Test
#' @name pyvals
#' @docType data
#' @keywords data
NULL
#'Test
#' @name x
#' @docType data
#' @keywords data
NULL
#'Test
#' @name sdx
#' @docType data
#' @keywords data
NULL
|
library(imp4p)
### Name: prob.mcar
### Title: Estimation of a vector of probabilities that missing values are
### MCAR.
### Aliases: prob.mcar
### Keywords: Missing value analysis
### ** Examples
## No test:
#Simulating data
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Imputation of missing values with the slsa algorithm
dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);
#Estimation of the mixture model
res=estim.mix(tab=res.sim$dat.obs, tab.imp=dat.slsa, conditions=res.sim$condition);
#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);
#Computing probabilities to be MCAR in the first column of result$tab.mod
proba=prob.mcar(b.l=born$tab.lower[,1],b.u=born$tab.upper[,1],absc=res$abs.mod,
pi.mcar=res$pi.mcar[1], F.tot=res$F.tot[,1], F.na=res$F.na[,1]);
## End(No test)
| /data/genthat_extracted_code/imp4p/examples/prob_mcar.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 992 | r | library(imp4p)
### Name: prob.mcar
### Title: Estimation of a vector of probabilities that missing values are
### MCAR.
### Aliases: prob.mcar
### Keywords: Missing value analysis
### ** Examples
## No test:
#Simulating data
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Imputation of missing values with the slsa algorithm
dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);
#Estimation of the mixture model
res=estim.mix(tab=res.sim$dat.obs, tab.imp=dat.slsa, conditions=res.sim$condition);
#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);
#Computing probabilities to be MCAR in the first column of result$tab.mod
proba=prob.mcar(b.l=born$tab.lower[,1],b.u=born$tab.upper[,1],absc=res$abs.mod,
pi.mcar=res$pi.mcar[1], F.tot=res$F.tot[,1], F.na=res$F.na[,1]);
## End(No test)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interpolateImageValues.R
\name{interpolateImageValues}
\alias{interpolateImageValues}
\title{interpolateImageValues}
\usage{
interpolateImageValues(img, points, type = "point",
interpolation = "linear")
}
\arguments{
\item{img}{an antsImage}
\item{points}{the locations of interest}
\item{type}{'point' or 'index'}
\item{interpolation}{options are: 'linear'}
}
\description{
return image values at points or indices
}
| /man/interpolateImageValues.Rd | no_license | jeffduda/DANTsR | R | false | true | 501 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interpolateImageValues.R
\name{interpolateImageValues}
\alias{interpolateImageValues}
\title{interpolateImageValues}
\usage{
interpolateImageValues(img, points, type = "point",
interpolation = "linear")
}
\arguments{
\item{img}{an antsImage}
\item{points}{the locations of interest}
\item{type}{'point' or 'index'}
\item{interpolation}{options are: 'linear'}
}
\description{
return image values at points or indices
}
|
rm(list = ls())
# XXX Manuscript uses asreml to do models (must pay for license)
## used asreml because easiest way to get 95% CIs on variance estimates
have_asreml <- FALSE #<-- change to TRUE if computer has the asreml software
# If do not have asreml, can still get same models with nlme, just can't do
## profile likelihood method to get 95% CIs on variance estimates.
library(nlme)
if(have_asreml){
library(asreml)
# create function to extract 95% CIs from profile likelihood
## uses nadiv:::proLik4 to profile the likelihood for a single variance
proCI <- function(x){
if(is(x) != "proLik") error("x is not a profile likelihood/nadiv::proLik")
unlist(x[c("LCL", "UCL")])
} #<-- end function
}
# need nadiv for use with asreml (need LRT function)
library(nadiv)
#FIXME set to your own path here
#setwd("<< Insert path on local computer >>")
# load data
microhab <- read.table(file = "microhabitat.txt", header = TRUE,
sep = "\t") #<-- XXX important to include tab-separated
## Create subset of just artificial nests
art <- microhab[which(microhab$NestRand == 1), ]
################################################################################
# BEFORE ANALYSES, need to make categorical variables into factors
## Do this so won't make mistake using an integer and R interprets this as a covariate when you want it to be a categorical factor
str(microhab)
# Just doing the main ones, others *could* be added if used later
# Also, Mean center and standardize iButton depth and canopy openness: use as covariates
microhab <- within(microhab, {
NestRandFac <- as.factor(NestRand) #<-- create new column/don't write over
SiteTypeFac <- as.factor(SiteType)
NestClusterFac <- as.factor(NestCluster)
scibdepth <- scale(ibdepth)
scCanopy <- scale(Canopy)
})
# order based on nest type for consistency (and for asreml)
microhab <- microhab[order(microhab$NestRandFac), ]
# now for artificial nest subset
art <- within(art, {
NestRandFac <- as.factor(NestRand) #<-- create new column/don't write over
SiteTypeFac <- as.factor(SiteType)
NestClusterFac <- as.factor(NestCluster)
scibdepth <- scale(ibdepth)
scCanopy <- scale(Canopy)
})
############################################################################
##################################
# ARTIFICIAL NEST SUBSET ANALYSES
##################################
artmod.Slope <- lm(Slope ~ SiteTypeFac, data = art, na.action = na.omit)
summary(artmod.Slope)
anova(artmod.Slope)
artmod.Canopy <- lm(Canopy ~ SiteTypeFac, data = art, na.action = na.omit)
summary(artmod.Canopy)
anova(artmod.Canopy)
# Temperature variables also include continuous covariate of iButton depth
artmod.dailyMean_C <- lm(dailyMean_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMean_C)
anova(artmod.dailyMean_C)
artmod.dailyMax_C <- lm(dailyMax_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMax_C)
anova(artmod.dailyMax_C)
artmod.dailyMin_C <- lm(dailyMin_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMin_C)
anova(artmod.dailyMin_C)
artmod.range <- lm(range ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.range)
anova(artmod.range)
############################################################################
############################################################################
############################################################################
############################################################################
################################
# MICROHABITAT ANALYSES
################################
#### Real vs. random locations for each microhabitat variable, plus urbanization covar. and urb*nestrand interaction
###random effect of nest cluster
##################################################
# Distance to water
##################################################
modDistTW <- lme(DistTW ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# use `modDistTW` (separate residual variances)
## but now see whether slopes differ or should we use a model with a single slope
anova(modDistTW) #<-- no significant interaction
modDistTWb <- lme(DistTW ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for different residual variances
modDistTWc <- lme(DistTW ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
anova(modDistTW, modDistTWc)
summary(modDistTWb) #<-- XXX use no interaction but separate residual variances
##############################
# asreml
if(have_asreml){
asrDistTW <- asreml(DistTW ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrDistTWb <- asreml(DistTW ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrDistTW, ~ sqrt(V1)),
vpredict(asrDistTW, ~ sqrt(V2)),
vpredict(asrDistTW, ~ sqrt(V3) / sqrt(V2)))
summary(modDistTW)
# Profile likelihood confidence intervals
DistTW.v1 <- proLik4(asrDistTW, component = ~ V1, parallel = TRUE, ncores = 8)
DistTW.v2 <- proLik4(asrDistTW, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
DistTW.v3 <- proLik4(asrDistTW, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrDistTWb, ~ sqrt(V1)),
vpredict(asrDistTWb, ~ sqrt(V2)))
summary(modDistTWc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrDistTW$loglik, asrDistTWb$loglik, df = 1)
anova(modDistTW, modDistTWc)
#XXX asreml agrees with variance components of lme
}
##############################
##################################################
# Slope
##################################################
modSlope <- lme(Slope ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
## see whether slopes differ or should use model with single slope
anova(modSlope) #<-- no significant interaction
modSlopeb <- lme(Slope ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for different residual variances
modSlopec <- lme(Slope ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
anova(modSlope,modSlopec) #<-- no significant diff: use 1 residual variance
summary(modSlope)
summary(modSlopec) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrSlope <- asreml(Slope ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrSlopeb <- asreml(Slope ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrSlope, ~ sqrt(V1)),
vpredict(asrSlope, ~ sqrt(V2)),
vpredict(asrSlope, ~ sqrt(V3) / sqrt(V2)))
summary(modSlope)
# Profile likelihood confidence intervals
Slope.v1 <- proLik4(asrSlope, component = ~ V1, parallel = TRUE, ncores = 8)
Slope.v2 <- proLik4(asrSlope, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
Slope.v3 <- proLik4(asrSlope, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrSlopeb, ~ sqrt(V1)),
vpredict(asrSlopeb, ~ sqrt(V2)))
summary(modSlopec)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrSlope$loglik, asrSlopeb$loglik, df = 1)
anova(modSlope, modSlopec)
#XXX asreml agrees with variance components of lme
}
##############################
##################################################
# Canopy Openness
##################################################
modCanopy <- lme(Canopy ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modCanopy)
## but now see whether slopes differ or should we use a model with a single slope
anova(modCanopy) #<-- NO significant interaction
modCanopyb <- lme(Canopy ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modCanopyb) #<-- XXX Use this model
# Test for different residual variances
modCanopyc <- lme(Canopy ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(modCanopyc)
anova(modCanopy,modCanopyc) #<-- XXX significant diff: use 2 residual variance
##############################
# asreml
if(have_asreml){
asrCanopy <- asreml(Canopy ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrCanopyb <- asreml(Canopy ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrCanopy, ~ sqrt(V1)),
vpredict(asrCanopy, ~ sqrt(V2)),
vpredict(asrCanopy, ~ sqrt(V3) / sqrt(V2)))
summary(modCanopy)
# Profile likelihood confidence intervals
Canopy.v1 <- proLik4(asrCanopy, component = ~ V1, parallel = TRUE, ncores = 8)
Canopy.v2 <- proLik4(asrCanopy, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
Canopy.v3 <- proLik4(asrCanopy, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrCanopyb, ~ sqrt(V1)),
vpredict(asrCanopyb, ~ sqrt(V2)))
summary(modCanopyc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrCanopy$loglik, asrCanopyb$loglik, df = 1)
anova(modCanopy, modCanopyc)
#XXX asreml agrees with variance components of lme
}
##############################
# use `modCanopyb` (separate residual variances)
##################################################
# Daily Mean Temperature
##################################################
moddailyMean_C <- lme(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMean_C)
## but now see whether slopes differ or should we use a model with a single slope
anova(moddailyMean_C) #<-- NO significant interaction
moddailyMean_Cb <- lme(dailyMean_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMean_Cb)
# See if separate residual variances needed
moddailyMean_Cc <- lme(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMean_Cc) #<-- XXX Use this model
anova(moddailyMean_C,moddailyMean_Cc) #<--XXX No significant diff.
##############################
# asreml
if(have_asreml){
asrdailyMean_C <- asreml(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMean_C <- update(asrdailyMean_C, maxit = 10)
asrdailyMean_C <- update(asrdailyMean_C, maxit = 10)
asrdailyMean_Cb <- asreml(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMean_C, ~ sqrt(V1)),
vpredict(asrdailyMean_C, ~ sqrt(V3)),
vpredict(asrdailyMean_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMean_C)
# Profile likelihood confidence intervals
dailyMean_C.v1 <- proLik4(asrdailyMean_C, component = ~ V1,
parallel = TRUE, ncores = 8)
dailyMean_C.v2 <- proLik4(asrdailyMean_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMean_C.v3 <- proLik4(asrdailyMean_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(dailyMean_C.v1)
plot(dailyMean_C.v2)
plot(dailyMean_C.v3)
rbind(vpredict(asrdailyMean_Cb, ~ sqrt(V1)),
vpredict(asrdailyMean_Cb, ~ sqrt(V2)))
summary(moddailyMean_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMean_C$loglik, asrdailyMean_Cb$loglik, df = 1)
anova(moddailyMean_C, moddailyMean_Cc)
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
# use `moddailyMean_C` (2 residual variances) based on profile likelihood CIs
##################################################
# Daily Max Temperature
##################################################
moddailyMax_C <- lme(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMax_C)
## see whether slopes differ or should use model with single slope
anova(moddailyMax_C) #<-- NO significant interaction
moddailyMax_Cb <- lme(dailyMax_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMax_Cb)
# Test for separate residual variances
moddailyMax_Cc <- lme(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMax_Cc)
anova(moddailyMax_C, moddailyMax_Cc)
# use `moddailyMax_Cc` (No difference between residual variances)
summary(moddailyMax_Cc) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrdailyMax_C <- asreml(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMax_C <- update(asrdailyMax_C, maxit = 10)
asrdailyMax_Cb <- asreml(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMax_C, ~ sqrt(V1)),
vpredict(asrdailyMax_C, ~ sqrt(V3)),
vpredict(asrdailyMax_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMax_C)
# Profile likelihood confidence intervals
dailyMax_C.v1 <- proLik4(asrdailyMax_C, component = ~ V1,
parallel = TRUE, ncores = 8,
nsample.units = 1, nse = 4)
dailyMax_C.v2 <- proLik4(asrdailyMax_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMax_C.v3 <- proLik4(asrdailyMax_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8,
nsample.units = 1, nse = 1.5)
# Have to find upper CI limit manually
#XXX MUST do var.estimate replacement FIRST XXX
dailyMax_C.v3$var.estimates <- dailyMax_C.v3$var.estimates[!is.na(dailyMax_C.v3$lambdas)]
#XXX
dailyMax_C.v3$lambdas <- dailyMax_C.v3$lambdas[!is.na(dailyMax_C.v3$lambdas)]
chi.val <- 0.5 * qchisq(1 - 0.05, df = 1)
fllmd <- asreml::update.asreml(object = asrdailyMax_C,
start.values = TRUE)$vparameters.table
fllmd2 <- fllmd
fllmd2[3, 3] <- "F"
for(v in seq(17.05, 17.15, by = 0.005)){
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
}
dailyMax_C.v3
chi.val
for(v in seq(17.14, 17.145, by = 0.0001)){
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
}
dailyMax_C.v3
chi.val
v <- 17.14016
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
dailyMax_C.v3
chi.val
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- lambdas[order(var.estimates)]
var.estimates <- var.estimates[order(var.estimates)]})
dailyMax_C.v3$UCL <- with(dailyMax_C.v3, var.estimates[var.estimates > 10][which.min(abs(lambdas[var.estimates > 10] - chi.val))])
par(mfrow = c(3, 1))
plot(dailyMax_C.v1)
plot(dailyMax_C.v2)
plot(dailyMax_C.v3)
rbind(vpredict(asrdailyMax_Cb, ~ sqrt(V1)),
vpredict(asrdailyMax_Cb, ~ sqrt(V2)))
summary(moddailyMax_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMax_C$loglik, asrdailyMax_Cb$loglik, df = 1)
anova(moddailyMax_C, moddailyMax_Cc)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
##################################################
# Daily Min Temperature
##################################################
moddailyMin_C <- lme(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMin_C)
anova(moddailyMin_C) #<-- NO significant interaction
moddailyMin_Cb <- lme(dailyMin_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for separate residual variances
## drop separate residual variances
moddailyMin_Cc <- lme(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMin_Cc)
anova(moddailyMin_C,moddailyMin_Cc) #<-- no significant diff. XXX p=0.08 XXX
# use `moddailyMin_C` (2 different residual variances since profile 95%CI do NOT overalp)
summary(moddailyMin_C) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrdailyMin_C <- asreml(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMin_C <- update(asrdailyMin_C, maxit = 10)
asrdailyMin_Cb <- asreml(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMin_C, ~ sqrt(V1)),
vpredict(asrdailyMin_C, ~ sqrt(V3)),
vpredict(asrdailyMin_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMin_C)
# Profile likelihood confidence intervals
dailyMin_C.v1 <- proLik4(asrdailyMin_C, component = ~ V1,
parallel = TRUE, ncores = 8)
dailyMin_C.v2 <- proLik4(asrdailyMin_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMin_C.v3 <- proLik4(asrdailyMin_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(dailyMin_C.v1)
plot(dailyMin_C.v2)
plot(dailyMin_C.v3)
rbind(vpredict(asrdailyMin_Cb, ~ sqrt(V1)),
vpredict(asrdailyMin_Cb, ~ sqrt(V2)))
summary(moddailyMin_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMin_C$loglik, asrdailyMin_Cb$loglik, df = 1)
anova(moddailyMin_C, moddailyMin_Cc)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
##################################################
# Daily Temperature Range
##################################################
modrange <- lme(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modrange)
anova(modrange) #<-- NO significant interaction
modrangeb <- lme(range ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modrangeb)
# Now test for separate residual variances
modrangec <- lme(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(modrangec) #<-- XXX Use this model
anova(modrange,modrangec) #<-- XXX Non-significance difference between residuals
# use `modrangec` (No difference between residual variances)
##############################
# asreml
if(have_asreml){
asrrange <- asreml(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrrangeb <- asreml(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrrange, ~ sqrt(V1)),
vpredict(asrrange, ~ sqrt(V3)),
vpredict(asrrange, ~ sqrt(V2) / sqrt(V3)))
summary(modrange)
# Profile likelihood confidence intervals
range.v1 <- proLik4(asrrange, component = ~ V1,
parallel = TRUE, ncores = 8)
range.v2 <- proLik4(asrrange, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
range.v3 <- proLik4(asrrange, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(range.v1)
plot(range.v2)
plot(range.v3)
rbind(vpredict(asrrangeb, ~ sqrt(V1)),
vpredict(asrrangeb, ~ sqrt(V2)))
summary(modrangec)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrrange$loglik, asrrangeb$loglik, df = 1)
anova(modrange, modrangec)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
###############################################################################
###############################################################################
#### Save asreml results if necessary
if(have_asreml){
ciBaseNms <- c("Canopy", "dailyMax_C", "dailyMean_C", "dailyMin_C",
"DistTW", "range", "Slope")
# Save raw profile likelihoods
save(list = c(paste0(ciBaseNms, ".v1"),
paste0(ciBaseNms, ".v2"),
paste0(ciBaseNms, ".v3")),
file = "./microhabProfileCIs.rda")
}
###############################################################################
###############################################################################
# Figure with urbanization scores by location/location type
###############################################################################
# First, make simple dataframe with sites
locations <- microhab[which(!duplicated(microhab$siteAbbr)),
match(c("SiteName", "siteAbbr", "SiteType", "HumDist", "urbPC1", "urbPC2"),
names(microhab))]
# Give each location a number corresponding to table in main text of Manuscript
## Missing the Agricultural Heritage park (4) and Notasulga pond (15)
locations$ind <- c(3, 2, 11, 10, 5, 7, 6, 14, 1, 13, 12, 9, 8)
# now make small dataframe for plotting the indices at the urbPC1 scores
## and condense to a single unique urbPC1
### sort first, so indices will increase when paste duplicated ones back in
locTcks <- locations[order(locations$ind), c("urbPC1", "ind")]
locTcks <- locTcks[which(!duplicated(locTcks$urbPC1)), ]
locTcks$ind <- as.character(locTcks$ind)
# Now find matches and paste those indices in with existing ones
for(i in 1:nrow(locTcks)){
locTcks$ind[i] <- paste(locations$ind[which(locTcks$urbPC1[i] ==
locations$urbPC1)],
collapse = ",")
}
## order by urbPC1
locTcks <- locTcks[order(locTcks$urbPC1), ]
# Add a vertical adjustment to space out text on figure
locTcks$vadj <- rep(c(0, 0.2), nrow(locTcks))[1:nrow(locTcks)]
# Snag some colors from base R's `Okabe-Ito` palette
## suitable palette for color-related vision accessibility
### first have a look
palOI <- palette.colors(NULL, "Okabe-Ito")
pie(rep(1, length(palOI)), col = palOI, labels = names(palOI))
# grab subset of 3 colors for figure
palOI3 <- palOI[c("skyblue", "bluishgreen", "vermillion")]
dev.off() #<-- turn off pie chart
# Need multiple "rug" plots to do different colors
## set rug plot arguments here
tcksz <- 0.05 #<-- "ticksize" positive then ticks plot in towards center of fig.
tcklwd <- 4 #<-- "lwd"
rpos <- 0.26 #<-- "pos" to make sure end of line doesn't extend below axis
xpos <- 0.25 #<-- "pos" of axis(1) to bring down to make room for indnices
#XXX for saving figures - use:
pdf(file = "./Fig1_HumDisturbCat_vs_urb.pdf",
width = 9, height = 5)
# use `par()` to set up some features of the entire figure
## `mfrow` designates the number of rows x columns to create in the figure
par(mar = c(4.5, 5.5, 2.2, 0.1), #<-- space around panel (bottom, left, top, right)
mgp = c(3, 1, 0), #<-- adjustment of axis title, labels, and line
cex.axis = 1.25, cex.lab = 1.6) #<-- scaling for axis labels and axis title
boxplot(urbPC1 ~ SiteType, data = locations, horizontal = TRUE, axes = FALSE,
col = palOI3,
xlab = "Urbanization level (PC1)", ylab = "Study area disturbance level",
xlim = c(0.5, 3.5), #<-- switched: for final y-axis when `horizontal = TRUE`
ylim = c(-1.6, 5.3)) #<-- switched so eventual x-axis
rug(locations$urbPC1[locations$HumDist == "High"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[3]) #<-- color for ticks
rug(locations$urbPC1[locations$HumDist == "Intermediate"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[2]) #<-- color for ticks
rug(locations$urbPC1[locations$HumDist == "Low"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[1]) #<-- color for ticks
# indices to associate rug plot ticks with table in main manuscript
#text(x = locTcks$urbPC1, y = 0.5 + locTcks$vadj, labels = locTcks$ind, cex = 0.8)
axis(1, at = seq(-1.6, 5.2, 0.4), labels = FALSE, lwd = 1.6, pos = xpos) #<-- just the axis
axis(1, at = seq(-1.6, 5.2, 0.8), lwd = 0, pos = xpos) #<-- just the labels
axis(2, at = seq.int(3), labels = c("Low", "Intermediate", "High"), lwd = 1.6)
dev.off() #<-- XXX MUST do this to close pdf file connection
###############################################################################
# 7-panel Figure of all above variables
###############################################################################
## First, to create prediction lines need a new dataset that gives values over which we want predictions
### choose even spacing between minimum and maximum urbanization scores
### assign this to both random and nest sites
ndata <- with(microhab, #<--avoid `microhab$` each time
data.frame(urbPC1 = rep(seq(from = min(urbPC1, na.rm = TRUE),
to = max(urbPC1, na.rm = TRUE),
length.out = 100), 2),
NestRandFac = as.factor(rep(c(0, 1), each = 100)),
scibdepth = rep(seq(from = min(scibdepth, na.rm = TRUE),
to = max(scibdepth, na.rm = TRUE),
length.out = 100), 2)))
# Set up a few things that will be used over and over again among all 8 plots
xlab_in <- "Urbanization level (PC1)"
xaxis <- seq(from = -2, to = 5, by = 1)
# what should we set x-axis limits at
range(microhab$urbPC1, na.rm = TRUE)
xlim_in <- c(-2.05, 5.2)
degCexpr <- "(\u00B0C)" #<-- degrees Celsius expression to paste in
nestPtSymb <- 21
nestPtCols <- c(bg = "#03244d", brd = "grey40") #<-- "BLUE"
nestPtCx <- 1.7
randPtSymb <- 22
randPtCols <- c(bg = "#e86823", brd = "grey20") #<-- "ORANGE"
randPtCx <- 1.1
jitfac <- 3.6 #<-- jitter factor
reglinewd <- 3.2 #<-- line width of all regression lines
ptLwd <- 1.0 #<-- line width of point border
#XXX for saving figures - use:
pdf(file = "./Fig3_microhab_vs_urb.pdf",
width = 9, height = 12)
# use `par()` to set up some features of the entire figure
## `mfrow` designates the number of rows x columns to create in the figure
par(mfrow = c(4, 2), #<-- (rows, columns)
mar = c(5, 6.2, 1.9, 0.5), #<-- space around each panel (bottom, left, top, right)
mgp = c(3, 1, 0), #<-- adjustment of axis title, labels, and line
cex.axis = 1.5, cex.lab = 1.6) #<-- scaling for axis labels and axis title
################################
# Distance to Water (DistTW)
################################
plot(DistTW ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 265),
xlab = xlab_in,
ylab = "Distance to water (m)")
# points first (to put in background)
## Nest first
points(DistTW ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(DistTW ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modDistTW, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"]) #<-- make thicker so not covered
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 250, 50))
mtext(text = expression((bold(A))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# Slope
################################
plot(Slope ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 65),
xlab = xlab_in,
ylab = "Slope (degrees)")
# points first (to put in background)
## Nest first
points(Slope ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(Slope ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modSlope, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 60, 20))
mtext(text = expression((bold(B))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# Canopy
################################
plot(Canopy ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 100),
xlab = xlab_in,
ylab = "Canopy openness (%)")
# points first (to put in background)
## Nest first
points(Canopy ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(Canopy ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modCanopy, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 100, 20))
mtext(text = expression((bold(C))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# dailyMean_C
################################
plot(dailyMean_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(22.5, 34),
xlab = xlab_in,
ylab = paste("Daily mean temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMean_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMean_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMean_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(24, 34, 2))
mtext(text = expression((bold(D))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# dailyMax_C
################################
plot(dailyMax_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(23, 45),
xlab = xlab_in,
ylab = paste("Daily max. temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMax_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMax_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMax_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(25, 45, 5))
mtext(text = expression((bold(E))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# dailyMin_C
################################
plot(dailyMin_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in,
xlab = xlab_in,
ylab = paste("Daily min. temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMin_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMin_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMin_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2)
mtext(text = expression((bold(F))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# range
################################
plot(range ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 16),
xlab = xlab_in,
ylab = paste("Daily temp. range ", degCexpr))
# points first (to put in background)
## Nest first
points(range ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(range ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modrange, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 16, 4))
mtext(text = expression((bold(G))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
# END OF PLOTTING #############################
dev.off() #<-- XXX MUST do this to close pdf file connection
################################################################################
################################################################################
# For results tables Using `asreml`
## "NestRandFac0" == natural and "NestRandFac1" == artifical
# Create quick function to extract fixed effect estimates and standard errors from asreml
fxdFun <- function(mod){
fxdtmp <- cbind(rev(mod$coefficients$fixed),
rev(sqrt(mod$vcoeff$fixed)))
dimnames(fxdtmp) <- list(rev(rownames(mod$coefficients$fixed)),
c("Est", "Std.Err"))
fxdtmp[which(fxdtmp[, 1] != 0.00 & fxdtmp[, 2] != 0.00), ]
}
#######################
fxdFun(asrDistTW)
wald(asrDistTW)
summary(asrDistTW)$varcomp
rbind(proCI(DistTW.v2), proCI(DistTW.v3))
lrt(asrDistTWb, asrDistTW)
fxdFun(asrSlope)
wald(asrSlope)
summary(asrSlope)$varcomp
rbind(proCI(Slope.v2), proCI(Slope.v3))
lrt(asrSlopeb, asrSlope)
fxdFun(asrCanopy)
wald(asrCanopy)
summary(asrCanopy)$varcomp
rbind(proCI(Canopy.v2), proCI(Canopy.v3))
lrt(asrCanopyb, asrCanopy)
fxdFun(asrdailyMean_C)
wald(asrdailyMean_C)
summary(asrdailyMean_C)$varcomp
rbind(proCI(dailyMean_C.v2), proCI(dailyMean_C.v3))
lrt(asrdailyMean_Cb, asrdailyMean_C)
fxdFun(asrdailyMax_C)
wald(asrdailyMax_C)
summary(asrdailyMax_C)$varcomp
rbind(proCI(dailyMax_C.v2), proCI(dailyMax_C.v3))
lrt(asrdailyMax_Cb, asrdailyMax_C)
fxdFun(asrdailyMin_C)
wald(asrdailyMin_C)
summary(asrdailyMin_C)$varcomp
rbind(proCI(dailyMin_C.v2), proCI(dailyMin_C.v3))
lrt(asrdailyMin_Cb, asrdailyMin_C)
fxdFun(asrrange)
wald(asrrange)
summary(asrrange)$varcomp
rbind(proCI(range.v2), proCI(range.v3))
lrt(asrrangeb, asrrange)
| /microhabitat_ANALYSES.R | permissive | qgevoeco/Caldwell_turtle_nest-choice-predation | R | false | false | 41,700 | r | rm(list = ls())
# XXX Manuscript uses asreml to do models (must pay for license)
## used asreml because easiest way to get 95% CIs on variance estimates
have_asreml <- FALSE #<-- change to TRUE if computer has the asreml software
# If do not have asreml, can still get same models with nlme, just can't do
## profile likelihood method to get 95% CIs on variance estimates.
library(nlme)
if(have_asreml){
library(asreml)
# create function to extract 95% CIs from profile likelihood
## uses nadiv:::proLik4 to profile the likelihood for a single variance
proCI <- function(x){
if(is(x) != "proLik") error("x is not a profile likelihood/nadiv::proLik")
unlist(x[c("LCL", "UCL")])
} #<-- end function
}
# need nadiv for use with asreml (need LRT function)
library(nadiv)
#FIXME set to your own path here
#setwd("<< Insert path on local computer >>")
# load data
microhab <- read.table(file = "microhabitat.txt", header = TRUE,
sep = "\t") #<-- XXX important to include tab-separated
## Create subset of just artificial nests
art <- microhab[which(microhab$NestRand == 1), ]
################################################################################
# BEFORE ANALYSES, need to make categorical variables into factors
## Do this so won't make mistake using an integer and R interprets this as a covariate when you want it to be a categorical factor
str(microhab)
# Just doing the main ones, others *could* be added if used later
# Also, Mean center and standardize iButton depth and canopy openness: use as covariates
microhab <- within(microhab, {
NestRandFac <- as.factor(NestRand) #<-- create new column/don't write over
SiteTypeFac <- as.factor(SiteType)
NestClusterFac <- as.factor(NestCluster)
scibdepth <- scale(ibdepth)
scCanopy <- scale(Canopy)
})
# order based on nest type for consistency (and for asreml)
microhab <- microhab[order(microhab$NestRandFac), ]
# now for artificial nest subset
art <- within(art, {
NestRandFac <- as.factor(NestRand) #<-- create new column/don't write over
SiteTypeFac <- as.factor(SiteType)
NestClusterFac <- as.factor(NestCluster)
scibdepth <- scale(ibdepth)
scCanopy <- scale(Canopy)
})
############################################################################
##################################
# ARTIFICIAL NEST SUBSET ANALYSES
##################################
artmod.Slope <- lm(Slope ~ SiteTypeFac, data = art, na.action = na.omit)
summary(artmod.Slope)
anova(artmod.Slope)
artmod.Canopy <- lm(Canopy ~ SiteTypeFac, data = art, na.action = na.omit)
summary(artmod.Canopy)
anova(artmod.Canopy)
# Temperature variables also include continuous covariate of iButton depth
artmod.dailyMean_C <- lm(dailyMean_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMean_C)
anova(artmod.dailyMean_C)
artmod.dailyMax_C <- lm(dailyMax_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMax_C)
anova(artmod.dailyMax_C)
artmod.dailyMin_C <- lm(dailyMin_C ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.dailyMin_C)
anova(artmod.dailyMin_C)
artmod.range <- lm(range ~ SiteTypeFac + ibdepth, data = art,
na.action = na.omit)
summary(artmod.range)
anova(artmod.range)
############################################################################
############################################################################
############################################################################
############################################################################
################################
# MICROHABITAT ANALYSES
################################
#### Real vs. random locations for each microhabitat variable, plus urbanization covar. and urb*nestrand interaction
###random effect of nest cluster
##################################################
# Distance to water
##################################################
modDistTW <- lme(DistTW ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# use `modDistTW` (separate residual variances)
## but now see whether slopes differ or should we use a model with a single slope
anova(modDistTW) #<-- no significant interaction
modDistTWb <- lme(DistTW ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for different residual variances
modDistTWc <- lme(DistTW ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
anova(modDistTW, modDistTWc)
summary(modDistTWb) #<-- XXX use no interaction but separate residual variances
##############################
# asreml
if(have_asreml){
asrDistTW <- asreml(DistTW ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrDistTWb <- asreml(DistTW ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrDistTW, ~ sqrt(V1)),
vpredict(asrDistTW, ~ sqrt(V2)),
vpredict(asrDistTW, ~ sqrt(V3) / sqrt(V2)))
summary(modDistTW)
# Profile likelihood confidence intervals
DistTW.v1 <- proLik4(asrDistTW, component = ~ V1, parallel = TRUE, ncores = 8)
DistTW.v2 <- proLik4(asrDistTW, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
DistTW.v3 <- proLik4(asrDistTW, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrDistTWb, ~ sqrt(V1)),
vpredict(asrDistTWb, ~ sqrt(V2)))
summary(modDistTWc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrDistTW$loglik, asrDistTWb$loglik, df = 1)
anova(modDistTW, modDistTWc)
#XXX asreml agrees with variance components of lme
}
##############################
##################################################
# Slope
##################################################
modSlope <- lme(Slope ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
## see whether slopes differ or should use model with single slope
anova(modSlope) #<-- no significant interaction
modSlopeb <- lme(Slope ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for different residual variances
modSlopec <- lme(Slope ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
anova(modSlope,modSlopec) #<-- no significant diff: use 1 residual variance
summary(modSlope)
summary(modSlopec) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrSlope <- asreml(Slope ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrSlopeb <- asreml(Slope ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrSlope, ~ sqrt(V1)),
vpredict(asrSlope, ~ sqrt(V2)),
vpredict(asrSlope, ~ sqrt(V3) / sqrt(V2)))
summary(modSlope)
# Profile likelihood confidence intervals
Slope.v1 <- proLik4(asrSlope, component = ~ V1, parallel = TRUE, ncores = 8)
Slope.v2 <- proLik4(asrSlope, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
Slope.v3 <- proLik4(asrSlope, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrSlopeb, ~ sqrt(V1)),
vpredict(asrSlopeb, ~ sqrt(V2)))
summary(modSlopec)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrSlope$loglik, asrSlopeb$loglik, df = 1)
anova(modSlope, modSlopec)
#XXX asreml agrees with variance components of lme
}
##############################
##################################################
# Canopy Openness
##################################################
modCanopy <- lme(Canopy ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modCanopy)
## but now see whether slopes differ or should we use a model with a single slope
anova(modCanopy) #<-- NO significant interaction
modCanopyb <- lme(Canopy ~ urbPC1 + NestRandFac,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modCanopyb) #<-- XXX Use this model
# Test for different residual variances
modCanopyc <- lme(Canopy ~ urbPC1*NestRandFac,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(modCanopyc)
anova(modCanopy,modCanopyc) #<-- XXX significant diff: use 2 residual variance
##############################
# asreml
if(have_asreml){
asrCanopy <- asreml(Canopy ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrCanopyb <- asreml(Canopy ~ urbPC1*NestRandFac,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrCanopy, ~ sqrt(V1)),
vpredict(asrCanopy, ~ sqrt(V2)),
vpredict(asrCanopy, ~ sqrt(V3) / sqrt(V2)))
summary(modCanopy)
# Profile likelihood confidence intervals
Canopy.v1 <- proLik4(asrCanopy, component = ~ V1, parallel = TRUE, ncores = 8)
Canopy.v2 <- proLik4(asrCanopy, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
Canopy.v3 <- proLik4(asrCanopy, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
rbind(vpredict(asrCanopyb, ~ sqrt(V1)),
vpredict(asrCanopyb, ~ sqrt(V2)))
summary(modCanopyc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrCanopy$loglik, asrCanopyb$loglik, df = 1)
anova(modCanopy, modCanopyc)
#XXX asreml agrees with variance components of lme
}
##############################
# use `modCanopyb` (separate residual variances)
##################################################
# Daily Mean Temperature
##################################################
moddailyMean_C <- lme(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMean_C)
## but now see whether slopes differ or should we use a model with a single slope
anova(moddailyMean_C) #<-- NO significant interaction
moddailyMean_Cb <- lme(dailyMean_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMean_Cb)
# See if separate residual variances needed
moddailyMean_Cc <- lme(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMean_Cc) #<-- XXX Use this model
anova(moddailyMean_C,moddailyMean_Cc) #<--XXX No significant diff.
##############################
# asreml
if(have_asreml){
asrdailyMean_C <- asreml(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMean_C <- update(asrdailyMean_C, maxit = 10)
asrdailyMean_C <- update(asrdailyMean_C, maxit = 10)
asrdailyMean_Cb <- asreml(dailyMean_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMean_C, ~ sqrt(V1)),
vpredict(asrdailyMean_C, ~ sqrt(V3)),
vpredict(asrdailyMean_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMean_C)
# Profile likelihood confidence intervals
dailyMean_C.v1 <- proLik4(asrdailyMean_C, component = ~ V1,
parallel = TRUE, ncores = 8)
dailyMean_C.v2 <- proLik4(asrdailyMean_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMean_C.v3 <- proLik4(asrdailyMean_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(dailyMean_C.v1)
plot(dailyMean_C.v2)
plot(dailyMean_C.v3)
rbind(vpredict(asrdailyMean_Cb, ~ sqrt(V1)),
vpredict(asrdailyMean_Cb, ~ sqrt(V2)))
summary(moddailyMean_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMean_C$loglik, asrdailyMean_Cb$loglik, df = 1)
anova(moddailyMean_C, moddailyMean_Cc)
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
# use `moddailyMean_C` (2 residual variances) based on profile likelihood CIs
##################################################
# Daily Max Temperature
##################################################
moddailyMax_C <- lme(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMax_C)
## see whether slopes differ or should use model with single slope
anova(moddailyMax_C) #<-- NO significant interaction
moddailyMax_Cb <- lme(dailyMax_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMax_Cb)
# Test for separate residual variances
moddailyMax_Cc <- lme(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMax_Cc)
anova(moddailyMax_C, moddailyMax_Cc)
# use `moddailyMax_Cc` (No difference between residual variances)
summary(moddailyMax_Cc) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrdailyMax_C <- asreml(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMax_C <- update(asrdailyMax_C, maxit = 10)
asrdailyMax_Cb <- asreml(dailyMax_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMax_C, ~ sqrt(V1)),
vpredict(asrdailyMax_C, ~ sqrt(V3)),
vpredict(asrdailyMax_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMax_C)
# Profile likelihood confidence intervals
dailyMax_C.v1 <- proLik4(asrdailyMax_C, component = ~ V1,
parallel = TRUE, ncores = 8,
nsample.units = 1, nse = 4)
dailyMax_C.v2 <- proLik4(asrdailyMax_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMax_C.v3 <- proLik4(asrdailyMax_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8,
nsample.units = 1, nse = 1.5)
# Have to find upper CI limit manually
#XXX MUST do var.estimate replacement FIRST XXX
dailyMax_C.v3$var.estimates <- dailyMax_C.v3$var.estimates[!is.na(dailyMax_C.v3$lambdas)]
#XXX
dailyMax_C.v3$lambdas <- dailyMax_C.v3$lambdas[!is.na(dailyMax_C.v3$lambdas)]
chi.val <- 0.5 * qchisq(1 - 0.05, df = 1)
fllmd <- asreml::update.asreml(object = asrdailyMax_C,
start.values = TRUE)$vparameters.table
fllmd2 <- fllmd
fllmd2[3, 3] <- "F"
for(v in seq(17.05, 17.15, by = 0.005)){
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
}
dailyMax_C.v3
chi.val
for(v in seq(17.14, 17.145, by = 0.0001)){
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
}
dailyMax_C.v3
chi.val
v <- 17.14016
fllmd2[3, 2] <- v
conMod <- update.asreml(asrdailyMax_C, random = ~., R.param = fllmd2)
conMod <- update(conMod, maxiter = 10)
lout <- asreml::lrt(conMod, asrdailyMax_C)$"LR-statistic"
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- c(lambdas, lout)
var.estimates <- c(var.estimates, v)})
dailyMax_C.v3
chi.val
dailyMax_C.v3 <- within(dailyMax_C.v3, {
lambdas <- lambdas[order(var.estimates)]
var.estimates <- var.estimates[order(var.estimates)]})
dailyMax_C.v3$UCL <- with(dailyMax_C.v3, var.estimates[var.estimates > 10][which.min(abs(lambdas[var.estimates > 10] - chi.val))])
par(mfrow = c(3, 1))
plot(dailyMax_C.v1)
plot(dailyMax_C.v2)
plot(dailyMax_C.v3)
rbind(vpredict(asrdailyMax_Cb, ~ sqrt(V1)),
vpredict(asrdailyMax_Cb, ~ sqrt(V2)))
summary(moddailyMax_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMax_C$loglik, asrdailyMax_Cb$loglik, df = 1)
anova(moddailyMax_C, moddailyMax_Cc)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
##################################################
# Daily Min Temperature
##################################################
moddailyMin_C <- lme(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(moddailyMin_C)
anova(moddailyMin_C) #<-- NO significant interaction
moddailyMin_Cb <- lme(dailyMin_C ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
# Test for separate residual variances
## drop separate residual variances
moddailyMin_Cc <- lme(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(moddailyMin_Cc)
anova(moddailyMin_C,moddailyMin_Cc) #<-- no significant diff. XXX p=0.08 XXX
# use `moddailyMin_C` (2 different residual variances since profile 95%CI do NOT overalp)
summary(moddailyMin_C) #<-- XXX Use this model
##############################
# asreml
if(have_asreml){
asrdailyMin_C <- asreml(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrdailyMin_C <- update(asrdailyMin_C, maxit = 10)
asrdailyMin_Cb <- asreml(dailyMin_C ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrdailyMin_C, ~ sqrt(V1)),
vpredict(asrdailyMin_C, ~ sqrt(V3)),
vpredict(asrdailyMin_C, ~ sqrt(V2) / sqrt(V3)))
summary(moddailyMin_C)
# Profile likelihood confidence intervals
dailyMin_C.v1 <- proLik4(asrdailyMin_C, component = ~ V1,
parallel = TRUE, ncores = 8)
dailyMin_C.v2 <- proLik4(asrdailyMin_C, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
dailyMin_C.v3 <- proLik4(asrdailyMin_C, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(dailyMin_C.v1)
plot(dailyMin_C.v2)
plot(dailyMin_C.v3)
rbind(vpredict(asrdailyMin_Cb, ~ sqrt(V1)),
vpredict(asrdailyMin_Cb, ~ sqrt(V2)))
summary(moddailyMin_Cc)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrdailyMin_C$loglik, asrdailyMin_Cb$loglik, df = 1)
anova(moddailyMin_C, moddailyMin_Cc)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
##################################################
# Daily Temperature Range
##################################################
modrange <- lme(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modrange)
anova(modrange) #<-- NO significant interaction
modrangeb <- lme(range ~ urbPC1 + NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
weights = varIdent(form = ~ 1 | NestRandFac),
data = microhab, na.action = na.omit)
summary(modrangeb)
# Now test for separate residual variances
modrangec <- lme(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ 1 | NestClusterFac,
data = microhab, na.action = na.omit)
summary(modrangec) #<-- XXX Use this model
anova(modrange,modrangec) #<-- XXX Non-significance difference between residuals
# use `modrangec` (No difference between residual variances)
##############################
# asreml
if(have_asreml){
asrrange <- asreml(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ dsum( ~ units | NestRandFac),
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
asrrangeb <- asreml(range ~ urbPC1*NestRandFac + scibdepth,
random = ~ NestClusterFac,
residual = ~ idv(units), #<--necessary so model NOT parameterized ratios: var/sigma
data = microhab, na.action = list(y = "omit", x = "omit"),
maxit = 20)
# convert asreml variances into lme SD and SD ratio
## use delta method to approximate std. errors on calculated SD and SD ratio
rbind(vpredict(asrrange, ~ sqrt(V1)),
vpredict(asrrange, ~ sqrt(V3)),
vpredict(asrrange, ~ sqrt(V2) / sqrt(V3)))
summary(modrange)
# Profile likelihood confidence intervals
range.v1 <- proLik4(asrrange, component = ~ V1,
parallel = TRUE, ncores = 8)
range.v2 <- proLik4(asrrange, component = ~ V2, G = FALSE,
parallel = TRUE, ncores = 8)
range.v3 <- proLik4(asrrange, component = ~ V3, G = FALSE,
parallel = TRUE, ncores = 8)
par(mfrow = c(3, 1))
plot(range.v1)
plot(range.v2)
plot(range.v3)
rbind(vpredict(asrrangeb, ~ sqrt(V1)),
vpredict(asrrangeb, ~ sqrt(V2)))
summary(modrangec)
# do the likelihood ratio test of asreml models to compare to anova() results
LRTest(asrrange$loglik, asrrangeb$loglik, df = 1)
anova(modrange, modrangec)
#XXX asreml MOSTLY agrees with variance components of lme
## the test of whether or not the variances differ yields the same interpretation
### across ALL software
}
##############################
###############################################################################
###############################################################################
#### Save asreml results if necessary
if(have_asreml){
ciBaseNms <- c("Canopy", "dailyMax_C", "dailyMean_C", "dailyMin_C",
"DistTW", "range", "Slope")
# Save raw profile likelihoods
save(list = c(paste0(ciBaseNms, ".v1"),
paste0(ciBaseNms, ".v2"),
paste0(ciBaseNms, ".v3")),
file = "./microhabProfileCIs.rda")
}
###############################################################################
###############################################################################
# Figure with urbanization scores by location/location type
###############################################################################
# First, make simple dataframe with sites
locations <- microhab[which(!duplicated(microhab$siteAbbr)),
match(c("SiteName", "siteAbbr", "SiteType", "HumDist", "urbPC1", "urbPC2"),
names(microhab))]
# Give each location a number corresponding to table in main text of Manuscript
## Missing the Agricultural Heritage park (4) and Notasulga pond (15)
locations$ind <- c(3, 2, 11, 10, 5, 7, 6, 14, 1, 13, 12, 9, 8)
# now make small dataframe for plotting the indices at the urbPC1 scores
## and condense to a single unique urbPC1
### sort first, so indices will increase when paste duplicated ones back in
locTcks <- locations[order(locations$ind), c("urbPC1", "ind")]
locTcks <- locTcks[which(!duplicated(locTcks$urbPC1)), ]
locTcks$ind <- as.character(locTcks$ind)
# Now find matches and paste those indices in with existing ones
for(i in 1:nrow(locTcks)){
locTcks$ind[i] <- paste(locations$ind[which(locTcks$urbPC1[i] ==
locations$urbPC1)],
collapse = ",")
}
## order by urbPC1
locTcks <- locTcks[order(locTcks$urbPC1), ]
# Add a vertical adjustment to space out text on figure
locTcks$vadj <- rep(c(0, 0.2), nrow(locTcks))[1:nrow(locTcks)]
# Snag some colors from base R's `Okabe-Ito` palette
## suitable palette for color-related vision accessibility
### first have a look
palOI <- palette.colors(NULL, "Okabe-Ito")
pie(rep(1, length(palOI)), col = palOI, labels = names(palOI))
# grab subset of 3 colors for figure
palOI3 <- palOI[c("skyblue", "bluishgreen", "vermillion")]
dev.off() #<-- turn off pie chart
# Need multiple "rug" plots to do different colors
## set rug plot arguments here
tcksz <- 0.05 #<-- "ticksize" positive then ticks plot in towards center of fig.
tcklwd <- 4 #<-- "lwd"
rpos <- 0.26 #<-- "pos" to make sure end of line doesn't extend below axis
xpos <- 0.25 #<-- "pos" of axis(1) to bring down to make room for indnices
#XXX for saving figures - use:
pdf(file = "./Fig1_HumDisturbCat_vs_urb.pdf",
width = 9, height = 5)
# use `par()` to set up some features of the entire figure
## `mfrow` designates the number of rows x columns to create in the figure
par(mar = c(4.5, 5.5, 2.2, 0.1), #<-- space around panel (bottom, left, top, right)
mgp = c(3, 1, 0), #<-- adjustment of axis title, labels, and line
cex.axis = 1.25, cex.lab = 1.6) #<-- scaling for axis labels and axis title
boxplot(urbPC1 ~ SiteType, data = locations, horizontal = TRUE, axes = FALSE,
col = palOI3,
xlab = "Urbanization level (PC1)", ylab = "Study area disturbance level",
xlim = c(0.5, 3.5), #<-- switched: for final y-axis when `horizontal = TRUE`
ylim = c(-1.6, 5.3)) #<-- switched so eventual x-axis
rug(locations$urbPC1[locations$HumDist == "High"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[3]) #<-- color for ticks
rug(locations$urbPC1[locations$HumDist == "Intermediate"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[2]) #<-- color for ticks
rug(locations$urbPC1[locations$HumDist == "Low"],
side = 1, #<-- side = 1 is bottom
ticksize = tcksz, lwd = tcklwd, pos = rpos,
col = palOI3[1]) #<-- color for ticks
# indices to associate rug plot ticks with table in main manuscript
#text(x = locTcks$urbPC1, y = 0.5 + locTcks$vadj, labels = locTcks$ind, cex = 0.8)
axis(1, at = seq(-1.6, 5.2, 0.4), labels = FALSE, lwd = 1.6, pos = xpos) #<-- just the axis
axis(1, at = seq(-1.6, 5.2, 0.8), lwd = 0, pos = xpos) #<-- just the labels
axis(2, at = seq.int(3), labels = c("Low", "Intermediate", "High"), lwd = 1.6)
dev.off() #<-- XXX MUST do this to close pdf file connection
###############################################################################
# 7-panel Figure of all above variables
###############################################################################
## First, to create prediction lines need a new dataset that gives values over which we want predictions
### choose even spacing between minimum and maximum urbanization scores
### assign this to both random and nest sites
ndata <- with(microhab, #<--avoid `microhab$` each time
data.frame(urbPC1 = rep(seq(from = min(urbPC1, na.rm = TRUE),
to = max(urbPC1, na.rm = TRUE),
length.out = 100), 2),
NestRandFac = as.factor(rep(c(0, 1), each = 100)),
scibdepth = rep(seq(from = min(scibdepth, na.rm = TRUE),
to = max(scibdepth, na.rm = TRUE),
length.out = 100), 2)))
# Set up a few things that will be used over and over again among all 8 plots
xlab_in <- "Urbanization level (PC1)"
xaxis <- seq(from = -2, to = 5, by = 1)
# what should we set x-axis limits at
range(microhab$urbPC1, na.rm = TRUE)
xlim_in <- c(-2.05, 5.2)
degCexpr <- "(\u00B0C)" #<-- degrees Celsius expression to paste in
nestPtSymb <- 21
nestPtCols <- c(bg = "#03244d", brd = "grey40") #<-- "BLUE"
nestPtCx <- 1.7
randPtSymb <- 22
randPtCols <- c(bg = "#e86823", brd = "grey20") #<-- "ORANGE"
randPtCx <- 1.1
jitfac <- 3.6 #<-- jitter factor
reglinewd <- 3.2 #<-- line width of all regression lines
ptLwd <- 1.0 #<-- line width of point border
#XXX for saving figures - use:
pdf(file = "./Fig3_microhab_vs_urb.pdf",
width = 9, height = 12)
# use `par()` to set up some features of the entire figure
## `mfrow` designates the number of rows x columns to create in the figure
par(mfrow = c(4, 2), #<-- (rows, columns)
mar = c(5, 6.2, 1.9, 0.5), #<-- space around each panel (bottom, left, top, right)
mgp = c(3, 1, 0), #<-- adjustment of axis title, labels, and line
cex.axis = 1.5, cex.lab = 1.6) #<-- scaling for axis labels and axis title
################################
# Distance to Water (DistTW)
################################
plot(DistTW ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 265),
xlab = xlab_in,
ylab = "Distance to water (m)")
# points first (to put in background)
## Nest first
points(DistTW ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(DistTW ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modDistTW, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"]) #<-- make thicker so not covered
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 250, 50))
mtext(text = expression((bold(A))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# Slope
################################
plot(Slope ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 65),
xlab = xlab_in,
ylab = "Slope (degrees)")
# points first (to put in background)
## Nest first
points(Slope ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(Slope ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modSlope, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 60, 20))
mtext(text = expression((bold(B))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# Canopy
################################
plot(Canopy ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 100),
xlab = xlab_in,
ylab = "Canopy openness (%)")
# points first (to put in background)
## Nest first
points(Canopy ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(Canopy ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modCanopy, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 100, 20))
mtext(text = expression((bold(C))),
side = 3, line = -0.4, adj = -0.25, cex = 1.3)
################################
# dailyMean_C
################################
plot(dailyMean_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(22.5, 34),
xlab = xlab_in,
ylab = paste("Daily mean temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMean_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMean_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMean_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(24, 34, 2))
mtext(text = expression((bold(D))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# dailyMax_C
################################
plot(dailyMax_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(23, 45),
xlab = xlab_in,
ylab = paste("Daily max. temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMax_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMax_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMax_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(25, 45, 5))
mtext(text = expression((bold(E))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# dailyMin_C
################################
plot(dailyMin_C ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in,
xlab = xlab_in,
ylab = paste("Daily min. temp. ", degCexpr))
# points first (to put in background)
## Nest first
points(dailyMin_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(dailyMin_C ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(moddailyMin_C, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2)
mtext(text = expression((bold(F))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
################################
# range
################################
plot(range ~ urbPC1, data = microhab, type = "n", #<-- just set up
axes = FALSE, #<-- make our own fancy ones
xlim = xlim_in, ylim = c(0, 16),
xlab = xlab_in,
ylab = paste("Daily temp. range ", degCexpr))
# points first (to put in background)
## Nest first
points(range ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 0,
pch = nestPtSymb, bg = nestPtCols["bg"], col = nestPtCols["brd"],
cex = nestPtCx, lwd = ptLwd)
## Random second
points(range ~ jitter(urbPC1, jitfac), data = microhab,
subset = NestRand == 1,
pch = randPtSymb, bg = randPtCols["bg"], col = randPtCols["brd"],
cex = randPtCx, lwd = ptLwd)
# Lines from model
## predict from the model
ndata$pred <- predict(modrange, #<-- XXX change for each response variable
newdata = ndata, level = 0)
## Nest first
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "0",
lwd = reglinewd * 1.2, col = nestPtCols["bg"])
## Random second
lines(pred ~ urbPC1, data = ndata, subset = NestRandFac == "1",
lwd = reglinewd, lty = "dashed", col = randPtCols["bg"])
# X-axis followed by Y-axis
axis(1, xaxis)
axis(2, seq(0, 16, 4))
mtext(text = expression((bold(G))),
side = 3, line = -0.2, adj = -0.24, cex = 1.3)
# END OF PLOTTING #############################
dev.off() #<-- XXX MUST do this to close pdf file connection
################################################################################
################################################################################
# For results tables Using `asreml`
## "NestRandFac0" == natural and "NestRandFac1" == artifical
# Create quick function to extract fixed effect estimates and standard errors from asreml
fxdFun <- function(mod){
fxdtmp <- cbind(rev(mod$coefficients$fixed),
rev(sqrt(mod$vcoeff$fixed)))
dimnames(fxdtmp) <- list(rev(rownames(mod$coefficients$fixed)),
c("Est", "Std.Err"))
fxdtmp[which(fxdtmp[, 1] != 0.00 & fxdtmp[, 2] != 0.00), ]
}
#######################
fxdFun(asrDistTW)
wald(asrDistTW)
summary(asrDistTW)$varcomp
rbind(proCI(DistTW.v2), proCI(DistTW.v3))
lrt(asrDistTWb, asrDistTW)
fxdFun(asrSlope)
wald(asrSlope)
summary(asrSlope)$varcomp
rbind(proCI(Slope.v2), proCI(Slope.v3))
lrt(asrSlopeb, asrSlope)
fxdFun(asrCanopy)
wald(asrCanopy)
summary(asrCanopy)$varcomp
rbind(proCI(Canopy.v2), proCI(Canopy.v3))
lrt(asrCanopyb, asrCanopy)
fxdFun(asrdailyMean_C)
wald(asrdailyMean_C)
summary(asrdailyMean_C)$varcomp
rbind(proCI(dailyMean_C.v2), proCI(dailyMean_C.v3))
lrt(asrdailyMean_Cb, asrdailyMean_C)
fxdFun(asrdailyMax_C)
wald(asrdailyMax_C)
summary(asrdailyMax_C)$varcomp
rbind(proCI(dailyMax_C.v2), proCI(dailyMax_C.v3))
lrt(asrdailyMax_Cb, asrdailyMax_C)
fxdFun(asrdailyMin_C)
wald(asrdailyMin_C)
summary(asrdailyMin_C)$varcomp
rbind(proCI(dailyMin_C.v2), proCI(dailyMin_C.v3))
lrt(asrdailyMin_Cb, asrdailyMin_C)
fxdFun(asrrange)
wald(asrrange)
summary(asrrange)$varcomp
rbind(proCI(range.v2), proCI(range.v3))
lrt(asrrangeb, asrrange)
|
library(dplyr)
## read and parse data (same for all plots)
allData <- read.table("household_power_consumption.txt", na.strings="?", stringsAsFactors=FALSE, sep=";", header=TRUE)
allData$Date<-as.Date(allData$Date,"%d/%m/%Y")
interestingdata<-allData %>%
subset(Date<=as.Date("2007-02-02","%Y-%m-%d")) %>%
subset(Date>=as.Date("2007-02-01","%Y-%m-%d"))
rm(allData)
interestingdata<-mutate(interestingdata,datetime=paste(interestingdata$Date,interestingdata$Time))
interestingdata$datetime<-strptime(interestingdata$datetime,"%Y-%m-%d %H:%M:%S")
## draw Plot 4 - four subplots
png(file="plot4.png",width = 480, height = 480, units = "px")
par(mfcol = c(2, 2))
##Subplot 1 = old plot2 (almost)
with(interestingdata,plot(datetime,Global_active_power,type="l",xlab="",ylab="Global Active Power"))
##Subplot 2 = old plot3 (almost)
with(interestingdata,plot(datetime,Sub_metering_1,type="l",xlab="",ylab="Energy sub metering",col="black"))
lines(interestingdata$datetime,interestingdata$Sub_metering_2,type="l",col="red")
lines(interestingdata$datetime,interestingdata$Sub_metering_3,type="l",col="blue")
legend("topright",legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),col=c("black","red","blue"),lwd=1,bty="n")
##Subplot 3 - Voltage
with(interestingdata,plot(datetime,Voltage,type="l",xlab="datetime",ylab="Voltage"))
##Subplot 4 - Reactive power
with(interestingdata,plot(datetime,Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power"))
dev.off()
rm(interestingdata)
| /plot4.R | no_license | MikeCrookenden/ExData_Plotting1 | R | false | false | 1,551 | r | library(dplyr)
## read and parse data (same for all plots)
allData <- read.table("household_power_consumption.txt", na.strings="?", stringsAsFactors=FALSE, sep=";", header=TRUE)
allData$Date<-as.Date(allData$Date,"%d/%m/%Y")
interestingdata<-allData %>%
subset(Date<=as.Date("2007-02-02","%Y-%m-%d")) %>%
subset(Date>=as.Date("2007-02-01","%Y-%m-%d"))
rm(allData)
interestingdata<-mutate(interestingdata,datetime=paste(interestingdata$Date,interestingdata$Time))
interestingdata$datetime<-strptime(interestingdata$datetime,"%Y-%m-%d %H:%M:%S")
## draw Plot 4 - four subplots
png(file="plot4.png",width = 480, height = 480, units = "px")
par(mfcol = c(2, 2))
##Subplot 1 = old plot2 (almost)
with(interestingdata,plot(datetime,Global_active_power,type="l",xlab="",ylab="Global Active Power"))
##Subplot 2 = old plot3 (almost)
with(interestingdata,plot(datetime,Sub_metering_1,type="l",xlab="",ylab="Energy sub metering",col="black"))
lines(interestingdata$datetime,interestingdata$Sub_metering_2,type="l",col="red")
lines(interestingdata$datetime,interestingdata$Sub_metering_3,type="l",col="blue")
legend("topright",legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),col=c("black","red","blue"),lwd=1,bty="n")
##Subplot 3 - Voltage
with(interestingdata,plot(datetime,Voltage,type="l",xlab="datetime",ylab="Voltage"))
##Subplot 4 - Reactive power
with(interestingdata,plot(datetime,Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power"))
dev.off()
rm(interestingdata)
|
wine<-read.csv("wine.csv")
set.seed(1234)
str(wine)
library(class)
##descriptive
summary(wine)
hist(wine$fixedacidity)
hist(wine$volatileacidity)
hist(wine$citricacid)
hist(wine$residualsugar)
hist(wine$chlorides)
hist(wine$freesulfurdioxide)
hist(wine$totalsulfurdioxide)
##outliers
boxplot(wine$fixedacidity)
boxplot(wine$volatileacidity)
boxplot(wine$citricacid)
boxplot(wine$residualsugar)
boxplot(wine$chlorides)
boxplot(wine$freesulfurdioxide)
boxplot(wine$totalsulfurdioxide)
##splitting
t<-floor(0.7*nrow(wine))
t1<-sample(seq_len(nrow(wine)),size = t)
train<-wine[t1,]
test<-wine[-t1,]
##min max and knn
minmax<-function(x){
xnew<-(x-min(x))/(max(x)-min(x))
}
train[,1:11]<-apply(train[,-12],2,minmax)
test[,1:11]<-apply(test[,-12],2,minmax)
data<-knn1(train[,-12], test[,-12], as.factor(train[,12]))
ac<-length(which(data==as.factor(test[,12]),T))/length((test[,12]))
cv1<-c()
for(i in 1 :35){
cvr<-knn.cv(train[,-12], as.factor(train[,12]),k=i,l=0,prob=F,use.all = T)
cv1<-c(cv1,length(which(cvr==as.factor(train[,12]),T))/length(train[,12]))
}
data1<-knn(train[,-12], test[,-12], as.factor(train[,12]),k=1,l=0,prob=F,use.all = T)
ac1<-length(which(data1==as.factor(test[,12]),T))/length((test[,12]))
keep<-condense(train[,-12],as.factor(train[,12]))
keep1<-reduce.nn(train[,-12], keep,as.factor(train[,12]))
data2<-knn(train[keep1,-12], test[,-12], as.factor(train[keep1,12]),k=1,l=0,prob=F,use.all = T)
ac2<-length(which(data2==as.factor(test[,12]),T))/length((test[,12]))
data3<-knn(train[keep,-12], test[,-12], as.factor(train[keep,12]),k=1,l=0,prob=F,use.all = T)
ac3<-length(which(data3==as.factor(test[,12]),T))/length((test[,12]))
##quality wise splitting
q5<-subset(wine,wine$quality==5)
q6<-subset(wine,wine$quality==6)
q7<-subset(wine,wine$quality==7)
q4<-subset(wine,wine$quality==4)
q8<-subset(wine,wine$quality==8)
q3<-subset(wine,wine$quality==3)
##splitting quality wise data for train and test
train5<-floor(seq_len(.8*nrow(q5)))
test5<-q5[-train5,]
train6<-floor(seq_len(.8*nrow(q6)))
test6<-q6[-train6,]
train7<-floor(seq_len(.8*nrow(q7)))
test7<-q7[-train7,]
train8<-floor(seq_len(.8*nrow(q8)))
test8<-q8[-train8,]
train3<-floor(seq_len(.8*nrow(q3)))
test3<-q3[-train3,]
train4<-floor(seq_len(.8*nrow(q4)))
test4<-q4[-train4,]
traini5<-q5[train5,]
traini6<-q6[train6,]
traini7<-q7[train7,]
traini8<-q8[train8,]
traini3<-q3[train3,]
traini4<-q4[train4,]
##combine quality wise data
library(class)
trainbig<-rbind(traini5,traini6,traini7,traini8,traini3,traini4)
testbig<-rbind(test5,test6,test7,test8,test3,test4)
knn4<-knn(train=trainbig[,-12],test=testbig[,-12],as.factor(trainbig[,12]),k=5,l=5)
ac2<-length(which(knn4==as.factor(testbig[,12]),T))/length((testbig[,12]))
qv<-matrix(0,10,10)
for(i in 1:10){
for(j in 1:10){
knn9<-knn.cv(trainbig[,-12],as.factor(trainbig[,12]),k=i,l=j-1)
qv[i,j]<-length(which(knn9==as.factor(trainbig[,12]),T))/length(trainbig[,12])
}
}
qv
| /wine data k neaest.R | no_license | drblur/Academic-Projects | R | false | false | 3,038 | r | wine<-read.csv("wine.csv")
set.seed(1234)
str(wine)
library(class)
##descriptive
summary(wine)
hist(wine$fixedacidity)
hist(wine$volatileacidity)
hist(wine$citricacid)
hist(wine$residualsugar)
hist(wine$chlorides)
hist(wine$freesulfurdioxide)
hist(wine$totalsulfurdioxide)
##outliers
boxplot(wine$fixedacidity)
boxplot(wine$volatileacidity)
boxplot(wine$citricacid)
boxplot(wine$residualsugar)
boxplot(wine$chlorides)
boxplot(wine$freesulfurdioxide)
boxplot(wine$totalsulfurdioxide)
##splitting
t<-floor(0.7*nrow(wine))
t1<-sample(seq_len(nrow(wine)),size = t)
train<-wine[t1,]
test<-wine[-t1,]
##min max and knn
minmax<-function(x){
xnew<-(x-min(x))/(max(x)-min(x))
}
train[,1:11]<-apply(train[,-12],2,minmax)
test[,1:11]<-apply(test[,-12],2,minmax)
data<-knn1(train[,-12], test[,-12], as.factor(train[,12]))
ac<-length(which(data==as.factor(test[,12]),T))/length((test[,12]))
cv1<-c()
for(i in 1 :35){
cvr<-knn.cv(train[,-12], as.factor(train[,12]),k=i,l=0,prob=F,use.all = T)
cv1<-c(cv1,length(which(cvr==as.factor(train[,12]),T))/length(train[,12]))
}
data1<-knn(train[,-12], test[,-12], as.factor(train[,12]),k=1,l=0,prob=F,use.all = T)
ac1<-length(which(data1==as.factor(test[,12]),T))/length((test[,12]))
keep<-condense(train[,-12],as.factor(train[,12]))
keep1<-reduce.nn(train[,-12], keep,as.factor(train[,12]))
data2<-knn(train[keep1,-12], test[,-12], as.factor(train[keep1,12]),k=1,l=0,prob=F,use.all = T)
ac2<-length(which(data2==as.factor(test[,12]),T))/length((test[,12]))
data3<-knn(train[keep,-12], test[,-12], as.factor(train[keep,12]),k=1,l=0,prob=F,use.all = T)
ac3<-length(which(data3==as.factor(test[,12]),T))/length((test[,12]))
##quality wise splitting
q5<-subset(wine,wine$quality==5)
q6<-subset(wine,wine$quality==6)
q7<-subset(wine,wine$quality==7)
q4<-subset(wine,wine$quality==4)
q8<-subset(wine,wine$quality==8)
q3<-subset(wine,wine$quality==3)
##splitting quality wise data for train and test
train5<-floor(seq_len(.8*nrow(q5)))
test5<-q5[-train5,]
train6<-floor(seq_len(.8*nrow(q6)))
test6<-q6[-train6,]
train7<-floor(seq_len(.8*nrow(q7)))
test7<-q7[-train7,]
train8<-floor(seq_len(.8*nrow(q8)))
test8<-q8[-train8,]
train3<-floor(seq_len(.8*nrow(q3)))
test3<-q3[-train3,]
train4<-floor(seq_len(.8*nrow(q4)))
test4<-q4[-train4,]
traini5<-q5[train5,]
traini6<-q6[train6,]
traini7<-q7[train7,]
traini8<-q8[train8,]
traini3<-q3[train3,]
traini4<-q4[train4,]
##combine quality wise data
library(class)
trainbig<-rbind(traini5,traini6,traini7,traini8,traini3,traini4)
testbig<-rbind(test5,test6,test7,test8,test3,test4)
knn4<-knn(train=trainbig[,-12],test=testbig[,-12],as.factor(trainbig[,12]),k=5,l=5)
ac2<-length(which(knn4==as.factor(testbig[,12]),T))/length((testbig[,12]))
qv<-matrix(0,10,10)
for(i in 1:10){
for(j in 1:10){
knn9<-knn.cv(trainbig[,-12],as.factor(trainbig[,12]),k=i,l=j-1)
qv[i,j]<-length(which(knn9==as.factor(trainbig[,12]),T))/length(trainbig[,12])
}
}
qv
|
library(igraph)
# Source: A tutorial by Esteban Moro
# <http://estebanmoro.org/2012/11/temporal-networks-with-igraph-and-r-with-20-lines-of-code/>
#load the edges with time stamp
#there are three columns in edges: id1,id2,time
#edges <- read.table("edges.csv",header=T)
#generate the full graph
#g <- graph.edgelist(as.matrix(edges[,c(1,2)]),directed=F)
# Initial part of "Fur Ellise"
g <- graph.formula(75-+76, 76-+75, 75-+76, 76-+71, 71-+74, 74-+72, 72-+69,
69-+45, 45-+52, 52-+57, 57-+60, 60-+64, 64-+69, 69-+71,
71-+52, 52-+56, 56-+59, 59-+64, 64-+68, 68-+71, 71-+72,
72-+45, 45-+52, 52-+57, 57-+64, 64-+76, 76-+75, 75-+76,
76-+75, 75-+76, 76-+71, 71-+74, 74-+72, 72-+69, 69-+45,
45-+52, 52-+57, 57-+60, 60-+64, 64-+69, 69-+71, 71-+52,
52-+56, 56-+59, 59-+64, 64-+72, 72-+71, 71-+69, 69-+45,
45-+52, 52-+57, 57-+76, 76-+75, 75-+76, 76-+75, 75-+76,
76-+71, 71-+74, 74-+72, 72-+69, 69-+45, 45-+52, 52-+57,
57-+60, 60-+64, 64-+69, 69-+71)
E(g)$time <- 1:68
#generate a cool palette for the graph
YlOrBr <- c("#FFFFD4", "#FED98E", "#FE9929", "#D95F0E", "#993404")
YlOrBr.Lab <- colorRampPalette(YlOrBr, space = "Lab")
#colors for the nodes are chosen from the very beginning
vcolor <- rev(YlOrBr.Lab(vcount(g)))
#time in the edges goes from 1 to 300. We kick off at time 3
ti <- 3
#weights of edges formed up to time ti is 1. Future edges are weighted 0
E(g)$weight <- ifelse(E(g)$time < ti,1,0)
#generate first layout using weights.
layout.old <- layout.fruchterman.reingold(g,params=list(weights=E(g)$weight))
#total time of the dynamics
total_time <- max(E(g)$time)
#This is the time interval for the animation. In this case is taken to be 1/10
#of the time (i.e. 10 snapshots) between adding two consecutive nodes
dt <- 0.1
#Output for each frame will be a png with HD size 1600x900 :)
png(file="example%03d.png", width=1600,height=900)
nsteps <- max(E(g)$time)
#Time loop starts
for(ti in seq(3,total_time,dt)){
#define weight for edges present up to time ti.
E(g)$weight <- ifelse(E(g)$time < ti,1,0)
#Edges with non-zero weight are in gray. The rest are transparent
E(g)$color <- ifelse(E(g)$time < ti,"gray",rgb(0,0,0,0))
#Nodes with at least a non-zero weighted edge are in color. The rest are transparent
V(g)$color <- ifelse(graph.strength(g)==0,rgb(0,0,0,0),vcolor)
#given the new weights, we update the layout a little bit
layout.new <- layout.fruchterman.reingold(g,params=list(niter=10,start=layout.old,weights=E(g)$weight,maxdelta=1))
#plot the new graph
plot(g,layout=layout.new,vertex.label="",vertex.size=1+2*log(graph.strength(g)),vertex.frame.color=V(g)$color,edge.width=1.5,asp=9/16,margin=-0.15)
#use the new layout in the next round
layout.old <- layout.new
}
dev.off()
| /playground/net.R | no_license | abaghan/music.as.network | R | false | false | 2,904 | r | library(igraph)
# Source: A tutorial by Esteban Moro
# <http://estebanmoro.org/2012/11/temporal-networks-with-igraph-and-r-with-20-lines-of-code/>
#load the edges with time stamp
#there are three columns in edges: id1,id2,time
#edges <- read.table("edges.csv",header=T)
#generate the full graph
#g <- graph.edgelist(as.matrix(edges[,c(1,2)]),directed=F)
# Initial part of "Fur Ellise"
g <- graph.formula(75-+76, 76-+75, 75-+76, 76-+71, 71-+74, 74-+72, 72-+69,
69-+45, 45-+52, 52-+57, 57-+60, 60-+64, 64-+69, 69-+71,
71-+52, 52-+56, 56-+59, 59-+64, 64-+68, 68-+71, 71-+72,
72-+45, 45-+52, 52-+57, 57-+64, 64-+76, 76-+75, 75-+76,
76-+75, 75-+76, 76-+71, 71-+74, 74-+72, 72-+69, 69-+45,
45-+52, 52-+57, 57-+60, 60-+64, 64-+69, 69-+71, 71-+52,
52-+56, 56-+59, 59-+64, 64-+72, 72-+71, 71-+69, 69-+45,
45-+52, 52-+57, 57-+76, 76-+75, 75-+76, 76-+75, 75-+76,
76-+71, 71-+74, 74-+72, 72-+69, 69-+45, 45-+52, 52-+57,
57-+60, 60-+64, 64-+69, 69-+71)
E(g)$time <- 1:68
#generate a cool palette for the graph
YlOrBr <- c("#FFFFD4", "#FED98E", "#FE9929", "#D95F0E", "#993404")
YlOrBr.Lab <- colorRampPalette(YlOrBr, space = "Lab")
#colors for the nodes are chosen from the very beginning
vcolor <- rev(YlOrBr.Lab(vcount(g)))
#time in the edges goes from 1 to 300. We kick off at time 3
ti <- 3
#weights of edges formed up to time ti is 1. Future edges are weighted 0
E(g)$weight <- ifelse(E(g)$time < ti,1,0)
#generate first layout using weights.
layout.old <- layout.fruchterman.reingold(g,params=list(weights=E(g)$weight))
#total time of the dynamics
total_time <- max(E(g)$time)
#This is the time interval for the animation. In this case is taken to be 1/10
#of the time (i.e. 10 snapshots) between adding two consecutive nodes
dt <- 0.1
#Output for each frame will be a png with HD size 1600x900 :)
png(file="example%03d.png", width=1600,height=900)
nsteps <- max(E(g)$time)
#Time loop starts
for(ti in seq(3,total_time,dt)){
#define weight for edges present up to time ti.
E(g)$weight <- ifelse(E(g)$time < ti,1,0)
#Edges with non-zero weight are in gray. The rest are transparent
E(g)$color <- ifelse(E(g)$time < ti,"gray",rgb(0,0,0,0))
#Nodes with at least a non-zero weighted edge are in color. The rest are transparent
V(g)$color <- ifelse(graph.strength(g)==0,rgb(0,0,0,0),vcolor)
#given the new weights, we update the layout a little bit
layout.new <- layout.fruchterman.reingold(g,params=list(niter=10,start=layout.old,weights=E(g)$weight,maxdelta=1))
#plot the new graph
plot(g,layout=layout.new,vertex.label="",vertex.size=1+2*log(graph.strength(g)),vertex.frame.color=V(g)$color,edge.width=1.5,asp=9/16,margin=-0.15)
#use the new layout in the next round
layout.old <- layout.new
}
dev.off()
|
# Testing code for the RCMIP5 scripts in 'RCMIP5.R'
# Uses the testthat package
# See http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
library(testthat)
# To run this code:
# source("RCMIP5.R")
# library(testthat)
# test_file("tests/testthat/test_internalHelpers.R")
context("cmip5data")
implementations <- c("data.frame", "array")
test_that("cmip5data handles bad input", {
expect_error(cmip5data("hi"))
expect_error(cmip5data(1, monthly=123))
expect_error(cmip5data(1, Z=123))
expect_error(cmip5data(1, lev=123))
expect_error(cmip5data(1, randomize="hi"))
})
# helper function: test basic structural integrity
structuretest <- function(d, i) { # d is cmip5data object, i is info
expect_is(d, "cmip5data")
expect_false(xor(is.null(d$lon), is.null(d$lat))) # both NULL, or both not
if(!is.null(d$lon)) {
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
}
expect_is(d$model, "character", info=i)
expect_is(d$variable, "character", info=i)
expect_is(d$experiment, "character", info=i)
expect_is(d$valUnit, "character", info=i)
expect_is(d$debug, "list", info=i)
if(!is.null(d$time)) {
expect_is(d$debug$timeFreqStr, "character", info=i)
expect_is(d$debug$calendarStr, "character", info=i)
expect_is(d$debug$timeUnit, "character", info=i)
}
if(is.data.frame(d$val)) { # Data-frame specific tests
expect_equal(ncol(d$val), 5, info=i)
if(is.null(d$lon)) {
expect_true(all(is.na(d$val$lon)))
expect_true(all(is.na(d$val$lat)))
}
else {
expect_equal(length(unique(d$val$lon)), length(unique(as.numeric(d$lon))), info=i)
expect_equal(length(unique(d$val$lat)), length(unique(as.numeric(d$lat))), info=i)
}
if(is.null(d$Z)) {
expect_true(all(is.na(d$val$Z)), info=i)
}
else {
expect_equal(length(unique(d$val$Z)), length(unique(as.numeric(d$Z))), info=i)
}
if(is.null(d$time)) {
expect_true(all(is.na(d$val$time)), info=i)
}
else {
expect_equal(length(unique(d$val$time)), length(unique(as.numeric(d$time))), info=i)
}
} else if(is.array(d$val)) { # Array-frame specific tests
dm <- dim(d$val)
expect_equal(length(dm), 4, info=i)
if(is.null(d$lon)) {
expect_equal(dm[1], 1, info=i)
expect_equal(dm[2], 1, info=i)
} else {
expect_equal(dm[1], length(unique(as.numeric(d$lon))), info=i)
expect_equal(dm[2], length(unique(as.numeric(d$lat))), info=i)
}
if(is.null(d$Z)) {
expect_equal(dm[3], 1, info=i)
} else {
expect_equal(dm[3], length(unique(as.numeric(d$Z))), info=i)
}
if(is.null(d$time)) {
expect_equal(dm[4], 1, info=i)
} else {
expect_equal(dm[4], length(unique(as.numeric(d$time))), info=i)
}
} else stop("Unknown val type")
}
test_that("cmip5data generates annual and monthly data", {
for(i in implementations) {
d <- cmip5data(1, loadAs=i)
structuretest(d, paste("A1", i))
expect_equal(length(dim(d$lon)), 2, info=i)
expect_equal(length(dim(d$lat)), 2, info=i)
expect_equal(length(d$time), 12, info=i)
expect_equal(d$debug$timeFreqStr, "mon", info=i)
d <- cmip5data(1, monthly=F, loadAs=i)
structuretest(d, paste("A2", i))
expect_equal(length(dim(d$lon)), 2, info=i)
expect_equal(length(dim(d$lat)), 2, info=i)
expect_equal(length(d$time), 1, info=i)
expect_equal(d$debug$timeFreqStr, "yr", info=i)
}
})
test_that("cmip5data obeys randomize", {
for(i in implementations) {
d1 <- cmip5data(1, randomize=T, loadAs=i)
d2 <- cmip5data(1, randomize=T, loadAs=i)
expect_false(all(RCMIP5:::vals(d1) == RCMIP5:::vals(d2)), info=i)
}
})
test_that("cmip5data creates area-only data", {
lonsize <- 10
latsize <- 10
for(i in implementations) {
d <- cmip5data(1, lonsize=lonsize, latsize=latsize, time=F, verbose=F, loadAs=i)
structuretest(d, paste("D1", i))
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
expect_null(d$Z, info=i)
expect_null(d$time, info=i)
}
})
test_that("cmip5data creates area and Z data", {
lonsize <- 10
latsize <- 10
Zsize <- 5
for(i in implementations) {
d <- cmip5data(1, lonsize=lonsize, latsize=latsize, Z=T, Zsize=Zsize, time=F, verbose=F, loadAs=i)
structuretest(d, paste("E1", i))
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
expect_is(d$Z, "integer", info=i)
expect_null(d$time, info=i)
}
})
test_that("cmip5data creates time-only data", {
for(i in implementations) {
d <- cmip5data(1, lonlat=F, Z=F, verbose=F, loadAs=i)
structuretest(d, paste("F1", i))
expect_null(d$lon, info=i)
expect_null(d$lat, info=i)
expect_null(d$Z, info=i)
expect_is(d$time, "numeric", info=i)
}
})
test_that("cmip5data creates irregular grids", {
for(i in implementations) {
d <- cmip5data(1, irregular=FALSE, verbose=F, loadAs=i)
# test that no rows/columns exhibit varying values
expect_true(all(apply(d$lon, 1, function(x) duplicated(x)[-1])), info=i)
expect_true(all(apply(d$lat, 2, function(x) duplicated(x)[-1])), info=i)
d <- cmip5data(1, irregular=TRUE, verbose=F, loadAs=i)
# test that rows/columns have varying values
expect_true(sum(apply(d$lon, 1, function(x) duplicated(x)[-1])) > 0, info=i)
expect_true(sum(apply(d$lat, 2, function(x) duplicated(x)[-1])) > 0, info=i)
}
})
| /tests/testthat/test_cmip5data.R | no_license | cran/RCMIP5 | R | false | false | 6,058 | r | # Testing code for the RCMIP5 scripts in 'RCMIP5.R'
# Uses the testthat package
# See http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
library(testthat)
# To run this code:
# source("RCMIP5.R")
# library(testthat)
# test_file("tests/testthat/test_internalHelpers.R")
context("cmip5data")
implementations <- c("data.frame", "array")
test_that("cmip5data handles bad input", {
expect_error(cmip5data("hi"))
expect_error(cmip5data(1, monthly=123))
expect_error(cmip5data(1, Z=123))
expect_error(cmip5data(1, lev=123))
expect_error(cmip5data(1, randomize="hi"))
})
# helper function: test basic structural integrity
structuretest <- function(d, i) { # d is cmip5data object, i is info
expect_is(d, "cmip5data")
expect_false(xor(is.null(d$lon), is.null(d$lat))) # both NULL, or both not
if(!is.null(d$lon)) {
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
}
expect_is(d$model, "character", info=i)
expect_is(d$variable, "character", info=i)
expect_is(d$experiment, "character", info=i)
expect_is(d$valUnit, "character", info=i)
expect_is(d$debug, "list", info=i)
if(!is.null(d$time)) {
expect_is(d$debug$timeFreqStr, "character", info=i)
expect_is(d$debug$calendarStr, "character", info=i)
expect_is(d$debug$timeUnit, "character", info=i)
}
if(is.data.frame(d$val)) { # Data-frame specific tests
expect_equal(ncol(d$val), 5, info=i)
if(is.null(d$lon)) {
expect_true(all(is.na(d$val$lon)))
expect_true(all(is.na(d$val$lat)))
}
else {
expect_equal(length(unique(d$val$lon)), length(unique(as.numeric(d$lon))), info=i)
expect_equal(length(unique(d$val$lat)), length(unique(as.numeric(d$lat))), info=i)
}
if(is.null(d$Z)) {
expect_true(all(is.na(d$val$Z)), info=i)
}
else {
expect_equal(length(unique(d$val$Z)), length(unique(as.numeric(d$Z))), info=i)
}
if(is.null(d$time)) {
expect_true(all(is.na(d$val$time)), info=i)
}
else {
expect_equal(length(unique(d$val$time)), length(unique(as.numeric(d$time))), info=i)
}
} else if(is.array(d$val)) { # Array-frame specific tests
dm <- dim(d$val)
expect_equal(length(dm), 4, info=i)
if(is.null(d$lon)) {
expect_equal(dm[1], 1, info=i)
expect_equal(dm[2], 1, info=i)
} else {
expect_equal(dm[1], length(unique(as.numeric(d$lon))), info=i)
expect_equal(dm[2], length(unique(as.numeric(d$lat))), info=i)
}
if(is.null(d$Z)) {
expect_equal(dm[3], 1, info=i)
} else {
expect_equal(dm[3], length(unique(as.numeric(d$Z))), info=i)
}
if(is.null(d$time)) {
expect_equal(dm[4], 1, info=i)
} else {
expect_equal(dm[4], length(unique(as.numeric(d$time))), info=i)
}
} else stop("Unknown val type")
}
test_that("cmip5data generates annual and monthly data", {
for(i in implementations) {
d <- cmip5data(1, loadAs=i)
structuretest(d, paste("A1", i))
expect_equal(length(dim(d$lon)), 2, info=i)
expect_equal(length(dim(d$lat)), 2, info=i)
expect_equal(length(d$time), 12, info=i)
expect_equal(d$debug$timeFreqStr, "mon", info=i)
d <- cmip5data(1, monthly=F, loadAs=i)
structuretest(d, paste("A2", i))
expect_equal(length(dim(d$lon)), 2, info=i)
expect_equal(length(dim(d$lat)), 2, info=i)
expect_equal(length(d$time), 1, info=i)
expect_equal(d$debug$timeFreqStr, "yr", info=i)
}
})
test_that("cmip5data obeys randomize", {
for(i in implementations) {
d1 <- cmip5data(1, randomize=T, loadAs=i)
d2 <- cmip5data(1, randomize=T, loadAs=i)
expect_false(all(RCMIP5:::vals(d1) == RCMIP5:::vals(d2)), info=i)
}
})
test_that("cmip5data creates area-only data", {
lonsize <- 10
latsize <- 10
for(i in implementations) {
d <- cmip5data(1, lonsize=lonsize, latsize=latsize, time=F, verbose=F, loadAs=i)
structuretest(d, paste("D1", i))
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
expect_null(d$Z, info=i)
expect_null(d$time, info=i)
}
})
test_that("cmip5data creates area and Z data", {
lonsize <- 10
latsize <- 10
Zsize <- 5
for(i in implementations) {
d <- cmip5data(1, lonsize=lonsize, latsize=latsize, Z=T, Zsize=Zsize, time=F, verbose=F, loadAs=i)
structuretest(d, paste("E1", i))
expect_is(d$lon, "matrix", info=i)
expect_is(d$lat, "matrix", info=i)
expect_is(d$Z, "integer", info=i)
expect_null(d$time, info=i)
}
})
test_that("cmip5data creates time-only data", {
for(i in implementations) {
d <- cmip5data(1, lonlat=F, Z=F, verbose=F, loadAs=i)
structuretest(d, paste("F1", i))
expect_null(d$lon, info=i)
expect_null(d$lat, info=i)
expect_null(d$Z, info=i)
expect_is(d$time, "numeric", info=i)
}
})
test_that("cmip5data creates irregular grids", {
for(i in implementations) {
d <- cmip5data(1, irregular=FALSE, verbose=F, loadAs=i)
# test that no rows/columns exhibit varying values
expect_true(all(apply(d$lon, 1, function(x) duplicated(x)[-1])), info=i)
expect_true(all(apply(d$lat, 2, function(x) duplicated(x)[-1])), info=i)
d <- cmip5data(1, irregular=TRUE, verbose=F, loadAs=i)
# test that rows/columns have varying values
expect_true(sum(apply(d$lon, 1, function(x) duplicated(x)[-1])) > 0, info=i)
expect_true(sum(apply(d$lat, 2, function(x) duplicated(x)[-1])) > 0, info=i)
}
})
|
table = read.delim('C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_motif_density_table.txt', header=TRUE)
scatter_table = cbind(table[,1:5],table[,10:14],log2(table[,12]/table[,11]))
colnames(scatter_table)=c("SUBPEAK_ID","CHROM","START","STOP","REGION_ID","BRD4_UNSTIM","BRD4_2H","BRD4_24H","LOG2FC_2v0","LOG2FC_24v0",'LOG2FC_24v2')
plot(scatter_table[,9],scatter_table[,11], xlim=c(-3,3),ylim=c(-3,3),xlab="Log2FC 2H versus Unstim", ylab="Log2FC 24H versus 2H", pch=1, cex=1)
abline(h=0, v=0, lwd=2)
#==================================================================
#===========================DEPENDENCIES===========================
#==================================================================
library(ggplot2)
vector1 = scatter_table[,9]
vector2 = scatter_table[,11]
#==================================================================
#=========================DENSITY PLOTS============================
#==================================================================
#for some reason ggplot doesn't like looping
print('Plotting contour scatter plot for RASMC subpeak regions')
pdf(file='C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_scatter_plot_2v0_vs24v2.pdf',width =6.5,height =5)
dataset = structure(list(x= vector1,y=vector2),class = "data.frame")
ggplot(dataset, aes(x, y)) +
geom_point(data = dataset,cex=0.5,pch=16,col=rgb(0.5,0.5,0.5,0.5)) + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) +
stat_density2d(aes(alpha=..level.., fill=..level..), size=2, bins=50, geom="polygon") +
scale_fill_gradient(low = "yellow", high = "red") +
scale_alpha(range = c(0.00, 0.5), guide = FALSE) +
geom_density2d(colour=rgb(0.5,0.5,0.5,.8)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
dev.off()
vector1 = scatter_table[,9]
vector2 = scatter_table[,10]
print('Plotting contour scatter plot for RASMC subpeak regions')
pdf(file='C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_scatter_plot_2v0_vs24v0.pdf',width =6.5,height =5)
dataset = structure(list(x= vector1,y=vector2),class = "data.frame")
ggplot(dataset, aes(x, y)) +
geom_point(data = dataset,cex=0.5,pch=16,col=rgb(0.5,0.5,0.5,0.5)) + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) +
stat_density2d(aes(alpha=..level.., fill=..level..), size=2, bins=50, geom="polygon") +
scale_fill_gradient(low = "yellow", high = "red") +
scale_alpha(range = c(0.00, 0.5), guide = FALSE) +
geom_density2d(colour=rgb(0.5,0.5,0.5,.8)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
dev.off()
| /r_scripts/subpeak_regions_scatter_plot.R | permissive | linlabbcm/RASMC_Phenotypic_Switching | R | false | false | 2,901 | r | table = read.delim('C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_motif_density_table.txt', header=TRUE)
scatter_table = cbind(table[,1:5],table[,10:14],log2(table[,12]/table[,11]))
colnames(scatter_table)=c("SUBPEAK_ID","CHROM","START","STOP","REGION_ID","BRD4_UNSTIM","BRD4_2H","BRD4_24H","LOG2FC_2v0","LOG2FC_24v0",'LOG2FC_24v2')
plot(scatter_table[,9],scatter_table[,11], xlim=c(-3,3),ylim=c(-3,3),xlab="Log2FC 2H versus Unstim", ylab="Log2FC 24H versus 2H", pch=1, cex=1)
abline(h=0, v=0, lwd=2)
#==================================================================
#===========================DEPENDENCIES===========================
#==================================================================
library(ggplot2)
vector1 = scatter_table[,9]
vector2 = scatter_table[,11]
#==================================================================
#=========================DENSITY PLOTS============================
#==================================================================
#for some reason ggplot doesn't like looping
print('Plotting contour scatter plot for RASMC subpeak regions')
pdf(file='C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_scatter_plot_2v0_vs24v2.pdf',width =6.5,height =5)
dataset = structure(list(x= vector1,y=vector2),class = "data.frame")
ggplot(dataset, aes(x, y)) +
geom_point(data = dataset,cex=0.5,pch=16,col=rgb(0.5,0.5,0.5,0.5)) + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) +
stat_density2d(aes(alpha=..level.., fill=..level..), size=2, bins=50, geom="polygon") +
scale_fill_gradient(low = "yellow", high = "red") +
scale_alpha(range = c(0.00, 0.5), guide = FALSE) +
geom_density2d(colour=rgb(0.5,0.5,0.5,.8)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
dev.off()
vector1 = scatter_table[,9]
vector2 = scatter_table[,10]
print('Plotting contour scatter plot for RASMC subpeak regions')
pdf(file='C:/Users/rhirsch/Desktop/rasmc_enhancer_promoter/brd4_tables/subpeak_to_region_scatter_plot_2v0_vs24v0.pdf',width =6.5,height =5)
dataset = structure(list(x= vector1,y=vector2),class = "data.frame")
ggplot(dataset, aes(x, y)) +
geom_point(data = dataset,cex=0.5,pch=16,col=rgb(0.5,0.5,0.5,0.5)) + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) +
stat_density2d(aes(alpha=..level.., fill=..level..), size=2, bins=50, geom="polygon") +
scale_fill_gradient(low = "yellow", high = "red") +
scale_alpha(range = c(0.00, 0.5), guide = FALSE) +
geom_density2d(colour=rgb(0.5,0.5,0.5,.8)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
dev.off()
|
#data clean
setwd("/Users/Ryan/Desktop/EDAV/")
survey <- read.csv("Survey+Response.csv")
#naming vars and removing empty columns
names(survey) <- c("waitlist","program","tools","exp.Rmodeling","b5","b6","b7","b8","b9","b10","b11","gender","primaryeditor","exp.Rgraphics","exp.Radvanced","exp.documentation","exp.Matlab","exp.Github","b19","b20","b21","b22","b23","b24","b25","b26","b27","b28","b29","b30","b31","b32","b33","b34","b35","b36","b37","b38")
survey <- survey[,c(-5:-11,-19:-38)]
#dummy variables for each language/tool in tools
tooldummies = c()
toolList <- c("Matlab","lattice","Github","Excel","SQL","RStudio","ggplot2","shell", "C/C","Python","LaTeX","(grep)","Sweave/knitr","XML","Web: html css js","dropbox","google drive","SPSS","Stata")
for(t in toolList){
tooldummies <- cbind(tooldummies,grepl(t,survey$tools))
}
tooldummies <- cbind(tooldummies,(grepl("R,",survey$tools)==TRUE | (grepl("R",survey$tools)==TRUE & grepl("RStudio",survey$tools)==FALSE)))
colnames(tooldummies) <- c("Matlab","lattice","GitHub","Excel","SQL","RStudio","ggplot2","shell", "C","Python","LaTeX","grep","Sweave","XML","Web","dropbox","googledrive","SPSS","Stata","R")
survey <- cbind(survey,tooldummies)
#quick summary info
summary(survey)
par(las = 2)
par(mar=c(5,5,5,5))
barplot(apply(survey[,12:31],2,mean),ylab = "Proportion of Class",ylim = c(0,1))
library(corrplot)
corrplot(cor(survey[,12:31]),method='square')
| /data_clean.R | no_license | rwalsh03/EDAV_HW1 | R | false | false | 1,420 | r | #data clean
setwd("/Users/Ryan/Desktop/EDAV/")
survey <- read.csv("Survey+Response.csv")
#naming vars and removing empty columns
names(survey) <- c("waitlist","program","tools","exp.Rmodeling","b5","b6","b7","b8","b9","b10","b11","gender","primaryeditor","exp.Rgraphics","exp.Radvanced","exp.documentation","exp.Matlab","exp.Github","b19","b20","b21","b22","b23","b24","b25","b26","b27","b28","b29","b30","b31","b32","b33","b34","b35","b36","b37","b38")
survey <- survey[,c(-5:-11,-19:-38)]
#dummy variables for each language/tool in tools
tooldummies = c()
toolList <- c("Matlab","lattice","Github","Excel","SQL","RStudio","ggplot2","shell", "C/C","Python","LaTeX","(grep)","Sweave/knitr","XML","Web: html css js","dropbox","google drive","SPSS","Stata")
for(t in toolList){
tooldummies <- cbind(tooldummies,grepl(t,survey$tools))
}
tooldummies <- cbind(tooldummies,(grepl("R,",survey$tools)==TRUE | (grepl("R",survey$tools)==TRUE & grepl("RStudio",survey$tools)==FALSE)))
colnames(tooldummies) <- c("Matlab","lattice","GitHub","Excel","SQL","RStudio","ggplot2","shell", "C","Python","LaTeX","grep","Sweave","XML","Web","dropbox","googledrive","SPSS","Stata","R")
survey <- cbind(survey,tooldummies)
#quick summary info
summary(survey)
par(las = 2)
par(mar=c(5,5,5,5))
barplot(apply(survey[,12:31],2,mean),ylab = "Proportion of Class",ylim = c(0,1))
library(corrplot)
corrplot(cor(survey[,12:31]),method='square')
|
#' @title PCA on a distributed dataset
#' @description This function is similar to the R function princomp applied on the covariance matrix of the distributed dataset.
#' It has the side effect of creating a scores dataframe on each node - that can be used by subsequent calls to 'biplot'.
#' @param df a character name of the dataframe. The dataframe can contain character columns or factors in which case only the numeric columns will be considered.
#' @param type a character which represents the type of analysis to carry out.
#' If type is set to 'combine', global column means are calculated if type is set to 'split', the column means are
#' calculated separately for each node.
#' @param center a logical, should the columns be centered? Default TRUE.
#' @param scale a logical, should the columns be scaled? Default FALSE.
#' @param scores.suffix a character. The name of the scores dataframe will be the concatenation between df and scores.suffix.
#' @param async a logical, see datashield.aggregate
#' @param datasources a list of opal objects obtained after logging into the opal servers (see datashield.login)
#' @return a list with one element for each node (or one $global element if type='combine'). Each element contains
#' a stripped down princomp object (the 'scores' element is replaced with the name of the scores dataframe on the remote nodes)
#' @export
#'
dssPrincomp <- function(df, type = 'combine', center = TRUE, scale = FALSE, scores.suffix = '_scores',
async = TRUE, datasources = NULL){
if(!(type %in% c('combine', 'split'))){
stop('Function argument "type" has to be either "combine" or "split"')
}
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
covlist <- dssCov(df, type = type, async = async, datasources = datasources)
pca.builder <- function(x){
pca <- princomp(covmat = x$vcov)
# to get an exact value of the loadings we need to recalculate the eigen vectors without assuming a symmetric matrix
# then replace the loadings with the result
# this allows correct matching of directions between the distributed and the local versions of the same dataset (e.g 'iris')
xx <- eigen(x$vcov, symmetric = FALSE)
pca$loadings[] <-xx$vectors[]
pca$n.obs <- x$nrows
pca$lam <- pca$sdev * sqrt(x$nrows) # for biplot
pca$scores <- paste0(df, scores.suffix)
class(pca) <- append( 'dssPrincomp', class(pca))
pca
}
pcalist <- Map(pca.builder, covlist)
scores.builder <- function(x){
loadings <- .encode.arg(unname(pcalist[[x]]$loadings[]))
means <- .encode.arg(unname(covlist[[x]]$means))
sds <- .encode.arg(pcalist[[x]]$sdev)
lam <- .encode.arg(pcalist[[x]]$lam)
# expr <- paste0('pca.scores(',df, ',"', loadings, '",', center, ',', scale, ',', FALSE, ',"' , means, '","', sds, '","', lam, '")')
#build expr as a list to be sent as.call
expr <- list(as.symbol('pcaScores'), as.symbol(df), loadings, center, scale, FALSE, means, sds, lam)
if(x == 'global'){
nodes <- datasources
} else {
nodes <- datasources[x]
}
#datashield.assign(nodes, paste0(df, scores.suffix), as.symbol(expr), async = async)
#send expr as.call and not as.symbol (10 thousand char limit):
datashield.assign(nodes, paste0(df, scores.suffix), as.call(expr), async = async)
}
if(type == 'combine'){
mappee <- c('global')
} else {
mappee <- names(datasources)
}
Map(scores.builder, mappee)
pcalist
}
#' @title Biplot a dssPrincomp object
#' @description Biplot implementation for PCA on distributed datasets
#' @param x an object of class dssPrincomp.
#' @param choices length 2 vector specifying the components to plot (same as for biplot.princomp)
#' @param type a character which represents the type of analysis to carry out.
#' If type is set to 'combine', global column means are calculated if type is set to 'split', the column means are
#' calculated separately for each node.
#' @param levels a character the name of a factor. If provided, the plot will be colour coded by the levels of this factor
#' @param draw.arrows a logical, should I draw arrows representing the underlying variables? Default TRUE.
#' @param ... further arguments to be passed to dssSmooth2d (see doc)
#' @param datasources a list of opal objects obtained after logging into the opal servers (see datashield.login)
#' @export
#'
biplot.dssPrincomp <- function (x, choices = 1L:2L, type = 'combine', levels = NULL, draw.arrows = TRUE, ..., datasources = NULL){
if(!(type %in% c('combine', 'split'))){
stop('Function argument "type" has to be either "combine" or "split"')
}
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
if (length(choices) != 2L){
stop("length of choices must be 2")
}
y <- t(t(x$loadings[,choices])* x$lam[choices])
v1 <- paste0(x$scores, '$Comp.', choices[1])
v2 <- paste0(x$scores, '$Comp.', choices[2])
ylabs <- dimnames(y)[[1L]]
if(!is.null(levels)){
if(length(levels) >1){
warn("Only one column allowed in 'levels'. Will use the first one.")
levels <- levels[1]
}
pl <- dssSmooth2d(v1, v2, categories = levels, draw.image = TRUE, ..., datasources = datasources)
} else {
pl <- dssSmooth2d(v1, v2, draw.image = TRUE, ..., datasources = datasources)
}
if(draw.arrows){
rangx1 <- dssRange(v1, datasources = datasources )
rangx2 <- dssRange(v2, datasources = datasources)
rangy1 <- range(y[, 1L])
rangy2 <- range(y[, 2L])
xlim <- ylim <- rangx1 <- rangx2 <- range(rangx1, rangx2)
ratio <- max(rangy1/rangx1, rangy2/rangx2)
par(new = TRUE)
plot(y, axes = FALSE, type = "n", xlim = xlim * ratio, ylim = ylim * ratio,
xlab = "", ylab = "", col = 'red')
axis(3, col = 'red')
axis(4, col = 'red')
text(y, labels = ylabs, cex = 1, col = 'red')
arrows(0,0,y[,1] *0.8 , y[,2] *0.8, col = 'red', length = 0.1)
}
invisible()
}
| /R/dssPrincomp.R | no_license | neelsoumya/dsSwissKnifeClient | R | false | false | 6,007 | r | #' @title PCA on a distributed dataset
#' @description This function is similar to the R function princomp applied on the covariance matrix of the distributed dataset.
#' It has the side effect of creating a scores dataframe on each node - that can be used by subsequent calls to 'biplot'.
#' @param df a character name of the dataframe. The dataframe can contain character columns or factors in which case only the numeric columns will be considered.
#' @param type a character which represents the type of analysis to carry out.
#' If type is set to 'combine', global column means are calculated if type is set to 'split', the column means are
#' calculated separately for each node.
#' @param center a logical, should the columns be centered? Default TRUE.
#' @param scale a logical, should the columns be scaled? Default FALSE.
#' @param scores.suffix a character. The name of the scores dataframe will be the concatenation between df and scores.suffix.
#' @param async a logical, see datashield.aggregate
#' @param datasources a list of opal objects obtained after logging into the opal servers (see datashield.login)
#' @return a list with one element for each node (or one $global element if type='combine'). Each element contains
#' a stripped down princomp object (the 'scores' element is replaced with the name of the scores dataframe on the remote nodes)
#' @export
#'
dssPrincomp <- function(df, type = 'combine', center = TRUE, scale = FALSE, scores.suffix = '_scores',
async = TRUE, datasources = NULL){
if(!(type %in% c('combine', 'split'))){
stop('Function argument "type" has to be either "combine" or "split"')
}
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
covlist <- dssCov(df, type = type, async = async, datasources = datasources)
pca.builder <- function(x){
pca <- princomp(covmat = x$vcov)
# to get an exact value of the loadings we need to recalculate the eigen vectors without assuming a symmetric matrix
# then replace the loadings with the result
# this allows correct matching of directions between the distributed and the local versions of the same dataset (e.g 'iris')
xx <- eigen(x$vcov, symmetric = FALSE)
pca$loadings[] <-xx$vectors[]
pca$n.obs <- x$nrows
pca$lam <- pca$sdev * sqrt(x$nrows) # for biplot
pca$scores <- paste0(df, scores.suffix)
class(pca) <- append( 'dssPrincomp', class(pca))
pca
}
pcalist <- Map(pca.builder, covlist)
scores.builder <- function(x){
loadings <- .encode.arg(unname(pcalist[[x]]$loadings[]))
means <- .encode.arg(unname(covlist[[x]]$means))
sds <- .encode.arg(pcalist[[x]]$sdev)
lam <- .encode.arg(pcalist[[x]]$lam)
# expr <- paste0('pca.scores(',df, ',"', loadings, '",', center, ',', scale, ',', FALSE, ',"' , means, '","', sds, '","', lam, '")')
#build expr as a list to be sent as.call
expr <- list(as.symbol('pcaScores'), as.symbol(df), loadings, center, scale, FALSE, means, sds, lam)
if(x == 'global'){
nodes <- datasources
} else {
nodes <- datasources[x]
}
#datashield.assign(nodes, paste0(df, scores.suffix), as.symbol(expr), async = async)
#send expr as.call and not as.symbol (10 thousand char limit):
datashield.assign(nodes, paste0(df, scores.suffix), as.call(expr), async = async)
}
if(type == 'combine'){
mappee <- c('global')
} else {
mappee <- names(datasources)
}
Map(scores.builder, mappee)
pcalist
}
#' @title Biplot a dssPrincomp object
#' @description Biplot implementation for PCA on distributed datasets
#' @param x an object of class dssPrincomp.
#' @param choices length 2 vector specifying the components to plot (same as for biplot.princomp)
#' @param type a character which represents the type of analysis to carry out.
#' If type is set to 'combine', global column means are calculated if type is set to 'split', the column means are
#' calculated separately for each node.
#' @param levels a character the name of a factor. If provided, the plot will be colour coded by the levels of this factor
#' @param draw.arrows a logical, should I draw arrows representing the underlying variables? Default TRUE.
#' @param ... further arguments to be passed to dssSmooth2d (see doc)
#' @param datasources a list of opal objects obtained after logging into the opal servers (see datashield.login)
#' @export
#'
biplot.dssPrincomp <- function (x, choices = 1L:2L, type = 'combine', levels = NULL, draw.arrows = TRUE, ..., datasources = NULL){
if(!(type %in% c('combine', 'split'))){
stop('Function argument "type" has to be either "combine" or "split"')
}
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
if (length(choices) != 2L){
stop("length of choices must be 2")
}
y <- t(t(x$loadings[,choices])* x$lam[choices])
v1 <- paste0(x$scores, '$Comp.', choices[1])
v2 <- paste0(x$scores, '$Comp.', choices[2])
ylabs <- dimnames(y)[[1L]]
if(!is.null(levels)){
if(length(levels) >1){
warn("Only one column allowed in 'levels'. Will use the first one.")
levels <- levels[1]
}
pl <- dssSmooth2d(v1, v2, categories = levels, draw.image = TRUE, ..., datasources = datasources)
} else {
pl <- dssSmooth2d(v1, v2, draw.image = TRUE, ..., datasources = datasources)
}
if(draw.arrows){
rangx1 <- dssRange(v1, datasources = datasources )
rangx2 <- dssRange(v2, datasources = datasources)
rangy1 <- range(y[, 1L])
rangy2 <- range(y[, 2L])
xlim <- ylim <- rangx1 <- rangx2 <- range(rangx1, rangx2)
ratio <- max(rangy1/rangx1, rangy2/rangx2)
par(new = TRUE)
plot(y, axes = FALSE, type = "n", xlim = xlim * ratio, ylim = ylim * ratio,
xlab = "", ylab = "", col = 'red')
axis(3, col = 'red')
axis(4, col = 'red')
text(y, labels = ylabs, cex = 1, col = 'red')
arrows(0,0,y[,1] *0.8 , y[,2] *0.8, col = 'red', length = 0.1)
}
invisible()
}
|
wk1d <- read.table("household_power_consumption.txt", sep=";",header=T)
data <- wk1d[wk1d$Date=='1/2/2007'| wk1d$Date=='2/2/2007',]
data$dt<-paste(data$Date,data$Time)
data$datetime<-strptime(data$dt,"%d/%m/%Y %H:%M:%S")
par(mfcol=c(2,2))
with(data, {
plot(data$datetime,as.numeric(as.character(data$Global_active_power)), type='l', xlab="",ylab="Global Active Power(kilowatts)")
plot(data$datetime,as.numeric(as.character(data$Sub_metering_1)), type='l', xlab="",ylab="Energy sub metering")
lines(data$datetime,as.numeric(as.character(data$Sub_metering_2)), col="red")
lines(data$datetime,as.numeric(as.character(data$Sub_metering_3)), col="green")
legend("topright",c("Sub metering 1","Sub metering 2","Sub metering 3"), lty=1, col=c("black","red","green"),cex=0.6)
plot(data$datetime,as.numeric(as.character(data$Voltage)), type='l', xlab="datetime",ylab="voltage")
plot(data$datetime,as.numeric(as.character(data$Global_reactive_power)), type='l', xlab="datetime",ylab="Global reactive power")
})
dev.copy(png, file="Plot4.png") | /plot4.R | no_license | blsingh/ExpAnalysis | R | false | false | 1,066 | r | wk1d <- read.table("household_power_consumption.txt", sep=";",header=T)
data <- wk1d[wk1d$Date=='1/2/2007'| wk1d$Date=='2/2/2007',]
data$dt<-paste(data$Date,data$Time)
data$datetime<-strptime(data$dt,"%d/%m/%Y %H:%M:%S")
par(mfcol=c(2,2))
with(data, {
plot(data$datetime,as.numeric(as.character(data$Global_active_power)), type='l', xlab="",ylab="Global Active Power(kilowatts)")
plot(data$datetime,as.numeric(as.character(data$Sub_metering_1)), type='l', xlab="",ylab="Energy sub metering")
lines(data$datetime,as.numeric(as.character(data$Sub_metering_2)), col="red")
lines(data$datetime,as.numeric(as.character(data$Sub_metering_3)), col="green")
legend("topright",c("Sub metering 1","Sub metering 2","Sub metering 3"), lty=1, col=c("black","red","green"),cex=0.6)
plot(data$datetime,as.numeric(as.character(data$Voltage)), type='l', xlab="datetime",ylab="voltage")
plot(data$datetime,as.numeric(as.character(data$Global_reactive_power)), type='l', xlab="datetime",ylab="Global reactive power")
})
dev.copy(png, file="Plot4.png") |
cov.dist.cat <- function(vars, dataset, exposure) {
out_list1 <- as.list(NULL)
for(i in 1:length(vars)) {
print(vars[i])
dataset[[vars[i]]] <- as.factor(dataset[[vars[i]]])
dataset[[vars[i]]] <- droplevels(dataset[[vars[i]]])
ds0 <- dataset[dataset[[exposure]]==0,]
ds1 <- dataset[dataset[[exposure]]==1,]
n_all <- as.numeric(table(dataset[[vars[i]]]))
n0 <- as.numeric(table(ds0[[vars[i]]]))
n1 <- as.numeric(table(ds1[[vars[i]]]))
p_all <- n_all / nrow(dataset)
p0 <- n0 / nrow(ds0)
p1 <- n1 / nrow(ds1)
var_all <- p_all*(1-p_all)
var0 <- p0*(1-p0)
var1 <- p1*(1-p1)
K = nlevels(dataset[[vars[i]]])
if(K==1) {abs_std_diff <- 0}
if(K>1) {
diff <- p1[-1] - p0[-1]
k=rep(2:K,times=K-1)
l=rep(2:K,each=K-1)
s <- ifelse(k==l, (var0[k] + var1[l]) / 2, (p0[k]*p0[l] + p1[k]*p1[l]) / 2)
s <- matrix(s, nrow=K-1, byrow=FALSE)
abs_std_diff <- abs(as.numeric(sqrt(diff %*% solve(s) %*% diff)))
abs_std_diff <- c(abs_std_diff, rep("",K-1))
}
out_list1[[i]] <- as.data.frame(cbind(c(vars[i], rep("", nlevels(dataset[[vars[i]]])-1)),
levels(dataset[[vars[i]]]),
n_all, p_all, n0, p0, n1, p1, abs_std_diff))
}
out_df1 <- out_list1[[1]]
if(length(vars)>1) {
for(i in 2:length(vars)) {out_df1 <- rbind(out_df1, out_list1[[i]])}
}
rownames(out_df1) <- NULL
colnames(out_df1)[1:2] <- c("characteristic", "level")
return(out_df1)
}
| /analysis/cov_dist_cat.R | permissive | opensafely/MH_pandemic | R | false | false | 1,606 | r | cov.dist.cat <- function(vars, dataset, exposure) {
out_list1 <- as.list(NULL)
for(i in 1:length(vars)) {
print(vars[i])
dataset[[vars[i]]] <- as.factor(dataset[[vars[i]]])
dataset[[vars[i]]] <- droplevels(dataset[[vars[i]]])
ds0 <- dataset[dataset[[exposure]]==0,]
ds1 <- dataset[dataset[[exposure]]==1,]
n_all <- as.numeric(table(dataset[[vars[i]]]))
n0 <- as.numeric(table(ds0[[vars[i]]]))
n1 <- as.numeric(table(ds1[[vars[i]]]))
p_all <- n_all / nrow(dataset)
p0 <- n0 / nrow(ds0)
p1 <- n1 / nrow(ds1)
var_all <- p_all*(1-p_all)
var0 <- p0*(1-p0)
var1 <- p1*(1-p1)
K = nlevels(dataset[[vars[i]]])
if(K==1) {abs_std_diff <- 0}
if(K>1) {
diff <- p1[-1] - p0[-1]
k=rep(2:K,times=K-1)
l=rep(2:K,each=K-1)
s <- ifelse(k==l, (var0[k] + var1[l]) / 2, (p0[k]*p0[l] + p1[k]*p1[l]) / 2)
s <- matrix(s, nrow=K-1, byrow=FALSE)
abs_std_diff <- abs(as.numeric(sqrt(diff %*% solve(s) %*% diff)))
abs_std_diff <- c(abs_std_diff, rep("",K-1))
}
out_list1[[i]] <- as.data.frame(cbind(c(vars[i], rep("", nlevels(dataset[[vars[i]]])-1)),
levels(dataset[[vars[i]]]),
n_all, p_all, n0, p0, n1, p1, abs_std_diff))
}
out_df1 <- out_list1[[1]]
if(length(vars)>1) {
for(i in 2:length(vars)) {out_df1 <- rbind(out_df1, out_list1[[i]])}
}
rownames(out_df1) <- NULL
colnames(out_df1)[1:2] <- c("characteristic", "level")
return(out_df1)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R
\docType{class}
\name{intsp-class}
\alias{intsp-class}
\alias{intsp}
\title{An interval extension of a SpatialPointsDataFrame}
\description{
An interval extension of a SpatialPointsDataFrame
}
\section{Slots}{
\describe{
\item{\code{interval}}{A matrix of two columns representing the lower and upper
endpoints of an interval.}
}}
| /man/intsp-class.Rd | no_license | beanb2/intkrige | R | false | true | 422 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R
\docType{class}
\name{intsp-class}
\alias{intsp-class}
\alias{intsp}
\title{An interval extension of a SpatialPointsDataFrame}
\description{
An interval extension of a SpatialPointsDataFrame
}
\section{Slots}{
\describe{
\item{\code{interval}}{A matrix of two columns representing the lower and upper
endpoints of an interval.}
}}
|
#' Run a Python REPL
#'
#' This function provides a Python REPL in the \R session, which can be used
#' to interactively run Python code. All code executed within the REPL is
#' run within the Python main module, and any generated Python objects will
#' persist in the Python session after the REPL is detached.
#'
#' When working with R and Python scripts interactively, one can activate
#' the Python REPL with `repl_python()`, run Python code, and later run `exit`
#' to return to the \R console.
#'
#' @examples \dontrun{
#'
#' # enter the Python REPL, create a dictionary, and exit
#' repl_python()
#' dictionary = {'alpha': 1, 'beta': 2}
#' exit
#'
#' # access the created dictionary from R
#' py$dictionary
#' # $alpha
#' # [1] 1
#' #
#' # $beta
#' # [1] 2
#'
#' }
#'
#' @param module An (optional) Python module to be imported before
#' the REPL is launched.
#'
#' @param quiet Boolean; print a startup banner when launching the REPL? If
#' `TRUE`, the banner will be suppressed.
#'
#' @seealso [py], for accessing objects created using the Python REPL.
#'
#' @importFrom utils packageVersion
#' @export
repl_python <- function(
module = NULL,
quiet = getOption("reticulate.repl.quiet", default = FALSE))
{
# load module if requested
if (is.character(module))
import(module)
# run hooks for initialize, teardown
initialize <- getOption("reticulate.repl.initialize")
if (is.function(initialize)) {
initialize()
}
teardown <- getOption("reticulate.repl.teardown")
if (is.function(teardown)) {
on.exit(teardown(), add = TRUE)
}
# import other required modules for the REPL
builtins <- import_builtins(convert = FALSE)
sys <- import("sys", convert = TRUE)
codeop <- import("codeop", convert = TRUE)
# grab references to the locals, globals of the main module
locals <- py_run_string("locals()")
globals <- py_run_string("globals()")
# check to see if the current environment supports history
# (check for case where working directory not writable)
use_history <-
!"--vanilla" %in% commandArgs() &&
!"--no-save" %in% commandArgs() &&
!is.null(getwd()) &&
tryCatch(
{ utils::savehistory(tempfile()); TRUE },
error = function(e) FALSE
)
if (use_history) {
# if we have history, save and then restore the current
# R history
utils::savehistory()
on.exit(utils::loadhistory(), add = TRUE)
# file to be used for command history during session
histfile <- getOption("reticulate.repl.histfile")
if (is.null(histfile))
histfile <- file.path(tempdir(), ".reticulatehistory")
# load history (create empty file if none exists yet)
if (!file.exists(histfile))
file.create(histfile)
utils::loadhistory(histfile)
}
# buffer of pending console input (we don't evaluate code
# until the user has submitted a complete Python statement)
#
# we return an environment of functions bound in a local environment
# so that hook can manipulate the buffer if required
buffer <- new_stack()
# command compiler (used to check if we've received a complete piece
# of Python input)
compiler <- codeop$CommandCompiler()
# record whether the used has requested a quit
quit_requested <- FALSE
# inform others that the reticulate REPL is active
.globals$py_repl_active <- TRUE
on.exit(.globals$py_repl_active <- FALSE, add = TRUE)
# handle errors produced during REPL actions
handle_error <- function(output) {
failed <- inherits(output, "error")
if (failed) {
error <- py_last_error()
message(paste(error$type, error$value, sep = ": "))
}
failed
}
# submit code for evaluation. return TRUE if evaluation succeeded
process <- function(code) {
# Python's command compiler complains if the only thing you submit
# is a comment, so detect that case first
if (grepl("^\\s*#", code))
return(TRUE)
# Python is picky about trailing whitespace, so ensure only a single
# newline follows the code to be submitted
code <- sub("\\s*$", "\n", code)
# now compile and run the code. we use 'single' mode to ensure that
# python auto-prints the statement as it is evaluated.
compiled <- tryCatch(builtins$compile(code, '<string>', 'single'), error = identity)
if (handle_error(compiled))
return(FALSE)
output <- tryCatch(builtins$eval(compiled, globals, locals), error = identity)
if (handle_error(output))
return(FALSE)
# ensure stdout, stderr flushed (required for Python 3)
sys$stdout$flush()
sys$stderr$flush()
TRUE
}
repl <- function() {
# read user input
prompt <- if (buffer$empty()) ">>> " else "... "
contents <- readline(prompt = prompt)
# NULL implies the user sent EOF -- time to leave
if (is.null(contents)) {
writeLines("exit", con = stdout())
quit_requested <<- TRUE
return()
}
# trim whitespace for handling of special commands
trimmed <- gsub("^\\s*|\\s*$", "", contents)
# run hook provided by front-end (in case special actions
# need to be taken in response to console input)
hook <- getOption("reticulate.repl.hook")
if (is.function(hook)) {
status <- tryCatch(hook(buffer, contents, trimmed), error = identity)
# report errors to the user
if (inherits(status, "error")) {
message(paste("Error:", conditionMessage(status)))
return()
}
# a TRUE return implies the hook handled this input
if (isTRUE(status))
return()
}
# special handling for top-level commands (when buffer is empty)
if (buffer$empty()) {
# handle user requests to quit
if (trimmed %in% c("quit", "exit")) {
quit_requested <<- TRUE
return()
}
# special handling for help requests prefixed with '?'
if (regexpr("?", trimmed, fixed = TRUE) == 1) {
code <- sprintf("help(\"%s\")", substring(trimmed, 2))
py_run_string(code)
return()
}
# similar handling for help requests postfixed with '?'
if (grepl("[?]\\s*$", trimmed)) {
replaced <- sub("[?]\\s*$", "", trimmed)
code <- sprintf("help(\"%s\")", replaced)
py_run_string(code)
return()
}
# if the user submitted a blank line at the top level,
# ignore it (note that we intentionally submit whitespace-only
# lines that might terminate a block)
if (!nzchar(trimmed))
return()
}
# update history file
if (use_history) {
write(contents, file = histfile, append = TRUE)
utils::loadhistory(histfile)
}
# update buffer
previous <- buffer$data()
buffer$push(contents)
# generate code to be sent to command interpreter
code <- paste(buffer$data(), collapse = "\n")
ready <- tryCatch(compiler(code), condition = identity)
# a NULL return implies that we can accept more input
if (is.null(ready))
return()
# on error, attempt to submit the previous buffer and then handle
# the newest line of code independently. this allows us to handle
# python constructs such as:
#
# def foo():
# return 42
# foo()
#
# try:
# print 1
# except:
# print 2
# print 3
#
# which would otherwise fail
if (length(previous) && inherits(ready, "error")) {
# submit previous code
process(paste(previous, collapse = "\n"))
# now, handle the newest line of code submitted
buffer$set(contents)
code <- contents
ready <- tryCatch(compiler(code), condition = identity)
# a NULL return implies that we can accept more input
if (is.null(ready))
return()
}
# otherwise, we should have received a code output object
# so we can just run the code submitted thus far
buffer$clear()
process(code)
}
# notify the user we're entering the REPL (when requested)
if (!quiet) {
version <- paste(
sys$version_info$major,
sys$version_info$minor,
sys$version_info$micro,
sep = "."
)
# NOTE: we used to use sys.executable but that would report
# the R process rather than the Python process
config <- py_config()
executable <- config$python
fmt <- c(
"Python %s (%s)",
"Reticulate %s REPL -- A Python interpreter in R."
)
msg <- sprintf(
paste(fmt, collapse = "\n"),
version,
executable,
utils::packageVersion("reticulate")
)
message(msg)
}
# enter the REPL loop
repeat {
if (quit_requested)
break
repl()
}
}
# Check Whether the Python REPL is Active
#
# Check to see whether the Python REPL is active. This is primarily
# for use by R front-ends, which might want to toggle or affect
# the state of the Python REPL while it is running.
py_repl_active <- function() {
.globals$py_repl_active
}
| /R/repl.R | permissive | hitfuture/reticulate | R | false | false | 8,944 | r | #' Run a Python REPL
#'
#' This function provides a Python REPL in the \R session, which can be used
#' to interactively run Python code. All code executed within the REPL is
#' run within the Python main module, and any generated Python objects will
#' persist in the Python session after the REPL is detached.
#'
#' When working with R and Python scripts interactively, one can activate
#' the Python REPL with `repl_python()`, run Python code, and later run `exit`
#' to return to the \R console.
#'
#' @examples \dontrun{
#'
#' # enter the Python REPL, create a dictionary, and exit
#' repl_python()
#' dictionary = {'alpha': 1, 'beta': 2}
#' exit
#'
#' # access the created dictionary from R
#' py$dictionary
#' # $alpha
#' # [1] 1
#' #
#' # $beta
#' # [1] 2
#'
#' }
#'
#' @param module An (optional) Python module to be imported before
#' the REPL is launched.
#'
#' @param quiet Boolean; print a startup banner when launching the REPL? If
#' `TRUE`, the banner will be suppressed.
#'
#' @seealso [py], for accessing objects created using the Python REPL.
#'
#' @importFrom utils packageVersion
#' @export
repl_python <- function(
module = NULL,
quiet = getOption("reticulate.repl.quiet", default = FALSE))
{
# load module if requested
if (is.character(module))
import(module)
# run hooks for initialize, teardown
initialize <- getOption("reticulate.repl.initialize")
if (is.function(initialize)) {
initialize()
}
teardown <- getOption("reticulate.repl.teardown")
if (is.function(teardown)) {
on.exit(teardown(), add = TRUE)
}
# import other required modules for the REPL
builtins <- import_builtins(convert = FALSE)
sys <- import("sys", convert = TRUE)
codeop <- import("codeop", convert = TRUE)
# grab references to the locals, globals of the main module
locals <- py_run_string("locals()")
globals <- py_run_string("globals()")
# check to see if the current environment supports history
# (check for case where working directory not writable)
use_history <-
!"--vanilla" %in% commandArgs() &&
!"--no-save" %in% commandArgs() &&
!is.null(getwd()) &&
tryCatch(
{ utils::savehistory(tempfile()); TRUE },
error = function(e) FALSE
)
if (use_history) {
# if we have history, save and then restore the current
# R history
utils::savehistory()
on.exit(utils::loadhistory(), add = TRUE)
# file to be used for command history during session
histfile <- getOption("reticulate.repl.histfile")
if (is.null(histfile))
histfile <- file.path(tempdir(), ".reticulatehistory")
# load history (create empty file if none exists yet)
if (!file.exists(histfile))
file.create(histfile)
utils::loadhistory(histfile)
}
# buffer of pending console input (we don't evaluate code
# until the user has submitted a complete Python statement)
#
# we return an environment of functions bound in a local environment
# so that hook can manipulate the buffer if required
buffer <- new_stack()
# command compiler (used to check if we've received a complete piece
# of Python input)
compiler <- codeop$CommandCompiler()
# record whether the used has requested a quit
quit_requested <- FALSE
# inform others that the reticulate REPL is active
.globals$py_repl_active <- TRUE
on.exit(.globals$py_repl_active <- FALSE, add = TRUE)
# handle errors produced during REPL actions
handle_error <- function(output) {
failed <- inherits(output, "error")
if (failed) {
error <- py_last_error()
message(paste(error$type, error$value, sep = ": "))
}
failed
}
# submit code for evaluation. return TRUE if evaluation succeeded
process <- function(code) {
# Python's command compiler complains if the only thing you submit
# is a comment, so detect that case first
if (grepl("^\\s*#", code))
return(TRUE)
# Python is picky about trailing whitespace, so ensure only a single
# newline follows the code to be submitted
code <- sub("\\s*$", "\n", code)
# now compile and run the code. we use 'single' mode to ensure that
# python auto-prints the statement as it is evaluated.
compiled <- tryCatch(builtins$compile(code, '<string>', 'single'), error = identity)
if (handle_error(compiled))
return(FALSE)
output <- tryCatch(builtins$eval(compiled, globals, locals), error = identity)
if (handle_error(output))
return(FALSE)
# ensure stdout, stderr flushed (required for Python 3)
sys$stdout$flush()
sys$stderr$flush()
TRUE
}
repl <- function() {
# read user input
prompt <- if (buffer$empty()) ">>> " else "... "
contents <- readline(prompt = prompt)
# NULL implies the user sent EOF -- time to leave
if (is.null(contents)) {
writeLines("exit", con = stdout())
quit_requested <<- TRUE
return()
}
# trim whitespace for handling of special commands
trimmed <- gsub("^\\s*|\\s*$", "", contents)
# run hook provided by front-end (in case special actions
# need to be taken in response to console input)
hook <- getOption("reticulate.repl.hook")
if (is.function(hook)) {
status <- tryCatch(hook(buffer, contents, trimmed), error = identity)
# report errors to the user
if (inherits(status, "error")) {
message(paste("Error:", conditionMessage(status)))
return()
}
# a TRUE return implies the hook handled this input
if (isTRUE(status))
return()
}
# special handling for top-level commands (when buffer is empty)
if (buffer$empty()) {
# handle user requests to quit
if (trimmed %in% c("quit", "exit")) {
quit_requested <<- TRUE
return()
}
# special handling for help requests prefixed with '?'
if (regexpr("?", trimmed, fixed = TRUE) == 1) {
code <- sprintf("help(\"%s\")", substring(trimmed, 2))
py_run_string(code)
return()
}
# similar handling for help requests postfixed with '?'
if (grepl("[?]\\s*$", trimmed)) {
replaced <- sub("[?]\\s*$", "", trimmed)
code <- sprintf("help(\"%s\")", replaced)
py_run_string(code)
return()
}
# if the user submitted a blank line at the top level,
# ignore it (note that we intentionally submit whitespace-only
# lines that might terminate a block)
if (!nzchar(trimmed))
return()
}
# update history file
if (use_history) {
write(contents, file = histfile, append = TRUE)
utils::loadhistory(histfile)
}
# update buffer
previous <- buffer$data()
buffer$push(contents)
# generate code to be sent to command interpreter
code <- paste(buffer$data(), collapse = "\n")
ready <- tryCatch(compiler(code), condition = identity)
# a NULL return implies that we can accept more input
if (is.null(ready))
return()
# on error, attempt to submit the previous buffer and then handle
# the newest line of code independently. this allows us to handle
# python constructs such as:
#
# def foo():
# return 42
# foo()
#
# try:
# print 1
# except:
# print 2
# print 3
#
# which would otherwise fail
if (length(previous) && inherits(ready, "error")) {
# submit previous code
process(paste(previous, collapse = "\n"))
# now, handle the newest line of code submitted
buffer$set(contents)
code <- contents
ready <- tryCatch(compiler(code), condition = identity)
# a NULL return implies that we can accept more input
if (is.null(ready))
return()
}
# otherwise, we should have received a code output object
# so we can just run the code submitted thus far
buffer$clear()
process(code)
}
# notify the user we're entering the REPL (when requested)
if (!quiet) {
version <- paste(
sys$version_info$major,
sys$version_info$minor,
sys$version_info$micro,
sep = "."
)
# NOTE: we used to use sys.executable but that would report
# the R process rather than the Python process
config <- py_config()
executable <- config$python
fmt <- c(
"Python %s (%s)",
"Reticulate %s REPL -- A Python interpreter in R."
)
msg <- sprintf(
paste(fmt, collapse = "\n"),
version,
executable,
utils::packageVersion("reticulate")
)
message(msg)
}
# enter the REPL loop
repeat {
if (quit_requested)
break
repl()
}
}
# Check Whether the Python REPL is Active
#
# Check to see whether the Python REPL is active. This is primarily
# for use by R front-ends, which might want to toggle or affect
# the state of the Python REPL while it is running.
py_repl_active <- function() {
.globals$py_repl_active
}
|
library("vars")
data("Canada")
summary(Canada)
str(Canada)
plot(Canada, nc = 2, xlab = "")
adf1 <- summary(ur.df(Canada[, "prod"], type = "trend", lags = 2))
adf1
Canada <- Canada[, c("prod", "e", "U", "rw")]
p1ct <- VAR(Canada, p = 1, type = "both")
p1ct
summary(p1ct, equation = "e")
plot(p1ct, names = "e")
ser11 <- serial.test(p1ct, lags.pt = 16, type = "PT.asymptotic")
ser11$serial
norm1 <- normality.test(p1ct)
norm1$jb.mul
arch1 <- arch.test(p1ct, lags.multi = 5)
arch1$arch.mul
plot(arch1, names = "e")
plot(stability(p1ct), nc = 2)
summary(ca.jo(Canada, type = "trace", ecdet = "trend", K = 3, spec = "transitory"))
summary(ca.jo(Canada, type = "trace", ecdet = "trend", K = 2, spec = "transitory"))
vecm <- ca.jo(Canada[, c("rw", "prod", "e", "U")], type = "trace", ecdet = "trend", K = 3, spec = "transitory")
vecm.r1 <- cajorls(vecm, r = 1)
vecm <- ca.jo(Canada[, c("prod", "e", "U", "rw")], type = "trace", ecdet = "trend", K = 3, spec = "transitory")
SR <- matrix(NA, nrow = 4, ncol = 4)
SR[4, 2] <- 0
LR <- matrix(NA, nrow = 4, ncol = 4)
LR[1, 2:4] <- 0
LR[2:4, 4] <- 0
svec <- SVEC(vecm, LR = LR, SR = SR, r = 1, lrtest = FALSE, boot = TRUE, runs = 100)
summary(svec)
LR[3, 3] <- 0
svec.oi <- update(svec, LR = LR, lrtest = TRUE, boot = FALSE)
svec.oi$LRover
svec.irf <- irf(svec, response = "U", n.ahead = 48, boot = TRUE)
plot(svec.irf)
fevd.U <- fevd(svec, n.ahead = 48)$U
| /vars.R | no_license | jhuato/postwar_global_capitalism | R | false | false | 1,393 | r | library("vars")
data("Canada")
summary(Canada)
str(Canada)
plot(Canada, nc = 2, xlab = "")
adf1 <- summary(ur.df(Canada[, "prod"], type = "trend", lags = 2))
adf1
Canada <- Canada[, c("prod", "e", "U", "rw")]
p1ct <- VAR(Canada, p = 1, type = "both")
p1ct
summary(p1ct, equation = "e")
plot(p1ct, names = "e")
ser11 <- serial.test(p1ct, lags.pt = 16, type = "PT.asymptotic")
ser11$serial
norm1 <- normality.test(p1ct)
norm1$jb.mul
arch1 <- arch.test(p1ct, lags.multi = 5)
arch1$arch.mul
plot(arch1, names = "e")
plot(stability(p1ct), nc = 2)
summary(ca.jo(Canada, type = "trace", ecdet = "trend", K = 3, spec = "transitory"))
summary(ca.jo(Canada, type = "trace", ecdet = "trend", K = 2, spec = "transitory"))
vecm <- ca.jo(Canada[, c("rw", "prod", "e", "U")], type = "trace", ecdet = "trend", K = 3, spec = "transitory")
vecm.r1 <- cajorls(vecm, r = 1)
vecm <- ca.jo(Canada[, c("prod", "e", "U", "rw")], type = "trace", ecdet = "trend", K = 3, spec = "transitory")
SR <- matrix(NA, nrow = 4, ncol = 4)
SR[4, 2] <- 0
LR <- matrix(NA, nrow = 4, ncol = 4)
LR[1, 2:4] <- 0
LR[2:4, 4] <- 0
svec <- SVEC(vecm, LR = LR, SR = SR, r = 1, lrtest = FALSE, boot = TRUE, runs = 100)
summary(svec)
LR[3, 3] <- 0
svec.oi <- update(svec, LR = LR, lrtest = TRUE, boot = FALSE)
svec.oi$LRover
svec.irf <- irf(svec, response = "U", n.ahead = 48, boot = TRUE)
plot(svec.irf)
fevd.U <- fevd(svec, n.ahead = 48)$U
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun1.R
\name{fun1}
\alias{fun1}
\title{MLR Assumptions Function}
\usage{
fun1(lm, k)
}
\arguments{
\item{lm}{a linear model}
\item{k}{the number of independent variables in the model}
}
\value{
Shapiro-Wilk Normality Test
Breusch-Pagan Test
Durbin-Watson Test
Variance Inflation Factor
plots of the residuals against each independent variable
a plot of the fitted values vs. residuals
a QQ plot
}
\description{
MLR Assumptions Function
}
\examples{
y <- rnorm(100, 1, 0)
x1 <- rnorm(100, 2, 1)
x2 <- rnorm(100, 3, 2)
ylm <- lm(y ~ x1 + x2)
fun1(ylm, 2)
}
| /man/fun1.Rd | no_license | leahpom/F2020Projt3LP | R | false | true | 640 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun1.R
\name{fun1}
\alias{fun1}
\title{MLR Assumptions Function}
\usage{
fun1(lm, k)
}
\arguments{
\item{lm}{a linear model}
\item{k}{the number of independent variables in the model}
}
\value{
Shapiro-Wilk Normality Test
Breusch-Pagan Test
Durbin-Watson Test
Variance Inflation Factor
plots of the residuals against each independent variable
a plot of the fitted values vs. residuals
a QQ plot
}
\description{
MLR Assumptions Function
}
\examples{
y <- rnorm(100, 1, 0)
x1 <- rnorm(100, 2, 1)
x2 <- rnorm(100, 3, 2)
ylm <- lm(y ~ x1 + x2)
fun1(ylm, 2)
}
|
## Load in command args:
args <- commandArgs(TRUE)
args <- as.list(args)
args[[2]] <- as.numeric(args[[2]])
print(args)
rm(list=ls(all=TRUE))
setwd("~/repos/bmr/R/")
args <- list("tetrapods_gs", 10000, "_fixed_r5")
## Load tree and data into R
require(ape)
require(aRbor)
require(devtools)
require(mvnfast)
require(Matrix)
#load_all("~/repos/bayou/bayou_1.0")
#install_github("uyedaj/bayou", ref="dev")
load_all("~/repos/bayou/bayou_1.0")
#require(bayou)
td <- readRDS(paste("../output/data/", args[[1]], ".rds", sep=""))
tree <- td$phy
dat <- td$dat
tree <- multi2di(tree)
tree$edge.length[tree$edge.length==0] <- .Machine$double.eps
td <- make.treedata(tree, dat)
td <- reorder(td, "postorder")
#td_gs <- filter(td, !is.na(lnGenSize))
#td <- mutate(td, lnMass = log(mean.mass), lnBMR = log(q10smr))
#td <- filter(td, !is.na(lnMass), !is.na(lnBMR), !is.na(lnGenSize))
tree <- td$phy
dat <- td$dat
## BM likelihood function for genome size
#gs.lik <- bm.lik(td_gs$phy, setNames(td_gs$dat[[3]], td_gs$phy$tip.label), SE=0, model="BM")
#par(mfrow=c(1,2))
#plot(tree, show.tip.label=FALSE)
#plot(dat$lnMass, dat$lnBMR)
lnBMR <- setNames(dat[['lnBMR']], tree$tip.label)
lnMass <- setNames(dat[['lnMass']], tree$tip.label)
pred <- cbind(setNames(dat[['lnMass']], tree$tip.label))
#pred <- cbind(setNames(dat[['lnMass']], tree$tip.label),setNames(dat[['lnMass']]^2, tree$tip.label),setNames(dat[['lnGenSize']], tree$tip.label))
cache <- bayou:::.prepare.ou.univariate(tree, setNames(dat$lnBMR, tree$tip.label))
#identifyBranches(cache$phy, 2)
endonodes <- cache$edge[c(841, 1703),2]
endo <- unlist(cache$desc$tips[endonodes])
endo <- cache$phy$tip.label[endo[endo <= cache$n]]
pred <- cbind(pred, as.numeric(cache$phy$tip.label %in% endo))
cache <- bayou:::.prepare.ou.univariate(tree, setNames(dat$lnBMR, tree$tip.label), pred = pred)
#missing <- which(is.na(cache$pred[,3]))
getPreValues <- function(cache){
V <- vcvPhylo(cache$phy, anc.nodes=FALSE)
X <- cache$pred[,3]
unknown <- is.na(X)
known <- !unknown
Vkk <- V[known, known]
Vuu <- V[unknown, unknown]
Vku <- V[known, unknown]
Vuk <- V[unknown, known]
iVkk <- solve(Vkk)
sigmabar <- as.matrix(forceSymmetric(Vuu - Vuk%*%iVkk%*%Vku))
cholSigmabar <- chol(sigmabar)
mubarmat <- Vuk%*%iVkk
return(list(V=V, X=X, unknown=unknown, known=known, Vkk=Vkk, Vuu=Vuu, Vku=Vku, Vuk=Vuk, iVkk=iVkk, sigmabar=sigmabar, mubarmat=mubarmat, cholSigmabar=cholSigmabar))
}
#pv <- getPreValues(cache)
cMVNorm <- function(cache, pars, prevalues=pv, known=FALSE){
X <- prevalues$X
known <- prevalues$known
unknown <- prevalues$unknown
mu <- rep(pars$pred.root, cache$n)
muk <- mu[known]
muu <- mu[unknown]
mubar <- t(muu + prevalues$mubarmat%*%(X[known]-muk))
#sigmabar <- pars$pred.sig2*prevalues$sigmabar
myChol <-sqrt(pars$pred.sig2)*prevalues$cholSigmabar
res <- dmvn(pars$missing.pred, mu=mubar, sigma = myChol, log=TRUE, isChol=TRUE)
return(res)
}
## Proposal function to simulate conditional draws from a multivariate normal distribution
.imputePredBM <- function(cache, pars, d, move,ct=NULL, prevalues=pv){
#(tree, dat, sig2, plot=TRUE, ...){
X <- prevalues$X
Vuk <- pars$pred.sig2*prevalues$Vuk
iVkk <- (1/pars$pred.sig2)*prevalues$iVkk
Vku <- pars$pred.sig2*prevalues$Vku
Vuu <- pars$pred.sig2*prevalues$Vuu
known <- prevalues$known
unknown <- prevalues$unknown
mu <- rep(pars$pred.root, cache$n)
muk <- mu[known]
muu <- mu[unknown]
mubar <- t(muu + Vuk%*%iVkk%*%(X[known]-muk))
sigmabar <- Vuu - Vuk%*%iVkk%*%Vku
res <- MASS::mvrnorm(1, mubar, sigmabar)
pars.new <- pars
pars.new$missing.pred <- res
hr=Inf
type="impute"
return(list(pars=pars.new, hr=hr, decision = type))
}
## We're going to define a custom likelihood function; this is nearly identical to bayou.lik (bayou's standard
## OU function), except we use pars$beta1 to calculate the residuals after accounting for lnMass.
custom.lik <- function(pars, cache, X, model="Custom"){
n <- cache$n
X <- cache$dat
pred <- cache$pred
#pred[is.na(pred[,3]),3] <- pars$missing.pred
map <- bayou:::.pars2map(pars,cache)
tipreg <- rev(map$theta)
ntipreg <- rev(map$branch)
#ntipreg <- names(map$theta)
dups <- !duplicated(ntipreg) & ntipreg %in% (1:nrow(cache$edge))[cache$externalEdge]
tipreg <- tipreg[which(dups)]
ntipreg <- ntipreg[which(dups)]
o <- order(cache$edge[as.numeric(ntipreg), 2])
betaID <- tipreg[o]
#betaID <- sapply(tipreglist[cache$externalEdge][o], function(x) x[length(x)])
#beta <- cbind(sapply(c("beta1", "beta2"), function(x) pars[[x]][betaID]), pars$beta3)
X = X - pars$beta1[betaID]*pred[,1]
cache$dat <- X
pars$theta[c(15,16)] <- pars$endo + pars$theta[c(15,16)]
X.c <- bayou:::C_weightmatrix(cache, pars)$resid
transf.phy <- bayou:::C_transf_branch_lengths(cache, 1, X.c, pars$alpha)
transf.phy$edge.length[cache$externalEdge] <- transf.phy$edge[cache$externalEdge] + cache$SE[cache$phy$edge[cache$externalEdge, 2]]^2*(2*pars$alpha)/pars$sig2
comp <- bayou:::C_threepoint(list(n=n, N=cache$N, anc=cache$phy$edge[, 1], des=cache$phy$edge[, 2], diagMatrix=transf.phy$diagMatrix, P=X.c, root=transf.phy$root.edge, len=transf.phy$edge.length))
if(pars$alpha==0){
inv.yVy <- comp$PP
detV <- comp$logd
} else {
inv.yVy <- comp$PP*(2*pars$alpha)/(pars$sig2)
detV <- comp$logd+n*log(pars$sig2/(2*pars$alpha))
}
llh <- -0.5*(n*log(2*pi)+detV+inv.yVy)
#llh <- llh + gs.lik(c(pars$pred.sig2, pars$pred.root), root=ROOT.GIVEN)
return(list(loglik=llh, theta=pars$theta,resid=X.c, comp=comp, transf.phy=transf.phy))
}
#plotSimmap(pars2simmap(pars, cache$phy)$tree, colors=pars2simmap(pars, cache$phy)$col, ftype="off")
sb <- c("Tetrapods"=1707, "Amniota"=1705,"Varanus_exanthematicus"=563, "Pseudoeurycea_brunnata"=277, "Sceloporus_variabilis"=515, "Sceloporus_occidentalis"=509, "Uta_stansburiana"=518, "Masticophis_flagellum"=443, "Typhlogobius_californiensis"=54, "Sebastolobus_altivelis"=85, "Lacertidae"=614, "Boidae"=501, "Hyla_arborea"=159, "Aves"=841, "Mammals"=1703, "Lichanura_trivirgata"=495, "Bunopus_tuberculatus"=655, "Salamandridae"=378, "Fishes"=115, "Scaphiophidae"=261, "Euphlyctis"=227, "Labeo"=7, "Bolitoglossinae"=294, "Salmonidae"=48, "Plethodontidae"=344, "Cricetidae"=1222, "Ambystoma_mexicanum"=368)
k <- length(sb)
startpar <- list(alpha=0.1, sig2=3, beta1=rnorm(k+1, 0.7, 0.1), endo=3 , k=k, ntheta=k+1, theta=rnorm(k+1, 0, 1), sb=sb, loc=rep(0, k), t2=2:(k+1))
## Get optimized starting values and trait evolutionary models for genome size.
#require(phylolm)
#tdgs <- filter(td, !is.na(lnGenSize))
#fits <- lapply(c("BM", "OUrandomRoot", "OUfixedRoot", "EB"), function(x) phylolm(lnGenSize~1, data=tdgs$dat, phy=tdgs$phy, model=x))
#aics <- sapply(fits, function(x) x$aic)
#bestfit <- fits[[which(aics == min(aics))]]
#phenogram(tdgs$phy, setNames(tdgs$dat$lnGenSize, tdgs$phy$tip.label), fsize=0.5)
#startpar$pred.root <- unname(bestfit$coeff)
#startpar$pred.sig2 <- unname(bestfit$sigma2)
#phenogram(td$phy, setNames(tmppred3[[1]], td$phy$tip.label), ftype="off", colors = makeTransparent("black", 0))
#startpar <- .imputePredBM(cache, startpar, d=1, NULL, ct=NULL, prevalues=pv)$pars
#tmppred3 <- as.vector(td$dat[,3])
#tmppred3[is.na(tmppred3), ] <- startpar$missing.pred
#phenogram(td$phy, setNames(tmppred3[[1]], td$phy$tip.label), ftype="off", colors = makeTransparent("black", 5), add=TRUE)
## This is a function to monitor the output of bayou for our custom model
BetaBMR.monitor = function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "rbeta1", "endo", "k")
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i"
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2, pars$beta1[1], pars$endo, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
## We're going to define a custom model with variable slopes (beta1)
model.BetaBMR <- list(moves = list(alpha=".multiplierProposal", sig2=".multiplierProposal", beta1=".vectorMultiplier", endo=".slidingWindowProposal", theta=".adjustTheta", slide=".slide"),
control.weights = list("alpha"=5,"sig2"=2,"beta1"=10,"endo"=3, "theta"=10,"slide"=2,"k"=0),
D = list(alpha=0.5, sig2= 0.5, beta1=0.75, endo=0.25, theta=2, slide=1),
parorder = c("alpha", "sig2", "beta1", "endo", "k", "ntheta", "theta"),
rjpars = c("beta1", "theta"),
shiftpars = c("sb", "loc", "t2"),
monitor.fn = BetaBMR.monitor,
lik.fn = custom.lik)
## Now we define the prior:
prior <- make.prior(tree, plot.prior = TRUE, dists=list(dalpha="dhalfcauchy", dsig2="dhalfcauchy",
dbeta1="dnorm", dendo="dnorm", dsb="fixed", dk="fixed", dtheta="dnorm"),
param=list(dalpha=list(scale=1), dsig2=list(scale=1),
dbeta1=list(mean=0.7, sd=0.1), dendo=list(mean=0, sd=4), dk="fixed", dsb="fixed",
dtheta=list(mean=0, sd=4)), model="ffancova", fixed=list(sb=startpar$sb, k=startpar$k))
fixed.pars <- list(k=length(sb), ntheta=length(sb)+1, sb=unname(sb), t2=2:(length(sb)+1), loc=rep(0, length(sb)))
tr <- pars2simmap(fixed.pars, cache$phy)
plotSimmap(tr$tree, colors=setNames(rainbow(length(sb))[sample(1:(length(sb)),length(sb), replace=FALSE)], 1:length(sb)), fsize=0.5)
prior(startpar)
custom.lik(startpar, cache, cache$dat)$loglik
#tr <- pars2simmap(startpar, tree)
#plotSimmap(tr$tree, colors=tr$col)
mymcmc <- bayou.makeMCMC(tree, lnBMR, pred=pred, SE=0, model=model.BetaBMR, prior=prior, startpar=startpar, new.dir=paste("../output/runs/",args[[1]],sep=""), outname=paste(args[[1]],args[[3]],sep=""), plot.freq=NULL, ticker.freq=1000, samp = 100)
## Now run the chain for 50,000 generations. This will write to a set of files in your temporary directory.
#sink(paste(mymcmc$dir,mymcmc$outname,".log",sep=""), append=TRUE)
mymcmc$run(args[[2]])
#sink(NULL)
chain <- mymcmc$load()
chain <- set.burnin(chain, 0.3)
out <- summary(chain)
print(out)
par(mar=c(10, 3, 1,1))
beta1 <- do.call(rbind, chain$beta1)
colnames(beta1) <- c("root", names(sb))
o <- order(apply(beta1,2, mean))
beta1 <- beta1[,o]
boxplot(beta1, las=2)
par(mar=c(10, 3, 1,1))
theta <- do.call(rbind, chain$theta)
colnames(theta) <- c("root", names(sb))
o <- order(apply(theta,2, mean))
theta <- theta[,o]
boxplot(theta, las=2)
plot(c(0, length(chain$gen)), c(-5, 5), type="n")
lapply(1:ncol(theta), function(x) lines(theta[,x], col=x))
summary(coda::mcmc(theta))
summary(coda::mcmc(beta1))
apply(coda::mcmc(theta), 2, effectiveSize)
apply(coda::mcmc(beta1), 2, effectiveSize)
require(foreach)
require(doParallel)
registerDoParallel(cores=10)
Bk <- seq(0, 1, length.out=10)
ss <- mymcmc$steppingstone(args[[2]], chain, Bk, burnin=0.3, plot=TRUE)
saveRDS(chain, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".chain.rds",sep=""))
saveRDS(mymcmc, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".mcmc.rds",sep=""))
saveRDS(ss, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".ss.rds",sep=""))
#plotSimmap.mcmc(tree, chain, burnin=0.3)
#chain <- mymcmc$load()
#out <- list(tree=mymcmc$tree, dat=mymcmc$dat, outname="newmammals_quadr1", model="custom", model.pars=mymcmc$model.pars, dir="~/repos/bmr/output/runs/newmammals/")
#chain <- load.bayou(out,save.Rdata = TRUE, file="../output/runs/newmammals/newmammals_quad.rds")
#pdf("../output/figures/QuadraticMammals.pdf")
#plotSimmap.mcmc(tree, chain, fsize=0.25, burnin=0.3)
#postburn <- round(0.3*length(chain$gen), 0):length(chain$gen)
#plot(density(sapply(chain$beta2[postburn], function(x) x[1])))#
#par(mfrow=c(2,1))
#plot(c(0, length(chain$gen)), c(0.5, 1), type="n", xlab="Sample", ylab="beta1")
#dum <- lapply(1:length(chain$gen), function(x) points(rep(x, length(chain$beta1[[x]])), chain$beta1[[x]], pch=".", col=1:length(chain$beta2[[x]])))
#plot(c(0, length(chain$gen)), c(-0.03, 0.03), type="n", xlab="Sample", ylab="beta2")
#dum <- lapply(1:length(chain$gen), function(x) points(rep(x, length(chain$beta2[[x]])), chain$beta2[[x]], pch=".", col=1:length(chain$beta2[[x]])))
#par(mfrow=c(1,1))
#samp <- round(seq(postburn[1], postburn[length(postburn)], length.out=1000),0)
#{burnin=0.3
#LP <- Lposterior(chain, tree, burnin=burnin)
#focalSB <- c(0, which(LP[,1] > cutoff))
#PostSB <- lapply(focalSB, function (y) getBranchPosterior(y, chain, burnin=burnin, thin=thin))
#postBetas <- lapply(1:length(PostSB), function(x) cbind(x, PostSB[[x]][[1]]))
#postThetas <- lapply(1:length(PostSB), function(x) cbind(x, PostSB[[x]][[2]]))
#postBetas <- data.frame(do.call(rbind, postBetas))
#postBetas$var <- rep("Beta", nrow(postBetas))
#postThetas <- data.frame(do.call(rbind, postThetas))
#postThetas$var <- rep("Theta", nrow(postThetas))
#allpar <- rbind(postBetas, postThetas)
#allpar$x <- factor(allpar$x)
#focalpars <- list(k=length(focalSB[-1]),
# ntheta=length(focalSB),
# sb=focalSB[-1],
# loc=rep(0, length(focalSB[-1])),
# t2=2:length(focalSB))#
#tr <- pars2simmap(focalpars, tree)
#tipreg <- bayou:::.tipregime(focalpars, tree)
#plot(lnMass, lnBMR, type="n")
#beta1s <- chain$beta1[samp]
#beta2s <- chain$beta2[samp]
#thetas <- chain$theta[samp]
#lapply(1:length(samp), function(j) sapply(1:length(beta1s[[j]]), function(y) curve(thetas[[j]][y]+beta1s[[j]][y]*x + beta2s[[j]][y]*x^2, add=TRUE, col=makeTransparent(y, 5))))
#points(lnMass, lnBMR, pch=21, bg=makeTransparent(tipreg,alpha=50), col=makeTransparent(tipreg,alpha=50))#
#}
#dev.off()
| /R/runBetaBayou_tetrapods_1pred_eFixedShifts.R | permissive | uyedaj/bmr | R | false | false | 13,877 | r | ## Load in command args:
args <- commandArgs(TRUE)
args <- as.list(args)
args[[2]] <- as.numeric(args[[2]])
print(args)
rm(list=ls(all=TRUE))
setwd("~/repos/bmr/R/")
args <- list("tetrapods_gs", 10000, "_fixed_r5")
## Load tree and data into R
require(ape)
require(aRbor)
require(devtools)
require(mvnfast)
require(Matrix)
#load_all("~/repos/bayou/bayou_1.0")
#install_github("uyedaj/bayou", ref="dev")
load_all("~/repos/bayou/bayou_1.0")
#require(bayou)
td <- readRDS(paste("../output/data/", args[[1]], ".rds", sep=""))
tree <- td$phy
dat <- td$dat
tree <- multi2di(tree)
tree$edge.length[tree$edge.length==0] <- .Machine$double.eps
td <- make.treedata(tree, dat)
td <- reorder(td, "postorder")
#td_gs <- filter(td, !is.na(lnGenSize))
#td <- mutate(td, lnMass = log(mean.mass), lnBMR = log(q10smr))
#td <- filter(td, !is.na(lnMass), !is.na(lnBMR), !is.na(lnGenSize))
tree <- td$phy
dat <- td$dat
## BM likelihood function for genome size
#gs.lik <- bm.lik(td_gs$phy, setNames(td_gs$dat[[3]], td_gs$phy$tip.label), SE=0, model="BM")
#par(mfrow=c(1,2))
#plot(tree, show.tip.label=FALSE)
#plot(dat$lnMass, dat$lnBMR)
lnBMR <- setNames(dat[['lnBMR']], tree$tip.label)
lnMass <- setNames(dat[['lnMass']], tree$tip.label)
pred <- cbind(setNames(dat[['lnMass']], tree$tip.label))
#pred <- cbind(setNames(dat[['lnMass']], tree$tip.label),setNames(dat[['lnMass']]^2, tree$tip.label),setNames(dat[['lnGenSize']], tree$tip.label))
cache <- bayou:::.prepare.ou.univariate(tree, setNames(dat$lnBMR, tree$tip.label))
#identifyBranches(cache$phy, 2)
endonodes <- cache$edge[c(841, 1703),2]
endo <- unlist(cache$desc$tips[endonodes])
endo <- cache$phy$tip.label[endo[endo <= cache$n]]
pred <- cbind(pred, as.numeric(cache$phy$tip.label %in% endo))
cache <- bayou:::.prepare.ou.univariate(tree, setNames(dat$lnBMR, tree$tip.label), pred = pred)
#missing <- which(is.na(cache$pred[,3]))
getPreValues <- function(cache){
V <- vcvPhylo(cache$phy, anc.nodes=FALSE)
X <- cache$pred[,3]
unknown <- is.na(X)
known <- !unknown
Vkk <- V[known, known]
Vuu <- V[unknown, unknown]
Vku <- V[known, unknown]
Vuk <- V[unknown, known]
iVkk <- solve(Vkk)
sigmabar <- as.matrix(forceSymmetric(Vuu - Vuk%*%iVkk%*%Vku))
cholSigmabar <- chol(sigmabar)
mubarmat <- Vuk%*%iVkk
return(list(V=V, X=X, unknown=unknown, known=known, Vkk=Vkk, Vuu=Vuu, Vku=Vku, Vuk=Vuk, iVkk=iVkk, sigmabar=sigmabar, mubarmat=mubarmat, cholSigmabar=cholSigmabar))
}
#pv <- getPreValues(cache)
cMVNorm <- function(cache, pars, prevalues=pv, known=FALSE){
X <- prevalues$X
known <- prevalues$known
unknown <- prevalues$unknown
mu <- rep(pars$pred.root, cache$n)
muk <- mu[known]
muu <- mu[unknown]
mubar <- t(muu + prevalues$mubarmat%*%(X[known]-muk))
#sigmabar <- pars$pred.sig2*prevalues$sigmabar
myChol <-sqrt(pars$pred.sig2)*prevalues$cholSigmabar
res <- dmvn(pars$missing.pred, mu=mubar, sigma = myChol, log=TRUE, isChol=TRUE)
return(res)
}
## Proposal function to simulate conditional draws from a multivariate normal distribution
.imputePredBM <- function(cache, pars, d, move,ct=NULL, prevalues=pv){
#(tree, dat, sig2, plot=TRUE, ...){
X <- prevalues$X
Vuk <- pars$pred.sig2*prevalues$Vuk
iVkk <- (1/pars$pred.sig2)*prevalues$iVkk
Vku <- pars$pred.sig2*prevalues$Vku
Vuu <- pars$pred.sig2*prevalues$Vuu
known <- prevalues$known
unknown <- prevalues$unknown
mu <- rep(pars$pred.root, cache$n)
muk <- mu[known]
muu <- mu[unknown]
mubar <- t(muu + Vuk%*%iVkk%*%(X[known]-muk))
sigmabar <- Vuu - Vuk%*%iVkk%*%Vku
res <- MASS::mvrnorm(1, mubar, sigmabar)
pars.new <- pars
pars.new$missing.pred <- res
hr=Inf
type="impute"
return(list(pars=pars.new, hr=hr, decision = type))
}
## We're going to define a custom likelihood function; this is nearly identical to bayou.lik (bayou's standard
## OU function), except we use pars$beta1 to calculate the residuals after accounting for lnMass.
custom.lik <- function(pars, cache, X, model="Custom"){
n <- cache$n
X <- cache$dat
pred <- cache$pred
#pred[is.na(pred[,3]),3] <- pars$missing.pred
map <- bayou:::.pars2map(pars,cache)
tipreg <- rev(map$theta)
ntipreg <- rev(map$branch)
#ntipreg <- names(map$theta)
dups <- !duplicated(ntipreg) & ntipreg %in% (1:nrow(cache$edge))[cache$externalEdge]
tipreg <- tipreg[which(dups)]
ntipreg <- ntipreg[which(dups)]
o <- order(cache$edge[as.numeric(ntipreg), 2])
betaID <- tipreg[o]
#betaID <- sapply(tipreglist[cache$externalEdge][o], function(x) x[length(x)])
#beta <- cbind(sapply(c("beta1", "beta2"), function(x) pars[[x]][betaID]), pars$beta3)
X = X - pars$beta1[betaID]*pred[,1]
cache$dat <- X
pars$theta[c(15,16)] <- pars$endo + pars$theta[c(15,16)]
X.c <- bayou:::C_weightmatrix(cache, pars)$resid
transf.phy <- bayou:::C_transf_branch_lengths(cache, 1, X.c, pars$alpha)
transf.phy$edge.length[cache$externalEdge] <- transf.phy$edge[cache$externalEdge] + cache$SE[cache$phy$edge[cache$externalEdge, 2]]^2*(2*pars$alpha)/pars$sig2
comp <- bayou:::C_threepoint(list(n=n, N=cache$N, anc=cache$phy$edge[, 1], des=cache$phy$edge[, 2], diagMatrix=transf.phy$diagMatrix, P=X.c, root=transf.phy$root.edge, len=transf.phy$edge.length))
if(pars$alpha==0){
inv.yVy <- comp$PP
detV <- comp$logd
} else {
inv.yVy <- comp$PP*(2*pars$alpha)/(pars$sig2)
detV <- comp$logd+n*log(pars$sig2/(2*pars$alpha))
}
llh <- -0.5*(n*log(2*pi)+detV+inv.yVy)
#llh <- llh + gs.lik(c(pars$pred.sig2, pars$pred.root), root=ROOT.GIVEN)
return(list(loglik=llh, theta=pars$theta,resid=X.c, comp=comp, transf.phy=transf.phy))
}
#plotSimmap(pars2simmap(pars, cache$phy)$tree, colors=pars2simmap(pars, cache$phy)$col, ftype="off")
sb <- c("Tetrapods"=1707, "Amniota"=1705,"Varanus_exanthematicus"=563, "Pseudoeurycea_brunnata"=277, "Sceloporus_variabilis"=515, "Sceloporus_occidentalis"=509, "Uta_stansburiana"=518, "Masticophis_flagellum"=443, "Typhlogobius_californiensis"=54, "Sebastolobus_altivelis"=85, "Lacertidae"=614, "Boidae"=501, "Hyla_arborea"=159, "Aves"=841, "Mammals"=1703, "Lichanura_trivirgata"=495, "Bunopus_tuberculatus"=655, "Salamandridae"=378, "Fishes"=115, "Scaphiophidae"=261, "Euphlyctis"=227, "Labeo"=7, "Bolitoglossinae"=294, "Salmonidae"=48, "Plethodontidae"=344, "Cricetidae"=1222, "Ambystoma_mexicanum"=368)
k <- length(sb)
startpar <- list(alpha=0.1, sig2=3, beta1=rnorm(k+1, 0.7, 0.1), endo=3 , k=k, ntheta=k+1, theta=rnorm(k+1, 0, 1), sb=sb, loc=rep(0, k), t2=2:(k+1))
## Get optimized starting values and trait evolutionary models for genome size.
#require(phylolm)
#tdgs <- filter(td, !is.na(lnGenSize))
#fits <- lapply(c("BM", "OUrandomRoot", "OUfixedRoot", "EB"), function(x) phylolm(lnGenSize~1, data=tdgs$dat, phy=tdgs$phy, model=x))
#aics <- sapply(fits, function(x) x$aic)
#bestfit <- fits[[which(aics == min(aics))]]
#phenogram(tdgs$phy, setNames(tdgs$dat$lnGenSize, tdgs$phy$tip.label), fsize=0.5)
#startpar$pred.root <- unname(bestfit$coeff)
#startpar$pred.sig2 <- unname(bestfit$sigma2)
#phenogram(td$phy, setNames(tmppred3[[1]], td$phy$tip.label), ftype="off", colors = makeTransparent("black", 0))
#startpar <- .imputePredBM(cache, startpar, d=1, NULL, ct=NULL, prevalues=pv)$pars
#tmppred3 <- as.vector(td$dat[,3])
#tmppred3[is.na(tmppred3), ] <- startpar$missing.pred
#phenogram(td$phy, setNames(tmppred3[[1]], td$phy$tip.label), ftype="off", colors = makeTransparent("black", 5), add=TRUE)
## This is a function to monitor the output of bayou for our custom model
BetaBMR.monitor = function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "rbeta1", "endo", "k")
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i"
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2, pars$beta1[1], pars$endo, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
## We're going to define a custom model with variable slopes (beta1)
model.BetaBMR <- list(moves = list(alpha=".multiplierProposal", sig2=".multiplierProposal", beta1=".vectorMultiplier", endo=".slidingWindowProposal", theta=".adjustTheta", slide=".slide"),
control.weights = list("alpha"=5,"sig2"=2,"beta1"=10,"endo"=3, "theta"=10,"slide"=2,"k"=0),
D = list(alpha=0.5, sig2= 0.5, beta1=0.75, endo=0.25, theta=2, slide=1),
parorder = c("alpha", "sig2", "beta1", "endo", "k", "ntheta", "theta"),
rjpars = c("beta1", "theta"),
shiftpars = c("sb", "loc", "t2"),
monitor.fn = BetaBMR.monitor,
lik.fn = custom.lik)
## Now we define the prior:
prior <- make.prior(tree, plot.prior = TRUE, dists=list(dalpha="dhalfcauchy", dsig2="dhalfcauchy",
dbeta1="dnorm", dendo="dnorm", dsb="fixed", dk="fixed", dtheta="dnorm"),
param=list(dalpha=list(scale=1), dsig2=list(scale=1),
dbeta1=list(mean=0.7, sd=0.1), dendo=list(mean=0, sd=4), dk="fixed", dsb="fixed",
dtheta=list(mean=0, sd=4)), model="ffancova", fixed=list(sb=startpar$sb, k=startpar$k))
fixed.pars <- list(k=length(sb), ntheta=length(sb)+1, sb=unname(sb), t2=2:(length(sb)+1), loc=rep(0, length(sb)))
tr <- pars2simmap(fixed.pars, cache$phy)
plotSimmap(tr$tree, colors=setNames(rainbow(length(sb))[sample(1:(length(sb)),length(sb), replace=FALSE)], 1:length(sb)), fsize=0.5)
prior(startpar)
custom.lik(startpar, cache, cache$dat)$loglik
#tr <- pars2simmap(startpar, tree)
#plotSimmap(tr$tree, colors=tr$col)
mymcmc <- bayou.makeMCMC(tree, lnBMR, pred=pred, SE=0, model=model.BetaBMR, prior=prior, startpar=startpar, new.dir=paste("../output/runs/",args[[1]],sep=""), outname=paste(args[[1]],args[[3]],sep=""), plot.freq=NULL, ticker.freq=1000, samp = 100)
## Now run the chain for 50,000 generations. This will write to a set of files in your temporary directory.
#sink(paste(mymcmc$dir,mymcmc$outname,".log",sep=""), append=TRUE)
mymcmc$run(args[[2]])
#sink(NULL)
chain <- mymcmc$load()
chain <- set.burnin(chain, 0.3)
out <- summary(chain)
print(out)
par(mar=c(10, 3, 1,1))
beta1 <- do.call(rbind, chain$beta1)
colnames(beta1) <- c("root", names(sb))
o <- order(apply(beta1,2, mean))
beta1 <- beta1[,o]
boxplot(beta1, las=2)
par(mar=c(10, 3, 1,1))
theta <- do.call(rbind, chain$theta)
colnames(theta) <- c("root", names(sb))
o <- order(apply(theta,2, mean))
theta <- theta[,o]
boxplot(theta, las=2)
plot(c(0, length(chain$gen)), c(-5, 5), type="n")
lapply(1:ncol(theta), function(x) lines(theta[,x], col=x))
summary(coda::mcmc(theta))
summary(coda::mcmc(beta1))
apply(coda::mcmc(theta), 2, effectiveSize)
apply(coda::mcmc(beta1), 2, effectiveSize)
require(foreach)
require(doParallel)
registerDoParallel(cores=10)
Bk <- seq(0, 1, length.out=10)
ss <- mymcmc$steppingstone(args[[2]], chain, Bk, burnin=0.3, plot=TRUE)
saveRDS(chain, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".chain.rds",sep=""))
saveRDS(mymcmc, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".mcmc.rds",sep=""))
saveRDS(ss, file=paste("../output/runs/",args[[1]],"/",args[[1]],"_",args[[3]],".ss.rds",sep=""))
#plotSimmap.mcmc(tree, chain, burnin=0.3)
#chain <- mymcmc$load()
#out <- list(tree=mymcmc$tree, dat=mymcmc$dat, outname="newmammals_quadr1", model="custom", model.pars=mymcmc$model.pars, dir="~/repos/bmr/output/runs/newmammals/")
#chain <- load.bayou(out,save.Rdata = TRUE, file="../output/runs/newmammals/newmammals_quad.rds")
#pdf("../output/figures/QuadraticMammals.pdf")
#plotSimmap.mcmc(tree, chain, fsize=0.25, burnin=0.3)
#postburn <- round(0.3*length(chain$gen), 0):length(chain$gen)
#plot(density(sapply(chain$beta2[postburn], function(x) x[1])))#
#par(mfrow=c(2,1))
#plot(c(0, length(chain$gen)), c(0.5, 1), type="n", xlab="Sample", ylab="beta1")
#dum <- lapply(1:length(chain$gen), function(x) points(rep(x, length(chain$beta1[[x]])), chain$beta1[[x]], pch=".", col=1:length(chain$beta2[[x]])))
#plot(c(0, length(chain$gen)), c(-0.03, 0.03), type="n", xlab="Sample", ylab="beta2")
#dum <- lapply(1:length(chain$gen), function(x) points(rep(x, length(chain$beta2[[x]])), chain$beta2[[x]], pch=".", col=1:length(chain$beta2[[x]])))
#par(mfrow=c(1,1))
#samp <- round(seq(postburn[1], postburn[length(postburn)], length.out=1000),0)
#{burnin=0.3
#LP <- Lposterior(chain, tree, burnin=burnin)
#focalSB <- c(0, which(LP[,1] > cutoff))
#PostSB <- lapply(focalSB, function (y) getBranchPosterior(y, chain, burnin=burnin, thin=thin))
#postBetas <- lapply(1:length(PostSB), function(x) cbind(x, PostSB[[x]][[1]]))
#postThetas <- lapply(1:length(PostSB), function(x) cbind(x, PostSB[[x]][[2]]))
#postBetas <- data.frame(do.call(rbind, postBetas))
#postBetas$var <- rep("Beta", nrow(postBetas))
#postThetas <- data.frame(do.call(rbind, postThetas))
#postThetas$var <- rep("Theta", nrow(postThetas))
#allpar <- rbind(postBetas, postThetas)
#allpar$x <- factor(allpar$x)
#focalpars <- list(k=length(focalSB[-1]),
# ntheta=length(focalSB),
# sb=focalSB[-1],
# loc=rep(0, length(focalSB[-1])),
# t2=2:length(focalSB))#
#tr <- pars2simmap(focalpars, tree)
#tipreg <- bayou:::.tipregime(focalpars, tree)
#plot(lnMass, lnBMR, type="n")
#beta1s <- chain$beta1[samp]
#beta2s <- chain$beta2[samp]
#thetas <- chain$theta[samp]
#lapply(1:length(samp), function(j) sapply(1:length(beta1s[[j]]), function(y) curve(thetas[[j]][y]+beta1s[[j]][y]*x + beta2s[[j]][y]*x^2, add=TRUE, col=makeTransparent(y, 5))))
#points(lnMass, lnBMR, pch=21, bg=makeTransparent(tipreg,alpha=50), col=makeTransparent(tipreg,alpha=50))#
#}
#dev.off()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ratios.R
\name{pw_ratios_matrix}
\alias{pw_ratios_matrix}
\title{Pairwise ratios of taxa from matrix of multiple samples}
\usage{
pw_ratios_matrix(x, na.rm = FALSE)
}
\description{
Pairwise ratios of taxa from matrix of multiple samples
}
| /man/pw_ratios_matrix.Rd | permissive | marcelladane/metacal | R | false | true | 317 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ratios.R
\name{pw_ratios_matrix}
\alias{pw_ratios_matrix}
\title{Pairwise ratios of taxa from matrix of multiple samples}
\usage{
pw_ratios_matrix(x, na.rm = FALSE)
}
\description{
Pairwise ratios of taxa from matrix of multiple samples
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ML_PLSModel.R
\name{PLSModel}
\alias{PLSModel}
\title{Partial Least Squares Model}
\usage{
PLSModel(ncomp = 1, scale = FALSE)
}
\arguments{
\item{ncomp}{number of components to include in the model.}
\item{scale}{logical indicating whether to scale the predictors by the
sample standard deviation.}
}
\value{
\code{MLModel} class object.
}
\description{
Function to perform partial least squares regression.
}
\details{
\describe{
\item{Response Types:}{\code{factor}, \code{numeric}}
\item{\link[=TunedModel]{Automatic Tuning} of Grid Parameters:}{
\code{ncomp}
}
}
Further model details can be found in the source link below.
}
\examples{
fit(sale_amount ~ ., data = ICHomes, model = PLSModel)
}
\seealso{
\code{\link[pls]{mvr}}, \code{\link{fit}}, \code{\link{resample}}
}
| /man/PLSModel.Rd | no_license | chen061218/MachineShop | R | false | true | 867 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ML_PLSModel.R
\name{PLSModel}
\alias{PLSModel}
\title{Partial Least Squares Model}
\usage{
PLSModel(ncomp = 1, scale = FALSE)
}
\arguments{
\item{ncomp}{number of components to include in the model.}
\item{scale}{logical indicating whether to scale the predictors by the
sample standard deviation.}
}
\value{
\code{MLModel} class object.
}
\description{
Function to perform partial least squares regression.
}
\details{
\describe{
\item{Response Types:}{\code{factor}, \code{numeric}}
\item{\link[=TunedModel]{Automatic Tuning} of Grid Parameters:}{
\code{ncomp}
}
}
Further model details can be found in the source link below.
}
\examples{
fit(sale_amount ~ ., data = ICHomes, model = PLSModel)
}
\seealso{
\code{\link[pls]{mvr}}, \code{\link{fit}}, \code{\link{resample}}
}
|
##############################################################################################
##############################################################################################
##### CREATED 1/28/2018 #####
#install.packages("lqa")
###predictors correlation of 0.2
rm(list=ls())
library("lqa") # version 1.0-3
library(MASS) # version 3.3.1
library("mgcv")
require(glmnet)
#########################################################
#########################################################
DIREC="/home/tkzhou/PSPP_Project/PENCOMP_OneTimePoint/variableSelection5/case2/"
DIRECOUT="/home/tkzhou/PSPP_Project/PENCOMP_OneTimePoint/variableSelection5/case2/sampleSize200"
funLoc = "Functions/"
source(paste0(DIREC, funLoc, "addFun.R"))
source(paste0(DIREC, funLoc, "formulaConstruct.R"))
source(paste0(DIREC, funLoc, "simDataAll.R"))
source(paste0(DIREC, funLoc, "variableSelectY.R"))
source(paste0(DIREC, funLoc, "variableSelectT.R"))
source(paste0(DIREC, funLoc, "variableSelectT2.R"))
source(paste0(DIREC, funLoc, "Stepwise.R"))
source(paste0(DIREC, funLoc, "pencompFit.R"))
numRun=500
############IPTW, AIPTW and PENCOMP estimators###############
###standard CI
estFinal_iptw=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp=matrix(NA, nrow=numRun, ncol=4)
###bagging estimator
estFinal_iptw_bag=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw_bag=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp_bag=matrix(NA, nrow=numRun, ncol=4)
###percentiles
estFinal_iptw_per=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw_per=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp_per=matrix(NA, nrow=numRun, ncol=4)
###Rubin's combining rule
estFinal_pencomp_rubin=matrix(NA, nrow=numRun, ncol=4)
varSelPropY=matrix(NA, nrow=numRun, ncol = 20)
varSelPropT=matrix(NA, nrow=numRun, ncol = 20)
start=201
end=300
for(d in start:end)
{
tryCatch (
{
numT=1000
sampleSize=200
numPred=20 ##number of predictors
level="low"
simdatG=simulateDate(sampleSize=sampleSize, numPred=numPred, overlapL=level, seed.num=d, rho=0, treatEff=2)
simdat=simdatG[[1]]
varList=simdatG[[2]]
outcome.varname="Y"
treat.varname="A"
splineTerm="s(pslogit, bs=\"ps\", k=15)" ###
firstNum="outcome" ###adaptive lasso on the outcome first and then propensity score model
Method="REML"
##for both propensity and prediction models
modelType="stepwise" ###seperate adaptive lasso on the propensity and prediction models
outcomeVarList0=NULL
outcomeVarList1=NULL
propenVarList=NULL
##############################################################################################################################
####################################################################################################################
# print out IPTW, AIPTW and PENCOMP estimates corresponding to smallest wAMD value
estimate.out=NULL
estimate.out=pencompStepwise(dataOR=simdat, data=simdat, varList=varList, propenVarList=propenVarList,
outcomeVarList0=outcomeVarList0, outcomeVarList1=outcomeVarList1,
treat.varname=treat.varname, outcome.varname=outcome.varname)
if( typeof(estimate.out) == "list" ){ ###if output is list, should be right
estFinal_iptw[d,1]=estimate.out$out[1]
estFinal_aiptw[d,1]=estimate.out$out[2]
estFinal_pencomp[d,1]=estimate.out$out[3]
}
estimate.boot=matrix(NA, nrow=numT, ncol=3) ###IPTW, AIPTW and PENCOMP estimates from each bootstrap sample
pencomp.rubin=matrix(NA, nrow=numT, ncol=2) ###variance of PENCOMP
pencomp.numKnot=matrix(NA, nrow=numT, ncol=2) ###number of knots in PENCOMP
varSelectTreat=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in propensity model
varSelectY1=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in the outcome model Y0
varSelectY0=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in the outcome modely1
countMat=matrix(NA, ncol=nrow(simdat), nrow=numT)
for(ind in 1:numT){
tryCatch (
{
set.seed(ind)
bootSample = simdat[sample(1:nrow(simdat),replace=T),] ###random bootstraps
tempCount=numeric(nrow(bootSample))
for(countIndex in 1:length(tempCount)){
tempCount[countIndex] = sum(bootSample$id2==countIndex)
}
mulResult = pencompStepwise(dataOR=simdat, data=bootSample, varList=varList, propenVarList=propenVarList,
outcomeVarList0=outcomeVarList0, outcomeVarList1=outcomeVarList1,
treat.varname=treat.varname, outcome.varname=outcome.varname )
if( typeof(mulResult) == "list" ){ ###if output is list, should be right
estimate.boot[ind,] = (mulResult$out)[c(1, 2, 3)]
pencomp.rubin[ind,] = (mulResult$out)[c(4, 5)]
varSelectTreat[ind,]=mulResult$varTreat
varSelectY1[ind,]=mulResult$varY1
varSelectY0[ind,]=mulResult$varY0
countMat[ind,]=tempCount
pencomp.numKnot[ind,]=mulResult$numK ###number of knots in PENCOMP
}
}
,
error=function(e) { }
)
}
if(d < 10){
####store bootstrap estimates
bootResult=cbind(estimate.out$out[1], estimate.out$out[2], estimate.out$out[3], estimate.boot, pencomp.rubin, pencomp.numKnot)
write.table(bootResult, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, ".txt",sep=""), row.name=F, quote=F, sep="\t",
col.names = c("iptwOR", "aiptwOR","pencompOR", "iptw", "aiptw","pencompBoot", "pencompRubin", "pencompRubinVar", "K0", "K1"))
}
####store counts of each datapoint in each bootstrap (for calculating Brad Efron's CI)
#write.table(countMat, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, "countMat.txt",sep=""), row.name=F, quote=F, sep="\t")
varSelPropY[d,]=colMeans(varSelectY0, na.rm = T)
varSelPropT[d,]=colMeans(varSelectTreat, na.rm = T)
write.table(varSelPropY, paste(DIRECOUT, "/Results/", modelType, "_", level, "_start_", start, "varSelectY.txt",sep=""), row.name=F, quote=F, col.names = varList,
sep="\t")
write.table(varSelPropT, paste(DIRECOUT, "/Results/", modelType, "_", level, "_start_", start, "varSelectTreat.txt",sep=""), row.name=F, quote=F, col.names = varList,
sep="\t")
####store coefficients of outcome model Y1
#write.table(varSelectY1, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, "varSelectY1.txt",sep=""), row.name=F, quote=F, col.names = varList,
# sep="\t")
#########standard confidence interval, Rubin's combining rule for PENCOMP
estFinal_pencomp_rubin[d,]=processPENCOMP(t(pencomp.rubin))
estFinal_iptw[d, 2:4]=c( sd(estimate.boot[,1], na.rm = T), estFinal_iptw[d,1] + c(-1, 1)*1.96*sd(estimate.boot[, 1], na.rm = T) )
estFinal_aiptw[d, 2:4]=c( sd(estimate.boot[,2], na.rm = T), estFinal_aiptw[d,1] + c(-1, 1)*1.96*sd(estimate.boot[,2], na.rm = T) )
estFinal_pencomp[d, 2:4]=c( sd(estimate.boot[,3], na.rm = T), estFinal_pencomp[d,1] + c(-1, 1)*1.96*sd(estimate.boot[,3], na.rm = T) )
##############################################
#### bagging estimator accounting for model selection
estFinal_iptw_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,1], sampleSize=sampleSize)
estFinal_aiptw_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,2], sampleSize=sampleSize)
estFinal_pencomp_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,3], sampleSize=sampleSize)
##############################################
#### confidence interval based on quantiles
estFinal_iptw_per[d,]=percentile(estimate=estimate.boot[,1])
estFinal_aiptw_per[d,]=percentile(estimate=estimate.boot[,2])
estFinal_pencomp_per[d,]=percentile(estimate=estimate.boot[,3])
resultTable=NULL
resultTable=data.frame(estFinal_pencomp, estFinal_pencomp_bag, estFinal_pencomp_per, estFinal_pencomp_rubin)
write.table(resultTable, paste(DIRECOUT, "/Results/pencomp_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
resultTable=NULL
resultTable=data.frame(estFinal_iptw, estFinal_iptw_bag, estFinal_iptw_per)
write.table(resultTable, paste(DIRECOUT, "/Results/iptw_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
resultTable=NULL
resultTable=data.frame(estFinal_aiptw, estFinal_aiptw_bag, estFinal_aiptw_per)
write.table(resultTable, paste(DIRECOUT, "/Results/aiptw_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
}
,
error=function(e) { }
)
}
| /simulation/case2/sampleSize200/stepwise_low_start_201.R | no_license | TingtingKayla/Stats_Robust_Causal_Estimation | R | false | false | 8,715 | r | ##############################################################################################
##############################################################################################
##### CREATED 1/28/2018 #####
#install.packages("lqa")
###predictors correlation of 0.2
rm(list=ls())
library("lqa") # version 1.0-3
library(MASS) # version 3.3.1
library("mgcv")
require(glmnet)
#########################################################
#########################################################
DIREC="/home/tkzhou/PSPP_Project/PENCOMP_OneTimePoint/variableSelection5/case2/"
DIRECOUT="/home/tkzhou/PSPP_Project/PENCOMP_OneTimePoint/variableSelection5/case2/sampleSize200"
funLoc = "Functions/"
source(paste0(DIREC, funLoc, "addFun.R"))
source(paste0(DIREC, funLoc, "formulaConstruct.R"))
source(paste0(DIREC, funLoc, "simDataAll.R"))
source(paste0(DIREC, funLoc, "variableSelectY.R"))
source(paste0(DIREC, funLoc, "variableSelectT.R"))
source(paste0(DIREC, funLoc, "variableSelectT2.R"))
source(paste0(DIREC, funLoc, "Stepwise.R"))
source(paste0(DIREC, funLoc, "pencompFit.R"))
numRun=500
############IPTW, AIPTW and PENCOMP estimators###############
###standard CI
estFinal_iptw=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp=matrix(NA, nrow=numRun, ncol=4)
###bagging estimator
estFinal_iptw_bag=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw_bag=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp_bag=matrix(NA, nrow=numRun, ncol=4)
###percentiles
estFinal_iptw_per=matrix(NA, nrow=numRun, ncol=4)
estFinal_aiptw_per=matrix(NA, nrow=numRun, ncol=4)
estFinal_pencomp_per=matrix(NA, nrow=numRun, ncol=4)
###Rubin's combining rule
estFinal_pencomp_rubin=matrix(NA, nrow=numRun, ncol=4)
varSelPropY=matrix(NA, nrow=numRun, ncol = 20)
varSelPropT=matrix(NA, nrow=numRun, ncol = 20)
start=201
end=300
for(d in start:end)
{
tryCatch (
{
numT=1000
sampleSize=200
numPred=20 ##number of predictors
level="low"
simdatG=simulateDate(sampleSize=sampleSize, numPred=numPred, overlapL=level, seed.num=d, rho=0, treatEff=2)
simdat=simdatG[[1]]
varList=simdatG[[2]]
outcome.varname="Y"
treat.varname="A"
splineTerm="s(pslogit, bs=\"ps\", k=15)" ###
firstNum="outcome" ###adaptive lasso on the outcome first and then propensity score model
Method="REML"
##for both propensity and prediction models
modelType="stepwise" ###seperate adaptive lasso on the propensity and prediction models
outcomeVarList0=NULL
outcomeVarList1=NULL
propenVarList=NULL
##############################################################################################################################
####################################################################################################################
# print out IPTW, AIPTW and PENCOMP estimates corresponding to smallest wAMD value
estimate.out=NULL
estimate.out=pencompStepwise(dataOR=simdat, data=simdat, varList=varList, propenVarList=propenVarList,
outcomeVarList0=outcomeVarList0, outcomeVarList1=outcomeVarList1,
treat.varname=treat.varname, outcome.varname=outcome.varname)
if( typeof(estimate.out) == "list" ){ ###if output is list, should be right
estFinal_iptw[d,1]=estimate.out$out[1]
estFinal_aiptw[d,1]=estimate.out$out[2]
estFinal_pencomp[d,1]=estimate.out$out[3]
}
estimate.boot=matrix(NA, nrow=numT, ncol=3) ###IPTW, AIPTW and PENCOMP estimates from each bootstrap sample
pencomp.rubin=matrix(NA, nrow=numT, ncol=2) ###variance of PENCOMP
pencomp.numKnot=matrix(NA, nrow=numT, ncol=2) ###number of knots in PENCOMP
varSelectTreat=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in propensity model
varSelectY1=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in the outcome model Y0
varSelectY0=matrix( NA, nrow=numT, ncol=length(varList) ) ### coefficients of variables in the outcome modely1
countMat=matrix(NA, ncol=nrow(simdat), nrow=numT)
for(ind in 1:numT){
tryCatch (
{
set.seed(ind)
bootSample = simdat[sample(1:nrow(simdat),replace=T),] ###random bootstraps
tempCount=numeric(nrow(bootSample))
for(countIndex in 1:length(tempCount)){
tempCount[countIndex] = sum(bootSample$id2==countIndex)
}
mulResult = pencompStepwise(dataOR=simdat, data=bootSample, varList=varList, propenVarList=propenVarList,
outcomeVarList0=outcomeVarList0, outcomeVarList1=outcomeVarList1,
treat.varname=treat.varname, outcome.varname=outcome.varname )
if( typeof(mulResult) == "list" ){ ###if output is list, should be right
estimate.boot[ind,] = (mulResult$out)[c(1, 2, 3)]
pencomp.rubin[ind,] = (mulResult$out)[c(4, 5)]
varSelectTreat[ind,]=mulResult$varTreat
varSelectY1[ind,]=mulResult$varY1
varSelectY0[ind,]=mulResult$varY0
countMat[ind,]=tempCount
pencomp.numKnot[ind,]=mulResult$numK ###number of knots in PENCOMP
}
}
,
error=function(e) { }
)
}
if(d < 10){
####store bootstrap estimates
bootResult=cbind(estimate.out$out[1], estimate.out$out[2], estimate.out$out[3], estimate.boot, pencomp.rubin, pencomp.numKnot)
write.table(bootResult, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, ".txt",sep=""), row.name=F, quote=F, sep="\t",
col.names = c("iptwOR", "aiptwOR","pencompOR", "iptw", "aiptw","pencompBoot", "pencompRubin", "pencompRubinVar", "K0", "K1"))
}
####store counts of each datapoint in each bootstrap (for calculating Brad Efron's CI)
#write.table(countMat, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, "countMat.txt",sep=""), row.name=F, quote=F, sep="\t")
varSelPropY[d,]=colMeans(varSelectY0, na.rm = T)
varSelPropT[d,]=colMeans(varSelectTreat, na.rm = T)
write.table(varSelPropY, paste(DIRECOUT, "/Results/", modelType, "_", level, "_start_", start, "varSelectY.txt",sep=""), row.name=F, quote=F, col.names = varList,
sep="\t")
write.table(varSelPropT, paste(DIRECOUT, "/Results/", modelType, "_", level, "_start_", start, "varSelectTreat.txt",sep=""), row.name=F, quote=F, col.names = varList,
sep="\t")
####store coefficients of outcome model Y1
#write.table(varSelectY1, paste(DIRECOUT, "/bootResults/sample", d, modelType, "_", level, "varSelectY1.txt",sep=""), row.name=F, quote=F, col.names = varList,
# sep="\t")
#########standard confidence interval, Rubin's combining rule for PENCOMP
estFinal_pencomp_rubin[d,]=processPENCOMP(t(pencomp.rubin))
estFinal_iptw[d, 2:4]=c( sd(estimate.boot[,1], na.rm = T), estFinal_iptw[d,1] + c(-1, 1)*1.96*sd(estimate.boot[, 1], na.rm = T) )
estFinal_aiptw[d, 2:4]=c( sd(estimate.boot[,2], na.rm = T), estFinal_aiptw[d,1] + c(-1, 1)*1.96*sd(estimate.boot[,2], na.rm = T) )
estFinal_pencomp[d, 2:4]=c( sd(estimate.boot[,3], na.rm = T), estFinal_pencomp[d,1] + c(-1, 1)*1.96*sd(estimate.boot[,3], na.rm = T) )
##############################################
#### bagging estimator accounting for model selection
estFinal_iptw_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,1], sampleSize=sampleSize)
estFinal_aiptw_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,2], sampleSize=sampleSize)
estFinal_pencomp_bag[d,]=bagging2(countMat=countMat, estimate=estimate.boot[,3], sampleSize=sampleSize)
##############################################
#### confidence interval based on quantiles
estFinal_iptw_per[d,]=percentile(estimate=estimate.boot[,1])
estFinal_aiptw_per[d,]=percentile(estimate=estimate.boot[,2])
estFinal_pencomp_per[d,]=percentile(estimate=estimate.boot[,3])
resultTable=NULL
resultTable=data.frame(estFinal_pencomp, estFinal_pencomp_bag, estFinal_pencomp_per, estFinal_pencomp_rubin)
write.table(resultTable, paste(DIRECOUT, "/Results/pencomp_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
resultTable=NULL
resultTable=data.frame(estFinal_iptw, estFinal_iptw_bag, estFinal_iptw_per)
write.table(resultTable, paste(DIRECOUT, "/Results/iptw_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
resultTable=NULL
resultTable=data.frame(estFinal_aiptw, estFinal_aiptw_bag, estFinal_aiptw_per)
write.table(resultTable, paste(DIRECOUT, "/Results/aiptw_", modelType, "_", level, "_start_", start, ".txt",sep=""), row.name=F, quote=F, sep="\t")
}
,
error=function(e) { }
)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilhisse.R
\name{m_scatterplot_cp}
\alias{m_scatterplot_cp}
\title{Plot diversification rates estimated by a HiSSE model with means and standard deviations across tips and a two-dimensional colorplane for color}
\usage{
m_scatterplot_cp(
processed_recon,
parameter = "turnover",
focal_character = c("prob_0x", "prob_x0"),
focal_character_label,
second_character_label,
colors,
plot_as_waiting_time = FALSE
)
}
\arguments{
\item{processed_recon}{An object produced with \code{m_process_recon}}
\item{parameter}{The diversification parameter to be plotted on the y axis. Possible options are turnover, extinct.frac, net.div, speciation, extinction}
\item{focal_character}{Specifies the x axis. Either \code{prob_0x} to plot the probability of state 0 for the first character, or \code{prob_x0} to plot the probability for state 0 for the second character.}
\item{focal_character_label}{Label for the x axis of the scatterplot and two-dimensional color gradient. This should match the focal probability.}
\item{second_character_label}{Label for the y axis of the scatterplot and two-dimensional color gradient.}
\item{colors}{A vector of three colors in the order: (1) zero color (color when the two traits are in state 0), (2) horizontal_color (color to interpolate towards state 1 of the focal character) and (2) vertical_color (color to interpolate towards state 1 of the second character). See \code{?colorplaner::color_projections} for details.}
\item{plot_as_waiting_time}{Logical, whether to convert the rate to waiting time (1/rate)}
}
\value{
A scatterplot with focal probability (0 or 1) on the x axis and the chosen diversification parameter on the y axis with means and error bars (mean +/- SD) for each state color coded with in two-dimensional colorplane.
}
\description{
A function to plot a jittered scatterplot of (model-averaged) diversification rates in the alternative states.
}
\examples{
library("colorplaner")
data("diatoms")
processed_muhisse <- m_process_recon(muhisse_recon=diatoms$muhisse_recon)
m_scatterplot_cp(
processed_recon = processed_muhisse,
parameter = "turnover",
focal_character = "prob_0x",
focal_character_label = "p(mar)",
second_character_label = "p(pla)",
colors = c("#21908CFF", "#440154FF", "#FDE725FF"),
plot_as_waiting_time = TRUE) +
labs(y="Net turnover\n(waiting time in millions of years)")
}
| /man/m_scatterplot_cp.Rd | no_license | discindo/gghisse | R | false | true | 2,450 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilhisse.R
\name{m_scatterplot_cp}
\alias{m_scatterplot_cp}
\title{Plot diversification rates estimated by a HiSSE model with means and standard deviations across tips and a two-dimensional colorplane for color}
\usage{
m_scatterplot_cp(
processed_recon,
parameter = "turnover",
focal_character = c("prob_0x", "prob_x0"),
focal_character_label,
second_character_label,
colors,
plot_as_waiting_time = FALSE
)
}
\arguments{
\item{processed_recon}{An object produced with \code{m_process_recon}}
\item{parameter}{The diversification parameter to be plotted on the y axis. Possible options are turnover, extinct.frac, net.div, speciation, extinction}
\item{focal_character}{Specifies the x axis. Either \code{prob_0x} to plot the probability of state 0 for the first character, or \code{prob_x0} to plot the probability for state 0 for the second character.}
\item{focal_character_label}{Label for the x axis of the scatterplot and two-dimensional color gradient. This should match the focal probability.}
\item{second_character_label}{Label for the y axis of the scatterplot and two-dimensional color gradient.}
\item{colors}{A vector of three colors in the order: (1) zero color (color when the two traits are in state 0), (2) horizontal_color (color to interpolate towards state 1 of the focal character) and (2) vertical_color (color to interpolate towards state 1 of the second character). See \code{?colorplaner::color_projections} for details.}
\item{plot_as_waiting_time}{Logical, whether to convert the rate to waiting time (1/rate)}
}
\value{
A scatterplot with focal probability (0 or 1) on the x axis and the chosen diversification parameter on the y axis with means and error bars (mean +/- SD) for each state color coded with in two-dimensional colorplane.
}
\description{
A function to plot a jittered scatterplot of (model-averaged) diversification rates in the alternative states.
}
\examples{
library("colorplaner")
data("diatoms")
processed_muhisse <- m_process_recon(muhisse_recon=diatoms$muhisse_recon)
m_scatterplot_cp(
processed_recon = processed_muhisse,
parameter = "turnover",
focal_character = "prob_0x",
focal_character_label = "p(mar)",
second_character_label = "p(pla)",
colors = c("#21908CFF", "#440154FF", "#FDE725FF"),
plot_as_waiting_time = TRUE) +
labs(y="Net turnover\n(waiting time in millions of years)")
}
|
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
library(rCharts)
shinyUI(fluidPage(
# Application title
titlePanel("Overview"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
conditionalPanel(condition="input.tabs=='Main'",
"Below we list all of the available sites - users can select which sites to focus on or leave all available",
uiOutput('sites'),
checkboxInput('selected','Select All',value=TRUE)
),
conditionalPanel(condition="input.tabs=='Upload'",
fileInput('file1', 'Choose file to upload')
)),
# Show a plot of the generated distribution
mainPanel(
tabsetPanel(id="tabs",
tabPanel("Main",
"This web-application provides high level summary data to the user for a selected list of sites. BELOW we provide an
option to provide cumulative data rather than for individual time points.",
checkboxInput('cumsum','Check for cumulative sums'),
"Data can be shown in simple tabular format or as a graphic via the toggle below",
radioButtons('table','Show Table or Graphic?',list("Table"='table',"Graphic"='graphic')),
conditionalPanel(condition="input.table=='graphic'",
"With Graphic selected we provide a number of options for which metrics to show in the graph. The graphics summarize the
selected data visually",
selectInput('measure','Select Measurement',list('Inquiries'='Inquiries','Referrals'='Referrals','Screens'='Screens')),
showOutput("day2",'highcharts'),
showOutput("day3",'highcharts')),
conditionalPanel(condition="input.table=='table'",
dataTableOutput("table"))
),
tabPanel("Upload",
"This second tab provides a user-interface for uploading new data that powers the visualizations in the first page. This currently
only accepts CSVs and requires a previously defined file layout. This restriction could be relaxed in the future. A preview of the
uploaded data is shown below for verification before submitting to the database.",
dataTableOutput("preview"))
)
)
)
)) | /review/ui.R | no_license | danieltgustafson/gidb | R | false | false | 2,355 | r |
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
library(rCharts)
shinyUI(fluidPage(
# Application title
titlePanel("Overview"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
conditionalPanel(condition="input.tabs=='Main'",
"Below we list all of the available sites - users can select which sites to focus on or leave all available",
uiOutput('sites'),
checkboxInput('selected','Select All',value=TRUE)
),
conditionalPanel(condition="input.tabs=='Upload'",
fileInput('file1', 'Choose file to upload')
)),
# Show a plot of the generated distribution
mainPanel(
tabsetPanel(id="tabs",
tabPanel("Main",
"This web-application provides high level summary data to the user for a selected list of sites. BELOW we provide an
option to provide cumulative data rather than for individual time points.",
checkboxInput('cumsum','Check for cumulative sums'),
"Data can be shown in simple tabular format or as a graphic via the toggle below",
radioButtons('table','Show Table or Graphic?',list("Table"='table',"Graphic"='graphic')),
conditionalPanel(condition="input.table=='graphic'",
"With Graphic selected we provide a number of options for which metrics to show in the graph. The graphics summarize the
selected data visually",
selectInput('measure','Select Measurement',list('Inquiries'='Inquiries','Referrals'='Referrals','Screens'='Screens')),
showOutput("day2",'highcharts'),
showOutput("day3",'highcharts')),
conditionalPanel(condition="input.table=='table'",
dataTableOutput("table"))
),
tabPanel("Upload",
"This second tab provides a user-interface for uploading new data that powers the visualizations in the first page. This currently
only accepts CSVs and requires a previously defined file layout. This restriction could be relaxed in the future. A preview of the
uploaded data is shown below for verification before submitting to the database.",
dataTableOutput("preview"))
)
)
)
)) |
###
### Claire Kelling
### BIGSSS Computational Social Science Summer School on Migration
###
### Created: 05/24/19
### Last Updated: 06/12/19
###
# Start clean
rm(list = ls())
# Packages
#use install.packages("package_name") if you do not have the package installed
#install.packages("maptools") #for example
library(maptools)
library(sp)
library(rgdal)
library(spatstat)
library(ggplot2)
library(dplyr)
library(spdep)
library(CARBayes)
# Set working directory to bigsss_spatial_stats folder path
# Note: directories have to include "/" not "\"
setwd("~/spatial_stats_workshop")
###
### 1.) Load spatial polygon data data (Source: US Census)
###
load(file = "data/det_bg.Rdata")
det_bg <- det_bg_geog
rm(det_bg_geog)
plot(det_bg, main = "Block Groups in Detroit")
###
### 2.) Load spatial points/events (Source: Police Data Iniatiative)
###
load(file = "data/detroit_data.Rdata")
events <- detroit_data
rm(detroit_data)
#Only use a subset of the data, for simplicity (otherwise, takes a while to plot)
set.seed(2)
rand_ind <- runif(10000, 1, nrow(events))
events <- events[rand_ind,]
#converting to spatial points dataframe
sp_point <- cbind(events$Longitude, events$Latitude)
colnames(sp_point) <- c("LONG","LAT")
proj <- CRS("+proj=longlat +datum=WGS84")
data.sp <- SpatialPointsDataFrame(coords=sp_point, data=events, proj4string=proj)
plot(data.sp, pch=16, cex=.5, axes=T)
# try overlaying the plots
plot(det_bg)
plot(data.sp, col = "blue", pch=16, cex=0.6, add = T) #some points actually lie outside of Detroit
#clean up
rm(proj, sp_point, rand_ind)
###
### 3.) Count events per areal unit
###
#Build a dataset with event counts per block group
#Needs to have same projection as spatial points
det_bg <- spTransform(det_bg, proj4string(data.sp))
overlap_set <- over(data.sp, det_bg)
nrow(data.sp)
nrow(overlap_set) #it has classified each of the points in the dataset into a block group
sum(is.na(overlap_set$STATEFP)) #there are some events that actually occur outside of city boundaries
detroit_df <- as.data.frame(data.sp)
det_dat_over <- cbind(detroit_df, overlap_set)
#det_dat_over <- det_dat_over[!is.na(over(domv_dat_detroit,det_bg)),]
det_dat_ov <- det_dat_over[!is.na(det_dat_over$GEOID),]
agg_dat <- plyr::count(det_dat_ov, c('GEOID'))
agg_dat$GEOID <- as.factor(agg_dat$GEOID)
#now I would like to create a plot that illustrates how many events are occuring per block group
num_per_bg <- as.numeric(agg_dat$freq)
#Now I will create the data structure that I need to create a plot
sp_f <- fortify(det_bg)
det_bg$id <- row.names(det_bg)
det_bg@data <- left_join(det_bg@data, agg_dat, by = (GEOID = "GEOID"))
sp_f <- left_join(sp_f, det_bg@data)
#make a color or grayscale plot to illustrate this
count_by_bg <- ggplot() + geom_polygon(data = sp_f, aes(long, lat, group = group, fill = freq)) + coord_equal() +
labs(fill = "No. of \nEvents")+ geom_polygon(data=sp_f,aes(long,lat, group = group),
fill = NA, col = "black") +
ggtitle("Number of Events per Block Group")+ scale_fill_gradient(low = "lightblue", high = "navyblue")
count_by_bg
rm(count_by_bg, sp_f, overlap_set, detroit_df, det_dat_over, det_dat_ov, agg_dat, num_per_bg, GEOID)
###
### 4.) Areal Unit modeling
###
length(which(is.na(det_bg@data$freq))) #some block groups with no crime
det_bg$freq[which(is.na(det_bg@data$freq))] <- 0 #replace with 0, instead of NA
#Create neighborhood matrix from shape file
#Create neighborhood matrix in different formats for different functions
W.nb <- poly2nb(det_bg, row.names = rownames(det_bg@data))
W.list <- nb2listw(W.nb, style="B")
W.mat <- nb2mat(W.nb, style="B")
View(head(W.mat[,1:10], n =10))
#Plot neighborhood matrix
coords <- coordinates(det_bg)
plot(det_bg, border = "gray", main = "Neighorhood Matrix")
plot(W.nb, coords, pch = 1, cex = 0.6, col="blue", add = TRUE)
#Preliminary test using Moran's I
#non-spatial modeling (just linear model)
form <- freq ~ median_income + upemp_rate+total_pop+perc_male+med_age+herf_index
model <- lm(formula=form, data=det_bg@data)
resid.model <- residuals(model)
moran.mc(x=resid.model, listw=W.list, nsim=5000)
#Fit model using neighborhood matrix
#rownames(W.mat) <- NULL #need this for test if matrix is symmetric
# Takes a couple of minutes to run
# *** Should increase n.sample and burnin when running for your data***
model.bym <- S.CARbym(formula=form, data=det_bg@data,
family="poisson", W=W.mat, burnin=2000, n.sample=5000, thin=10)
summary(model.bym)
model.bym$modelfit
model.bym$summary.results[,1:3] #should consider standardization of variables
rm(W.list, W.mat, W.nb, model, coords, form, resid.model, model.bym)
###
### 5.) Point Process Modeling
###
#Need to make smaller dataset for point process modeling
data.sp <- data.sp[runif(500, 1, length(data.sp)),]
## Preliminrary test using Ripley's K
# Transform our data into ppp object
bg_owin <- as.owin(det_bg)
xyzt <- as.matrix(coordinates(data.sp))
event_ppp <- as.ppp(xyzt, bg_owin) #some lie outside of the specified window
#Get Ripley's K plot with bootstrapped CIs
# simultaneous (takes a VERY long time)
# sig level = (nrank/(1+nsim))
#k_sim <- envelope(event_ppp, fun = Kest, global = FALSE, nrank = 20, nsim = 800)
#F test using ECDF's
# *** Should use a larger "nsim" when using on your data ***
# Takes a couple of minutes to run
f_sim <- envelope(event_ppp, fun = Fest, global = FALSE, nrank = 20, nsim = 100)
plot(f_sim, main = "F function Envelope, Pointwise", ylab = "F function")
# Plot ppp object
plot(event_ppp)
# More visualizations
plot(density(event_ppp))
persp(density(event_ppp))
| /src/bigsss_preliminaries.R | no_license | ckelling/spatial_stats_workshop | R | false | false | 5,670 | r | ###
### Claire Kelling
### BIGSSS Computational Social Science Summer School on Migration
###
### Created: 05/24/19
### Last Updated: 06/12/19
###
# Start clean
rm(list = ls())
# Packages
#use install.packages("package_name") if you do not have the package installed
#install.packages("maptools") #for example
library(maptools)
library(sp)
library(rgdal)
library(spatstat)
library(ggplot2)
library(dplyr)
library(spdep)
library(CARBayes)
# Set working directory to bigsss_spatial_stats folder path
# Note: directories have to include "/" not "\"
setwd("~/spatial_stats_workshop")
###
### 1.) Load spatial polygon data data (Source: US Census)
###
load(file = "data/det_bg.Rdata")
det_bg <- det_bg_geog
rm(det_bg_geog)
plot(det_bg, main = "Block Groups in Detroit")
###
### 2.) Load spatial points/events (Source: Police Data Iniatiative)
###
load(file = "data/detroit_data.Rdata")
events <- detroit_data
rm(detroit_data)
#Only use a subset of the data, for simplicity (otherwise, takes a while to plot)
set.seed(2)
rand_ind <- runif(10000, 1, nrow(events))
events <- events[rand_ind,]
#converting to spatial points dataframe
sp_point <- cbind(events$Longitude, events$Latitude)
colnames(sp_point) <- c("LONG","LAT")
proj <- CRS("+proj=longlat +datum=WGS84")
data.sp <- SpatialPointsDataFrame(coords=sp_point, data=events, proj4string=proj)
plot(data.sp, pch=16, cex=.5, axes=T)
# try overlaying the plots
plot(det_bg)
plot(data.sp, col = "blue", pch=16, cex=0.6, add = T) #some points actually lie outside of Detroit
#clean up
rm(proj, sp_point, rand_ind)
###
### 3.) Count events per areal unit
###
#Build a dataset with event counts per block group
#Needs to have same projection as spatial points
det_bg <- spTransform(det_bg, proj4string(data.sp))
overlap_set <- over(data.sp, det_bg)
nrow(data.sp)
nrow(overlap_set) #it has classified each of the points in the dataset into a block group
sum(is.na(overlap_set$STATEFP)) #there are some events that actually occur outside of city boundaries
detroit_df <- as.data.frame(data.sp)
det_dat_over <- cbind(detroit_df, overlap_set)
#det_dat_over <- det_dat_over[!is.na(over(domv_dat_detroit,det_bg)),]
det_dat_ov <- det_dat_over[!is.na(det_dat_over$GEOID),]
agg_dat <- plyr::count(det_dat_ov, c('GEOID'))
agg_dat$GEOID <- as.factor(agg_dat$GEOID)
#now I would like to create a plot that illustrates how many events are occuring per block group
num_per_bg <- as.numeric(agg_dat$freq)
#Now I will create the data structure that I need to create a plot
sp_f <- fortify(det_bg)
det_bg$id <- row.names(det_bg)
det_bg@data <- left_join(det_bg@data, agg_dat, by = (GEOID = "GEOID"))
sp_f <- left_join(sp_f, det_bg@data)
#make a color or grayscale plot to illustrate this
count_by_bg <- ggplot() + geom_polygon(data = sp_f, aes(long, lat, group = group, fill = freq)) + coord_equal() +
labs(fill = "No. of \nEvents")+ geom_polygon(data=sp_f,aes(long,lat, group = group),
fill = NA, col = "black") +
ggtitle("Number of Events per Block Group")+ scale_fill_gradient(low = "lightblue", high = "navyblue")
count_by_bg
rm(count_by_bg, sp_f, overlap_set, detroit_df, det_dat_over, det_dat_ov, agg_dat, num_per_bg, GEOID)
###
### 4.) Areal Unit modeling
###
length(which(is.na(det_bg@data$freq))) #some block groups with no crime
det_bg$freq[which(is.na(det_bg@data$freq))] <- 0 #replace with 0, instead of NA
#Create neighborhood matrix from shape file
#Create neighborhood matrix in different formats for different functions
W.nb <- poly2nb(det_bg, row.names = rownames(det_bg@data))
W.list <- nb2listw(W.nb, style="B")
W.mat <- nb2mat(W.nb, style="B")
View(head(W.mat[,1:10], n =10))
#Plot neighborhood matrix
coords <- coordinates(det_bg)
plot(det_bg, border = "gray", main = "Neighorhood Matrix")
plot(W.nb, coords, pch = 1, cex = 0.6, col="blue", add = TRUE)
#Preliminary test using Moran's I
#non-spatial modeling (just linear model)
form <- freq ~ median_income + upemp_rate+total_pop+perc_male+med_age+herf_index
model <- lm(formula=form, data=det_bg@data)
resid.model <- residuals(model)
moran.mc(x=resid.model, listw=W.list, nsim=5000)
#Fit model using neighborhood matrix
#rownames(W.mat) <- NULL #need this for test if matrix is symmetric
# Takes a couple of minutes to run
# *** Should increase n.sample and burnin when running for your data***
model.bym <- S.CARbym(formula=form, data=det_bg@data,
family="poisson", W=W.mat, burnin=2000, n.sample=5000, thin=10)
summary(model.bym)
model.bym$modelfit
model.bym$summary.results[,1:3] #should consider standardization of variables
rm(W.list, W.mat, W.nb, model, coords, form, resid.model, model.bym)
###
### 5.) Point Process Modeling
###
#Need to make smaller dataset for point process modeling
data.sp <- data.sp[runif(500, 1, length(data.sp)),]
## Preliminrary test using Ripley's K
# Transform our data into ppp object
bg_owin <- as.owin(det_bg)
xyzt <- as.matrix(coordinates(data.sp))
event_ppp <- as.ppp(xyzt, bg_owin) #some lie outside of the specified window
#Get Ripley's K plot with bootstrapped CIs
# simultaneous (takes a VERY long time)
# sig level = (nrank/(1+nsim))
#k_sim <- envelope(event_ppp, fun = Kest, global = FALSE, nrank = 20, nsim = 800)
#F test using ECDF's
# *** Should use a larger "nsim" when using on your data ***
# Takes a couple of minutes to run
f_sim <- envelope(event_ppp, fun = Fest, global = FALSE, nrank = 20, nsim = 100)
plot(f_sim, main = "F function Envelope, Pointwise", ylab = "F function")
# Plot ppp object
plot(event_ppp)
# More visualizations
plot(density(event_ppp))
persp(density(event_ppp))
|
## To minimize execution of repetitive cpu-intensive operations
## on the same (large) dataset such as Compution of inverse of
## a large matrix, R provides the mechanism to cache the result
## of the operation in memory for fast retrivial and reuse.
## --------------------------------------------------------------
## makeCacheMatrix:
##
## This function provides the interface to function (cacheSolve)
## that computes the inverse of the mattrix given in the argument.
## It defines the following functions and initiates the cache for
## storing the matrix and its inverse.
## setmatrix - called to perfrom the inverse compution for a matrix
## with different value or diminsions. It inititates the
## cache for the new matrix and its inverse.
## getmatrix - returns the matrix.
## setinverse - stores the invered matrix in the cache
## getinverse - fectes the invered matrix from the cache.
## NULL indicated the inverse hasn't been stored in
## the cache.
## Returned value: the list of defined functions.
##
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
setmatrix <- function(y) {
x <<- y
m <<- NULL
}
getmatrix <- function() x
setinverse <- function(inverse) m <<- inverse
getinverse <- function() m
list(setmatrix = setmatrix, getmatrix = getmatrix,
setinverse = setinverse,
getinverse = getinverse)
}
##
## This function perfroms the compution of inverse matrix.
## Input: list of functions from makeCacheMatrix:
## (setmatrix, getmatrix, setinverse, getinverse)
## Logic: if inverse is already in cache (m) return the inverse
## matrix, o.w. get the matrix, compute the inverse, and
## store the inverse in cache (through setinverse function)
##
cacheSolve <- function(x, ...) {
m <- x$getinverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$getmatrix()
m <- solve(data, ...)
x$setinverse(m)
m
} | /cachematrix.R | no_license | hfarsi/ProgrammingAssignment2 | R | false | false | 2,026 | r | ## To minimize execution of repetitive cpu-intensive operations
## on the same (large) dataset such as Compution of inverse of
## a large matrix, R provides the mechanism to cache the result
## of the operation in memory for fast retrivial and reuse.
## --------------------------------------------------------------
## makeCacheMatrix:
##
## This function provides the interface to function (cacheSolve)
## that computes the inverse of the mattrix given in the argument.
## It defines the following functions and initiates the cache for
## storing the matrix and its inverse.
## setmatrix - called to perfrom the inverse compution for a matrix
## with different value or diminsions. It inititates the
## cache for the new matrix and its inverse.
## getmatrix - returns the matrix.
## setinverse - stores the invered matrix in the cache
## getinverse - fectes the invered matrix from the cache.
## NULL indicated the inverse hasn't been stored in
## the cache.
## Returned value: the list of defined functions.
##
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
setmatrix <- function(y) {
x <<- y
m <<- NULL
}
getmatrix <- function() x
setinverse <- function(inverse) m <<- inverse
getinverse <- function() m
list(setmatrix = setmatrix, getmatrix = getmatrix,
setinverse = setinverse,
getinverse = getinverse)
}
##
## This function perfroms the compution of inverse matrix.
## Input: list of functions from makeCacheMatrix:
## (setmatrix, getmatrix, setinverse, getinverse)
## Logic: if inverse is already in cache (m) return the inverse
## matrix, o.w. get the matrix, compute the inverse, and
## store the inverse in cache (through setinverse function)
##
cacheSolve <- function(x, ...) {
m <- x$getinverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$getmatrix()
m <- solve(data, ...)
x$setinverse(m)
m
} |
# Module UI
#' @title mod_select_substances_ui and mod_select_substances_server
#' @description A shiny Module.
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_select_substances
#'
#' @keywords internal
#' @export
#' @import dplyr
#' @import shiny
mod_select_substances_ui <- function(id){
ns <- NS(id)
tagList(
uiOutput(ns("substanceSelector"))
)
}
# Module Server
#' @rdname mod_select_substances
#' @export
#' @keywords internal
mod_select_substances_server <- function(input, output, session, data, departement, classe_substance){
n_top <- reactive({
filter(data, departement %in% departement(), classification %in% classe_substance()) %>%
pull(substance) %>%
n_distinct()
})
output$substanceSelector <- renderUI({
ns <- session$ns
if (n_top() == 0) {
HTML("<p style=\"color:grey; font-size:24px\"><br>Pas de données de ventes de substances pesticides</p>")
} else {
if (n_top() == 1) {
HTML("<p style=\"color:#39a9dc; font-size:24px\"><br>Une seule substance représente plus de 50% des ventes</p>")
} else {
sliderInput(inputId = ns("nb_subst"),
label = "Nombre de substances à représenter",
value = 5, min = 1, max = n_top(), step = 1, ticks = FALSE)
}
}
})
reactive(input$nb_subst)
}
## To be copied in the UI
# mod_select_substances_ui("select_substances_ui_1")
## To be copied in the server
# callModule(mod_select_substances_server, "select_substances_ui_1")
| /R/01.06_mod_select_substances.R | permissive | CedricMondy/dataviz_bnvd | R | false | false | 1,639 | r | # Module UI
#' @title mod_select_substances_ui and mod_select_substances_server
#' @description A shiny Module.
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_select_substances
#'
#' @keywords internal
#' @export
#' @import dplyr
#' @import shiny
mod_select_substances_ui <- function(id){
ns <- NS(id)
tagList(
uiOutput(ns("substanceSelector"))
)
}
# Module Server
#' @rdname mod_select_substances
#' @export
#' @keywords internal
mod_select_substances_server <- function(input, output, session, data, departement, classe_substance){
n_top <- reactive({
filter(data, departement %in% departement(), classification %in% classe_substance()) %>%
pull(substance) %>%
n_distinct()
})
output$substanceSelector <- renderUI({
ns <- session$ns
if (n_top() == 0) {
HTML("<p style=\"color:grey; font-size:24px\"><br>Pas de données de ventes de substances pesticides</p>")
} else {
if (n_top() == 1) {
HTML("<p style=\"color:#39a9dc; font-size:24px\"><br>Une seule substance représente plus de 50% des ventes</p>")
} else {
sliderInput(inputId = ns("nb_subst"),
label = "Nombre de substances à représenter",
value = 5, min = 1, max = n_top(), step = 1, ticks = FALSE)
}
}
})
reactive(input$nb_subst)
}
## To be copied in the UI
# mod_select_substances_ui("select_substances_ui_1")
## To be copied in the server
# callModule(mod_select_substances_server, "select_substances_ui_1")
|
library(ggplot2)
library(scales)
library(reshape2)
library(cowplot)
theme_set(theme_minimal_grid())
library(png)
library(grid)
library(RColorBrewer)
#exportar 1400 x 1400
C1_line = "solid"
C2_line = "solid"
C1 <- "#007849"
C2 <- "#82b135"
#FigureS4A
sugarcane4mo9mo_Valine_timecourses <- read.csv("4mo9mo_Valine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Valine_timecourses, aes(x=CT, y=Valine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Valine <- ggplot(data=sugarcane4mo9mo_Valine_timecourses, aes(x=CT, y=Valine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Valine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Valine
FinalMaxMin
#FigureS4B
sugarcane4mo9mo_Leucine_timecourses <- read.csv("4mo9mo_Leucine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Leucine_timecourses, aes(x=CT, y=Leucine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Leucine <- ggplot(data=sugarcane4mo9mo_Leucine_timecourses, aes(x=CT, y=Leucine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Leucine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Leucine
FinalMaxMin
#FigureS4C
sugarcane4mo9mo_Isoleucine_timecourses <- read.csv("4mo9mo_Isoleucine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Isoleucine_timecourses, aes(x=CT, y=Isoleucine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Isoleucine <- ggplot(data=sugarcane4mo9mo_Isoleucine_timecourses, aes(x=CT, y=Isoleucine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Isoleucine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Isoleucine
FinalMaxMin
#Figure4D
sugarcane4mo9mo_Citrate_timecourses <- read.csv("4mo9mo_Citrate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Citrate_timecourses, aes(x=CT, y=Citrate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[8:80, ]
fit4 <- fit2[82:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*23.66/71)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*25.85/77)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*23.66/71)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*25.85/77)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Citrate <- ggplot(data=sugarcane4mo9mo_Citrate_timecourses, aes(x=CT, y=Citrate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Citrate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Citrate
FinalMaxMin
#Figure4E
sugarcane4mo9mo_Glycerate_timecourses <- read.csv("4mo9mo_Glycerate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Glycerate_timecourses, aes(x=CT, y=Glycerate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Glycerate <- ggplot(data=sugarcane4mo9mo_Glycerate_timecourses, aes(x=CT, y=Glycerate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Glycerate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Glycerate
FinalMaxMin
#Figure4F
sugarcane4mo9mo_Malate_timecourses <- read.csv("4mo9mo_Malate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Malate_timecourses, aes(x=CT, y=Malate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Malate <- ggplot(data=sugarcane4mo9mo_Malate_timecourses, aes(x=CT, y=Malate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Malate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Malate
FinalMaxMin
#Figure4G
#changed interval
sugarcane4mo9mo_Aspartate_timecourses <- read.csv("4mo9mo_Aspartate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Aspartate_timecourses, aes(x=CT, y=Aspartate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[6:72, ]
fit4 <- fit2[86:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*21.69/65)+0.203
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*24.52/73)-0.293
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*21.69/65)+0.203
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/73)-0.293
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Aspartate <- ggplot(data=sugarcane4mo9mo_Aspartate_timecourses, aes(x=CT, y=Aspartate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Aspartate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Aspartate
FinalMaxMin
#Figure4H
sugarcane4mo9mo_Glycine_timecourses <- read.csv("4mo9mo_Glycine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Glycine_timecourses, aes(x=CT, y=Glycine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Glycine <- ggplot(data=sugarcane4mo9mo_Glycine_timecourses, aes(x=CT, y=Glycine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Glycine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Glycine
FinalMaxMin
#Figure4I
sugarcane4mo9mo_GABA_timecourses <- read.csv("4mo9mo_GABA.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_GABA_timecourses, aes(x=CT, y=GABA, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_GABA <- ggplot(data=sugarcane4mo9mo_GABA_timecourses, aes(x=CT, y=GABA, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "GABA", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_GABA
FinalMaxMin
plot_grid(sugarcane4mo9mo_Valine, sugarcane4mo9mo_Leucine, sugarcane4mo9mo_Isoleucine, sugarcane4mo9mo_Citrate, sugarcane4mo9mo_Glycerate, sugarcane4mo9mo_Malate, sugarcane4mo9mo_Aspartate, sugarcane4mo9mo_Glycine, sugarcane4mo9mo_GABA, labels = c("A","B","C","D","E","F","G","H","I"), ncol = 3, align = "none", rel_widths = c(1.15, 1, 1),rel_heights = c(1,1,1.15),label_size = 20)
| /02_figures/FigureS4/FigureS4.R | no_license | Jovanderson/Microenvironments | R | false | false | 24,257 | r | library(ggplot2)
library(scales)
library(reshape2)
library(cowplot)
theme_set(theme_minimal_grid())
library(png)
library(grid)
library(RColorBrewer)
#exportar 1400 x 1400
C1_line = "solid"
C2_line = "solid"
C1 <- "#007849"
C2 <- "#82b135"
#FigureS4A
sugarcane4mo9mo_Valine_timecourses <- read.csv("4mo9mo_Valine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Valine_timecourses, aes(x=CT, y=Valine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Valine <- ggplot(data=sugarcane4mo9mo_Valine_timecourses, aes(x=CT, y=Valine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Valine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Valine
FinalMaxMin
#FigureS4B
sugarcane4mo9mo_Leucine_timecourses <- read.csv("4mo9mo_Leucine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Leucine_timecourses, aes(x=CT, y=Leucine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Leucine <- ggplot(data=sugarcane4mo9mo_Leucine_timecourses, aes(x=CT, y=Leucine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Leucine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Leucine
FinalMaxMin
#FigureS4C
sugarcane4mo9mo_Isoleucine_timecourses <- read.csv("4mo9mo_Isoleucine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Isoleucine_timecourses, aes(x=CT, y=Isoleucine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Isoleucine <- ggplot(data=sugarcane4mo9mo_Isoleucine_timecourses, aes(x=CT, y=Isoleucine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Isoleucine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Isoleucine
FinalMaxMin
#Figure4D
sugarcane4mo9mo_Citrate_timecourses <- read.csv("4mo9mo_Citrate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Citrate_timecourses, aes(x=CT, y=Citrate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[8:80, ]
fit4 <- fit2[82:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*23.66/71)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*25.85/77)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*23.66/71)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*25.85/77)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Citrate <- ggplot(data=sugarcane4mo9mo_Citrate_timecourses, aes(x=CT, y=Citrate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Citrate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Citrate
FinalMaxMin
#Figure4E
sugarcane4mo9mo_Glycerate_timecourses <- read.csv("4mo9mo_Glycerate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Glycerate_timecourses, aes(x=CT, y=Glycerate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Glycerate <- ggplot(data=sugarcane4mo9mo_Glycerate_timecourses, aes(x=CT, y=Glycerate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Glycerate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Glycerate
FinalMaxMin
#Figure4F
sugarcane4mo9mo_Malate_timecourses <- read.csv("4mo9mo_Malate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Malate_timecourses, aes(x=CT, y=Malate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Malate <- ggplot(data=sugarcane4mo9mo_Malate_timecourses, aes(x=CT, y=Malate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Malate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
#axis.text.x = element_text(size=18),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Malate
FinalMaxMin
#Figure4G
#changed interval
sugarcane4mo9mo_Aspartate_timecourses <- read.csv("4mo9mo_Aspartate.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Aspartate_timecourses, aes(x=CT, y=Aspartate, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[6:72, ]
fit4 <- fit2[86:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*21.69/65)+0.203
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*24.52/73)-0.293
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*21.69/65)+0.203
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/73)-0.293
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Aspartate <- ggplot(data=sugarcane4mo9mo_Aspartate_timecourses, aes(x=CT, y=Aspartate, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Aspartate", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
axis.text.y = element_text(size=18),
#axis.text.y = element_blank(),
#axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Aspartate
FinalMaxMin
#Figure4H
sugarcane4mo9mo_Glycine_timecourses <- read.csv("4mo9mo_Glycine.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_Glycine_timecourses, aes(x=CT, y=Glycine, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_Glycine <- ggplot(data=sugarcane4mo9mo_Glycine_timecourses, aes(x=CT, y=Glycine, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "Glycine", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_Glycine
FinalMaxMin
#Figure4I
sugarcane4mo9mo_GABA_timecourses <- read.csv("4mo9mo_GABA.txt", sep="\t")
ggplot(data=sugarcane4mo9mo_GABA_timecourses, aes(x=CT, y=GABA, group=Campo)) +
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)
fit2 <- data.frame(fit2)
fit3 <- fit2[1:80, ]
fit4 <- fit2[81:160, ]
fit5 <- cbind(fit3, fit4)
fit5 <- data.frame(fit5)
maxC10 <- data.frame(which(fit5 == max(fit5$fit3), arr.ind=TRUE))
maxC1 <- ((maxC10$row-1)*25.96/79)-1.44
maxC20 <- data.frame(which(fit5 == max(fit5$fit4), arr.ind=TRUE))
maxC2 <- ((maxC20$row-1)*26.18/79)-1.95
minC10 <- data.frame(which(fit5 == min(fit5$fit3), arr.ind=TRUE))
minC1 <- ((minC10$row-1)*25.96/79)-1.44
minC20 <- data.frame(which(fit5 == min(fit5$fit4), arr.ind=TRUE))
minC2 <- ((minC20$row-1)*26.18/79)-1.95
FinalMaxMin <- cbind(maxC1, minC1, maxC2, minC2)
sugarcane4mo9mo_GABA <- ggplot(data=sugarcane4mo9mo_GABA_timecourses, aes(x=CT, y=GABA, group=Campo)) +
annotate("rect", xmin = -2, xmax = 0, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
annotate("rect", xmin = 12, xmax = 24, ymin = -3, ymax = 3, alpha = .5, fill = "#e3e3e3")+
geom_jitter(aes(col=Campo, shape = Campo),position=position_jitter(0.2), size = 3) +
stat_smooth(aes(group = Campo, colour = Campo, fill=Campo, outfit=fit2<<-..y..),method="loess", span = 0.5)+
scale_colour_manual(values=c(C1, C2))+
scale_fill_manual(values=c(C1, C2))+
scale_linetype_manual(values=c(C1_line, C2_line)) +
annotate("text", x = maxC1, y = 2.75, label = "\u25bc", size = 6, colour=C1)+
annotate("text", x = maxC2, y = 2.75, label = "\u25bc", size = 6, colour=C2)+
annotate("text", x = 22, y = -2.75, label = "GABA", size = 6, parse=TRUE)+
scale_x_continuous(breaks=seq(0,24,6), name="ZT (h)", limits=c(-2, 24.75))+
scale_y_continuous(breaks=seq(-3,3,1.5), name="Normalized Expression", limits=c(-3,3), labels = scales::number_format(accuracy = 0.1))+
theme(panel.grid.major = element_line(colour = "#efefef", size = 0.75),
text = element_text(size=18),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(size=18),
#axis.text.x = element_blank(),
#axis.title.x = element_blank(),
#axis.text.y = element_text(size=18),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
sugarcane4mo9mo_GABA
FinalMaxMin
plot_grid(sugarcane4mo9mo_Valine, sugarcane4mo9mo_Leucine, sugarcane4mo9mo_Isoleucine, sugarcane4mo9mo_Citrate, sugarcane4mo9mo_Glycerate, sugarcane4mo9mo_Malate, sugarcane4mo9mo_Aspartate, sugarcane4mo9mo_Glycine, sugarcane4mo9mo_GABA, labels = c("A","B","C","D","E","F","G","H","I"), ncol = 3, align = "none", rel_widths = c(1.15, 1, 1),rel_heights = c(1,1,1.15),label_size = 20)
|
#! /usr/bin/Rscript
# Plot graphics showing how well (or not) each primer set kept the community as determined by the Universal (Earth Microbiome Project) primers)
# Libraries
library(argparse)
library(ggplot2)
library(phyloseq)
options(stringsAsFactors=F)
# Command-line arguments
parser=ArgumentParser()
parser$add_argument("-i", "--infile", help="RDS file containing the phyloseq object to analyze (from step 2a)")
parser$add_argument("-o", "--outprefix", help="Prefix for all output files")
args=parser$parse_args()
# setwd('/home/jgwall/Projects/Microbiomes/MicrobiomeMethodsDevelopment/CompareSampleExtractionAndAmplification_Mohsen_Cecelia/2019 07 Cecelia Final Data/2_Analysis/')
# args=parser$parse_args(c("-i", "2b_filtered_data.phyloseq.Rdata", "-o", "99_tmp"))
# Load data
cat("Loading phyloseq data\n")
mydata=readRDS(args$infile)
# Split data into plant and non-plant samples
is_plant = get_variable(mydata, "sample.type") %in% c("leaf-maize", "leaf-soybean")
non_plant = get_variable(mydata, "sample.type") %in% c("defined-community", "soil-clay", "soil-flowerbed")
plants = prune_samples(is_plant, mydata)
nonplants = prune_samples(non_plant, mydata)
# Make my own function to collapse by levels, since taxa_glom() keeps giving integer overflow errors
collapse_taxa=function(phylo, level){
# Check that the requested phylogenetic level is actually in the data
if(! level %in% rank_names(phylo)){
warning("Illegal taxonomic rank requested; returning unchanged object")
return(phylo)
}
# Filter out OTUs with 0 reads
has_reads = which(taxa_sums(phylo)>0)
phylo = prune_taxa(names(has_reads), phylo)
# Iteratively merge groups
tempdata = phylo
mytaxa = unique(as.character(tax_table(tempdata)[,level]))
for(taxon in sort(mytaxa)){
# cat("Collapsing", taxon,"\n")
classifications = as.character(tax_table(tempdata)[,level])
tomerge = which(classifications == taxon)
tempdata = merge_taxa(tempdata, tomerge, archetype=1)
}
return(tempdata)
}
# Look over some high-level taxonomy to display results
for(level in c("Domain", "Phylum", "Class", "Order")){
cat("\tPlotting distortion barplots at level",level,"\n")
# Plant community data
subplants = collapse_taxa(plants, level=level)
subplants = transform_sample_counts(subplants, fun=function(x){x/sum(x)}) # Convert to relative abundance
p1 = plot_bar(subplants, fill=level, title=paste("Plant community distortion at",level,"level")) + facet_grid(.~sample.type, scales="free_x")
# Nonplant community data
subnonplants = collapse_taxa(nonplants, level=level)
subnonplants = transform_sample_counts(subnonplants, fun=function(x){x/sum(x)}) # Convert to relative abundance
p2 = plot_bar(subnonplants, fill=level, title=paste("Non-plant community distortion at",level,"level")) + facet_grid(.~sample.type, scales="free_x")
ggsave(paste(args$outprefix, level, "plant", "png", sep="."), plot=p1, width=12, height=8)
ggsave(paste(args$outprefix, level, "nonplant", "png", sep="."), plot=p2, width=12, height=8)
}
| /TestPrimers/2d_AssessCommunityDistortion_taxonomy.r | no_license | wallacelab/paper-giangacomo-16s-methods | R | false | false | 3,151 | r | #! /usr/bin/Rscript
# Plot graphics showing how well (or not) each primer set kept the community as determined by the Universal (Earth Microbiome Project) primers)
# Libraries
library(argparse)
library(ggplot2)
library(phyloseq)
options(stringsAsFactors=F)
# Command-line arguments
parser=ArgumentParser()
parser$add_argument("-i", "--infile", help="RDS file containing the phyloseq object to analyze (from step 2a)")
parser$add_argument("-o", "--outprefix", help="Prefix for all output files")
args=parser$parse_args()
# setwd('/home/jgwall/Projects/Microbiomes/MicrobiomeMethodsDevelopment/CompareSampleExtractionAndAmplification_Mohsen_Cecelia/2019 07 Cecelia Final Data/2_Analysis/')
# args=parser$parse_args(c("-i", "2b_filtered_data.phyloseq.Rdata", "-o", "99_tmp"))
# Load data
cat("Loading phyloseq data\n")
mydata=readRDS(args$infile)
# Split data into plant and non-plant samples
is_plant = get_variable(mydata, "sample.type") %in% c("leaf-maize", "leaf-soybean")
non_plant = get_variable(mydata, "sample.type") %in% c("defined-community", "soil-clay", "soil-flowerbed")
plants = prune_samples(is_plant, mydata)
nonplants = prune_samples(non_plant, mydata)
# Make my own function to collapse by levels, since taxa_glom() keeps giving integer overflow errors
collapse_taxa=function(phylo, level){
# Check that the requested phylogenetic level is actually in the data
if(! level %in% rank_names(phylo)){
warning("Illegal taxonomic rank requested; returning unchanged object")
return(phylo)
}
# Filter out OTUs with 0 reads
has_reads = which(taxa_sums(phylo)>0)
phylo = prune_taxa(names(has_reads), phylo)
# Iteratively merge groups
tempdata = phylo
mytaxa = unique(as.character(tax_table(tempdata)[,level]))
for(taxon in sort(mytaxa)){
# cat("Collapsing", taxon,"\n")
classifications = as.character(tax_table(tempdata)[,level])
tomerge = which(classifications == taxon)
tempdata = merge_taxa(tempdata, tomerge, archetype=1)
}
return(tempdata)
}
# Look over some high-level taxonomy to display results
for(level in c("Domain", "Phylum", "Class", "Order")){
cat("\tPlotting distortion barplots at level",level,"\n")
# Plant community data
subplants = collapse_taxa(plants, level=level)
subplants = transform_sample_counts(subplants, fun=function(x){x/sum(x)}) # Convert to relative abundance
p1 = plot_bar(subplants, fill=level, title=paste("Plant community distortion at",level,"level")) + facet_grid(.~sample.type, scales="free_x")
# Nonplant community data
subnonplants = collapse_taxa(nonplants, level=level)
subnonplants = transform_sample_counts(subnonplants, fun=function(x){x/sum(x)}) # Convert to relative abundance
p2 = plot_bar(subnonplants, fill=level, title=paste("Non-plant community distortion at",level,"level")) + facet_grid(.~sample.type, scales="free_x")
ggsave(paste(args$outprefix, level, "plant", "png", sep="."), plot=p1, width=12, height=8)
ggsave(paste(args$outprefix, level, "nonplant", "png", sep="."), plot=p2, width=12, height=8)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blob_copyurl.R, R/transfer_generics.R
\name{copy_url_to_storage}
\alias{copy_url_to_storage}
\alias{multicopy_url_to_storage}
\alias{copy_url_to_storage.blob_container}
\alias{multicopy_url_to_storage.blob_container}
\alias{storage_upload}
\alias{storage_upload.blob_container}
\alias{storage_upload.file_share}
\alias{storage_upload.adls_filesystem}
\alias{storage_multiupload}
\alias{storage_multiupload.blob_container}
\alias{storage_multiupload.file_share}
\alias{storage_multiupload.adls_filesystem}
\alias{storage_download}
\alias{storage_download.blob_container}
\alias{storage_download.file_share}
\alias{storage_download.adls_filesystem}
\alias{storage_multidownload}
\alias{storage_multidownload.blob_container}
\alias{storage_multidownload.file_share}
\alias{storage_multidownload.adls_filesystem}
\alias{download_from_url}
\alias{upload_to_url}
\title{Upload and download generics}
\usage{
copy_url_to_storage(container, src, dest, ...)
multicopy_url_to_storage(container, src, dest, ...)
\method{copy_url_to_storage}{blob_container}(container, src, dest, ...)
\method{multicopy_url_to_storage}{blob_container}(container, src, dest, ...)
storage_upload(container, ...)
\method{storage_upload}{blob_container}(container, ...)
\method{storage_upload}{file_share}(container, ...)
\method{storage_upload}{adls_filesystem}(container, ...)
storage_multiupload(container, ...)
\method{storage_multiupload}{blob_container}(container, ...)
\method{storage_multiupload}{file_share}(container, ...)
\method{storage_multiupload}{adls_filesystem}(container, ...)
storage_download(container, ...)
\method{storage_download}{blob_container}(container, ...)
\method{storage_download}{file_share}(container, ...)
\method{storage_download}{adls_filesystem}(container, ...)
storage_multidownload(container, ...)
\method{storage_multidownload}{blob_container}(container, ...)
\method{storage_multidownload}{file_share}(container, ...)
\method{storage_multidownload}{adls_filesystem}(container, ...)
download_from_url(src, dest, key = NULL, token = NULL, sas = NULL, ...,
overwrite = FALSE)
upload_to_url(src, dest, key = NULL, token = NULL, sas = NULL, ...)
}
\arguments{
\item{container}{A storage container object.}
\item{src, dest}{For \code{upload_to_url} and \code{download_from_url}, the source and destination files to transfer.}
\item{...}{Further arguments to pass to lower-level functions.}
\item{key, token, sas}{Authentication arguments: an access key, Azure Active Directory (AAD) token or a shared access signature (SAS). If multiple arguments are supplied, a key takes priority over a token, which takes priority over a SAS. For \code{upload_to_url} and \code{download_to_url}, you can also provide a SAS as part of the URL itself.}
\item{overwrite}{For downloading, whether to overwrite any destination files that exist.}
}
\description{
Upload and download generics
}
\details{
\code{copy_url_to_storage} transfers the contents of the file at the specified HTTP[S] URL directly to storage, without requiring a temporary local copy to be made. \code{multicopy_url_to_storage} does the same, for multiple URLs at once. Currently methods for these are only implemented for blob storage.
These functions allow you to transfer files to and from a storage account.
\code{storage_upload}, \code{storage_download}, \code{storage_multiupload} and \code{storage_multidownload} take as first argument a storage container, either for blob storage, file storage, or ADLSgen2. They dispatch to the corresponding file transfer functions for the given storage type.
\code{upload_to_url} and \code{download_to_url} allow you to transfer a file to or from Azure storage, given the URL of the source or destination. The storage details (endpoint, container name, and so on) are obtained from the URL.
By default, the upload and download functions will display a progress bar while they are downloading. To turn this off, use \code{options(azure_storage_progress_bar=FALSE)}. To turn the progress bar back on, use \code{options(azure_storage_progress_bar=TRUE)}.
}
\examples{
\dontrun{
# download from blob storage
bl <- storage_endpoint("https://mystorage.blob.core.windows.net/", key="access_key")
cont <- storage_container(bl, "mycontainer")
storage_download(cont, "bigfile.zip", "~/bigfile.zip")
# same download but directly from the URL
download_from_url("https://mystorage.blob.core.windows.net/mycontainer/bigfile.zip",
"~/bigfile.zip",
key="access_key")
# upload to ADLSgen2
ad <- storage_endpoint("https://myadls.dfs.core.windows.net/", token=mytoken)
cont <- storage_container(ad, "myfilesystem")
create_storage_dir(cont, "newdir")
storage_upload(cont, "files.zip", "newdir/files.zip")
# same upload but directly to the URL
upload_to_url("files.zip",
"https://myadls.dfs.core.windows.net/myfilesystem/newdir/files.zip",
token=mytoken)
}
}
\seealso{
\link{storage_container}, \link{blob_container}, \link{file_share}, \link{adls_filesystem}
\link{download_blob}, \link{download_azure_file}, \link{download_adls_file}, \link{call_azcopy}
}
| /man/file_transfer.Rd | permissive | Azure/AzureStor | R | false | true | 5,215 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blob_copyurl.R, R/transfer_generics.R
\name{copy_url_to_storage}
\alias{copy_url_to_storage}
\alias{multicopy_url_to_storage}
\alias{copy_url_to_storage.blob_container}
\alias{multicopy_url_to_storage.blob_container}
\alias{storage_upload}
\alias{storage_upload.blob_container}
\alias{storage_upload.file_share}
\alias{storage_upload.adls_filesystem}
\alias{storage_multiupload}
\alias{storage_multiupload.blob_container}
\alias{storage_multiupload.file_share}
\alias{storage_multiupload.adls_filesystem}
\alias{storage_download}
\alias{storage_download.blob_container}
\alias{storage_download.file_share}
\alias{storage_download.adls_filesystem}
\alias{storage_multidownload}
\alias{storage_multidownload.blob_container}
\alias{storage_multidownload.file_share}
\alias{storage_multidownload.adls_filesystem}
\alias{download_from_url}
\alias{upload_to_url}
\title{Upload and download generics}
\usage{
copy_url_to_storage(container, src, dest, ...)
multicopy_url_to_storage(container, src, dest, ...)
\method{copy_url_to_storage}{blob_container}(container, src, dest, ...)
\method{multicopy_url_to_storage}{blob_container}(container, src, dest, ...)
storage_upload(container, ...)
\method{storage_upload}{blob_container}(container, ...)
\method{storage_upload}{file_share}(container, ...)
\method{storage_upload}{adls_filesystem}(container, ...)
storage_multiupload(container, ...)
\method{storage_multiupload}{blob_container}(container, ...)
\method{storage_multiupload}{file_share}(container, ...)
\method{storage_multiupload}{adls_filesystem}(container, ...)
storage_download(container, ...)
\method{storage_download}{blob_container}(container, ...)
\method{storage_download}{file_share}(container, ...)
\method{storage_download}{adls_filesystem}(container, ...)
storage_multidownload(container, ...)
\method{storage_multidownload}{blob_container}(container, ...)
\method{storage_multidownload}{file_share}(container, ...)
\method{storage_multidownload}{adls_filesystem}(container, ...)
download_from_url(src, dest, key = NULL, token = NULL, sas = NULL, ...,
overwrite = FALSE)
upload_to_url(src, dest, key = NULL, token = NULL, sas = NULL, ...)
}
\arguments{
\item{container}{A storage container object.}
\item{src, dest}{For \code{upload_to_url} and \code{download_from_url}, the source and destination files to transfer.}
\item{...}{Further arguments to pass to lower-level functions.}
\item{key, token, sas}{Authentication arguments: an access key, Azure Active Directory (AAD) token or a shared access signature (SAS). If multiple arguments are supplied, a key takes priority over a token, which takes priority over a SAS. For \code{upload_to_url} and \code{download_to_url}, you can also provide a SAS as part of the URL itself.}
\item{overwrite}{For downloading, whether to overwrite any destination files that exist.}
}
\description{
Upload and download generics
}
\details{
\code{copy_url_to_storage} transfers the contents of the file at the specified HTTP[S] URL directly to storage, without requiring a temporary local copy to be made. \code{multicopy_url_to_storage} does the same, for multiple URLs at once. Currently methods for these are only implemented for blob storage.
These functions allow you to transfer files to and from a storage account.
\code{storage_upload}, \code{storage_download}, \code{storage_multiupload} and \code{storage_multidownload} take as first argument a storage container, either for blob storage, file storage, or ADLSgen2. They dispatch to the corresponding file transfer functions for the given storage type.
\code{upload_to_url} and \code{download_to_url} allow you to transfer a file to or from Azure storage, given the URL of the source or destination. The storage details (endpoint, container name, and so on) are obtained from the URL.
By default, the upload and download functions will display a progress bar while they are downloading. To turn this off, use \code{options(azure_storage_progress_bar=FALSE)}. To turn the progress bar back on, use \code{options(azure_storage_progress_bar=TRUE)}.
}
\examples{
\dontrun{
# download from blob storage
bl <- storage_endpoint("https://mystorage.blob.core.windows.net/", key="access_key")
cont <- storage_container(bl, "mycontainer")
storage_download(cont, "bigfile.zip", "~/bigfile.zip")
# same download but directly from the URL
download_from_url("https://mystorage.blob.core.windows.net/mycontainer/bigfile.zip",
"~/bigfile.zip",
key="access_key")
# upload to ADLSgen2
ad <- storage_endpoint("https://myadls.dfs.core.windows.net/", token=mytoken)
cont <- storage_container(ad, "myfilesystem")
create_storage_dir(cont, "newdir")
storage_upload(cont, "files.zip", "newdir/files.zip")
# same upload but directly to the URL
upload_to_url("files.zip",
"https://myadls.dfs.core.windows.net/myfilesystem/newdir/files.zip",
token=mytoken)
}
}
\seealso{
\link{storage_container}, \link{blob_container}, \link{file_share}, \link{adls_filesystem}
\link{download_blob}, \link{download_azure_file}, \link{download_adls_file}, \link{call_azcopy}
}
|
#==================================================
# DESCRIPCIoN: Estimación de datos faltantes en la estacion miraflores
#Udep y Senamhi
# AUTOR(ES): Nohelia e Isabella
#==================================================
## Establecer el Working Directory
setwd("C:/R/Tesis")
#installed.packages("installr")
##Cargar librerias
library(tibble)
library(openxlsx)
library(dplyr)
library(ggplot2)
library(assertive)
library(naniar)
library(lubridate)
library(MASS)
library(mosaicData)
library(simputation)
library(gtools)
#############Modelos de miraflores#########################################
miss_var_summary (data_temp_max)
model_Mtmax_1 <- lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max + chusis_temp_max+
miguel_temp_max + UDEP_temp_max,
data = data_temp_max)#0.9214
#model_Mtmax_2 <- lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max + chusis_temp_max+
# miguel_temp_max,
# data = data_temp_max)
model_Mtmax_2 <- lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max+
miguel_temp_max,
data = data_temp_max) # 0.9065
model_Mtmax_3 <-lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max+ mallares_temp_max,
data = data_temp_max) # 0.8865
model_Mtmax_4 <-lm(miraflores_temp_max ~ Esperanza_temp_max + miguel_temp_max+ mallares_temp_max,
data = data_temp_max) #0.9033
model_Mtmax_5 <-lm(miraflores_temp_max ~ miguel_temp_max + chusis_temp_max+ mallares_temp_max,
data = data_temp_max) #0.8961
model_Mtmax_6 <-lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max,
data = data_temp_max)# 0.8498
model_Mtmax_7 <-lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max,
data = data_temp_max) #0.8745
model_Mtmax_8 <-lm(miraflores_temp_max ~ Esperanza_temp_max + miguel_temp_max,
data = data_temp_max)#0.9021
model_Mtmax_9 <-lm(miraflores_temp_max ~ mallares_temp_max + chusis_temp_max,
data = data_temp_max) #0.8811
model_Mtmax_10 <-lm(miraflores_temp_max ~ mallares_temp_max + miguel_temp_max,
data = data_temp_max) #0.8739
model_Mtmax_11 <-lm(miraflores_temp_max ~ chusis_temp_max + miguel_temp_max,
data = data_temp_max) #0.9043
model_Mtmax_12 <-lm(miraflores_temp_max ~ chusis_temp_max + UDEP_temp_max,
data = data_temp_max)#0.9085
summary(model_Mtmax_13)
#Estimaciones con Cerritos
############Código de estimación###################
n=0
n <- c()
m <- 0
k=1
for (j in 1971:2019){
cont=0
for (i in 1:length(miraflores_bymonth$year)){
if(miraflores_bymonth$year[i] == j){
if(is.na(miraflores_bymonth$max_temp_max[i]) == TRUE){
cont = cont +1
}else {
cont=cont
}
print(cont)
}
}
n[k]=cont
k=k+1
}
#n = numero de meses faltantes por año (lluvia)
n
m
data_faltante <- c(1:588)
#data_faltante
#library(dplyr)
n <- data.frame(n)
#n
valor<-c()
n_datos <- data.frame(meses_faltantes = n)
#n_datos
for (i in 1:49) {
if (n[i,]>=1) {
valor[i]="yes"
}else{
valor[i]="no"
}
}
valor <-data.frame(valor)
valor
n_datos <- data.frame(año=1971:2019, valor)
#n_datos
#s es los meses
year<-0
h= c()
k=1
j=0
g = c()
jian = c()
yue = c()
est = c()
as.data.frame(faltantes)
data_predictora <- 0
for (i in 1:49) {
for (r in 1:672) { #Filas en los meses de lluvia
if (n_datos$valor[i]=="yes"){
for (s in 1:12){
if (data_temp_max$year[r]==n_datos$año[i] && data_temp_max$month[r]== s
#&& is.na(data_temp_max$miguel_temp_max[r])==FALSE
&& is.na(data_temp_max$UDEP_temp_max[r])==FALSE
&&is.na(data_temp_max$chusis_temp_max[r])==FALSE
#&& is.na(data_temp_max$Bernal_temp_max[r])==FALSE
#&& is.na(data_temp_max$Esperanza_temp_max[r])==FALSE
#&& is.na(data_temp_max$mallares_temp_max[r])==FALSE
&& is.na(data_temp_max$miraflores_temp_max[r])==TRUE){
j=j+1
model_Mtmax_12 <-lm(miraflores_temp_max ~ chusis_temp_max + UDEP_temp_max,
data = data_temp_max)
data_predictora <- tibble(UDEP_temp_max = data_temp_max[r,]$UDEP_temp_max,
#miguel_temp_max = data_temp_max[r,]$miguel_temp_max,
chusis_temp_max = data_temp_max[r,]$chusis_temp_max,
#Bernal_temp_max = data_temp_max[r,]$Bernal_temp_max,
#mallares_temp_max = data_temp_max[r,]$mallares_temp_max,
#Esperanza_temp_max = data_temp_max[r,]$Esperanza_temp_max,
)
g[j] = predict(model_Mtmax_12, data_predictora)
print (g)
#}else{
jian[j] = n_datos$año[i]
yue[j] = s
est[j] = g[j]
faltantes <- data.frame(jian, yue, est)
}
}
}
}
#k=k+1
#print(h)
}
print(data_predictora)
g
faltantes | /Script/Miraflores_estimado_temp_max.R | no_license | NohePS/Tesis | R | false | false | 5,217 | r | #==================================================
# DESCRIPCIoN: Estimación de datos faltantes en la estacion miraflores
#Udep y Senamhi
# AUTOR(ES): Nohelia e Isabella
#==================================================
## Establecer el Working Directory
setwd("C:/R/Tesis")
#installed.packages("installr")
##Cargar librerias
library(tibble)
library(openxlsx)
library(dplyr)
library(ggplot2)
library(assertive)
library(naniar)
library(lubridate)
library(MASS)
library(mosaicData)
library(simputation)
library(gtools)
#############Modelos de miraflores#########################################
miss_var_summary (data_temp_max)
model_Mtmax_1 <- lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max + chusis_temp_max+
miguel_temp_max + UDEP_temp_max,
data = data_temp_max)#0.9214
#model_Mtmax_2 <- lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max + chusis_temp_max+
# miguel_temp_max,
# data = data_temp_max)
model_Mtmax_2 <- lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max+
miguel_temp_max,
data = data_temp_max) # 0.9065
model_Mtmax_3 <-lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max+ mallares_temp_max,
data = data_temp_max) # 0.8865
model_Mtmax_4 <-lm(miraflores_temp_max ~ Esperanza_temp_max + miguel_temp_max+ mallares_temp_max,
data = data_temp_max) #0.9033
model_Mtmax_5 <-lm(miraflores_temp_max ~ miguel_temp_max + chusis_temp_max+ mallares_temp_max,
data = data_temp_max) #0.8961
model_Mtmax_6 <-lm(miraflores_temp_max ~ Esperanza_temp_max + mallares_temp_max,
data = data_temp_max)# 0.8498
model_Mtmax_7 <-lm(miraflores_temp_max ~ Esperanza_temp_max + chusis_temp_max,
data = data_temp_max) #0.8745
model_Mtmax_8 <-lm(miraflores_temp_max ~ Esperanza_temp_max + miguel_temp_max,
data = data_temp_max)#0.9021
model_Mtmax_9 <-lm(miraflores_temp_max ~ mallares_temp_max + chusis_temp_max,
data = data_temp_max) #0.8811
model_Mtmax_10 <-lm(miraflores_temp_max ~ mallares_temp_max + miguel_temp_max,
data = data_temp_max) #0.8739
model_Mtmax_11 <-lm(miraflores_temp_max ~ chusis_temp_max + miguel_temp_max,
data = data_temp_max) #0.9043
model_Mtmax_12 <-lm(miraflores_temp_max ~ chusis_temp_max + UDEP_temp_max,
data = data_temp_max)#0.9085
summary(model_Mtmax_13)
#Estimaciones con Cerritos
############Código de estimación###################
n=0
n <- c()
m <- 0
k=1
for (j in 1971:2019){
cont=0
for (i in 1:length(miraflores_bymonth$year)){
if(miraflores_bymonth$year[i] == j){
if(is.na(miraflores_bymonth$max_temp_max[i]) == TRUE){
cont = cont +1
}else {
cont=cont
}
print(cont)
}
}
n[k]=cont
k=k+1
}
#n = numero de meses faltantes por año (lluvia)
n
m
data_faltante <- c(1:588)
#data_faltante
#library(dplyr)
n <- data.frame(n)
#n
valor<-c()
n_datos <- data.frame(meses_faltantes = n)
#n_datos
for (i in 1:49) {
if (n[i,]>=1) {
valor[i]="yes"
}else{
valor[i]="no"
}
}
valor <-data.frame(valor)
valor
n_datos <- data.frame(año=1971:2019, valor)
#n_datos
#s es los meses
year<-0
h= c()
k=1
j=0
g = c()
jian = c()
yue = c()
est = c()
as.data.frame(faltantes)
data_predictora <- 0
for (i in 1:49) {
for (r in 1:672) { #Filas en los meses de lluvia
if (n_datos$valor[i]=="yes"){
for (s in 1:12){
if (data_temp_max$year[r]==n_datos$año[i] && data_temp_max$month[r]== s
#&& is.na(data_temp_max$miguel_temp_max[r])==FALSE
&& is.na(data_temp_max$UDEP_temp_max[r])==FALSE
&&is.na(data_temp_max$chusis_temp_max[r])==FALSE
#&& is.na(data_temp_max$Bernal_temp_max[r])==FALSE
#&& is.na(data_temp_max$Esperanza_temp_max[r])==FALSE
#&& is.na(data_temp_max$mallares_temp_max[r])==FALSE
&& is.na(data_temp_max$miraflores_temp_max[r])==TRUE){
j=j+1
model_Mtmax_12 <-lm(miraflores_temp_max ~ chusis_temp_max + UDEP_temp_max,
data = data_temp_max)
data_predictora <- tibble(UDEP_temp_max = data_temp_max[r,]$UDEP_temp_max,
#miguel_temp_max = data_temp_max[r,]$miguel_temp_max,
chusis_temp_max = data_temp_max[r,]$chusis_temp_max,
#Bernal_temp_max = data_temp_max[r,]$Bernal_temp_max,
#mallares_temp_max = data_temp_max[r,]$mallares_temp_max,
#Esperanza_temp_max = data_temp_max[r,]$Esperanza_temp_max,
)
g[j] = predict(model_Mtmax_12, data_predictora)
print (g)
#}else{
jian[j] = n_datos$año[i]
yue[j] = s
est[j] = g[j]
faltantes <- data.frame(jian, yue, est)
}
}
}
}
#k=k+1
#print(h)
}
print(data_predictora)
g
faltantes |
\name{Wang_Chen_Sim}
\alias{Wang_Chen_Sim}
\docType{data}
\title{
Simulated process data from a plastics manufacturer.
}
\description{
Fifty observations where 'D' represents depth, 'L' represents length, and 'W' represents width.
}
\usage{Wang_Chen_Sim}
\format{
A simulated data frame with 50 observations and the following 3 variables.
\describe{
\item{\code{D}}{depth}
\item{\code{L}}{length}
\item{\code{W}}{width}
}
}
\source{
Data simulated by Nelson Lee Afanador from average and covariance estimates provided in Wang F, Chen J (1998). "Capability index using principal components analysis." Quality Engineering, 11, 21-27.
}
| /man/Wang_Chen_Sim.Rd | no_license | cran/mvdalab | R | false | false | 653 | rd | \name{Wang_Chen_Sim}
\alias{Wang_Chen_Sim}
\docType{data}
\title{
Simulated process data from a plastics manufacturer.
}
\description{
Fifty observations where 'D' represents depth, 'L' represents length, and 'W' represents width.
}
\usage{Wang_Chen_Sim}
\format{
A simulated data frame with 50 observations and the following 3 variables.
\describe{
\item{\code{D}}{depth}
\item{\code{L}}{length}
\item{\code{W}}{width}
}
}
\source{
Data simulated by Nelson Lee Afanador from average and covariance estimates provided in Wang F, Chen J (1998). "Capability index using principal components analysis." Quality Engineering, 11, 21-27.
}
|
with(a949c7f65ee664349a8371e2024f67407, {ROOT <- 'D:/SEMOSS_v4.0.0_x64/SEMOSS_v4.0.0_x64/semosshome/db/Atadata2__3b3e4a3b-d382-4e98-9950-9b4e8b308c1c/version/1c4fa71c-191c-4da9-8102-b247ffddc5d3';options(digits.secs=NULL);FRAME950866[,(c('DATA_COLLECTION_TIME')) := lapply(.SD, function(x) as.POSIXct(fast_strptime(x, format='%Y-%m-%d %H:%M:%S'))), .SDcols = c('DATA_COLLECTION_TIME')]}); | /1c4fa71c-191c-4da9-8102-b247ffddc5d3/R/Temp/arTlu2CPsgV3M.R | no_license | ayanmanna8/test | R | false | false | 388 | r | with(a949c7f65ee664349a8371e2024f67407, {ROOT <- 'D:/SEMOSS_v4.0.0_x64/SEMOSS_v4.0.0_x64/semosshome/db/Atadata2__3b3e4a3b-d382-4e98-9950-9b4e8b308c1c/version/1c4fa71c-191c-4da9-8102-b247ffddc5d3';options(digits.secs=NULL);FRAME950866[,(c('DATA_COLLECTION_TIME')) := lapply(.SD, function(x) as.POSIXct(fast_strptime(x, format='%Y-%m-%d %H:%M:%S'))), .SDcols = c('DATA_COLLECTION_TIME')]}); |
#!/usr/bin/env Rscript
#' Makes sure the R_LIBS_USER directory is installed
#' R_LIBS_USER is set when R is executed
dir.create(Sys.getenv("R_LIBS_USER"), showWarnings = FALSE, recursive = TRUE)
#' installs packages which are not yet installed
#' @param pkg list of package names
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg,
dependencies = TRUE,
repos = "https://cran.rstudio.com")
sapply(pkg, require, character.only = TRUE)
}
### install base packages
base_packages <- c(
"devtools",
"tidyverse",
"reshape2"
)
ipak(base_packages)
### install tools from github
devtools::install_github("klutometis/roxygen") # for package generation
| /Install.R | permissive | riethmayer/incusyte | R | false | false | 784 | r | #!/usr/bin/env Rscript
#' Makes sure the R_LIBS_USER directory is installed
#' R_LIBS_USER is set when R is executed
dir.create(Sys.getenv("R_LIBS_USER"), showWarnings = FALSE, recursive = TRUE)
#' installs packages which are not yet installed
#' @param pkg list of package names
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg,
dependencies = TRUE,
repos = "https://cran.rstudio.com")
sapply(pkg, require, character.only = TRUE)
}
### install base packages
base_packages <- c(
"devtools",
"tidyverse",
"reshape2"
)
ipak(base_packages)
### install tools from github
devtools::install_github("klutometis/roxygen") # for package generation
|
summary.eaemg <- function(object, ...) {
res <- list(empirical = object$empirical, level = object$level, gp = dim(object$intervals)[1])
class(res) <- "summary.eaemg"
return(res)
}
| /biosignalEMG/R/summary.eaemg.R | no_license | ingted/R-Examples | R | false | false | 198 | r | summary.eaemg <- function(object, ...) {
res <- list(empirical = object$empirical, level = object$level, gp = dim(object$intervals)[1])
class(res) <- "summary.eaemg"
return(res)
}
|
#' Overlaid Normal QQ Plot
#'
#' Produces an overlaid normal QQ plot.
#'
#'
#' @param x a \code{fit.models} object.
#' @param fun a function to extract the desired quantity from \code{x}.
#' @param \dots additional arguments are passed to
#' \code{\link[lattice]{qqmath}}.
#' @return the \code{trellis} object is invisibly returned.
#' @keywords hplot
#' @importFrom lattice qqmath strip.default
#' @export
overlaidQQPlot <- function(x, fun, ...)
{
n.models <- length(x)
mod.names <- names(x)
y <- lapply(x, fun)
n.y <- sapply(y, length)
mod <- factor(rep(mod.names, n.y), levels = mod.names)
tdf <- data.frame(y = unlist(y), mod = mod)
p <- qqmath(~ y | "",
groups = mod,
data = tdf,
distribution = qnorm,
strip = function(...) strip.default(..., style = 1),
auto.key = list(corner = c(0.05, 0.95)),
...)
print(p)
invisible(p)
}
| /R/overlaidQQPlot.R | no_license | cran/fit.models | R | false | false | 944 | r | #' Overlaid Normal QQ Plot
#'
#' Produces an overlaid normal QQ plot.
#'
#'
#' @param x a \code{fit.models} object.
#' @param fun a function to extract the desired quantity from \code{x}.
#' @param \dots additional arguments are passed to
#' \code{\link[lattice]{qqmath}}.
#' @return the \code{trellis} object is invisibly returned.
#' @keywords hplot
#' @importFrom lattice qqmath strip.default
#' @export
overlaidQQPlot <- function(x, fun, ...)
{
n.models <- length(x)
mod.names <- names(x)
y <- lapply(x, fun)
n.y <- sapply(y, length)
mod <- factor(rep(mod.names, n.y), levels = mod.names)
tdf <- data.frame(y = unlist(y), mod = mod)
p <- qqmath(~ y | "",
groups = mod,
data = tdf,
distribution = qnorm,
strip = function(...) strip.default(..., style = 1),
auto.key = list(corner = c(0.05, 0.95)),
...)
print(p)
invisible(p)
}
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 162900
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 154980
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 154980
c
c Input Parameter (command line, file):
c input filename QBFLIB/Gent-Rowley/Connect9/cf_9_9x9_d_.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 910981
c no.of clauses 162900
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 154980
c
c QBFLIB/Gent-Rowley/Connect9/cf_9_9x9_d_.qdimacs 910981 162900 E1 [10101 10102 10103 10104 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152 10153 10154 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164 10165 10166 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176 10177 10178 10179 10180 10181 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248 10249 10250 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260 10261 10262 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272 10273 10274 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98419 98421 98423 98425 98427 98429 98431 98433 98435 98437 98439 98441 98443 98445 98447 98449 98451 98453 98455 98457 98459 98461 98463 98465 98467 98469 98471 98473 98475 98477 98479 98501 98503 98505 98507 98509 98511 98513 98515 98517 98519 98521 98523 98525 98527 98529 98531 98533 98535 98537 98539 98541 98543 98545 98547 98549 98551 98553 98555 98557 98559 98561 98563 98565 98567 98569 98571 98573 98575 98577 98579 98601 98603 98605 98607 98609 98611 98613 98615 98617 98619 98621 98623 98625 98627 98629 98631 98633 98635 98637 98639 98641 98643 98645 98647 98649 98651 98653 98655 98657 98659 98661 98663 98665 98667 98669 98671 98673 98675 98677 98679 98701 98703 98705 98707 98709 98711 98713 98715 98717 98719 98721 98723 98725 98727 98729 98731 98733 98735 98737 98739 98741 98743 98745 98747 98749 98751 98753 98755 98757 98759 98761 98763 98765 98767 98769 98771 98773 98775 98777 98779 98801 98803 98805 98807 98809 98811 98813 98815 98817 98819 98821 98823 98825 98827 98829 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99343 99345 99347 99349 99351 99353 99355 99357 99359 99361 99363 99365 99367 99369 99371 99373 99375 99377 99379 99401 99403 99405 99407 99409 99411 99413 99415 99417 99419 99421 99423 99425 99427 99429 99431 99433 99435 99437 99439 99441 99443 99445 99447 99449 99451 99453 99455 99457 99459 99461 99463 99465 99467 99469 99471 99473 99475 99477 99479 99501 99503 99505 99507 99509 99511 99513 99515 99517 99519 99521 99523 99525 99527 99529 99531 99533 99535 99537 99539 99541 99543 99545 99547 99549 99551 99553 99555 99557 99559 99561 99563 99565 99567 99569 99571 99573 99575 99577 99579 99601 99603 99605 99607 99609 99611 99613 99615 99617 99619 99621 99623 99625 99627 99629 99631 99633 99635 99637 99639 99641 99643 99645 99647 99649 99651 99653 99655 99657 99659 99661 99663 99665 99667 99669 99671 99673 99675 99677 99679 99701 99703 99705 99707 99709 99711 99713 99715 99717 99719 99721 99723 99725 99727 99729 99731 99733 99735 99737 99739 99741 99743 99745 99747 99749 99751 99753 99755 99757 99759 99761 99763 99765 99767 99769 99771 99773 99775 99777 99779 99801 99803 99805 99807 99809 99811 99813 99815 99817 99819 99821 99823 99825 99827 99829 99831 99833 99835 99837 99839 99841 99843 99845 99847 99849 99851 99853 99855 99857 99859 99861 99863 99865 99867 99869 99871 99873 99875 99877 99879 99901 99903 99905 99907 99909 99911 99913 99915 99917 99919 99921 99923 99925 99927 99929 99931 99933 99935 99937 99939 99941 99943 99945 99947 99949 99951 99953 99955 99957 99959 99961 99963 99965 99967 99969 99971 99973 99975 99977 99979] 0 360 36994 154980 RED
| /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Gent-Rowley/Connect9/cf_9_9x9_d_/cf_9_9x9_d_.R | no_license | arey0pushpa/dcnf-autarky | R | false | false | 56,998 | r | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 162900
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 154980
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 154980
c
c Input Parameter (command line, file):
c input filename QBFLIB/Gent-Rowley/Connect9/cf_9_9x9_d_.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 910981
c no.of clauses 162900
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 154980
c
c QBFLIB/Gent-Rowley/Connect9/cf_9_9x9_d_.qdimacs 910981 162900 E1 [10101 10102 10103 10104 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152 10153 10154 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164 10165 10166 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176 10177 10178 10179 10180 10181 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248 10249 10250 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260 10261 10262 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272 10273 10274 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|
# Install and load packages
package_names <- c("survey","dplyr","foreign","devtools")
lapply(package_names, function(x) if(!x %in% installed.packages()) install.packages(x))
lapply(package_names, require, character.only=T)
install_github("e-mitchell/meps_r_pkg/MEPS")
library(MEPS)
options(survey.lonely.psu="adjust")
# Load FYC file
FYC <- read.xport('C:/MEPS/.FYC..ssp');
year <- .year.
if(year <= 2001) FYC <- FYC %>% mutate(VARPSU = VARPSU.yy., VARSTR=VARSTR.yy.)
if(year <= 1998) FYC <- FYC %>% rename(PERWT.yy.F = WTDPER.yy.)
if(year == 1996) FYC <- FYC %>% mutate(AGE42X = AGE2X, AGE31X = AGE1X)
FYC <- FYC %>%
mutate_at(vars(starts_with("AGE")),funs(replace(., .< 0, NA))) %>%
mutate(AGELAST = coalesce(AGE.yy.X, AGE42X, AGE31X))
FYC$ind = 1
# Add aggregate event variables
FYC <- FYC %>% mutate(
HHTEXP.yy. = HHAEXP.yy. + HHNEXP.yy., # Home Health Agency + Independent providers
ERTEXP.yy. = ERFEXP.yy. + ERDEXP.yy., # Doctor + Facility Expenses for OP, ER, IP events
IPTEXP.yy. = IPFEXP.yy. + IPDEXP.yy.,
OPTEXP.yy. = OPFEXP.yy. + OPDEXP.yy., # All Outpatient
OPYEXP.yy. = OPVEXP.yy. + OPSEXP.yy., # Physician only
OPZEXP.yy. = OPOEXP.yy. + OPPEXP.yy., # Non-physician only
OMAEXP.yy. = VISEXP.yy. + OTHEXP.yy.) # Other medical equipment and services
FYC <- FYC %>% mutate(
TOTUSE.yy. = ((DVTOT.yy. > 0) + (RXTOT.yy. > 0) + (OBTOTV.yy. > 0) +
(OPTOTV.yy. > 0) + (ERTOT.yy. > 0) + (IPDIS.yy. > 0) +
(HHTOTD.yy. > 0) + (OMAEXP.yy. > 0))
)
# Age groups
# To compute for all age groups, replace 'agegrps' in the 'svyby' function with 'agegrps_v2X' or 'agegrps_v3X'
FYC <- FYC %>%
mutate(agegrps = cut(AGELAST,
breaks = c(-1, 4.5, 17.5, 44.5, 64.5, Inf),
labels = c("Under 5","5-17","18-44","45-64","65+"))) %>%
mutate(agegrps_v2X = cut(AGELAST,
breaks = c(-1, 17.5 ,64.5, Inf),
labels = c("Under 18","18-64","65+"))) %>%
mutate(agegrps_v3X = cut(AGELAST,
breaks = c(-1, 4.5, 6.5, 12.5, 17.5, 18.5, 24.5, 29.5, 34.5, 44.5, 54.5, 64.5, Inf),
labels = c("Under 5", "5-6", "7-12", "13-17", "18", "19-24", "25-29",
"30-34", "35-44", "45-54", "55-64", "65+")))
FYCdsgn <- svydesign(
id = ~VARPSU,
strata = ~VARSTR,
weights = ~PERWT.yy.F,
data = FYC,
nest = TRUE)
# Loop over event types
events <- c("TOT", "DVT", "RX", "OBV", "OBD", "OBO",
"OPT", "OPY", "OPZ", "ERT", "IPT", "HHT", "OMA")
results <- list()
for(ev in events) {
key <- paste0(ev, "EXP", ".yy.")
formula <- as.formula(sprintf("~%s", key))
results[[key]] <- svyby(formula, FUN = svyquantile, by = ~agegrps, design = subset(FYCdsgn, FYC[[key]] > 0), quantiles=c(0.5), ci=T, method="constant")
}
print(results)
| /mepstrends/hc_use/json/code/r/medEXP__agegrps__event__.r | permissive | RandomCriticalAnalysis/MEPS-summary-tables | R | false | false | 2,807 | r | # Install and load packages
package_names <- c("survey","dplyr","foreign","devtools")
lapply(package_names, function(x) if(!x %in% installed.packages()) install.packages(x))
lapply(package_names, require, character.only=T)
install_github("e-mitchell/meps_r_pkg/MEPS")
library(MEPS)
options(survey.lonely.psu="adjust")
# Load FYC file
FYC <- read.xport('C:/MEPS/.FYC..ssp');
year <- .year.
if(year <= 2001) FYC <- FYC %>% mutate(VARPSU = VARPSU.yy., VARSTR=VARSTR.yy.)
if(year <= 1998) FYC <- FYC %>% rename(PERWT.yy.F = WTDPER.yy.)
if(year == 1996) FYC <- FYC %>% mutate(AGE42X = AGE2X, AGE31X = AGE1X)
FYC <- FYC %>%
mutate_at(vars(starts_with("AGE")),funs(replace(., .< 0, NA))) %>%
mutate(AGELAST = coalesce(AGE.yy.X, AGE42X, AGE31X))
FYC$ind = 1
# Add aggregate event variables
FYC <- FYC %>% mutate(
HHTEXP.yy. = HHAEXP.yy. + HHNEXP.yy., # Home Health Agency + Independent providers
ERTEXP.yy. = ERFEXP.yy. + ERDEXP.yy., # Doctor + Facility Expenses for OP, ER, IP events
IPTEXP.yy. = IPFEXP.yy. + IPDEXP.yy.,
OPTEXP.yy. = OPFEXP.yy. + OPDEXP.yy., # All Outpatient
OPYEXP.yy. = OPVEXP.yy. + OPSEXP.yy., # Physician only
OPZEXP.yy. = OPOEXP.yy. + OPPEXP.yy., # Non-physician only
OMAEXP.yy. = VISEXP.yy. + OTHEXP.yy.) # Other medical equipment and services
FYC <- FYC %>% mutate(
TOTUSE.yy. = ((DVTOT.yy. > 0) + (RXTOT.yy. > 0) + (OBTOTV.yy. > 0) +
(OPTOTV.yy. > 0) + (ERTOT.yy. > 0) + (IPDIS.yy. > 0) +
(HHTOTD.yy. > 0) + (OMAEXP.yy. > 0))
)
# Age groups
# To compute for all age groups, replace 'agegrps' in the 'svyby' function with 'agegrps_v2X' or 'agegrps_v3X'
FYC <- FYC %>%
mutate(agegrps = cut(AGELAST,
breaks = c(-1, 4.5, 17.5, 44.5, 64.5, Inf),
labels = c("Under 5","5-17","18-44","45-64","65+"))) %>%
mutate(agegrps_v2X = cut(AGELAST,
breaks = c(-1, 17.5 ,64.5, Inf),
labels = c("Under 18","18-64","65+"))) %>%
mutate(agegrps_v3X = cut(AGELAST,
breaks = c(-1, 4.5, 6.5, 12.5, 17.5, 18.5, 24.5, 29.5, 34.5, 44.5, 54.5, 64.5, Inf),
labels = c("Under 5", "5-6", "7-12", "13-17", "18", "19-24", "25-29",
"30-34", "35-44", "45-54", "55-64", "65+")))
FYCdsgn <- svydesign(
id = ~VARPSU,
strata = ~VARSTR,
weights = ~PERWT.yy.F,
data = FYC,
nest = TRUE)
# Loop over event types
events <- c("TOT", "DVT", "RX", "OBV", "OBD", "OBO",
"OPT", "OPY", "OPZ", "ERT", "IPT", "HHT", "OMA")
results <- list()
for(ev in events) {
key <- paste0(ev, "EXP", ".yy.")
formula <- as.formula(sprintf("~%s", key))
results[[key]] <- svyby(formula, FUN = svyquantile, by = ~agegrps, design = subset(FYCdsgn, FYC[[key]] > 0), quantiles=c(0.5), ci=T, method="constant")
}
print(results)
|
# feature selection #
#' @title Features which are used to build a tree
#' @description The name of covariates which use to partition the covariate space.
#' @param tree :Tree structure made by getTree() function
#' @return An array of characters which is the name of those covariate used in the tree
#' @details The sequence only contain unique name(Only count one time even the single covariate use to split twice or more)
#' @examples
#' data(Lung,package="compound.Cox")
#' train_Lung=Lung[which(Lung[,"train"]==TRUE),] #select training data
#' t.vec=train_Lung[,1]
#' d.vec=train_Lung[,2]
#' x.mat=train_Lung[,-c(1,2,3)]
#' res=uni.tree(t.vec,d.vec,x.mat,P.value=0.01,d0=0.01,S.plot=FALSE,score=TRUE)
#' feature.selected(res)
#' @export
feature.selected=function(tree){
#split.covariate function can record the covariate use to split in current node
split.covariate=function(tree,covariate.seq=NULL){
if(as.character(tree[[1]]$Information)[1] == "terminal node"){
return(covariate.seq)
}else{
covariate.name=as.character(tree[[1]]$Information)[4] #[4] is the name which record the name of covariate
covariate.seq=c(covariate.seq,covariate.name) #combine new selected covariate
left_subtree = tree[[2]] #[[2]] call out the left child
right_subtree = tree[[3]] #[[3]] call out the right child
return(c(split.covariate(left_subtree,covariate.seq=covariate.seq),split.covariate(right_subtree,covariate.seq=covariate.seq)))
}
}
return(unique(split.covariate(tree,covariate.seq=NULL)))
}
| /R/feature.selected.R | no_license | lichkeam/uni.survival.tree | R | false | false | 1,549 | r | # feature selection #
#' @title Features which are used to build a tree
#' @description The name of covariates which use to partition the covariate space.
#' @param tree :Tree structure made by getTree() function
#' @return An array of characters which is the name of those covariate used in the tree
#' @details The sequence only contain unique name(Only count one time even the single covariate use to split twice or more)
#' @examples
#' data(Lung,package="compound.Cox")
#' train_Lung=Lung[which(Lung[,"train"]==TRUE),] #select training data
#' t.vec=train_Lung[,1]
#' d.vec=train_Lung[,2]
#' x.mat=train_Lung[,-c(1,2,3)]
#' res=uni.tree(t.vec,d.vec,x.mat,P.value=0.01,d0=0.01,S.plot=FALSE,score=TRUE)
#' feature.selected(res)
#' @export
feature.selected=function(tree){
#split.covariate function can record the covariate use to split in current node
split.covariate=function(tree,covariate.seq=NULL){
if(as.character(tree[[1]]$Information)[1] == "terminal node"){
return(covariate.seq)
}else{
covariate.name=as.character(tree[[1]]$Information)[4] #[4] is the name which record the name of covariate
covariate.seq=c(covariate.seq,covariate.name) #combine new selected covariate
left_subtree = tree[[2]] #[[2]] call out the left child
right_subtree = tree[[3]] #[[3]] call out the right child
return(c(split.covariate(left_subtree,covariate.seq=covariate.seq),split.covariate(right_subtree,covariate.seq=covariate.seq)))
}
}
return(unique(split.covariate(tree,covariate.seq=NULL)))
}
|
library(tidyverse) # version 1.2.1
library(readxl) # version 1.0.0
library(DT) # version 0.4
library(highcharter) # version 0.5.0.9999
library(treemap) # version 2.4-2
source("admitidos-pregrado.R", encoding = 'UTF-8')
source("funciones.R", encoding = 'UTF-8')
col <- c( "#8cc63f", # verde
"#f15a24", # naranja
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
ano <- 2018
semestre <- 1 # 1 o 2 según corresponda
periodo_actual_titulo <- " 2018-I"
# Desagregaciones temáticas:
############### Edad: ###############
col <- c( "#8cc63f", # verde, 17 o menos
"#f15a24", # naranja, 18 a 20
"#0071bc", # azul vivo, 21 a 25
"#6d6666", # gris, 26 o más
"#fbb03b", # amarillo, sin información
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
CAT_EDAD_TABLA <- tabla(
datos = Consolidado,
categoria = "CAT_EDAD",
variable = 'Rango de edad - en años - del admitido',
mensaje = "Número de admitidos por grupos de edad",
titulo = "Admitidos por grupos de edad"
);CAT_EDAD_TABLA
# saveWidget(CAT_EDAD_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
CAT_EDAD_SERIE <- series(
datos = Consolidado,
categoria = "CAT_EDAD",
colores = col,
titulo = "Número de admitidos por grupos de edad (en años)",
eje = "Número de admitidos (k: miles)"
);CAT_EDAD_SERIE
# saveWidget(CAT_EDAD_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
CAT_EDAD_BARRA <- barra_vertical(
datos = Consolidado,
categoria = "CAT_EDAD",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por grupos de edad",
eje = "Número de admitidos (k: miles)"
); CAT_EDAD_BARRA
# saveWidget(CAT_EDAD_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Sexo: ###############
col <- c( "#8cc63f", # verde, hombres
"#f15a24", # naranja, mujeres
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
SEXO_TABLA <- tabla(
datos = Consolidado,
categoria = "SEXO",
variable = 'Sexo del admitido',
mensaje = "Número de admitidos por sexo",
titulo = "Admitidos por sexo"
);SEXO_TABLA
# saveWidget(SEXO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
SEXO_SERIE <- series(
datos = Consolidado,
categoria = "SEXO",
colores = col,
titulo = "Número de admitidos por sexo",
eje = "Número de admitidos (k: miles)"
);SEXO_SERIE
# saveWidget(SEXO_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
SEXO_TORTA <- torta(
datos = Consolidado,
variable = "SEXO",
colores = col,
titulo = "Admitidos por sexo",
etiqueta = "Número de admitidos",
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo
);SEXO_TORTA
# saveWidget(SEXO_TORTA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_TORTA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Estrato socioeconómico: ###############
col <- c( "#8cc63f", # verde, estrato 2 o menos
"#f15a24", # naranja, estrato 3
"#0071bc", # azul vivo, estrato 4 o más
"#6d6666", # gris, ND/NE
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
ESTRATO_TABLA <- tabla(
datos = Consolidado,
categoria = "ESTRATO",
variable = 'Estrato socioeconómico del admitido',
mensaje = "Número de admitidos según el estrato socioeconómico",
titulo = "Admitidos según el estrato socioeconómico"
);ESTRATO_TABLA
# saveWidget(ESTRATO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
ESTRATO_SERIE <- series(
datos = Consolidado,
categoria = "ESTRATO",
colores = col,
titulo = "Número de admitidos por estrato socioeconómico",
eje = "Número de admitidos (k: miles)"
);ESTRATO_SERIE
# saveWidget(ESTRATO_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
ESTRATO_TORTA <- torta(
datos = Consolidado,
variable = "ESTRATO",
colores = col,
titulo = "Admitidos por estrato socioeconómico",
etiqueta = "Número de admitidos",
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo
);ESTRATO_TORTA
# saveWidget(ESTRATO_TORTA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_TORTA.html"),
# selfcontained = F, libdir = "libraryjs")
ESTRATO_BARRA <- barra_vertical(
datos = Consolidado,
categoria = "ESTRATO",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por estrato socioeconómico",
eje = "Número de admitidos (k: miles)"
); ESTRATO_BARRA
# saveWidget(CAT_EDAD_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Área de conocimiento SNIES: ###############
col <- c( "#93278f", # morado, agronomia..
"#29abe2", # azul claro, bellas artes
"#fbb03b", # amarillo, ciencias de...
"#f15a24", # naranja, ciencias sociales...
"#0071bc", # azul vivo, economia...
"#8cc63f", # verde, ingenieria...
"#6d6666", # gris, matemáticas...
"#c1272d", # rojo, sin informacion
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
AREAC_SNIES_TABLA <- tabla(
datos = Consolidado,
categoria = "AREAC_SNIES",
variable = 'Modalidades de los admitidos por área de conocimiento (SNIES)',
mensaje = "Número de admitidos por área de conocimiento (SNIES)",
titulo = "Admitidos por área de conocimiento (SNIES)"
);AREAC_SNIES_TABLA
# saveWidget(AREAC_SNIES_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
AREAC_SNIES_SERIE <- series(
datos = Consolidado,
categoria = "AREAC_SNIES",
colores = col,
titulo = "Número de admitidos por área de conocimiento (SNIES)",
eje = "Número de admitidos"
);AREAC_SNIES_SERIE
#
# saveWidget(AREAC_SNIES_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
AREAC_SNIES_BARRA <- barra_horizontal(
datos = Consolidado,
categoria = "AREAC_SNIES",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por área de conocimiento (SNIES)",
eje = "Número de admitidos"
); AREAC_SNIES_BARRA
# saveWidget(AREAC_SNIES_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Área de conocimiento CINE: ###############
col <- c( "#29abe2", # azul claro, Administración...
"#f15a24", # naranja, Agricultura...
"#fbb03b", # amarillo, Artes y humanidades
"#0071bc", # azul vivo, Ciencias naturales...
"#93278f", # morado, Ciencias sociales...
"#8cc63f", # verde, ingenieria...
"#6d6666", # gris, Salud y ...
"#8b7355", # cafe, sin información
"#c1272d", # rojo, TIC
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
AREA_CINE_TABLA <- tabla(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
variable = 'Modalidades de los admitidos por área de conocimiento (CINE)',
mensaje = "Número de admitidos por área de conocimiento (CINE)",
titulo = "Admitidos por área de conocimiento (CINE)"
);AREA_CINE_TABLA
# saveWidget(AREA_CINE_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
AREA_CINE_SERIE <- series(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
colores = col,
titulo = "Número de admitidos por área de conocimiento (CINE)",
eje = "Número de admitidos"
);AREA_CINE_SERIE
# saveWidget(AREA_CINE_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
AREA_CINE_BARRA <- barra_horizontal(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por área de conocimiento (CINE)",
eje = "Número de admitidos"
); AREA_CINE_BARRA
# saveWidget(AREA_CINE_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Serie de: Evolución Histórica Total de Admitidos ###############
col <- c( "#8cc63f", # verde, Total
"#f15a24", # naranja
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
EVOLUCION_TABLA <- tablaall(
datos = Consolidado,
categoria = "TOTAL",
mensaje = "Número de admitidos",
titulo = "Admitidos"
);EVOLUCION_TABLA
# saveWidget(ADMITIDO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "EVOLUCION_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
EVOLUCION_SERIE <- series(
datos = Consolidado,
categoria = "TOTAL",
colores = col,
titulo = "Evolución histórica del número total de admitidos a pregrado",
eje = "Número de admitidos (k: miles)"
);EVOLUCION_SERIE
# saveWidget(EVOLUCION_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "EVOLUCION_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
| /public/slides/admitidos-graphs.R | no_license | mamaciasq/martin | R | false | false | 12,238 | r | library(tidyverse) # version 1.2.1
library(readxl) # version 1.0.0
library(DT) # version 0.4
library(highcharter) # version 0.5.0.9999
library(treemap) # version 2.4-2
source("admitidos-pregrado.R", encoding = 'UTF-8')
source("funciones.R", encoding = 'UTF-8')
col <- c( "#8cc63f", # verde
"#f15a24", # naranja
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
ano <- 2018
semestre <- 1 # 1 o 2 según corresponda
periodo_actual_titulo <- " 2018-I"
# Desagregaciones temáticas:
############### Edad: ###############
col <- c( "#8cc63f", # verde, 17 o menos
"#f15a24", # naranja, 18 a 20
"#0071bc", # azul vivo, 21 a 25
"#6d6666", # gris, 26 o más
"#fbb03b", # amarillo, sin información
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
CAT_EDAD_TABLA <- tabla(
datos = Consolidado,
categoria = "CAT_EDAD",
variable = 'Rango de edad - en años - del admitido',
mensaje = "Número de admitidos por grupos de edad",
titulo = "Admitidos por grupos de edad"
);CAT_EDAD_TABLA
# saveWidget(CAT_EDAD_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
CAT_EDAD_SERIE <- series(
datos = Consolidado,
categoria = "CAT_EDAD",
colores = col,
titulo = "Número de admitidos por grupos de edad (en años)",
eje = "Número de admitidos (k: miles)"
);CAT_EDAD_SERIE
# saveWidget(CAT_EDAD_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
CAT_EDAD_BARRA <- barra_vertical(
datos = Consolidado,
categoria = "CAT_EDAD",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por grupos de edad",
eje = "Número de admitidos (k: miles)"
); CAT_EDAD_BARRA
# saveWidget(CAT_EDAD_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "CAT_EDAD_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Sexo: ###############
col <- c( "#8cc63f", # verde, hombres
"#f15a24", # naranja, mujeres
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
SEXO_TABLA <- tabla(
datos = Consolidado,
categoria = "SEXO",
variable = 'Sexo del admitido',
mensaje = "Número de admitidos por sexo",
titulo = "Admitidos por sexo"
);SEXO_TABLA
# saveWidget(SEXO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
SEXO_SERIE <- series(
datos = Consolidado,
categoria = "SEXO",
colores = col,
titulo = "Número de admitidos por sexo",
eje = "Número de admitidos (k: miles)"
);SEXO_SERIE
# saveWidget(SEXO_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
SEXO_TORTA <- torta(
datos = Consolidado,
variable = "SEXO",
colores = col,
titulo = "Admitidos por sexo",
etiqueta = "Número de admitidos",
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo
);SEXO_TORTA
# saveWidget(SEXO_TORTA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "SEXO_TORTA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Estrato socioeconómico: ###############
col <- c( "#8cc63f", # verde, estrato 2 o menos
"#f15a24", # naranja, estrato 3
"#0071bc", # azul vivo, estrato 4 o más
"#6d6666", # gris, ND/NE
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
ESTRATO_TABLA <- tabla(
datos = Consolidado,
categoria = "ESTRATO",
variable = 'Estrato socioeconómico del admitido',
mensaje = "Número de admitidos según el estrato socioeconómico",
titulo = "Admitidos según el estrato socioeconómico"
);ESTRATO_TABLA
# saveWidget(ESTRATO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
ESTRATO_SERIE <- series(
datos = Consolidado,
categoria = "ESTRATO",
colores = col,
titulo = "Número de admitidos por estrato socioeconómico",
eje = "Número de admitidos (k: miles)"
);ESTRATO_SERIE
# saveWidget(ESTRATO_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
ESTRATO_TORTA <- torta(
datos = Consolidado,
variable = "ESTRATO",
colores = col,
titulo = "Admitidos por estrato socioeconómico",
etiqueta = "Número de admitidos",
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo
);ESTRATO_TORTA
# saveWidget(ESTRATO_TORTA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_TORTA.html"),
# selfcontained = F, libdir = "libraryjs")
ESTRATO_BARRA <- barra_vertical(
datos = Consolidado,
categoria = "ESTRATO",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por estrato socioeconómico",
eje = "Número de admitidos (k: miles)"
); ESTRATO_BARRA
# saveWidget(CAT_EDAD_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "ESTRATO_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Área de conocimiento SNIES: ###############
col <- c( "#93278f", # morado, agronomia..
"#29abe2", # azul claro, bellas artes
"#fbb03b", # amarillo, ciencias de...
"#f15a24", # naranja, ciencias sociales...
"#0071bc", # azul vivo, economia...
"#8cc63f", # verde, ingenieria...
"#6d6666", # gris, matemáticas...
"#c1272d", # rojo, sin informacion
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
AREAC_SNIES_TABLA <- tabla(
datos = Consolidado,
categoria = "AREAC_SNIES",
variable = 'Modalidades de los admitidos por área de conocimiento (SNIES)',
mensaje = "Número de admitidos por área de conocimiento (SNIES)",
titulo = "Admitidos por área de conocimiento (SNIES)"
);AREAC_SNIES_TABLA
# saveWidget(AREAC_SNIES_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
AREAC_SNIES_SERIE <- series(
datos = Consolidado,
categoria = "AREAC_SNIES",
colores = col,
titulo = "Número de admitidos por área de conocimiento (SNIES)",
eje = "Número de admitidos"
);AREAC_SNIES_SERIE
#
# saveWidget(AREAC_SNIES_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
AREAC_SNIES_BARRA <- barra_horizontal(
datos = Consolidado,
categoria = "AREAC_SNIES",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por área de conocimiento (SNIES)",
eje = "Número de admitidos"
); AREAC_SNIES_BARRA
# saveWidget(AREAC_SNIES_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREAC_SNIES_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Área de conocimiento CINE: ###############
col <- c( "#29abe2", # azul claro, Administración...
"#f15a24", # naranja, Agricultura...
"#fbb03b", # amarillo, Artes y humanidades
"#0071bc", # azul vivo, Ciencias naturales...
"#93278f", # morado, Ciencias sociales...
"#8cc63f", # verde, ingenieria...
"#6d6666", # gris, Salud y ...
"#8b7355", # cafe, sin información
"#c1272d", # rojo, TIC
"#855b5b", # vinotinto
"#ed1e79") # rosado
################ 1. Tabla
AREA_CINE_TABLA <- tabla(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
variable = 'Modalidades de los admitidos por área de conocimiento (CINE)',
mensaje = "Número de admitidos por área de conocimiento (CINE)",
titulo = "Admitidos por área de conocimiento (CINE)"
);AREA_CINE_TABLA
# saveWidget(AREA_CINE_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
################ 2. Serie
AREA_CINE_SERIE <- series(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
colores = col,
titulo = "Número de admitidos por área de conocimiento (CINE)",
eje = "Número de admitidos"
);AREA_CINE_SERIE
# saveWidget(AREA_CINE_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
################ 3. Actual
AREA_CINE_BARRA <- barra_horizontal(
datos = Consolidado %>%
filter(is.na(Clase)==FALSE),
categoria = "AREA_CINE",
colores = col,
ano = ano,
periodo = semestre,
periodo_titulo = periodo_actual_titulo,
titulo = "Admitidos por área de conocimiento (CINE)",
eje = "Número de admitidos"
); AREA_CINE_BARRA
# saveWidget(AREA_CINE_BARRA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "AREA_CINE_BARRA.html"),
# selfcontained = F, libdir = "libraryjs")
############### Serie de: Evolución Histórica Total de Admitidos ###############
col <- c( "#8cc63f", # verde, Total
"#f15a24", # naranja
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
EVOLUCION_TABLA <- tablaall(
datos = Consolidado,
categoria = "TOTAL",
mensaje = "Número de admitidos",
titulo = "Admitidos"
);EVOLUCION_TABLA
# saveWidget(ADMITIDO_TABLA,
# file = file.path(getwd(), "Resultados/Admitidos",
# "EVOLUCION_TABLA.html"),
# selfcontained = F, libdir = "libraryjs")
EVOLUCION_SERIE <- series(
datos = Consolidado,
categoria = "TOTAL",
colores = col,
titulo = "Evolución histórica del número total de admitidos a pregrado",
eje = "Número de admitidos (k: miles)"
);EVOLUCION_SERIE
# saveWidget(EVOLUCION_SERIE,
# file = file.path(getwd(), "Resultados/Admitidos",
# "EVOLUCION_SERIE.html"),
# selfcontained = F, libdir = "libraryjs")
|
library(stringr)
setwd("D:/code/Parser/Parser_for_Biodiversity_Checklists_Formal")
getSciname <- function(x){
sciName_index <- grep("^[0-9]", x[, 1])
sciName <- x[sciName_index,]
sciName <- gsub("^[0-9]+. ", "", sciName)
return(sciName)
}
getDis <- function(x,length){
Distribution = c()
n = 0
cur_dis = ""
for (i in 1:length) {
if (str_detect(x[i, 1], "Distribution:") &&
(str_detect(x[i, 1], "[*. ]$"))) {
n = n + 1
cur_dis = str_split(x[i, 1], "Distribution: ")[[1]][2]
}
Distribution[n] = cur_dis
if ((str_detect(x[i, 1], "Distribution:")) &&
(str_detect(x[i, 1], "[*. ]$") == FALSE)) {
n = n + 1
dstr_line = ""
dstr_line = x[i, 1]
if (i < length - 1) {
j = i + 1
}
while (j < length - 1) {
if (grepl("^[0-9]", x[j + 1, 1]) |
grepl("^[[:alpha:]]*$", x[j + 1, 1])) {
dstr_line <- paste(dstr_line, x[j, 1])
break
} else{
dstr_line <- paste(dstr_line, x[j, 1])
j = j + 1
}
}
cur_dis <- str_split(dstr_line, "Distribution: ")[[1]][2]
dstr_line <- ""
}
Distribution[n] <- cur_dis
}
return(Distribution)
}
getFamily <- function(x,sciName){
family_index = grep("^[[:alpha:]]*$", x[, 1])
family_name = x[family_index,]
total_num = length(sciName)
cur_index = c()
cur_num = c()
for (i in 1:length(family_index)) {
cur_index[i] <-
(str_match(as.character(x[family_index[i] + 1, 1]), "^[0-9]+. "))
cur_index[i] <- str_split(cur_index[i], "[\\.]")[[1]][1]
cur_num[i] <- as.integer(cur_index[i])
}
cur_num <- c(cur_num, total_num + 1)
Family <- rep(family_name, diff(cur_num))
return(Family)
}
parse_taxolist <- function(filepath,filename,type,sep,output){
# read input file
file <- read_file(filepath, filename,type,sep)
length = length(file[, 1])
sciName = getSciname(file)
Distribution = getDis(file,length)
Family = getFamily(file,sciName)
full_scientific_name = sciName
genus = str_extract_all(full_scientific_name,"^[[:blank:]]?[A-z]+")
rest1 = gsub("^[[:blank:]]?[A-z]+","",full_scientific_name)
author = str_extract_all(rest1,"[A-Z]{1}.?\\s?&?\\s?[A-z]+.?(.*?)?,")
year = str_extract_all(full_scientific_name,"\\s?[0-9]+")
table = cbind(as.data.frame(Family),as.data.frame(Distribution))
table$genus = 1
table$author = 1
table$year = 1
for (i in 1:nrow(table)){
table[i,3] = genus[i]
table[i,4] = author[i]
table[i,5] = year[i]
}
write.csv(table, file = output, row.names = F)
return(table)
}
read_file < -function(filepath, filename,type,sep){
if (tolower(type) == "txt") {
file_path_name<-paste(filepath,"/",filename,sep="")
file <- read.table(file_path_name, sep = sep, header = FALSE)
}
if (tolower(type) == "csv") {
file_path_name<-paste(filepath,"/",filename,sep="")
file <- read.csv(file_path_name, sep = sep, header = FALSE)
}
if (tolower(type) == 'pdf') {
pdf_file <- file.path(filepath,filename)
context <- pdf_text(pdf_file)
file <- cat(context)
}
}
# a = parse_taxolist("taxo01.txt","txt","\t","taxo_out01.csv")
# b = parse_taxolist("testest.csv","csv","\t","taxo_out02.csv")
| /parse_taxolist.R | no_license | XingXiong/Parser_for_Biodiversity_Checklists_2017 | R | false | false | 3,239 | r | library(stringr)
setwd("D:/code/Parser/Parser_for_Biodiversity_Checklists_Formal")
getSciname <- function(x){
sciName_index <- grep("^[0-9]", x[, 1])
sciName <- x[sciName_index,]
sciName <- gsub("^[0-9]+. ", "", sciName)
return(sciName)
}
getDis <- function(x,length){
Distribution = c()
n = 0
cur_dis = ""
for (i in 1:length) {
if (str_detect(x[i, 1], "Distribution:") &&
(str_detect(x[i, 1], "[*. ]$"))) {
n = n + 1
cur_dis = str_split(x[i, 1], "Distribution: ")[[1]][2]
}
Distribution[n] = cur_dis
if ((str_detect(x[i, 1], "Distribution:")) &&
(str_detect(x[i, 1], "[*. ]$") == FALSE)) {
n = n + 1
dstr_line = ""
dstr_line = x[i, 1]
if (i < length - 1) {
j = i + 1
}
while (j < length - 1) {
if (grepl("^[0-9]", x[j + 1, 1]) |
grepl("^[[:alpha:]]*$", x[j + 1, 1])) {
dstr_line <- paste(dstr_line, x[j, 1])
break
} else{
dstr_line <- paste(dstr_line, x[j, 1])
j = j + 1
}
}
cur_dis <- str_split(dstr_line, "Distribution: ")[[1]][2]
dstr_line <- ""
}
Distribution[n] <- cur_dis
}
return(Distribution)
}
getFamily <- function(x,sciName){
family_index = grep("^[[:alpha:]]*$", x[, 1])
family_name = x[family_index,]
total_num = length(sciName)
cur_index = c()
cur_num = c()
for (i in 1:length(family_index)) {
cur_index[i] <-
(str_match(as.character(x[family_index[i] + 1, 1]), "^[0-9]+. "))
cur_index[i] <- str_split(cur_index[i], "[\\.]")[[1]][1]
cur_num[i] <- as.integer(cur_index[i])
}
cur_num <- c(cur_num, total_num + 1)
Family <- rep(family_name, diff(cur_num))
return(Family)
}
parse_taxolist <- function(filepath,filename,type,sep,output){
# read input file
file <- read_file(filepath, filename,type,sep)
length = length(file[, 1])
sciName = getSciname(file)
Distribution = getDis(file,length)
Family = getFamily(file,sciName)
full_scientific_name = sciName
genus = str_extract_all(full_scientific_name,"^[[:blank:]]?[A-z]+")
rest1 = gsub("^[[:blank:]]?[A-z]+","",full_scientific_name)
author = str_extract_all(rest1,"[A-Z]{1}.?\\s?&?\\s?[A-z]+.?(.*?)?,")
year = str_extract_all(full_scientific_name,"\\s?[0-9]+")
table = cbind(as.data.frame(Family),as.data.frame(Distribution))
table$genus = 1
table$author = 1
table$year = 1
for (i in 1:nrow(table)){
table[i,3] = genus[i]
table[i,4] = author[i]
table[i,5] = year[i]
}
write.csv(table, file = output, row.names = F)
return(table)
}
read_file < -function(filepath, filename,type,sep){
if (tolower(type) == "txt") {
file_path_name<-paste(filepath,"/",filename,sep="")
file <- read.table(file_path_name, sep = sep, header = FALSE)
}
if (tolower(type) == "csv") {
file_path_name<-paste(filepath,"/",filename,sep="")
file <- read.csv(file_path_name, sep = sep, header = FALSE)
}
if (tolower(type) == 'pdf') {
pdf_file <- file.path(filepath,filename)
context <- pdf_text(pdf_file)
file <- cat(context)
}
}
# a = parse_taxolist("taxo01.txt","txt","\t","taxo_out01.csv")
# b = parse_taxolist("testest.csv","csv","\t","taxo_out02.csv")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/noTouch.R
\name{noTouch}
\alias{noTouch}
\title{Extract and parse YAML metadata}
\usage{
noTouch(file = NULL)
}
\arguments{
\item{file}{Path to the metadata.yaml file}
}
\description{
Process metadata.yml content for use in a template
}
\details{
This function is only intended to be called from
within the YAML header of a Rmarkdown template provided
by the DODschools package. When this function in invoked,
the YAML metadata is read from \code{file}, processed and
then inserted in the appropriate field as needed by the template.
Calls to this function made within the YAML header of an Rmarkdown
document should not be touched (hence the name). Changes to a
document's metadata should be made in the metadata.yml file.
}
| /man/noTouch.Rd | no_license | Auburngrads/DODschools | R | false | true | 871 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/noTouch.R
\name{noTouch}
\alias{noTouch}
\title{Extract and parse YAML metadata}
\usage{
noTouch(file = NULL)
}
\arguments{
\item{file}{Path to the metadata.yaml file}
}
\description{
Process metadata.yml content for use in a template
}
\details{
This function is only intended to be called from
within the YAML header of a Rmarkdown template provided
by the DODschools package. When this function in invoked,
the YAML metadata is read from \code{file}, processed and
then inserted in the appropriate field as needed by the template.
Calls to this function made within the YAML header of an Rmarkdown
document should not be touched (hence the name). Changes to a
document's metadata should be made in the metadata.yml file.
}
|
sci_figure <- function (experiments, hide_stages = NULL, names_of_stages = TRUE)
{
if (!all(unlist(lapply(experiments, function(x) {
x %in% c("observed", "different", "unobserved", "incorrect")
})))) {
stop("Invalid cell value in experiments data frame.")
}
if (ncol(experiments) > 20) {
experiments <- experiments[, 1:20]
warning("Only showing the first 20 experiments for ease of plotting.")
}
idx <- !(rownames(experiments) %in% hide_stages)
stage_names <- c("Población", "Pregunta", "Hipótesis",
"Diseño Exp.", "Experimentador", "Datos", "Plan de Análisis",
"Analista", "Código", "Estimador", "Resultados")
stage_names <- stage_names[idx]
experiments <- experiments[idx, , drop = FALSE]
grid::grid.newpage()
gptext <- grid::gpar(fontsize = 16 - min(nrow(experiments),
7))
yht <- seq(0.95, 0.05, length = nrow(experiments))
if (names_of_stages) {
vp1 <- grid::viewport(x = 0.1, y = 0.5, width = 0.2,
height = 0.9)
grid::pushViewport(vp1)
grid::grid.text(stage_names, x = 0.9, y = yht, gp = gptext)
grid::upViewport()
}
icons <- scifigure::icons
vp2 <- grid::viewport(x = 0.5, y = 0.5, width = 0.6, height = 0.9)
grid::pushViewport(vp2)
for (j in 1:ncol(experiments)) {
for (i in 1:nrow(experiments)) {
grid::grid.raster(icons[[paste(rownames(experiments)[i],
experiments[i, j], sep = "_")]], x = j/(ncol(experiments) +
1), y = yht[i], height = 0.08 - 0.03 * (ncol(experiments) >
4), width = grid::unit(max(0.05, min(0.1, 1/((ncol(experiments) *
3)))), "snpc"))
}
}
grid::upViewport()
vp3 <- grid::viewport(x = 0.5, y = 0.95, width = 0.6, height = 0.1)
grid::pushViewport(vp3)
grid::grid.text(colnames(experiments), x = (1:ncol(experiments))/(ncol(experiments) +
1), y = 0.7, gp = gptext, rot = ifelse(ncol(experiments) >
12, 90, 0))
grid::upViewport()
vp4 <- grid::viewport(x = 0.9, y = 0.5, width = 0.2, height = 0.6)
grid::pushViewport(vp4)
cols <- c("#D20000", "#007888", "#CDCDCD", "black")
grid::grid.rect(width = 0.25, height = 0.1, x = 0.3, y = c(0.2,
0.4, 0.6, 0.8), gp = grid::gpar(fill = cols))
grid::grid.text(c("Incorrecto", "Diferente", "No Observado",
"Original"), x = 0.3, y = c(0.1, 0.3, 0.5, 0.7), gp = grid::gpar(fontsize = 14))
} | /src/sci_figure_ES.R | no_license | EstadisticaUNTDF/EACN-2018 | R | false | false | 3,048 | r | sci_figure <- function (experiments, hide_stages = NULL, names_of_stages = TRUE)
{
if (!all(unlist(lapply(experiments, function(x) {
x %in% c("observed", "different", "unobserved", "incorrect")
})))) {
stop("Invalid cell value in experiments data frame.")
}
if (ncol(experiments) > 20) {
experiments <- experiments[, 1:20]
warning("Only showing the first 20 experiments for ease of plotting.")
}
idx <- !(rownames(experiments) %in% hide_stages)
stage_names <- c("Población", "Pregunta", "Hipótesis",
"Diseño Exp.", "Experimentador", "Datos", "Plan de Análisis",
"Analista", "Código", "Estimador", "Resultados")
stage_names <- stage_names[idx]
experiments <- experiments[idx, , drop = FALSE]
grid::grid.newpage()
gptext <- grid::gpar(fontsize = 16 - min(nrow(experiments),
7))
yht <- seq(0.95, 0.05, length = nrow(experiments))
if (names_of_stages) {
vp1 <- grid::viewport(x = 0.1, y = 0.5, width = 0.2,
height = 0.9)
grid::pushViewport(vp1)
grid::grid.text(stage_names, x = 0.9, y = yht, gp = gptext)
grid::upViewport()
}
icons <- scifigure::icons
vp2 <- grid::viewport(x = 0.5, y = 0.5, width = 0.6, height = 0.9)
grid::pushViewport(vp2)
for (j in 1:ncol(experiments)) {
for (i in 1:nrow(experiments)) {
grid::grid.raster(icons[[paste(rownames(experiments)[i],
experiments[i, j], sep = "_")]], x = j/(ncol(experiments) +
1), y = yht[i], height = 0.08 - 0.03 * (ncol(experiments) >
4), width = grid::unit(max(0.05, min(0.1, 1/((ncol(experiments) *
3)))), "snpc"))
}
}
grid::upViewport()
vp3 <- grid::viewport(x = 0.5, y = 0.95, width = 0.6, height = 0.1)
grid::pushViewport(vp3)
grid::grid.text(colnames(experiments), x = (1:ncol(experiments))/(ncol(experiments) +
1), y = 0.7, gp = gptext, rot = ifelse(ncol(experiments) >
12, 90, 0))
grid::upViewport()
vp4 <- grid::viewport(x = 0.9, y = 0.5, width = 0.2, height = 0.6)
grid::pushViewport(vp4)
cols <- c("#D20000", "#007888", "#CDCDCD", "black")
grid::grid.rect(width = 0.25, height = 0.1, x = 0.3, y = c(0.2,
0.4, 0.6, 0.8), gp = grid::gpar(fill = cols))
grid::grid.text(c("Incorrecto", "Diferente", "No Observado",
"Original"), x = 0.3, y = c(0.1, 0.3, 0.5, 0.7), gp = grid::gpar(fontsize = 14))
} |
i_row <- function (x, i) x[i, ]
splt_REs <- function (b_mat, ind_RE) {
n <- length(ind_RE)
out <- vector("list", n)
for (i in seq_len(n)) out[[i]] <- b_mat[, ind_RE[[i]], drop = FALSE]
out
}
linpred_long <- function (X, betas, Z, b, id, type) {
out <- vector("list", length(X))
for (i in seq_along(X)) {
out[[i]] <- c(X[[i]] %*% betas[[i]])
if (type == "subject_specific") {
out[[i]] <- out[[i]] +
as.vector(rowSums(Z[[i]] * b[[i]][id[[i]], , drop = FALSE]))
}
}
out
}
mu_fun <- function (eta, link) {
switch (link, "identity" = eta, "inverse" = 1 / eta, "logit" = plogis(eta),
"probit" = pnorm(eta), "cloglog" = - exp(- exp(eta)) + 1.0,
"log" = exp(eta))
}
fix_NAs_preds <- function (preds, NAs, n) {
if (is.null(NAs)) preds else {
r <- rep(as.numeric(NA), n)
r[-NAs] <- preds
r
}
}
get_components_newdata <- function (object, newdata, n_samples, n_mcmc,
cores, seed) {
if (!exists(".Random.seed", envir = .GlobalEnv)) {
runif(1L)
}
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
# control
control <- object$control
# check for tibbles
if (inherits(newdata, "tbl_df") || inherits(newdata, "tbl")) {
newdata <- as.data.frame(newdata)
}
# extract idVar and time_var
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
# set dataL as newdata; almost the same code as in jm()
dataL <- if (!is.data.frame(newdata)) newdata[["newdataL"]] else newdata
idL <- dataL[[idVar]]
nY <- length(unique(idL))
# order data by idL and time_var
if (is.null(dataL[[time_var]])) {
stop("the variable specified in agument 'time_var' cannot be found ",
"in the database of the longitudinal models.")
}
dataL <- dataL[order(idL, dataL[[time_var]]), ]
# extract terms
respVars <- object$model_info$var_names$respVars
terms_FE <- object$model_info$terms$terms_FE
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
terms_Surv <- object$model_info$terms$terms_Surv_noResp
Xbar <- object$model_data$Xbar
# create model frames
mf_FE_dataL <- lapply(terms_FE, model.frame.default, data = dataL)
mf_RE_dataL <- lapply(terms_RE, model.frame.default, data = dataL)
# we need to account for missing data in the fixed and random effects model frames,
# in parallel across outcomes (i.e., we will allow that some subjects may have no data
# for some outcomes)
NAs_FE_dataL <- lapply(mf_FE_dataL, attr, "na.action")
NAs_RE_dataL <- lapply(mf_RE_dataL, attr, "na.action")
mf_FE_dataL <- mapply2(fix_NAs_fixed, mf_FE_dataL, NAs_FE_dataL, NAs_RE_dataL)
mf_RE_dataL <- mapply2(fix_NAs_random, mf_RE_dataL, NAs_RE_dataL, NAs_FE_dataL)
# create response vectors
y <- lapply(mf_FE_dataL, model.response)
y <- lapply(y, function (yy) {
if (is.factor(yy)) as.numeric(yy != levels(yy)[1L]) else yy
})
y[] <- lapply(y, as.matrix)
NAs <- mapply2(c, NAs_FE_dataL, NAs_RE_dataL)
times_y <- lapply(NAs, function (ind) if (!is.null(ind))
dataL[[time_var]][-ind] else dataL[[time_var]])
families <- object$model_info$families
family_names <- sapply(families, "[[", "family")
links <- sapply(families, "[[", "link")
# for family = binomial and when y has two columns, set the second column
# to the number of trials instead the number of failures
binomial_data <- family_names %in% c("binomial", "beta binomial")
trials_fun <- function (y) {
if (NCOL(y) == 2L) y[, 2L] <- y[, 1L] + y[, 2L]
y
}
y[binomial_data] <- lapply(y[binomial_data], trials_fun)
unq_id <- unique(idL)
idL <- mapply2(exclude_NAs, NAs_FE_dataL, NAs_RE_dataL,
MoreArgs = list(id = idL))
idL <- lapply(idL, match, table = unq_id)
idL_lp <- lapply(idL, function (x) match(x, unique(x)))
unq_idL <- lapply(idL, unique)
X <- mapply2(model.matrix.default, terms_FE, mf_FE_dataL)
Z <- mapply2(model.matrix.default, terms_RE, mf_RE_dataL)
################################
# extract terms
terms_Surv <- object$model_info$terms$terms_Surv
terms_Surv_noResp <- object$model_info$terms$terms_Surv_noResp
type_censoring <- object$model_info$type_censoring
dataS <- if (!is.data.frame(newdata)) newdata[["newdataE"]] else newdata
CR_MS <- object$model_info$CR_MS
if (!CR_MS) {
idT <- dataS[[idVar]]
dataS <- dataS[tapply(row.names(dataS), factor(idT, unique(idT)), tail, 1L), ]
}
idT <- dataS[[idVar]]
mf_surv_dataS <- model.frame.default(terms_Surv, data = dataS)
if (!is.null(NAs_surv <- attr(mf_surv_dataS, "na.action"))) {
idT <- idT[-NAs_surv]
dataS <- dataS[-NAs_surv, ]
}
idT <- factor(idT, levels = unique(idT))
nT <- length(unique(idT))
if (nY != nT) {
stop("the number of groups/subjects in the longitudinal and survival datasets ",
"do not seem to match. A potential reason why this may be happening is ",
"missing data in some covariates used in the individual models.")
}
Surv_Response <- model.response(mf_surv_dataS)
if (type_censoring == "right") {
Time_right <- unname(Surv_Response[, "time"])
Time_left <- Time_start <- trunc_Time <- rep(0.0, nrow(dataS))
delta <- unname(Surv_Response[, "status"])
} else if (type_censoring == "counting") {
Time_start <- unname(Surv_Response[, "start"])
Time_stop <- unname(Surv_Response[, "stop"])
delta <- unname(Surv_Response[, "status"])
Time_right <- Time_stop
trunc_Time <- Time_start # possible left truncation time
Time_left <- rep(0.0, nrow(dataS))
} else if (type_censoring == "interval") {
Time1 <- unname(Surv_Response[, "time1"])
Time2 <- unname(Surv_Response[, "time2"])
trunc_Time <- Time_start <- rep(0.0, nrow(dataS))
delta <- unname(Surv_Response[, "status"])
Time_right <- Time1
Time_right[delta == 3] <- Time2[delta == 3]
Time_right[delta == 2] <- 0.0
Time_left <- Time1
Time_left[delta <= 1] <- 0.0
}
if (type_censoring != "counting") {
names(Time_right) <- names(Time_left) <- names(Time_start) <- idT
}
which_event <- which(delta == 1)
which_right <- which(delta == 0)
which_left <- which(delta == 2)
which_interval <- which(delta == 3)
# extract strata if present otherwise all subjects in one stratum
ind_strata <- attr(terms_Surv, "specials")$strata
strata <- if (is.null(ind_strata)) {
rep(1, nrow(mf_surv_dataS))
} else {
unclass(mf_surv_dataS[[ind_strata]])
}
Time_integration <- Time_right
Time_integration[which_left] <- Time_left[which_left]
Time_integration[which_interval] <- Time_left[which_interval]
Time_integration2 <- rep(0.0, length(Time_integration))
if (length(which_interval)) {
Time_integration2[which_interval] <- Time_right[which_interval]
}
last_times <- switch(type_censoring,
"right" = unname(Surv_Response[, "time"]),
"counting" = unname(Surv_Response[, "stop"]),
"interval" = unname(Surv_Response[, "time1"]))
# create Gauss Kronrod points and weights
GK <- gaussKronrod(control$GK_k)
sk <- GK$sk
P <- c(Time_integration - trunc_Time) / 2
st <- outer(P, sk) + (c(Time_integration + trunc_Time) / 2)
log_Pwk <- unname(rep(log(P), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st)))
if (length(which_interval)) {
# we take the absolute value because for the subjects for whom we do not have
# interval censoring P2 will be negative and this will produce a NA when we take
# the log in 'log_Pwk2'
P2 <- abs(Time_integration2 - Time_integration) / 2
st2 <- outer(P2, sk) + (c(Time_integration2 + Time_integration) / 2)
log_Pwk2 <- rep(log(P2), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st2))
} else {
P2 <- st2 <- log_Pwk2 <- rep(0.0, nT * control$GK_k)
}
# knots for the log baseline hazard function
knots <- control$knots
# indices
ni_event <- tapply(idT, idT, length)
ni_event <- cbind(c(0, head(cumsum(ni_event), -1)), cumsum(ni_event))
id_H <- rep(paste0(idT, "_", unlist(tapply(idT, idT, seq_along))),
each = control$GK_k)
id_H <- match(id_H, unique(id_H))
# id_H_ repeats each unique idT the number of quadrature points
id_H_ <- rep(idT, each = control$GK_k)
id_H_ <- match(id_H_, unique(id_H_))
id_h <- unclass(idT)
# Functional forms
functional_forms <- object$model_info$functional_forms
FunForms_per_outcome <- object$model_info$FunForms_per_outcome
collapsed_functional_forms <- object$model_info$collapsed_functional_forms
FunForms_cpp <- object$model_info$FunForms_cpp
FunForms_ind <- object$model_info$FunForms_ind
Funs_FunForms <- object$model_info$Funs_FunForms
eps <- object$model_info$eps
direction <- object$model_info$direction
# Design matrices
strata_H <- rep(strata, each = control$GK_k)
W0_H <- create_W0(c(t(st)), knots, control$Bsplines_degree + 1, strata_H)
dataS_H <- SurvData_HazardModel(st, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_H)
W_H <- construct_Wmat(terms_Surv_noResp, mf)
any_gammas <- as.logical(ncol(W_H))
if (!any_gammas) {
W_H <- matrix(0.0, nrow = nrow(W_H), ncol = 1L)
}
attr <- lapply(functional_forms, extract_attributes, data = dataS_H)
eps <- lapply(attr, "[[", 1L)
direction <- lapply(attr, "[[", 2L)
X_H <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H <- lapply(functional_forms, construct_Umat, dataS = dataS_H)
if (length(which_event)) {
W0_h <- create_W0(Time_right, knots, control$Bsplines_degree + 1,
strata)
dataS_h <- SurvData_HazardModel(Time_right, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_h)
W_h <- construct_Wmat(terms_Surv_noResp, mf)
if (!any_gammas) {
W_h <- matrix(0.0, nrow = nrow(W_h), ncol = 1L)
}
X_h <- design_matrices_functional_forms(Time_right, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_h <- design_matrices_functional_forms(Time_right, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_h <- lapply(functional_forms, construct_Umat, dataS = dataS_h)
} else {
W0_h <- W_h <- matrix(0.0)
X_h <- Z_h <- U_h <- rep(list(matrix(0.0)), length(respVars))
}
if (length(which_interval)) {
W0_H2 <- create_W0(c(t(st2)), knots, control$Bsplines_degree + 1,
strata_H)
dataS_H2 <- SurvData_HazardModel(st2, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf2 <- model.frame.default(terms_Surv_noResp, data = dataS_H2)
W_h <- construct_Wmat(terms_Surv_noResp, mf2)
if (!any_gammas) {
W_H2 <- matrix(0.0, nrow = nrow(W_H2), ncol = 1L)
}
X_H2 <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H2 <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H2 <- lapply(functional_forms, construct_Umat, dataS = dataS_H2)
} else {
W0_H2 <- W_H2 <- matrix(0.0)
X_H2 <- Z_H2 <- U_H2 <- rep(list(matrix(0.0)), length(respVars))
}
X_H[] <- lapply(X_H, docall_cbind)
X_h[] <- lapply(X_h, docall_cbind)
X_H2[] <- lapply(X_H2, docall_cbind)
Z_H[] <- lapply(Z_H, docall_cbind)
Z_h[] <- lapply(Z_h, docall_cbind)
Z_H2[] <- lapply(Z_H2, docall_cbind)
W_bar <- object$W_bar
W_sds <- object$W_sds
W_H <- center_fun(W_H, W_bar, W_sds)
W_h <- center_fun(W_h, W_bar, W_sds)
W_H2 <- center_fun(W_H2, W_bar, W_sds)
# MCMC sample
b <- lapply(sapply(Z, ncol), function (nc) matrix(0.0, nY, nc))
M <- sum(sapply(object$mcmc$bs_gammas, nrow))
get_param <- function (nam) {
tht <- object$mcmc[[nam]]
if (!is.null(tht)) docall_rbind(tht) else matrix(0.0, M, 1)
}
ind_betas <- grep("^betas", names(object$mcmc))
mcmc <- list(
b = b, bs_gammas = get_param("bs_gammas"), gammas = get_param("gammas"),
alphas = get_param("alphas"),
Wlong_std_alphas = get_param("Wlong_std_alphas"),
W_std_gammas = get_param("W_std_gammas"),
betas = lapply(object$mcmc[ind_betas], docall_rbind)
)
has_sigmas <- object$model_data$has_sigmas
mcmc$sigmas <- matrix(0.0, M, length(has_sigmas))
mcmc$sigmas[, has_sigmas > 0] <- get_param("sigmas")
D <- get_param("D")
mcmc$D <- array(0.0, c(dim(lowertri2mat(D[1L, ])), M))
for (i in seq_len(M)) {
mcmc$D[, , i] <- lowertri2mat(D[i, ])
}
Data <- list(
ind_RE = object$model_data$ind_RE,
W0_H = W0_H, W0_h = W0_h, W0_H2 = W0_H2,
W_H = W_H, W_h = W_h, W_H2 = W_H2,
X_H = X_H, X_h = X_h, X_H2 = X_H2,
Z_H = Z_H, Z_h = Z_h, Z_H2 = Z_H2,
U_H = U_H, U_h = U_h, U_H2 = U_H2,
Wlong_bar = object$Wlong_bar, Wlong_sds = object$Wlong_sds,
idT = match(idT, unique(idT)), log_Pwk = log_Pwk, log_Pwk2 = log_Pwk2,
id_H = id_H, id_H_ = id_H_, id_h = id_h, any_gammas = any_gammas,
which_event = which_event, which_right = which_right,
which_left = which_left, which_interval = which_interval,
ni_event = ni_event, FunForms_cpp = FunForms_cpp,
FunForms_ind = FunForms_ind, Funs_FunForms = Funs_FunForms,
X = X, Z = Z, y = y, family_names = family_names,
links = links, extra_parms = object$model_data$extra_parms,
unq_idL = unq_idL, idL_lp = idL_lp, idL = idL
)
if (n_samples > M) {
warning("the number of samples cannot be greater than the number of ",
"MCMC iterations in the fitted model.")
n_samples <- M
}
control <- list(GK_k = object$control$GK_k, n_samples = n_samples, n_iter = n_mcmc)
id_samples <-
split(seq_len(control$n_samples),
rep(seq_len(cores), each = ceiling(control$n_samples / cores),
length.out = control$n_samples))
sample_parallel <- function (id_samples, Data, mcmc, control) {
# keep only the samples from the MCMC used in the sampling
# of the random effects
mcmc$bs_gammas <- mcmc$bs_gammas[id_samples, , drop = FALSE]
mcmc$gammas <- mcmc$gammas[id_samples, , drop = FALSE]
mcmc$alphas <- mcmc$alphas[id_samples, , drop = FALSE]
mcmc$betas[] <- lapply(mcmc$betas, function (m, ind) m[ind, , drop = FALSE],
"ind" = id_samples)
mcmc$sigmas <- mcmc$sigmas[id_samples, , drop = FALSE]
mcmc$D <- mcmc$D[, , id_samples, drop = FALSE]
# update control n_samples
control$n_samples <- length(id_samples)
# update random effects
mcmc[["b"]] <- simulate_REs(Data, mcmc, control)
mcmc$Wlong_std_alphas <- mcmc$Wlong_std_alphas[id_samples, , drop = FALSE]
mcmc$W_std_gammas <- mcmc$W_std_gammas[id_samples, , drop = FALSE]
mcmc
}
if (cores > 1L) {
cl <- parallel::makeCluster(cores)
parallel::clusterSetRNGStream(cl = cl, iseed = seed)
out <- parallel::parLapply(cl, id_samples, sample_parallel,
Data = Data, mcmc = mcmc, control = control)
parallel::stopCluster(cl)
} else {
set.seed(seed)
out <- list(sample_parallel(id_samples[[1L]], Data = Data, mcmc = mcmc,
control = control))
}
combine <- function (x) {
n <- length(x)
res <- x[[1L]]
if (n > 1L) {
for (i in 2:n) {
res$bs_gammas <- rbind(res$bs_gammas, x[[i]][["bs_gammas"]])
res$gammas <- rbind(res$gammas, x[[i]][["gammas"]])
res$alphas <- rbind(res$alphas, x[[i]][["alphas"]])
res$sigmas <- rbind(res$sigmas, x[[i]][["sigmas"]])
res$Wlong_std_alphas <-
rbind(res$Wlong_std_alphas, x[[i]][["Wlong_std_alphas"]])
res$W_std_gammas <-
rbind(res$W_std_gammas, x[[i]][["W_std_gammas"]])
d1 <- dim(res$D)[3L]
d2 <- dim(x[[i]][["D"]])[3L]
a <- array(0.0, dim = c(dim(res$D)[1:2], d1 + d2))
a[, , seq(1, d1)] <- res$D
a[, , seq(d1 + 1, d1 + d2)] <- x[[i]][["D"]]
res$D <- a
d1 <- dim(res$b)[3L]
d2 <- dim(x[[i]][["b"]])[3L]
a <- array(0.0, dim = c(dim(res$b)[1:2], d1 + d2))
a[, , seq(1, d1)] <- res$b
a[, , seq(d1 + 1, d1 + d2)] <- x[[i]][["b"]]
res$b <- a
for (j in seq_along(res$betas)) {
res$betas[[j]] <- rbind(res$betas[[j]], x[[i]][["betas"]][[j]])
}
}
}
res
}
list(mcmc = combine(out), X = X, Z = Z, y = y, times_y = times_y, id = idL,
ind_RE = object$model_data$ind_RE, links = links,
respVars = lapply(respVars, "[", 1L),
NAs = mapply2(c, NAs_FE_dataL, NAs_RE_dataL),
last_times = last_times)
}
predict_Long <- function (object, components_newdata, newdata, newdata2, times,
type, type_pred, level, return_newdata) {
# Predictions for newdata
betas <- components_newdata$mcmc[["betas"]]
b_mat <- components_newdata$mcmc[["b"]]
ind_RE <- components_newdata$ind_RE
links <- components_newdata$links
K <- length(ind_RE)
M <- dim(b_mat)[3L]
out <- lapply(components_newdata$X, function (x) matrix(0.0, nrow(x), M))
names(out) <- components_newdata$respVars
for (i in seq_len(M)) {
eta_i <-
linpred_long(components_newdata$X, lapply(betas, i_row, i),
components_newdata$Z,
splt_REs(rbind(b_mat[, , i]), ind_RE),
components_newdata$id, type = type)
for (j in seq_len(K)) {
out[[j]][, i] <- if (type_pred == "response") {
mu_fun(eta_i[[j]], links[j])
} else eta_i[[j]]
}
}
res1 <- list(preds = lapply(out, rowMeans, na.rm = TRUE),
low = lapply(out, rowQuantiles, probs = (1 - level) / 2),
upp = lapply(out, rowQuantiles, probs = (1 + level) / 2))
if (return_newdata) {
n <- nrow(newdata)
preds <- mapply2(fix_NAs_preds, res1$preds, components_newdata$NAs,
MoreArgs = list(n = n))
names(preds) <- paste0("pred_", components_newdata$respVars)
low <- mapply2(fix_NAs_preds, res1$low, components_newdata$NAs,
MoreArgs = list(n = n))
names(low) <- paste0("low_", components_newdata$respVars)
upp <- mapply2(fix_NAs_preds, res1$upp, components_newdata$NAs,
MoreArgs = list(n = n))
names(upp) <- paste0("upp_", components_newdata$respVars)
l <- c(preds, low, upp)
l <- l[c(matrix(seq_along(l), ncol = length(preds), byrow = TRUE))]
res1 <- cbind(newdata, as.data.frame(do.call("cbind", l)))
}
############################################################################
############################################################################
# Predictions for newdata2
if (is.null(newdata2) && !is.null(times) && is.numeric(times)) {
last_times <- components_newdata$last_times
t_max <- max(object$model_data$Time_right)
test <- sapply(last_times, function (lt, tt) all(tt <= lt), tt = times)
if (any(test)) {
stop("according to the definition of argument 'times', for some ",
"subjects the last available time is\n\t larger than the ",
"maximum time to predict; redefine 'times' accordingly.")
}
f <- function (lt, tt, tm) c(lt, sort(tt[tt > lt & tt <= tm]))
times <- lapply(last_times, f, tt = times, tm = t_max)
n_times <- sapply(times, length)
newdata2 <- newdata
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
idT <- newdata2[[idVar]]
newdata2 <- newdata2[tapply(row.names(newdata2),
factor(idT, unique(idT)), tail, 1L), ]
newdata2 <- newdata2[rep(seq_along(times), n_times), ]
newdata2[[time_var]] <- unlist(times, use.names = FALSE)
}
if (!is.null(newdata2)) {
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
mf_FE <- lapply(terms_FE_noResp, model.frame.default, data = newdata2)
mf_RE <- lapply(terms_RE, model.frame.default, data = newdata2)
NAs_FE <- lapply(mf_FE, attr, "na.action")
NAs_RE <- lapply(mf_RE, attr, "na.action")
mf_FE <- mapply2(fix_NAs_fixed, mf_FE, NAs_FE, NAs_RE)
mf_RE <- mapply2(fix_NAs_random, mf_RE, NAs_RE, NAs_FE)
X <- mapply2(model.matrix.default, terms_FE_noResp, mf_FE)
Z <- mapply2(model.matrix.default, terms_RE, mf_RE)
NAs <- mapply2(c, NAs_FE, NAs_RE)
idL <- newdata2[[object$model_info$var_names$idVar]]
unq_id <- unique(idL)
idL <- mapply2(exclude_NAs, NAs_FE, NAs_RE, MoreArgs = list(id = idL))
idL <- lapply(idL, match, table = unq_id)
out <- lapply(X, function (x) matrix(0.0, nrow(x), M))
names(out) <- components_newdata$respVars
for (i in seq_len(M)) {
eta_i <-
linpred_long(X, lapply(betas, i_row, i), Z,
splt_REs(rbind(b_mat[, , i]), ind_RE),
idL, type = type)
for (j in seq_len(K)) {
out[[j]][, i] <- if (type_pred == "response") {
mu_fun(eta_i[[j]], links[j])
} else eta_i[[j]]
}
}
res2 <- list(preds = lapply(out, rowMeans, na.rm = TRUE),
low = lapply(out, rowQuantiles, probs = (1 - level) / 2),
upp = lapply(out, rowQuantiles, probs = (1 + level) / 2))
if (return_newdata) {
n <- nrow(newdata2)
preds <- mapply2(fix_NAs_preds, res2$preds, NAs,
MoreArgs = list(n = n))
names(preds) <- paste0("pred_", components_newdata$respVars)
low <- mapply2(fix_NAs_preds, res2$low, NAs,
MoreArgs = list(n = n))
names(low) <- paste0("low_", components_newdata$respVars)
upp <- mapply2(fix_NAs_preds, res2$upp, NAs,
MoreArgs = list(n = n))
names(upp) <- paste0("upp_", components_newdata$respVars)
l <- c(preds, low, upp)
l <- l[c(matrix(seq_along(l), ncol = length(preds), byrow = TRUE))]
res2 <- cbind(newdata2, as.data.frame(do.call("cbind", l)))
}
}
out <- if (is.null(newdata2)) {
res1
} else {
list(newdata = res1, newdata2 = res2)
}
class(out) <- c("predict_jm", class(out))
attr(out, "id_var") <- object$model_info$var_names$idVar
attr(out, "time_var") <- object$model_info$var_names$time_var
attr(out, "resp_vars") <- object$model_info$var_names$respVars_form
attr(out, "ranges") <- ranges <- lapply(object$model_data$y, range,
na.rm = TRUE)
attr(out, "last_times") <- components_newdata$last_times
attr(out, "y") <- components_newdata$y
attr(out, "times_y") <- components_newdata$times_y
attr(out, "id") <- components_newdata$id
attr(out, "process") <- "longitudinal"
out
}
predict_Event <- function (object, components_newdata, newdata, times,
level, return_newdata) {
control <- object$control
terms_FE <- object$model_info$terms$terms_FE
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
terms_Surv <- object$model_info$terms$terms_Surv
terms_Surv_noResp <- object$model_info$terms$terms_Surv_noResp
type_censoring <- object$model_info$type_censoring
dataL <- newdata
Xbar <- object$model_data$Xbar
data_pred <- newdata
idT <- data_pred[[idVar]]
data_pred <- data_pred[tapply(row.names(data_pred),
factor(idT, unique(idT)), tail, 1L), ]
mf_data_pred <- model.frame.default(terms_Surv, data = data_pred)
Surv_Response <- model.response(mf_data_pred)
ind_strata <- attr(terms_Surv, "specials")$strata
strata <- if (is.null(ind_strata)) {
rep(1, nrow(mf_data_pred))
} else {
unclass(mf_data_pred[[ind_strata]])
}
# The definition of last_times needs to be checked for counting and interval
last_times <- switch(type_censoring, "right" = unname(Surv_Response[, "time"]),
"counting" = unname(Surv_Response[, "stop"]),
"interval" = unname(Surv_Response[, "time1"]))
t_max <- quantile(object$model_data$Time_right, probs = 0.9)
if (is.null(times) || !is.numeric(times)) {
times <- lapply(last_times, seq, to = t_max, length.out = 21L)
} else {
t_max <- max(object$model_data$Time_right)
test <- sapply(last_times, function (lt, tt) all(tt <= lt), tt = times)
if (any(test)) {
stop("according to the definition of argument 'times', for some ",
"subjects the last available time is\n\t larger than the ",
"maximum time to predict; redefine 'times' accordingly.")
}
f <- function (lt, tt, tm) c(lt, sort(tt[tt > lt & tt <= tm]))
times <- lapply(last_times, f, tt = times, tm = t_max)
}
n_times <- sapply(times, length)
data_pred <- data_pred[rep(seq_along(times), n_times), ]
data_pred[[time_var]] <- unlist(times, use.names = FALSE)
idT <- data_pred[[idVar]]
idT <- factor(idT, levels = unique(idT))
strata <- rep(strata, n_times)
upp_limit <- data_pred[[time_var]]
Time_start <- last_times[unclass(idT)]
g <- function (t0, t) c(t0, head(t, -1))
low_limit <- unlist(mapply2(g, last_times, times), use.names = FALSE)
GK <- gaussKronrod(k = 7L)
sk <- GK$sk
P <- c(upp_limit - low_limit) / 2
st <- outer(P, sk) + (c(upp_limit + low_limit) / 2)
log_Pwk <- unname(rep(log(P), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st)))
# knots
knots <- control$knots
# indices
ni_event <- tapply(idT, idT, length)
ni_event <- cbind(c(0, head(cumsum(ni_event), -1)), cumsum(ni_event))
id_H <- rep(paste0(idT, "_", unlist(tapply(idT, idT, seq_along))),
each = 7L)
id_H <- match(id_H, unique(id_H))
# id_H_ repeats each unique idT the number of quadrature points
id_H_ <- rep(idT, each = 7L)
id_H_ <- match(id_H_, unique(id_H_))
id_h <- unclass(idT)
# Functional forms
functional_forms <- object$model_info$functional_forms
FunForms_per_outcome <- object$model_info$FunForms_per_outcome
collapsed_functional_forms <- object$model_info$collapsed_functional_forms
FunForms_cpp <- object$model_info$FunForms_cpp
FunForms_ind <- object$model_info$FunForms_ind
Funs_FunForms <- object$model_info$Funs_FunForms
eps <- object$model_info$eps
direction <- object$model_info$direction
strata_H <- rep(strata, each = 7L)
W0_H <- create_W0(c(t(st)), knots, control$Bsplines_degree + 1, strata_H)
dataS_H <- SurvData_HazardModel(st, data_pred, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_H)
W_H <- construct_Wmat(terms_Surv_noResp, mf)
any_gammas <- as.logical(ncol(W_H))
if (!any_gammas) {
W_H <- matrix(0.0, nrow = nrow(W_H), ncol = 1L)
}
attr <- lapply(functional_forms, extract_attributes, data = dataS_H)
eps <- lapply(attr, "[[", 1L)
direction <- lapply(attr, "[[", 2L)
X_H <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H <- lapply(functional_forms, construct_Umat, dataS = dataS_H)
X_H[] <- lapply(X_H, docall_cbind)
Z_H[] <- lapply(Z_H, docall_cbind)
Data <- list(
log_Pwk = log_Pwk, id_H = id_H, id_h = id_h, id_H_ = id_H_,
ind_RE = object$model_data$ind_RE,
W0_H = W0_H, W_H = W_H, U_H = U_H, X_H = X_H, Z_H = Z_H,
Wlong_bar = object$Wlong_bar, Wlong_sds = object$Wlong_sds,
any_gammas = any_gammas,
FunForms_cpp = FunForms_cpp, FunForms_ind = FunForms_ind,
Funs_FunForms = Funs_FunForms
)
H <- cum_haz(Data, components_newdata$mcmc)
index <- rep(seq_along(times), n_times)
for (i in seq_along(times)) {
H[index == i, ] <- colCumsums(H[index == i, ])
}
CIF <- 1.0 - pmax(exp(- H), .Machine$double.eps)
res <- list(pred = rowMeans(CIF),
low = rowQuantiles(CIF, probs = (1 - level) / 2),
upp = rowQuantiles(CIF, probs = (1 + level) / 2),
times = unlist(times, use.names = FALSE),
id = rep(levels(idT), n_times))
if (return_newdata) {
data_pred[["pred_CIF"]] <- res$pred
data_pred[["low_CIF"]] <- res$low
data_pred[["upp_CIF"]] <- res$upp
res <- data_pred
}
class(res) <- c("predict_jm", class(res))
attr(res, "id_var") <- object$model_info$var_names$idVar
attr(res, "time_var") <- object$model_info$var_names$time_var
attr(res, "resp_vars") <- object$model_info$var_names$respVars_form
attr(res, "ranges") <- ranges <- lapply(object$model_data$y, range,
na.rm = TRUE)
attr(res, "last_times") <- components_newdata$last_times
attr(res, "y") <- components_newdata$y
attr(res, "times_y") <- components_newdata$times_y
attr(res, "id") <- components_newdata$id
attr(res, "process") <- "event"
res
}
| /R/predict_funs.R | no_license | DaanNieboer/JMbayes2 | R | false | false | 32,748 | r | i_row <- function (x, i) x[i, ]
splt_REs <- function (b_mat, ind_RE) {
n <- length(ind_RE)
out <- vector("list", n)
for (i in seq_len(n)) out[[i]] <- b_mat[, ind_RE[[i]], drop = FALSE]
out
}
linpred_long <- function (X, betas, Z, b, id, type) {
out <- vector("list", length(X))
for (i in seq_along(X)) {
out[[i]] <- c(X[[i]] %*% betas[[i]])
if (type == "subject_specific") {
out[[i]] <- out[[i]] +
as.vector(rowSums(Z[[i]] * b[[i]][id[[i]], , drop = FALSE]))
}
}
out
}
mu_fun <- function (eta, link) {
switch (link, "identity" = eta, "inverse" = 1 / eta, "logit" = plogis(eta),
"probit" = pnorm(eta), "cloglog" = - exp(- exp(eta)) + 1.0,
"log" = exp(eta))
}
fix_NAs_preds <- function (preds, NAs, n) {
if (is.null(NAs)) preds else {
r <- rep(as.numeric(NA), n)
r[-NAs] <- preds
r
}
}
get_components_newdata <- function (object, newdata, n_samples, n_mcmc,
cores, seed) {
if (!exists(".Random.seed", envir = .GlobalEnv)) {
runif(1L)
}
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
# control
control <- object$control
# check for tibbles
if (inherits(newdata, "tbl_df") || inherits(newdata, "tbl")) {
newdata <- as.data.frame(newdata)
}
# extract idVar and time_var
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
# set dataL as newdata; almost the same code as in jm()
dataL <- if (!is.data.frame(newdata)) newdata[["newdataL"]] else newdata
idL <- dataL[[idVar]]
nY <- length(unique(idL))
# order data by idL and time_var
if (is.null(dataL[[time_var]])) {
stop("the variable specified in agument 'time_var' cannot be found ",
"in the database of the longitudinal models.")
}
dataL <- dataL[order(idL, dataL[[time_var]]), ]
# extract terms
respVars <- object$model_info$var_names$respVars
terms_FE <- object$model_info$terms$terms_FE
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
terms_Surv <- object$model_info$terms$terms_Surv_noResp
Xbar <- object$model_data$Xbar
# create model frames
mf_FE_dataL <- lapply(terms_FE, model.frame.default, data = dataL)
mf_RE_dataL <- lapply(terms_RE, model.frame.default, data = dataL)
# we need to account for missing data in the fixed and random effects model frames,
# in parallel across outcomes (i.e., we will allow that some subjects may have no data
# for some outcomes)
NAs_FE_dataL <- lapply(mf_FE_dataL, attr, "na.action")
NAs_RE_dataL <- lapply(mf_RE_dataL, attr, "na.action")
mf_FE_dataL <- mapply2(fix_NAs_fixed, mf_FE_dataL, NAs_FE_dataL, NAs_RE_dataL)
mf_RE_dataL <- mapply2(fix_NAs_random, mf_RE_dataL, NAs_RE_dataL, NAs_FE_dataL)
# create response vectors
y <- lapply(mf_FE_dataL, model.response)
y <- lapply(y, function (yy) {
if (is.factor(yy)) as.numeric(yy != levels(yy)[1L]) else yy
})
y[] <- lapply(y, as.matrix)
NAs <- mapply2(c, NAs_FE_dataL, NAs_RE_dataL)
times_y <- lapply(NAs, function (ind) if (!is.null(ind))
dataL[[time_var]][-ind] else dataL[[time_var]])
families <- object$model_info$families
family_names <- sapply(families, "[[", "family")
links <- sapply(families, "[[", "link")
# for family = binomial and when y has two columns, set the second column
# to the number of trials instead the number of failures
binomial_data <- family_names %in% c("binomial", "beta binomial")
trials_fun <- function (y) {
if (NCOL(y) == 2L) y[, 2L] <- y[, 1L] + y[, 2L]
y
}
y[binomial_data] <- lapply(y[binomial_data], trials_fun)
unq_id <- unique(idL)
idL <- mapply2(exclude_NAs, NAs_FE_dataL, NAs_RE_dataL,
MoreArgs = list(id = idL))
idL <- lapply(idL, match, table = unq_id)
idL_lp <- lapply(idL, function (x) match(x, unique(x)))
unq_idL <- lapply(idL, unique)
X <- mapply2(model.matrix.default, terms_FE, mf_FE_dataL)
Z <- mapply2(model.matrix.default, terms_RE, mf_RE_dataL)
################################
# extract terms
terms_Surv <- object$model_info$terms$terms_Surv
terms_Surv_noResp <- object$model_info$terms$terms_Surv_noResp
type_censoring <- object$model_info$type_censoring
dataS <- if (!is.data.frame(newdata)) newdata[["newdataE"]] else newdata
CR_MS <- object$model_info$CR_MS
if (!CR_MS) {
idT <- dataS[[idVar]]
dataS <- dataS[tapply(row.names(dataS), factor(idT, unique(idT)), tail, 1L), ]
}
idT <- dataS[[idVar]]
mf_surv_dataS <- model.frame.default(terms_Surv, data = dataS)
if (!is.null(NAs_surv <- attr(mf_surv_dataS, "na.action"))) {
idT <- idT[-NAs_surv]
dataS <- dataS[-NAs_surv, ]
}
idT <- factor(idT, levels = unique(idT))
nT <- length(unique(idT))
if (nY != nT) {
stop("the number of groups/subjects in the longitudinal and survival datasets ",
"do not seem to match. A potential reason why this may be happening is ",
"missing data in some covariates used in the individual models.")
}
Surv_Response <- model.response(mf_surv_dataS)
if (type_censoring == "right") {
Time_right <- unname(Surv_Response[, "time"])
Time_left <- Time_start <- trunc_Time <- rep(0.0, nrow(dataS))
delta <- unname(Surv_Response[, "status"])
} else if (type_censoring == "counting") {
Time_start <- unname(Surv_Response[, "start"])
Time_stop <- unname(Surv_Response[, "stop"])
delta <- unname(Surv_Response[, "status"])
Time_right <- Time_stop
trunc_Time <- Time_start # possible left truncation time
Time_left <- rep(0.0, nrow(dataS))
} else if (type_censoring == "interval") {
Time1 <- unname(Surv_Response[, "time1"])
Time2 <- unname(Surv_Response[, "time2"])
trunc_Time <- Time_start <- rep(0.0, nrow(dataS))
delta <- unname(Surv_Response[, "status"])
Time_right <- Time1
Time_right[delta == 3] <- Time2[delta == 3]
Time_right[delta == 2] <- 0.0
Time_left <- Time1
Time_left[delta <= 1] <- 0.0
}
if (type_censoring != "counting") {
names(Time_right) <- names(Time_left) <- names(Time_start) <- idT
}
which_event <- which(delta == 1)
which_right <- which(delta == 0)
which_left <- which(delta == 2)
which_interval <- which(delta == 3)
# extract strata if present otherwise all subjects in one stratum
ind_strata <- attr(terms_Surv, "specials")$strata
strata <- if (is.null(ind_strata)) {
rep(1, nrow(mf_surv_dataS))
} else {
unclass(mf_surv_dataS[[ind_strata]])
}
Time_integration <- Time_right
Time_integration[which_left] <- Time_left[which_left]
Time_integration[which_interval] <- Time_left[which_interval]
Time_integration2 <- rep(0.0, length(Time_integration))
if (length(which_interval)) {
Time_integration2[which_interval] <- Time_right[which_interval]
}
last_times <- switch(type_censoring,
"right" = unname(Surv_Response[, "time"]),
"counting" = unname(Surv_Response[, "stop"]),
"interval" = unname(Surv_Response[, "time1"]))
# create Gauss Kronrod points and weights
GK <- gaussKronrod(control$GK_k)
sk <- GK$sk
P <- c(Time_integration - trunc_Time) / 2
st <- outer(P, sk) + (c(Time_integration + trunc_Time) / 2)
log_Pwk <- unname(rep(log(P), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st)))
if (length(which_interval)) {
# we take the absolute value because for the subjects for whom we do not have
# interval censoring P2 will be negative and this will produce a NA when we take
# the log in 'log_Pwk2'
P2 <- abs(Time_integration2 - Time_integration) / 2
st2 <- outer(P2, sk) + (c(Time_integration2 + Time_integration) / 2)
log_Pwk2 <- rep(log(P2), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st2))
} else {
P2 <- st2 <- log_Pwk2 <- rep(0.0, nT * control$GK_k)
}
# knots for the log baseline hazard function
knots <- control$knots
# indices
ni_event <- tapply(idT, idT, length)
ni_event <- cbind(c(0, head(cumsum(ni_event), -1)), cumsum(ni_event))
id_H <- rep(paste0(idT, "_", unlist(tapply(idT, idT, seq_along))),
each = control$GK_k)
id_H <- match(id_H, unique(id_H))
# id_H_ repeats each unique idT the number of quadrature points
id_H_ <- rep(idT, each = control$GK_k)
id_H_ <- match(id_H_, unique(id_H_))
id_h <- unclass(idT)
# Functional forms
functional_forms <- object$model_info$functional_forms
FunForms_per_outcome <- object$model_info$FunForms_per_outcome
collapsed_functional_forms <- object$model_info$collapsed_functional_forms
FunForms_cpp <- object$model_info$FunForms_cpp
FunForms_ind <- object$model_info$FunForms_ind
Funs_FunForms <- object$model_info$Funs_FunForms
eps <- object$model_info$eps
direction <- object$model_info$direction
# Design matrices
strata_H <- rep(strata, each = control$GK_k)
W0_H <- create_W0(c(t(st)), knots, control$Bsplines_degree + 1, strata_H)
dataS_H <- SurvData_HazardModel(st, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_H)
W_H <- construct_Wmat(terms_Surv_noResp, mf)
any_gammas <- as.logical(ncol(W_H))
if (!any_gammas) {
W_H <- matrix(0.0, nrow = nrow(W_H), ncol = 1L)
}
attr <- lapply(functional_forms, extract_attributes, data = dataS_H)
eps <- lapply(attr, "[[", 1L)
direction <- lapply(attr, "[[", 2L)
X_H <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H <- lapply(functional_forms, construct_Umat, dataS = dataS_H)
if (length(which_event)) {
W0_h <- create_W0(Time_right, knots, control$Bsplines_degree + 1,
strata)
dataS_h <- SurvData_HazardModel(Time_right, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_h)
W_h <- construct_Wmat(terms_Surv_noResp, mf)
if (!any_gammas) {
W_h <- matrix(0.0, nrow = nrow(W_h), ncol = 1L)
}
X_h <- design_matrices_functional_forms(Time_right, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_h <- design_matrices_functional_forms(Time_right, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_h <- lapply(functional_forms, construct_Umat, dataS = dataS_h)
} else {
W0_h <- W_h <- matrix(0.0)
X_h <- Z_h <- U_h <- rep(list(matrix(0.0)), length(respVars))
}
if (length(which_interval)) {
W0_H2 <- create_W0(c(t(st2)), knots, control$Bsplines_degree + 1,
strata_H)
dataS_H2 <- SurvData_HazardModel(st2, dataS, Time_start,
paste0(idT, "_", strata), time_var)
mf2 <- model.frame.default(terms_Surv_noResp, data = dataS_H2)
W_h <- construct_Wmat(terms_Surv_noResp, mf2)
if (!any_gammas) {
W_H2 <- matrix(0.0, nrow = nrow(W_H2), ncol = 1L)
}
X_H2 <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H2 <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H2 <- lapply(functional_forms, construct_Umat, dataS = dataS_H2)
} else {
W0_H2 <- W_H2 <- matrix(0.0)
X_H2 <- Z_H2 <- U_H2 <- rep(list(matrix(0.0)), length(respVars))
}
X_H[] <- lapply(X_H, docall_cbind)
X_h[] <- lapply(X_h, docall_cbind)
X_H2[] <- lapply(X_H2, docall_cbind)
Z_H[] <- lapply(Z_H, docall_cbind)
Z_h[] <- lapply(Z_h, docall_cbind)
Z_H2[] <- lapply(Z_H2, docall_cbind)
W_bar <- object$W_bar
W_sds <- object$W_sds
W_H <- center_fun(W_H, W_bar, W_sds)
W_h <- center_fun(W_h, W_bar, W_sds)
W_H2 <- center_fun(W_H2, W_bar, W_sds)
# MCMC sample
b <- lapply(sapply(Z, ncol), function (nc) matrix(0.0, nY, nc))
M <- sum(sapply(object$mcmc$bs_gammas, nrow))
get_param <- function (nam) {
tht <- object$mcmc[[nam]]
if (!is.null(tht)) docall_rbind(tht) else matrix(0.0, M, 1)
}
ind_betas <- grep("^betas", names(object$mcmc))
mcmc <- list(
b = b, bs_gammas = get_param("bs_gammas"), gammas = get_param("gammas"),
alphas = get_param("alphas"),
Wlong_std_alphas = get_param("Wlong_std_alphas"),
W_std_gammas = get_param("W_std_gammas"),
betas = lapply(object$mcmc[ind_betas], docall_rbind)
)
has_sigmas <- object$model_data$has_sigmas
mcmc$sigmas <- matrix(0.0, M, length(has_sigmas))
mcmc$sigmas[, has_sigmas > 0] <- get_param("sigmas")
D <- get_param("D")
mcmc$D <- array(0.0, c(dim(lowertri2mat(D[1L, ])), M))
for (i in seq_len(M)) {
mcmc$D[, , i] <- lowertri2mat(D[i, ])
}
Data <- list(
ind_RE = object$model_data$ind_RE,
W0_H = W0_H, W0_h = W0_h, W0_H2 = W0_H2,
W_H = W_H, W_h = W_h, W_H2 = W_H2,
X_H = X_H, X_h = X_h, X_H2 = X_H2,
Z_H = Z_H, Z_h = Z_h, Z_H2 = Z_H2,
U_H = U_H, U_h = U_h, U_H2 = U_H2,
Wlong_bar = object$Wlong_bar, Wlong_sds = object$Wlong_sds,
idT = match(idT, unique(idT)), log_Pwk = log_Pwk, log_Pwk2 = log_Pwk2,
id_H = id_H, id_H_ = id_H_, id_h = id_h, any_gammas = any_gammas,
which_event = which_event, which_right = which_right,
which_left = which_left, which_interval = which_interval,
ni_event = ni_event, FunForms_cpp = FunForms_cpp,
FunForms_ind = FunForms_ind, Funs_FunForms = Funs_FunForms,
X = X, Z = Z, y = y, family_names = family_names,
links = links, extra_parms = object$model_data$extra_parms,
unq_idL = unq_idL, idL_lp = idL_lp, idL = idL
)
if (n_samples > M) {
warning("the number of samples cannot be greater than the number of ",
"MCMC iterations in the fitted model.")
n_samples <- M
}
control <- list(GK_k = object$control$GK_k, n_samples = n_samples, n_iter = n_mcmc)
id_samples <-
split(seq_len(control$n_samples),
rep(seq_len(cores), each = ceiling(control$n_samples / cores),
length.out = control$n_samples))
sample_parallel <- function (id_samples, Data, mcmc, control) {
# keep only the samples from the MCMC used in the sampling
# of the random effects
mcmc$bs_gammas <- mcmc$bs_gammas[id_samples, , drop = FALSE]
mcmc$gammas <- mcmc$gammas[id_samples, , drop = FALSE]
mcmc$alphas <- mcmc$alphas[id_samples, , drop = FALSE]
mcmc$betas[] <- lapply(mcmc$betas, function (m, ind) m[ind, , drop = FALSE],
"ind" = id_samples)
mcmc$sigmas <- mcmc$sigmas[id_samples, , drop = FALSE]
mcmc$D <- mcmc$D[, , id_samples, drop = FALSE]
# update control n_samples
control$n_samples <- length(id_samples)
# update random effects
mcmc[["b"]] <- simulate_REs(Data, mcmc, control)
mcmc$Wlong_std_alphas <- mcmc$Wlong_std_alphas[id_samples, , drop = FALSE]
mcmc$W_std_gammas <- mcmc$W_std_gammas[id_samples, , drop = FALSE]
mcmc
}
if (cores > 1L) {
cl <- parallel::makeCluster(cores)
parallel::clusterSetRNGStream(cl = cl, iseed = seed)
out <- parallel::parLapply(cl, id_samples, sample_parallel,
Data = Data, mcmc = mcmc, control = control)
parallel::stopCluster(cl)
} else {
set.seed(seed)
out <- list(sample_parallel(id_samples[[1L]], Data = Data, mcmc = mcmc,
control = control))
}
combine <- function (x) {
n <- length(x)
res <- x[[1L]]
if (n > 1L) {
for (i in 2:n) {
res$bs_gammas <- rbind(res$bs_gammas, x[[i]][["bs_gammas"]])
res$gammas <- rbind(res$gammas, x[[i]][["gammas"]])
res$alphas <- rbind(res$alphas, x[[i]][["alphas"]])
res$sigmas <- rbind(res$sigmas, x[[i]][["sigmas"]])
res$Wlong_std_alphas <-
rbind(res$Wlong_std_alphas, x[[i]][["Wlong_std_alphas"]])
res$W_std_gammas <-
rbind(res$W_std_gammas, x[[i]][["W_std_gammas"]])
d1 <- dim(res$D)[3L]
d2 <- dim(x[[i]][["D"]])[3L]
a <- array(0.0, dim = c(dim(res$D)[1:2], d1 + d2))
a[, , seq(1, d1)] <- res$D
a[, , seq(d1 + 1, d1 + d2)] <- x[[i]][["D"]]
res$D <- a
d1 <- dim(res$b)[3L]
d2 <- dim(x[[i]][["b"]])[3L]
a <- array(0.0, dim = c(dim(res$b)[1:2], d1 + d2))
a[, , seq(1, d1)] <- res$b
a[, , seq(d1 + 1, d1 + d2)] <- x[[i]][["b"]]
res$b <- a
for (j in seq_along(res$betas)) {
res$betas[[j]] <- rbind(res$betas[[j]], x[[i]][["betas"]][[j]])
}
}
}
res
}
list(mcmc = combine(out), X = X, Z = Z, y = y, times_y = times_y, id = idL,
ind_RE = object$model_data$ind_RE, links = links,
respVars = lapply(respVars, "[", 1L),
NAs = mapply2(c, NAs_FE_dataL, NAs_RE_dataL),
last_times = last_times)
}
predict_Long <- function (object, components_newdata, newdata, newdata2, times,
type, type_pred, level, return_newdata) {
# Predictions for newdata
betas <- components_newdata$mcmc[["betas"]]
b_mat <- components_newdata$mcmc[["b"]]
ind_RE <- components_newdata$ind_RE
links <- components_newdata$links
K <- length(ind_RE)
M <- dim(b_mat)[3L]
out <- lapply(components_newdata$X, function (x) matrix(0.0, nrow(x), M))
names(out) <- components_newdata$respVars
for (i in seq_len(M)) {
eta_i <-
linpred_long(components_newdata$X, lapply(betas, i_row, i),
components_newdata$Z,
splt_REs(rbind(b_mat[, , i]), ind_RE),
components_newdata$id, type = type)
for (j in seq_len(K)) {
out[[j]][, i] <- if (type_pred == "response") {
mu_fun(eta_i[[j]], links[j])
} else eta_i[[j]]
}
}
res1 <- list(preds = lapply(out, rowMeans, na.rm = TRUE),
low = lapply(out, rowQuantiles, probs = (1 - level) / 2),
upp = lapply(out, rowQuantiles, probs = (1 + level) / 2))
if (return_newdata) {
n <- nrow(newdata)
preds <- mapply2(fix_NAs_preds, res1$preds, components_newdata$NAs,
MoreArgs = list(n = n))
names(preds) <- paste0("pred_", components_newdata$respVars)
low <- mapply2(fix_NAs_preds, res1$low, components_newdata$NAs,
MoreArgs = list(n = n))
names(low) <- paste0("low_", components_newdata$respVars)
upp <- mapply2(fix_NAs_preds, res1$upp, components_newdata$NAs,
MoreArgs = list(n = n))
names(upp) <- paste0("upp_", components_newdata$respVars)
l <- c(preds, low, upp)
l <- l[c(matrix(seq_along(l), ncol = length(preds), byrow = TRUE))]
res1 <- cbind(newdata, as.data.frame(do.call("cbind", l)))
}
############################################################################
############################################################################
# Predictions for newdata2
if (is.null(newdata2) && !is.null(times) && is.numeric(times)) {
last_times <- components_newdata$last_times
t_max <- max(object$model_data$Time_right)
test <- sapply(last_times, function (lt, tt) all(tt <= lt), tt = times)
if (any(test)) {
stop("according to the definition of argument 'times', for some ",
"subjects the last available time is\n\t larger than the ",
"maximum time to predict; redefine 'times' accordingly.")
}
f <- function (lt, tt, tm) c(lt, sort(tt[tt > lt & tt <= tm]))
times <- lapply(last_times, f, tt = times, tm = t_max)
n_times <- sapply(times, length)
newdata2 <- newdata
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
idT <- newdata2[[idVar]]
newdata2 <- newdata2[tapply(row.names(newdata2),
factor(idT, unique(idT)), tail, 1L), ]
newdata2 <- newdata2[rep(seq_along(times), n_times), ]
newdata2[[time_var]] <- unlist(times, use.names = FALSE)
}
if (!is.null(newdata2)) {
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
mf_FE <- lapply(terms_FE_noResp, model.frame.default, data = newdata2)
mf_RE <- lapply(terms_RE, model.frame.default, data = newdata2)
NAs_FE <- lapply(mf_FE, attr, "na.action")
NAs_RE <- lapply(mf_RE, attr, "na.action")
mf_FE <- mapply2(fix_NAs_fixed, mf_FE, NAs_FE, NAs_RE)
mf_RE <- mapply2(fix_NAs_random, mf_RE, NAs_RE, NAs_FE)
X <- mapply2(model.matrix.default, terms_FE_noResp, mf_FE)
Z <- mapply2(model.matrix.default, terms_RE, mf_RE)
NAs <- mapply2(c, NAs_FE, NAs_RE)
idL <- newdata2[[object$model_info$var_names$idVar]]
unq_id <- unique(idL)
idL <- mapply2(exclude_NAs, NAs_FE, NAs_RE, MoreArgs = list(id = idL))
idL <- lapply(idL, match, table = unq_id)
out <- lapply(X, function (x) matrix(0.0, nrow(x), M))
names(out) <- components_newdata$respVars
for (i in seq_len(M)) {
eta_i <-
linpred_long(X, lapply(betas, i_row, i), Z,
splt_REs(rbind(b_mat[, , i]), ind_RE),
idL, type = type)
for (j in seq_len(K)) {
out[[j]][, i] <- if (type_pred == "response") {
mu_fun(eta_i[[j]], links[j])
} else eta_i[[j]]
}
}
res2 <- list(preds = lapply(out, rowMeans, na.rm = TRUE),
low = lapply(out, rowQuantiles, probs = (1 - level) / 2),
upp = lapply(out, rowQuantiles, probs = (1 + level) / 2))
if (return_newdata) {
n <- nrow(newdata2)
preds <- mapply2(fix_NAs_preds, res2$preds, NAs,
MoreArgs = list(n = n))
names(preds) <- paste0("pred_", components_newdata$respVars)
low <- mapply2(fix_NAs_preds, res2$low, NAs,
MoreArgs = list(n = n))
names(low) <- paste0("low_", components_newdata$respVars)
upp <- mapply2(fix_NAs_preds, res2$upp, NAs,
MoreArgs = list(n = n))
names(upp) <- paste0("upp_", components_newdata$respVars)
l <- c(preds, low, upp)
l <- l[c(matrix(seq_along(l), ncol = length(preds), byrow = TRUE))]
res2 <- cbind(newdata2, as.data.frame(do.call("cbind", l)))
}
}
out <- if (is.null(newdata2)) {
res1
} else {
list(newdata = res1, newdata2 = res2)
}
class(out) <- c("predict_jm", class(out))
attr(out, "id_var") <- object$model_info$var_names$idVar
attr(out, "time_var") <- object$model_info$var_names$time_var
attr(out, "resp_vars") <- object$model_info$var_names$respVars_form
attr(out, "ranges") <- ranges <- lapply(object$model_data$y, range,
na.rm = TRUE)
attr(out, "last_times") <- components_newdata$last_times
attr(out, "y") <- components_newdata$y
attr(out, "times_y") <- components_newdata$times_y
attr(out, "id") <- components_newdata$id
attr(out, "process") <- "longitudinal"
out
}
predict_Event <- function (object, components_newdata, newdata, times,
level, return_newdata) {
control <- object$control
terms_FE <- object$model_info$terms$terms_FE
terms_FE_noResp <- object$model_info$terms$terms_FE_noResp
terms_RE <- object$model_info$terms$terms_RE
idVar <- object$model_info$var_names$idVar
time_var <- object$model_info$var_names$time_var
terms_Surv <- object$model_info$terms$terms_Surv
terms_Surv_noResp <- object$model_info$terms$terms_Surv_noResp
type_censoring <- object$model_info$type_censoring
dataL <- newdata
Xbar <- object$model_data$Xbar
data_pred <- newdata
idT <- data_pred[[idVar]]
data_pred <- data_pred[tapply(row.names(data_pred),
factor(idT, unique(idT)), tail, 1L), ]
mf_data_pred <- model.frame.default(terms_Surv, data = data_pred)
Surv_Response <- model.response(mf_data_pred)
ind_strata <- attr(terms_Surv, "specials")$strata
strata <- if (is.null(ind_strata)) {
rep(1, nrow(mf_data_pred))
} else {
unclass(mf_data_pred[[ind_strata]])
}
# The definition of last_times needs to be checked for counting and interval
last_times <- switch(type_censoring, "right" = unname(Surv_Response[, "time"]),
"counting" = unname(Surv_Response[, "stop"]),
"interval" = unname(Surv_Response[, "time1"]))
t_max <- quantile(object$model_data$Time_right, probs = 0.9)
if (is.null(times) || !is.numeric(times)) {
times <- lapply(last_times, seq, to = t_max, length.out = 21L)
} else {
t_max <- max(object$model_data$Time_right)
test <- sapply(last_times, function (lt, tt) all(tt <= lt), tt = times)
if (any(test)) {
stop("according to the definition of argument 'times', for some ",
"subjects the last available time is\n\t larger than the ",
"maximum time to predict; redefine 'times' accordingly.")
}
f <- function (lt, tt, tm) c(lt, sort(tt[tt > lt & tt <= tm]))
times <- lapply(last_times, f, tt = times, tm = t_max)
}
n_times <- sapply(times, length)
data_pred <- data_pred[rep(seq_along(times), n_times), ]
data_pred[[time_var]] <- unlist(times, use.names = FALSE)
idT <- data_pred[[idVar]]
idT <- factor(idT, levels = unique(idT))
strata <- rep(strata, n_times)
upp_limit <- data_pred[[time_var]]
Time_start <- last_times[unclass(idT)]
g <- function (t0, t) c(t0, head(t, -1))
low_limit <- unlist(mapply2(g, last_times, times), use.names = FALSE)
GK <- gaussKronrod(k = 7L)
sk <- GK$sk
P <- c(upp_limit - low_limit) / 2
st <- outer(P, sk) + (c(upp_limit + low_limit) / 2)
log_Pwk <- unname(rep(log(P), each = length(sk)) +
rep_len(log(GK$wk), length.out = length(st)))
# knots
knots <- control$knots
# indices
ni_event <- tapply(idT, idT, length)
ni_event <- cbind(c(0, head(cumsum(ni_event), -1)), cumsum(ni_event))
id_H <- rep(paste0(idT, "_", unlist(tapply(idT, idT, seq_along))),
each = 7L)
id_H <- match(id_H, unique(id_H))
# id_H_ repeats each unique idT the number of quadrature points
id_H_ <- rep(idT, each = 7L)
id_H_ <- match(id_H_, unique(id_H_))
id_h <- unclass(idT)
# Functional forms
functional_forms <- object$model_info$functional_forms
FunForms_per_outcome <- object$model_info$FunForms_per_outcome
collapsed_functional_forms <- object$model_info$collapsed_functional_forms
FunForms_cpp <- object$model_info$FunForms_cpp
FunForms_ind <- object$model_info$FunForms_ind
Funs_FunForms <- object$model_info$Funs_FunForms
eps <- object$model_info$eps
direction <- object$model_info$direction
strata_H <- rep(strata, each = 7L)
W0_H <- create_W0(c(t(st)), knots, control$Bsplines_degree + 1, strata_H)
dataS_H <- SurvData_HazardModel(st, data_pred, Time_start,
paste0(idT, "_", strata), time_var)
mf <- model.frame.default(terms_Surv_noResp, data = dataS_H)
W_H <- construct_Wmat(terms_Surv_noResp, mf)
any_gammas <- as.logical(ncol(W_H))
if (!any_gammas) {
W_H <- matrix(0.0, nrow = nrow(W_H), ncol = 1L)
}
attr <- lapply(functional_forms, extract_attributes, data = dataS_H)
eps <- lapply(attr, "[[", 1L)
direction <- lapply(attr, "[[", 2L)
X_H <- design_matrices_functional_forms(st, terms_FE_noResp,
dataL, time_var, idVar, idT,
collapsed_functional_forms, Xbar,
eps, direction)
Z_H <- design_matrices_functional_forms(st, terms_RE,
dataL, time_var, idVar, idT,
collapsed_functional_forms, NULL,
eps, direction)
U_H <- lapply(functional_forms, construct_Umat, dataS = dataS_H)
X_H[] <- lapply(X_H, docall_cbind)
Z_H[] <- lapply(Z_H, docall_cbind)
Data <- list(
log_Pwk = log_Pwk, id_H = id_H, id_h = id_h, id_H_ = id_H_,
ind_RE = object$model_data$ind_RE,
W0_H = W0_H, W_H = W_H, U_H = U_H, X_H = X_H, Z_H = Z_H,
Wlong_bar = object$Wlong_bar, Wlong_sds = object$Wlong_sds,
any_gammas = any_gammas,
FunForms_cpp = FunForms_cpp, FunForms_ind = FunForms_ind,
Funs_FunForms = Funs_FunForms
)
H <- cum_haz(Data, components_newdata$mcmc)
index <- rep(seq_along(times), n_times)
for (i in seq_along(times)) {
H[index == i, ] <- colCumsums(H[index == i, ])
}
CIF <- 1.0 - pmax(exp(- H), .Machine$double.eps)
res <- list(pred = rowMeans(CIF),
low = rowQuantiles(CIF, probs = (1 - level) / 2),
upp = rowQuantiles(CIF, probs = (1 + level) / 2),
times = unlist(times, use.names = FALSE),
id = rep(levels(idT), n_times))
if (return_newdata) {
data_pred[["pred_CIF"]] <- res$pred
data_pred[["low_CIF"]] <- res$low
data_pred[["upp_CIF"]] <- res$upp
res <- data_pred
}
class(res) <- c("predict_jm", class(res))
attr(res, "id_var") <- object$model_info$var_names$idVar
attr(res, "time_var") <- object$model_info$var_names$time_var
attr(res, "resp_vars") <- object$model_info$var_names$respVars_form
attr(res, "ranges") <- ranges <- lapply(object$model_data$y, range,
na.rm = TRUE)
attr(res, "last_times") <- components_newdata$last_times
attr(res, "y") <- components_newdata$y
attr(res, "times_y") <- components_newdata$times_y
attr(res, "id") <- components_newdata$id
attr(res, "process") <- "event"
res
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/app.r
\name{appReadyToRun}
\alias{appReadyToRun}
\title{set the app ready to run}
\usage{
appReadyToRun(app = getApp(), ui = app$ui)
}
\description{
set the app ready to run
}
| /man/appReadyToRun.Rd | no_license | skranz/shinyEvents | R | false | true | 255 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/app.r
\name{appReadyToRun}
\alias{appReadyToRun}
\title{set the app ready to run}
\usage{
appReadyToRun(app = getApp(), ui = app$ui)
}
\description{
set the app ready to run
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MARX_functions.R
\name{mixed}
\alias{mixed}
\alias{mixed.default}
\alias{print.mixed}
\alias{summary.mixed}
\alias{mixed.default}
\alias{print.mixed}
\alias{summary.mixed}
\title{The MARX estimation function}
\usage{
mixed(y, x, p_C, p_NC)
\method{mixed}{default}(y, x, p_C, p_NC)
\method{print}{mixed}(x, ...)
\method{summary}{mixed}(object, ...)
}
\arguments{
\item{y}{Data vector of time series observations.}
\item{x}{Matrix of data (every column represents one time series). Specify NULL or "not" if not wanted.}
\item{p_C}{Number of lags to be included.}
\item{p_NC}{Number of leads to be included.}
\item{...}{Other parameters.}
\item{object}{An object of the class "mixed".}
}
\value{
An object of class \code{"mixed"} is a list containing the following components:
\item{coefficients}{Vector of estimated coefficients.}
\item{se}{Standard errors of estimated coefficients.}
\item{df.residual}{Degrees of freedom residuals.}
\item{residuals}{Residuals.}
\item{fitted.values}{Fitted values.}
\item{order}{Vector containing (r,s,q), i.e. causal order r, noncausal order s, number of exogenous regressors q.}
}
\description{
This function allows you to estimate mixed causal-noncausal MARX models by t-MLE (compatible with most functions in lm() class).
}
\examples{
data <- sim.marx(c('t',1,1), c('t',1,1),100,0.5,0.4,0.3)
object <- mixed(data$y, data$x, 1, 1)
class(object) <- "mixed"
summary(object)
}
\keyword{causal-noncausal}
\keyword{estimation}
| /man/mixed.Rd | no_license | alexstihi/MARX | R | false | true | 1,609 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MARX_functions.R
\name{mixed}
\alias{mixed}
\alias{mixed.default}
\alias{print.mixed}
\alias{summary.mixed}
\alias{mixed.default}
\alias{print.mixed}
\alias{summary.mixed}
\title{The MARX estimation function}
\usage{
mixed(y, x, p_C, p_NC)
\method{mixed}{default}(y, x, p_C, p_NC)
\method{print}{mixed}(x, ...)
\method{summary}{mixed}(object, ...)
}
\arguments{
\item{y}{Data vector of time series observations.}
\item{x}{Matrix of data (every column represents one time series). Specify NULL or "not" if not wanted.}
\item{p_C}{Number of lags to be included.}
\item{p_NC}{Number of leads to be included.}
\item{...}{Other parameters.}
\item{object}{An object of the class "mixed".}
}
\value{
An object of class \code{"mixed"} is a list containing the following components:
\item{coefficients}{Vector of estimated coefficients.}
\item{se}{Standard errors of estimated coefficients.}
\item{df.residual}{Degrees of freedom residuals.}
\item{residuals}{Residuals.}
\item{fitted.values}{Fitted values.}
\item{order}{Vector containing (r,s,q), i.e. causal order r, noncausal order s, number of exogenous regressors q.}
}
\description{
This function allows you to estimate mixed causal-noncausal MARX models by t-MLE (compatible with most functions in lm() class).
}
\examples{
data <- sim.marx(c('t',1,1), c('t',1,1),100,0.5,0.4,0.3)
object <- mixed(data$y, data$x, 1, 1)
class(object) <- "mixed"
summary(object)
}
\keyword{causal-noncausal}
\keyword{estimation}
|
dataFileName <- "data\\household_power_consumption.txt"
powerData <- read.table(dataFileName, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
#head(powerData)
subData <- powerData[powerData$Date %in% c("1/2/2007","2/2/2007"),]
#head(subData)
day <- strptime(paste(subData$Date,subData$Time,sep=" "),"%d/%m/%Y %H:%M:%S")
globalActivePower <- as.numeric(subData$Global_active_power)
sub_metering_1 <- as.numeric(subData$Sub_metering_1)
sub_metering_2 <- as.numeric(subData$Sub_metering_2)
sub_metering_3 <- as.numeric(subData$Sub_metering_3)
png("plot3.png", width=480, height=480)
#create line plot 1 for sub metering 1
plot(day, sub_metering_1, type="l", ylab="Energy Submetering", xlab="")
#create line plot 1 for sub metering 2
lines(day, sub_metering_2, type="l", col="red")
#create line plot 1 for sub metering 3
lines(day, sub_metering_3, type="l", col="blue")
#add legend for sub metering
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
#shutdown graphic device
dev.off()
| /plot3.R | no_license | minesweeper222/ExData_Plotting1 | R | false | false | 1,073 | r | dataFileName <- "data\\household_power_consumption.txt"
powerData <- read.table(dataFileName, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
#head(powerData)
subData <- powerData[powerData$Date %in% c("1/2/2007","2/2/2007"),]
#head(subData)
day <- strptime(paste(subData$Date,subData$Time,sep=" "),"%d/%m/%Y %H:%M:%S")
globalActivePower <- as.numeric(subData$Global_active_power)
sub_metering_1 <- as.numeric(subData$Sub_metering_1)
sub_metering_2 <- as.numeric(subData$Sub_metering_2)
sub_metering_3 <- as.numeric(subData$Sub_metering_3)
png("plot3.png", width=480, height=480)
#create line plot 1 for sub metering 1
plot(day, sub_metering_1, type="l", ylab="Energy Submetering", xlab="")
#create line plot 1 for sub metering 2
lines(day, sub_metering_2, type="l", col="red")
#create line plot 1 for sub metering 3
lines(day, sub_metering_3, type="l", col="blue")
#add legend for sub metering
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
#shutdown graphic device
dev.off()
|
## Programming Assignment #2 Lexical Scoping
## FUNCTION: makeCacheMatrix
## DESCRIPTION:creates a special "matrix" object that can cache its inverse
makeCacheMatrix <- function(v_matrix = matrix()) {
v_inverse <- NULL
set <- function(x) {
v_matrix <<- x ;
v_inverse <<- NULL ;
}
get <- function() return(v_matrix) ;
setinv <- function(inv) v_inverse <<- inv ;
getinv <- function() return(v_inverse) ;
return(list(set = set, get = get, setinv = setinv, getinv = getinv))
}
## Programming Assignment #2 Lexical Scoping
## FUNCTION: cacheSolve
## DESCRIPTION:This function computes the inverse of the special
## "matrix" returned by makeCacheMatrix.
## If the inverse has already been calculated
## (and the matrix has not changed), then the cachesolve
## should retrieve the inverse from the cache.
cacheSolve <- function(v_matrix, ...) {
v_inverse <- v_matrix$getinv()
if(!is.null(v_inverse)) {
message("get the data in the cache") ## Return a matrix that is the inverse of 'x'
return(v_inverse)
}
data <- v_matrix$get()
v_inverse <- solve(data, ...)
v_matrix$setinv(v_inverse)
return(v_inverse)
} | /cachematrix.R | no_license | timsharp/ProgrammingAssignment2 | R | false | false | 1,282 | r | ## Programming Assignment #2 Lexical Scoping
## FUNCTION: makeCacheMatrix
## DESCRIPTION:creates a special "matrix" object that can cache its inverse
makeCacheMatrix <- function(v_matrix = matrix()) {
v_inverse <- NULL
set <- function(x) {
v_matrix <<- x ;
v_inverse <<- NULL ;
}
get <- function() return(v_matrix) ;
setinv <- function(inv) v_inverse <<- inv ;
getinv <- function() return(v_inverse) ;
return(list(set = set, get = get, setinv = setinv, getinv = getinv))
}
## Programming Assignment #2 Lexical Scoping
## FUNCTION: cacheSolve
## DESCRIPTION:This function computes the inverse of the special
## "matrix" returned by makeCacheMatrix.
## 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(v_matrix, ...) {
v_inverse <- v_matrix$getinv()
if(!is.null(v_inverse)) {
message("get the data in the cache") ## Return a matrix that is the inverse of 'x'
return(v_inverse)
}
data <- v_matrix$get()
v_inverse <- solve(data, ...)
v_matrix$setinv(v_inverse)
return(v_inverse)
} |
# dat <- read.csv('RenegadesHistoryFormatted.csv')
# weekly = F
# Season = c(2016, 2017)
# statcat = 'HR'
# playoffs = F
# best = T
# numshow = 10
# ownerfilter = 'All'
# franchisefilter = 'All'
records.func <- function(dat = dat,
weekly = T,
season = c(2011:2017),
statcat = 'All',
playoffs = F,
best = T,
numshow = 5,
ownerfilter = 'All',
franchisefilter = 'All'){
dat <- dat %>% filter(AllStar == 0) %>%
mutate(Luck = Wins - xWins)
dat <- dat %>% filter(ifelse(rep(ownerfilter, nrow(dat)) == 'All',
TRUE,
TeamOwner == ownerfilter))
dat <- dat %>% filter(ifelse(rep(franchisefilter, nrow(dat)) == 'All',
TRUE,
CurrentName == franchisefilter))
if(!weekly){
dat <- dat %>%
filter(Playoffs == as.numeric(playoffs)) %>%
group_by(Season, Team) %>%
summarize(Owner = TeamOwner[1],
R = sum(R),
HR = sum(HR),
RBI = sum(RBI),
SB = sum(SB),
OBP = mean(OBP),
SLG = mean(SLG),
K = sum(K),
QS = sum(QS),
W = sum(W),
SV = sum(SV),
ERA = mean(ERA),
WHIP = mean(WHIP),
Luck = sum(Luck)) %>%
ungroup()
return(dat %>%
select_(.dots = c('Team', 'Season', 'Owner', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(Season %in% season) %>%
filter(row_number() <= numshow))
} else{
if(statcat == 'All'){
if(playoffs){
ranks <- data.frame(Team = dat$Team[dat$Playoffs == 1 & dat$Season %in% season],
Season = dat$Season[dat$Playoffs == 1 & dat$Season %in% season],
Week = dat$Week[dat$Playoffs == 1 & dat$Season %in% season],
sapply(dat %>% filter(Playoffs == 1 & Season %in% season) %>%
select(R:SV), rank),
sapply(dat %>% filter(dat$Playoffs == 1) %>%
select(ERA, WHIP), function(x) nrow(dat) + 1 - rank(x)))
ranks[['Score']] <- apply(ranks %>% select(R:WHIP), 1, sum)
return(ranks %>%
arrange_(ifelse(best, 'desc(Score)', 'Score')) %>%
filter(row_number() <= numshow) %>%
select(Team, Season, Week, Score) %>%
left_join(dat %>%
select(Team, Season, Week, Owner = TeamOwner, R:WHIP),
c('Team', 'Season', 'Week')) %>%
mutate(WinPct = Score / (nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) * 12)) %>%
select(-Score))
}else{
ranks <- data.frame(Team = dat$Team[dat$Playoffs == 0 & dat$Season %in% season],
Season = dat$Season[dat$Playoffs == 0 & dat$Season %in% season],
Week = dat$Week[dat$Playoffs == 0 & dat$Season %in% season],
sapply(dat %>% filter(Playoffs == 0 & Season %in% season) %>%
select(R:SV), rank),
sapply(dat %>% filter(Playoffs == 0 & Season %in% season) %>%
select(ERA, WHIP), function(x) nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) + 1 - rank(x)))
ranks[['Score']] <- apply(ranks %>% select(R:WHIP), 1, sum)
return(ranks %>%
arrange_(ifelse(best, 'desc(Score)', 'Score')) %>%
filter(row_number() <= numshow) %>%
select(Team, Season, Week, Score) %>%
left_join(dat %>%
select(Team, Season, Week, Owner = TeamOwner, R:WHIP),
c('Team', 'Season', 'Week')) %>%
mutate(WinPct = Score / (nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) * 12)) %>%
select(-Score))
}
} else{
if(playoffs){
return(dat %>%
filter(Playoffs == 1 & Season %in% season) %>%
select_(.dots = c('Team', 'Season', 'Week', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(row_number() <= numshow))
}else{
return(dat %>%
filter(Playoffs == 0 & Season %in% season) %>%
select_(.dots = c('Team', 'Season', 'Week', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(row_number() <= numshow))
}
}
}
}
# records.func(dat = dat,
# weekly = T,
# Season = 2016,
# statcat = 'R',
# playoffs = F,
# best = T,
# numshow = 5) | /RecordsFunction.R | no_license | mattyanselmo/RenegadesHistoryApp | R | false | false | 5,516 | r | # dat <- read.csv('RenegadesHistoryFormatted.csv')
# weekly = F
# Season = c(2016, 2017)
# statcat = 'HR'
# playoffs = F
# best = T
# numshow = 10
# ownerfilter = 'All'
# franchisefilter = 'All'
records.func <- function(dat = dat,
weekly = T,
season = c(2011:2017),
statcat = 'All',
playoffs = F,
best = T,
numshow = 5,
ownerfilter = 'All',
franchisefilter = 'All'){
dat <- dat %>% filter(AllStar == 0) %>%
mutate(Luck = Wins - xWins)
dat <- dat %>% filter(ifelse(rep(ownerfilter, nrow(dat)) == 'All',
TRUE,
TeamOwner == ownerfilter))
dat <- dat %>% filter(ifelse(rep(franchisefilter, nrow(dat)) == 'All',
TRUE,
CurrentName == franchisefilter))
if(!weekly){
dat <- dat %>%
filter(Playoffs == as.numeric(playoffs)) %>%
group_by(Season, Team) %>%
summarize(Owner = TeamOwner[1],
R = sum(R),
HR = sum(HR),
RBI = sum(RBI),
SB = sum(SB),
OBP = mean(OBP),
SLG = mean(SLG),
K = sum(K),
QS = sum(QS),
W = sum(W),
SV = sum(SV),
ERA = mean(ERA),
WHIP = mean(WHIP),
Luck = sum(Luck)) %>%
ungroup()
return(dat %>%
select_(.dots = c('Team', 'Season', 'Owner', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(Season %in% season) %>%
filter(row_number() <= numshow))
} else{
if(statcat == 'All'){
if(playoffs){
ranks <- data.frame(Team = dat$Team[dat$Playoffs == 1 & dat$Season %in% season],
Season = dat$Season[dat$Playoffs == 1 & dat$Season %in% season],
Week = dat$Week[dat$Playoffs == 1 & dat$Season %in% season],
sapply(dat %>% filter(Playoffs == 1 & Season %in% season) %>%
select(R:SV), rank),
sapply(dat %>% filter(dat$Playoffs == 1) %>%
select(ERA, WHIP), function(x) nrow(dat) + 1 - rank(x)))
ranks[['Score']] <- apply(ranks %>% select(R:WHIP), 1, sum)
return(ranks %>%
arrange_(ifelse(best, 'desc(Score)', 'Score')) %>%
filter(row_number() <= numshow) %>%
select(Team, Season, Week, Score) %>%
left_join(dat %>%
select(Team, Season, Week, Owner = TeamOwner, R:WHIP),
c('Team', 'Season', 'Week')) %>%
mutate(WinPct = Score / (nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) * 12)) %>%
select(-Score))
}else{
ranks <- data.frame(Team = dat$Team[dat$Playoffs == 0 & dat$Season %in% season],
Season = dat$Season[dat$Playoffs == 0 & dat$Season %in% season],
Week = dat$Week[dat$Playoffs == 0 & dat$Season %in% season],
sapply(dat %>% filter(Playoffs == 0 & Season %in% season) %>%
select(R:SV), rank),
sapply(dat %>% filter(Playoffs == 0 & Season %in% season) %>%
select(ERA, WHIP), function(x) nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) + 1 - rank(x)))
ranks[['Score']] <- apply(ranks %>% select(R:WHIP), 1, sum)
return(ranks %>%
arrange_(ifelse(best, 'desc(Score)', 'Score')) %>%
filter(row_number() <= numshow) %>%
select(Team, Season, Week, Score) %>%
left_join(dat %>%
select(Team, Season, Week, Owner = TeamOwner, R:WHIP),
c('Team', 'Season', 'Week')) %>%
mutate(WinPct = Score / (nrow(dat[dat$Playoffs == as.numeric(playoffs) & dat$Season %in% season,]) * 12)) %>%
select(-Score))
}
} else{
if(playoffs){
return(dat %>%
filter(Playoffs == 1 & Season %in% season) %>%
select_(.dots = c('Team', 'Season', 'Week', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(row_number() <= numshow))
}else{
return(dat %>%
filter(Playoffs == 0 & Season %in% season) %>%
select_(.dots = c('Team', 'Season', 'Week', statcat)) %>%
arrange_(ifelse((statcat %in% c('WHIP', 'ERA') & best) | (!(statcat %in% c('WHIP', 'ERA')) & !best), statcat, paste0('desc(', statcat, ')'))) %>%
filter(row_number() <= numshow))
}
}
}
}
# records.func(dat = dat,
# weekly = T,
# Season = 2016,
# statcat = 'R',
# playoffs = F,
# best = T,
# numshow = 5) |
# PLOT 1
# STEP 1: reading the data
# Dataset: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# Missing values are coded as ?.
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp <- tempfile()
download.file(url, temp)
unzip(temp, "household_power_consumption.txt")
data <- read.table("household_power_consumption.txt", header=T, sep=";", na.strings = "?")
unlink(temp)
names(data)
# STEP 2: Optimize and subset dataset
# Convert the Date and Time variables to Date/Time classes in R using the 𝚜𝚝𝚛𝚙𝚝𝚒𝚖𝚎() and 𝚊𝚜.𝙳𝚊𝚝𝚎()functions.
data$Date <- as.Date(data$Date, "%d/%m/%Y")
data$Time <- strptime(data$Time, "%H:%M:%S")
# data$Time <- sub(".* ", "", data$Time)
# head(data)
# class(data$Time)
# Use only data from the dates 2007-02-01 and 2007-02-02.
data.02.07 <- subset(data, data$Date == "2007-02-01" | data$Date == "2007-02-02")
# STEP 3: Create plot
# Create PNG file with a width of 480 pixels and a height of 480 pixels
# Name each of the plot files as plot1.png, plot2.png, etc.
dev.copy(png,'plot1.png', width = 480, height = 480)
# Histogram: Frequency ~ Global Active Power (kilowatts), col = red. "Global Active Power"
par(mfrow = c(1,1))
hist(data.02.07$Global_active_power, col = "red", main = "Global Active Power",
xlab = "Global Active Power (kilowatts)", ylab = "Frequency")
dev.off() | /plot1.R | no_license | lisahlmsch/ExData_Plotting1 | R | false | false | 1,447 | r | # PLOT 1
# STEP 1: reading the data
# Dataset: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# Missing values are coded as ?.
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp <- tempfile()
download.file(url, temp)
unzip(temp, "household_power_consumption.txt")
data <- read.table("household_power_consumption.txt", header=T, sep=";", na.strings = "?")
unlink(temp)
names(data)
# STEP 2: Optimize and subset dataset
# Convert the Date and Time variables to Date/Time classes in R using the 𝚜𝚝𝚛𝚙𝚝𝚒𝚖𝚎() and 𝚊𝚜.𝙳𝚊𝚝𝚎()functions.
data$Date <- as.Date(data$Date, "%d/%m/%Y")
data$Time <- strptime(data$Time, "%H:%M:%S")
# data$Time <- sub(".* ", "", data$Time)
# head(data)
# class(data$Time)
# Use only data from the dates 2007-02-01 and 2007-02-02.
data.02.07 <- subset(data, data$Date == "2007-02-01" | data$Date == "2007-02-02")
# STEP 3: Create plot
# Create PNG file with a width of 480 pixels and a height of 480 pixels
# Name each of the plot files as plot1.png, plot2.png, etc.
dev.copy(png,'plot1.png', width = 480, height = 480)
# Histogram: Frequency ~ Global Active Power (kilowatts), col = red. "Global Active Power"
par(mfrow = c(1,1))
hist(data.02.07$Global_active_power, col = "red", main = "Global Active Power",
xlab = "Global Active Power (kilowatts)", ylab = "Frequency")
dev.off() |
library(slam)
### Name: crossprod
### Title: Matrix Crossproduct
### Aliases: tcrossprod_simple_triplet_matrix
### crossprod_simple_triplet_matrix matprod_simple_triplet_matrix
### Keywords: algebra array
### ** Examples
##
x <- matrix(c(1, 0, 0, 2, 1, 0), nrow = 3)
x
s <- as.simple_triplet_matrix(x)
tcrossprod_simple_triplet_matrix(s, x)
##
tcrossprod_simple_triplet_matrix(s)
##
tcrossprod_simple_triplet_matrix(s[1L, ], s[2:3, ])
| /data/genthat_extracted_code/slam/examples/crossprod.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 445 | r | library(slam)
### Name: crossprod
### Title: Matrix Crossproduct
### Aliases: tcrossprod_simple_triplet_matrix
### crossprod_simple_triplet_matrix matprod_simple_triplet_matrix
### Keywords: algebra array
### ** Examples
##
x <- matrix(c(1, 0, 0, 2, 1, 0), nrow = 3)
x
s <- as.simple_triplet_matrix(x)
tcrossprod_simple_triplet_matrix(s, x)
##
tcrossprod_simple_triplet_matrix(s)
##
tcrossprod_simple_triplet_matrix(s[1L, ], s[2:3, ])
|
program <-
function(D_matrix, k=5, cancer_type = cancer_type) {
##
## YOUR CODE BEGINS HERE
##
if ( !{ "NMF" %in% installed.packages( ) } ) {
install.packages(pkgs = "NMF")
}
## we compute the estimation of A for the data set :
A_matrix <- NULL
if (!is.null(x = D_matrix) ) {
if (nrow(D_matrix) < 5000){
print("number of features < 5000")
D = D_matrix
} else {
print("number of features > 5000, variance based feature selection is applied")
D = medepir::feature_selection(D_matrix)
}
res <- NMF::nmf(x = D, rank = k, method = "snmf/r", seed = 1)
A <- apply(
X = res@fit@H
, MARGIN = 2
, FUN = function( x ) {
x / sum( x )
}
)
A_matrix <- A
T_matrix <- res@fit@W
remove(list = "res")
}
##
## YOUR CODE ENDS HERE
##
return( list(A_matrix = A_matrix,T_matrix = T_matrix) )
}
| /meteor_files/algorithm_744.r | no_license | cancer-heterogeneity/cancer-heterogeneity.github.io | R | false | false | 1,039 | r | program <-
function(D_matrix, k=5, cancer_type = cancer_type) {
##
## YOUR CODE BEGINS HERE
##
if ( !{ "NMF" %in% installed.packages( ) } ) {
install.packages(pkgs = "NMF")
}
## we compute the estimation of A for the data set :
A_matrix <- NULL
if (!is.null(x = D_matrix) ) {
if (nrow(D_matrix) < 5000){
print("number of features < 5000")
D = D_matrix
} else {
print("number of features > 5000, variance based feature selection is applied")
D = medepir::feature_selection(D_matrix)
}
res <- NMF::nmf(x = D, rank = k, method = "snmf/r", seed = 1)
A <- apply(
X = res@fit@H
, MARGIN = 2
, FUN = function( x ) {
x / sum( x )
}
)
A_matrix <- A
T_matrix <- res@fit@W
remove(list = "res")
}
##
## YOUR CODE ENDS HERE
##
return( list(A_matrix = A_matrix,T_matrix = T_matrix) )
}
|
library("car")
sqrt(vif(z)) > 2 - фактор инфляции дисперсии, если больше 2, то есть вероятность мультиколлинеарности | /multicollinear-check.R | no_license | savraska/Kata | R | false | false | 191 | r | library("car")
sqrt(vif(z)) > 2 - фактор инфляции дисперсии, если больше 2, то есть вероятность мультиколлинеарности |
needs("jsonlite");
#When running the code from RStudio, you need to comment the lines above and uncomment the lines bellow
# library(jsonlite);
getDocumentsWithNgram <-function(ngram, path_users){
con <- file(sprintf("%s/coordinates.json", path_users), encoding = "latin1");
test <- as.character(readLines(con, warn = FALSE));
close(con)
test <- iconv(test, to = "utf8")
coordinates <- fromJSON(test);
doclist <- c();
k <- 1;
for (i in 1:length(coordinates[,1])){
if ((grepl(ngram, coordinates[i,'body_preprocessed']))){
doclist[k] <- coordinates[i, 'name'];
k <- k +1;
}
}
return(doclist);
}
setRelevantNgramBatch <- function(ngram, path_users, corpus){
#retrieve docs with ngram
doclist <- getDocumentsWithNgram(ngram, path_users);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
label <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% focus[,'docname']))&&(!(doclist[i] %in% notrelevant[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
label[k] <- 1;
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title, "isbase" = FALSE, "label" = label);
focus <- rbind(focus, df);
write(toJSON(focus, pretty=TRUE), sprintf("%s/focuslist.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
}
setNotRelevantNgramBatch <- function(ngram, path_users, corpus){
#retrieve docs with ngram
doclist <- getDocumentsWithNgram(ngram, path_users);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
close(con)
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
close(con)
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% notrelevant[,'docname'])&&(!(doclist[i] %in% focus[,'docname'])))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title);
notrelevant <- rbind(notrelevant, df);
write(toJSON(notrelevant, pretty=TRUE), sprintf("%s/notrelevant.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
}
setRelevantSimilarBatch <- function(document, path_users, path_core, corpus, embtech){
source("init.R")
doclist <- c();
doclist <- getSimilarDocuments(document, path_core, path_users, embtech);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
label <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% focus[,'docname']))&&(!(doclist[i] %in% notrelevant[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
label[k] <- 0.5;
close(con);
k <- k + 1;
}
}
for (i in 1:length(focus[,1])){
if (focus[i,'docname'] == document){
focus[i, 'isbase'] = TRUE;
focus[i, 'label'] = 2;
break;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title, "isbase" = FALSE, "label" = label);
focus <- rbind(focus, df);
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
write(toJSON(focus, pretty=TRUE), sprintf("%s/focuslist.json", path_users));
resdf <- data.frame("response" = "success");
return(doclist);
}
setNotRelevantSimilarBatch <- function(document, path_users, path_core, corpus, embtech){
source("init.R")
doclist <- c();
doclist <- getSimilarDocuments(document, path_core, path_users, embtech);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on not relevant list - check
# #if document is on suggestion list - remove from suggestion + add to not relevant list
# #else if document is on not focus list - remove from focus + add to not relevant
k <- 1;
sugg <- 1;
title <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% notrelevant[,'docname']))&&(!(doclist[i] %in% focus[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title);
notrelevant <- rbind(notrelevant, df);
write(toJSON(notrelevant, pretty=TRUE), sprintf("%s/notrelevant.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
} | /Server/scripts/labelDocuments.R | no_license | amandagdias/TRIVIR | R | false | false | 11,116 | r | needs("jsonlite");
#When running the code from RStudio, you need to comment the lines above and uncomment the lines bellow
# library(jsonlite);
getDocumentsWithNgram <-function(ngram, path_users){
con <- file(sprintf("%s/coordinates.json", path_users), encoding = "latin1");
test <- as.character(readLines(con, warn = FALSE));
close(con)
test <- iconv(test, to = "utf8")
coordinates <- fromJSON(test);
doclist <- c();
k <- 1;
for (i in 1:length(coordinates[,1])){
if ((grepl(ngram, coordinates[i,'body_preprocessed']))){
doclist[k] <- coordinates[i, 'name'];
k <- k +1;
}
}
return(doclist);
}
setRelevantNgramBatch <- function(ngram, path_users, corpus){
#retrieve docs with ngram
doclist <- getDocumentsWithNgram(ngram, path_users);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
label <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% focus[,'docname']))&&(!(doclist[i] %in% notrelevant[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
label[k] <- 1;
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title, "isbase" = FALSE, "label" = label);
focus <- rbind(focus, df);
write(toJSON(focus, pretty=TRUE), sprintf("%s/focuslist.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
}
setNotRelevantNgramBatch <- function(ngram, path_users, corpus){
#retrieve docs with ngram
doclist <- getDocumentsWithNgram(ngram, path_users);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
close(con)
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
close(con)
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% notrelevant[,'docname'])&&(!(doclist[i] %in% focus[,'docname'])))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title);
notrelevant <- rbind(notrelevant, df);
write(toJSON(notrelevant, pretty=TRUE), sprintf("%s/notrelevant.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
}
setRelevantSimilarBatch <- function(document, path_users, path_core, corpus, embtech){
source("init.R")
doclist <- c();
doclist <- getSimilarDocuments(document, path_core, path_users, embtech);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on focus list - check
# #if document is on suggestion list - remove from suggestion + add to focus list
# #else if document is on not relevant list - remove from not relevant + add to focus list
k <- 1;
sugg <- 1;
title <- c();
label <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% focus[,'docname']))&&(!(doclist[i] %in% notrelevant[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
label[k] <- 0.5;
close(con);
k <- k + 1;
}
}
for (i in 1:length(focus[,1])){
if (focus[i,'docname'] == document){
focus[i, 'isbase'] = TRUE;
focus[i, 'label'] = 2;
break;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title, "isbase" = FALSE, "label" = label);
focus <- rbind(focus, df);
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
write(toJSON(focus, pretty=TRUE), sprintf("%s/focuslist.json", path_users));
resdf <- data.frame("response" = "success");
return(doclist);
}
setNotRelevantSimilarBatch <- function(document, path_users, path_core, corpus, embtech){
source("init.R")
doclist <- c();
doclist <- getSimilarDocuments(document, path_core, path_users, embtech);
con <- file(sprintf("%s/focuslist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
focus <- fromJSON(jsondata);
con <- file(sprintf("%s/notrelevant.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
notrelevant <- fromJSON(jsondata);
con <- file(sprintf("%s/suggestionlist.json", path_users), encoding = "latin1");
jsondata <- as.character(readLines(con, warn = FALSE));
jsondata <- iconv(jsondata, to = "utf8")
suggestion <- fromJSON(jsondata);
close(con)
# #run over each document and check
# #if document is not on not relevant list - check
# #if document is on suggestion list - remove from suggestion + add to not relevant list
# #else if document is on not focus list - remove from focus + add to not relevant
k <- 1;
sugg <- 1;
title <- c();
docsnames <- c();
for (i in 1:length(doclist)){
if ((!(doclist[i] %in% notrelevant[,'docname']))&&(!(doclist[i] %in% focus[,'docname']))){
if (length(suggestion) > 0){
if (doclist[i] %in% suggestion[,'docname']){
suggestion <- suggestion[suggestion$docname != doclist[i],];
sugg <- sugg + 1;
}
}
path <- sprintf("%s/%s", corpus, doclist[i]);
con <- file(path);
lines <- as.character(readLines(con, warn = FALSE));
for (j in 1:length(lines)){
if (lines[j] == "")
break;
}
if ((basename(corpus) == "cbr ilp ir son")||(basename(corpus) == "demo")){
titlelines <- lines[1:j];
title[k] <- concatenate(titlelines);
}else if (basename(corpus) == "WOS all"){
title[k] <- doclist[i];
title[k] <- substr(title[k], 1, nchar(title[k]) - 4)
}else{
title[k] <- lines[1];
}
docsnames[k] <- doclist[i];
close(con);
k <- k + 1;
}
}
if (k > 1){
df <- data.frame("docname" = docsnames, "title" = title);
notrelevant <- rbind(notrelevant, df);
write(toJSON(notrelevant, pretty=TRUE), sprintf("%s/notrelevant.json", path_users));
if (sugg > 1){
write(toJSON(suggestion, pretty=TRUE), sprintf("%s/suggestionlist.json", path_users));
}
}
} |
#' Rectangles
#'
#' `geom_rect()` and `geom_tile()` do the same thing, but are
#' parameterised differently: `geom_rect()` uses the locations of the four
#' corners (`xmin`, `xmax`, `ymin` and `ymax`), while
#' `geom_tile()` uses the center of the tile and its size (`x`,
#' `y`, `width`, `height`). `geom_raster()` is a high
#' performance special case for when all the tiles are the same size.
#'
#' @eval rd_aesthetics("geom", "tile")
#' @inheritParams layer
#' @inheritParams geom_point
#' @inheritParams geom_segment
#' @export
#' @examples
#' # The most common use for rectangles is to draw a surface. You always want
#' # to use geom_raster here because it's so much faster, and produces
#' # smaller output when saving to PDF
#' ggplot(faithfuld, aes(waiting, eruptions)) +
#' geom_raster(aes(fill = density))
#'
#' # Interpolation smooths the surface & is most helpful when rendering images.
#' ggplot(faithfuld, aes(waiting, eruptions)) +
#' geom_raster(aes(fill = density), interpolate = TRUE)
#'
#' # If you want to draw arbitrary rectangles, use geom_tile() or geom_rect()
#' df <- data.frame(
#' x = rep(c(2, 5, 7, 9, 12), 2),
#' y = rep(c(1, 2), each = 5),
#' z = factor(rep(1:5, each = 2)),
#' w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2)
#' )
#' ggplot(df, aes(x, y)) +
#' geom_tile(aes(fill = z), colour = "grey50")
#' ggplot(df, aes(x, y, width = w)) +
#' geom_tile(aes(fill = z), colour = "grey50")
#' ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) +
#' geom_rect(aes(fill = z), colour = "grey50")
#'
#' \donttest{
#' # Justification controls where the cells are anchored
#' df <- expand.grid(x = 0:5, y = 0:5)
#' set.seed(1)
#' df$z <- runif(nrow(df))
#' # default is compatible with geom_tile()
#' ggplot(df, aes(x, y, fill = z)) +
#' geom_raster()
#' # zero padding
#' ggplot(df, aes(x, y, fill = z)) +
#' geom_raster(hjust = 0, vjust = 0)
#'
#' # Inspired by the image-density plots of Ken Knoblauch
#' cars <- ggplot(mtcars, aes(mpg, factor(cyl)))
#' cars + geom_point()
#' cars + stat_bin2d(aes(fill = after_stat(count)), binwidth = c(3,1))
#' cars + stat_bin2d(aes(fill = after_stat(density)), binwidth = c(3,1))
#'
#' cars +
#' stat_density(
#' aes(fill = after_stat(density)),
#' geom = "raster",
#' position = "identity"
#' )
#' cars +
#' stat_density(
#' aes(fill = after_stat(count)),
#' geom = "raster",
#' position = "identity"
#' )
#' }
geom_tile <- function(mapping = NULL, data = NULL,
stat = "identity", position = "identity",
...,
linejoin = "mitre",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomTile,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list2(
linejoin = linejoin,
na.rm = na.rm,
...
)
)
}
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
#' @include geom-rect.r
GeomTile <- ggproto("GeomTile", GeomRect,
extra_params = c("na.rm"),
setup_data = function(data, params) {
data$width <- data$width %||% params$width %||% resolution(data$x, FALSE)
data$height <- data$height %||% params$height %||% resolution(data$y, FALSE)
transform(data,
xmin = x - width / 2, xmax = x + width / 2, width = NULL,
ymin = y - height / 2, ymax = y + height / 2, height = NULL
)
},
default_aes = aes(fill = "grey20", colour = NA, linewidth = 0.1, linetype = 1,
alpha = NA, width = NA, height = NA),
required_aes = c("x", "y"),
# These aes columns are created by setup_data(). They need to be listed here so
# that GeomRect$handle_na() properly removes any bars that fall outside the defined
# limits, not just those for which x and y are outside the limits
non_missing_aes = c("xmin", "xmax", "ymin", "ymax"),
draw_key = draw_key_polygon
)
| /R/geom-tile.r | permissive | zawkzaw/ggplot2 | R | false | false | 4,027 | r | #' Rectangles
#'
#' `geom_rect()` and `geom_tile()` do the same thing, but are
#' parameterised differently: `geom_rect()` uses the locations of the four
#' corners (`xmin`, `xmax`, `ymin` and `ymax`), while
#' `geom_tile()` uses the center of the tile and its size (`x`,
#' `y`, `width`, `height`). `geom_raster()` is a high
#' performance special case for when all the tiles are the same size.
#'
#' @eval rd_aesthetics("geom", "tile")
#' @inheritParams layer
#' @inheritParams geom_point
#' @inheritParams geom_segment
#' @export
#' @examples
#' # The most common use for rectangles is to draw a surface. You always want
#' # to use geom_raster here because it's so much faster, and produces
#' # smaller output when saving to PDF
#' ggplot(faithfuld, aes(waiting, eruptions)) +
#' geom_raster(aes(fill = density))
#'
#' # Interpolation smooths the surface & is most helpful when rendering images.
#' ggplot(faithfuld, aes(waiting, eruptions)) +
#' geom_raster(aes(fill = density), interpolate = TRUE)
#'
#' # If you want to draw arbitrary rectangles, use geom_tile() or geom_rect()
#' df <- data.frame(
#' x = rep(c(2, 5, 7, 9, 12), 2),
#' y = rep(c(1, 2), each = 5),
#' z = factor(rep(1:5, each = 2)),
#' w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2)
#' )
#' ggplot(df, aes(x, y)) +
#' geom_tile(aes(fill = z), colour = "grey50")
#' ggplot(df, aes(x, y, width = w)) +
#' geom_tile(aes(fill = z), colour = "grey50")
#' ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) +
#' geom_rect(aes(fill = z), colour = "grey50")
#'
#' \donttest{
#' # Justification controls where the cells are anchored
#' df <- expand.grid(x = 0:5, y = 0:5)
#' set.seed(1)
#' df$z <- runif(nrow(df))
#' # default is compatible with geom_tile()
#' ggplot(df, aes(x, y, fill = z)) +
#' geom_raster()
#' # zero padding
#' ggplot(df, aes(x, y, fill = z)) +
#' geom_raster(hjust = 0, vjust = 0)
#'
#' # Inspired by the image-density plots of Ken Knoblauch
#' cars <- ggplot(mtcars, aes(mpg, factor(cyl)))
#' cars + geom_point()
#' cars + stat_bin2d(aes(fill = after_stat(count)), binwidth = c(3,1))
#' cars + stat_bin2d(aes(fill = after_stat(density)), binwidth = c(3,1))
#'
#' cars +
#' stat_density(
#' aes(fill = after_stat(density)),
#' geom = "raster",
#' position = "identity"
#' )
#' cars +
#' stat_density(
#' aes(fill = after_stat(count)),
#' geom = "raster",
#' position = "identity"
#' )
#' }
geom_tile <- function(mapping = NULL, data = NULL,
stat = "identity", position = "identity",
...,
linejoin = "mitre",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomTile,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list2(
linejoin = linejoin,
na.rm = na.rm,
...
)
)
}
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
#' @include geom-rect.r
GeomTile <- ggproto("GeomTile", GeomRect,
extra_params = c("na.rm"),
setup_data = function(data, params) {
data$width <- data$width %||% params$width %||% resolution(data$x, FALSE)
data$height <- data$height %||% params$height %||% resolution(data$y, FALSE)
transform(data,
xmin = x - width / 2, xmax = x + width / 2, width = NULL,
ymin = y - height / 2, ymax = y + height / 2, height = NULL
)
},
default_aes = aes(fill = "grey20", colour = NA, linewidth = 0.1, linetype = 1,
alpha = NA, width = NA, height = NA),
required_aes = c("x", "y"),
# These aes columns are created by setup_data(). They need to be listed here so
# that GeomRect$handle_na() properly removes any bars that fall outside the defined
# limits, not just those for which x and y are outside the limits
non_missing_aes = c("xmin", "xmax", "ymin", "ymax"),
draw_key = draw_key_polygon
)
|
library(caret)
library(mlbench)
library(magrittr)
data(Sonar)
set.seed(107)
in_train <- createDataPartition(y = Sonar$Class, p = 0.75, list = FALSE)
training <- Sonar[in_train, ]
testing <- Sonar[-in_train, ]
ctrl <- trainControl(method = "repeatedcv",
repeats = 3,
classProbs = TRUE,
summaryFunction = twoClassSummary
)
# See names(getModelInfo()) for potential models
# or http://topepo.github.io/caret/bytag.html
model <- train(Class ~ .,
data = training,
method = "pls",
preProc = c("center", "scale"),
trControl = ctrl,
metric = "ROC",
tuneLength = 15
)
model
plot(model)
predictions <- predict(model, newdata = testing)
predictions_prob <- predict(model, newdata = testing, type = "prob")
accuracy <- confusionMatrix(data = predictions, testing$Class)
| /classifier.R | no_license | GCDigitalFellows/WebScraping | R | false | false | 956 | r | library(caret)
library(mlbench)
library(magrittr)
data(Sonar)
set.seed(107)
in_train <- createDataPartition(y = Sonar$Class, p = 0.75, list = FALSE)
training <- Sonar[in_train, ]
testing <- Sonar[-in_train, ]
ctrl <- trainControl(method = "repeatedcv",
repeats = 3,
classProbs = TRUE,
summaryFunction = twoClassSummary
)
# See names(getModelInfo()) for potential models
# or http://topepo.github.io/caret/bytag.html
model <- train(Class ~ .,
data = training,
method = "pls",
preProc = c("center", "scale"),
trControl = ctrl,
metric = "ROC",
tuneLength = 15
)
model
plot(model)
predictions <- predict(model, newdata = testing)
predictions_prob <- predict(model, newdata = testing, type = "prob")
accuracy <- confusionMatrix(data = predictions, testing$Class)
|
str(HBAT)
designMat <- HBAT[,c(2, 4, 14:18)]
str(designMat)
attach(designMat)
sex = factor(sex) #factor 지정
JP = factor(JP)
#일변량 이원배치분산분석
summary(aov(JS1 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS2 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS3 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS4 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS5 ~ sex+JP+sex:JP, data=designMat))
# 상호작용 그림
par(mfrow=c(1,2))
interaction.plot(sex, JP, JS1, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS1")
interaction.plot(JP.f, SEX.f, JS1, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
ylab = "Sex", xlab = "Job Performance", main = "Interaction Plot : JS1")
interaction.plot(SEX.f, JP.f, JS2, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS2")
interaction.plot(SEX.f, JP.f, JS3, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS3")
interaction.plot(SEX.f, JP.f, JS4, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS4")
interaction.plot(SEX.f, JP.f, JS5, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS5")
# 다중비교 실시
TukeyHSD(aov(JS2 ~ factor(SEX.f)+factor(JP.f)+factor(SEX.f)*factor(JP.f), data=designMat))
# 다변량 이원배치분산분석
JS = cbind(JS1, JS2, JS3, JS4, JS5) #응답벡터
fit = manova(JS ~ SEX.f + JP.f + SEX.f:JP.f)
summary(fit, test="Wilks") # Wilks' lambda
summary(fit, test="Pillai") # Pillai's trace
summary(fit, test="Roy") # Roy's grestest root
summary(fit, test="Hotelling") # Hotelling-Lawley trace
#공분산행렬의 동질성 검정
install.packages("biotools")
install.packages("rpanel")
install.packages("tcltk")
install.packages("BWidget")
library(biotools)
.libPaths()
install.packages("psych")
install.packages("GPArotation")
library(psych)
library(GPArotation)
data(Thurstone)
fit1 <- fa(Thurstone, nfactors = 3, rotate = "oblimin", fm="ml")
print(fit1)
install.packages("EFAutilities")
library(EFAutilities)
Thurstone
res1 <- efa(x=NULL, covmat=Thurstone, dist="normal", factors=3, n.obs=213, fm="ml",
rtype = 'oblique', rotation = 'CF-varimax', merror="YES", mnames=row.names(Thurstone))
print(res1)
res1$ModelF
res1$rotatedlow
res1$rotatedupper
res1$Residual
Target1 <- matrix(c(9, 0, 0,
9, 0, 0,
9, 0, 0,
0, 9, 0,
0, 9, 0,
0, 9, 0,
0, 0, 9,
0, 0, 9,
0, 0, 9), ncol=3, byrow=TURE)
MWeight1 <- matrix(0, ncol=3, nrow=9)
MWeight1[Target1==0] <- 1
| /TwoWayMANOVA.R | no_license | SungjiCho/R | R | false | false | 3,290 | r | str(HBAT)
designMat <- HBAT[,c(2, 4, 14:18)]
str(designMat)
attach(designMat)
sex = factor(sex) #factor 지정
JP = factor(JP)
#일변량 이원배치분산분석
summary(aov(JS1 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS2 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS3 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS4 ~ sex+JP+sex:JP, data=designMat))
summary(aov(JS5 ~ sex+JP+sex:JP, data=designMat))
# 상호작용 그림
par(mfrow=c(1,2))
interaction.plot(sex, JP, JS1, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS1")
interaction.plot(JP.f, SEX.f, JS1, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
ylab = "Sex", xlab = "Job Performance", main = "Interaction Plot : JS1")
interaction.plot(SEX.f, JP.f, JS2, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS2")
interaction.plot(SEX.f, JP.f, JS3, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS3")
interaction.plot(SEX.f, JP.f, JS4, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS4")
interaction.plot(SEX.f, JP.f, JS5, type="b", col=c(1:3),
leg.bty = "o", leg.bg = "beige", lwd=2, pch = c(18, 24, 22),
xlab = "Sex", ylab = "Job Performance", main = "Interaction Plot : JS5")
# 다중비교 실시
TukeyHSD(aov(JS2 ~ factor(SEX.f)+factor(JP.f)+factor(SEX.f)*factor(JP.f), data=designMat))
# 다변량 이원배치분산분석
JS = cbind(JS1, JS2, JS3, JS4, JS5) #응답벡터
fit = manova(JS ~ SEX.f + JP.f + SEX.f:JP.f)
summary(fit, test="Wilks") # Wilks' lambda
summary(fit, test="Pillai") # Pillai's trace
summary(fit, test="Roy") # Roy's grestest root
summary(fit, test="Hotelling") # Hotelling-Lawley trace
#공분산행렬의 동질성 검정
install.packages("biotools")
install.packages("rpanel")
install.packages("tcltk")
install.packages("BWidget")
library(biotools)
.libPaths()
install.packages("psych")
install.packages("GPArotation")
library(psych)
library(GPArotation)
data(Thurstone)
fit1 <- fa(Thurstone, nfactors = 3, rotate = "oblimin", fm="ml")
print(fit1)
install.packages("EFAutilities")
library(EFAutilities)
Thurstone
res1 <- efa(x=NULL, covmat=Thurstone, dist="normal", factors=3, n.obs=213, fm="ml",
rtype = 'oblique', rotation = 'CF-varimax', merror="YES", mnames=row.names(Thurstone))
print(res1)
res1$ModelF
res1$rotatedlow
res1$rotatedupper
res1$Residual
Target1 <- matrix(c(9, 0, 0,
9, 0, 0,
9, 0, 0,
0, 9, 0,
0, 9, 0,
0, 9, 0,
0, 0, 9,
0, 0, 9,
0, 0, 9), ncol=3, byrow=TURE)
MWeight1 <- matrix(0, ncol=3, nrow=9)
MWeight1[Target1==0] <- 1
|
## File Name: tam_args_replace_value.R
## File Version: 0.01
tam_args_replace_value <- function( args , variable=NULL , value=NULL)
{
if ( ! is.null(variable) ){
args[[ variable ]] <- value
}
return(args)
}
| /R/tam_args_replace_value.R | no_license | yaozeyang90/TAM | R | false | false | 213 | r | ## File Name: tam_args_replace_value.R
## File Version: 0.01
tam_args_replace_value <- function( args , variable=NULL , value=NULL)
{
if ( ! is.null(variable) ){
args[[ variable ]] <- value
}
return(args)
}
|
read_rdf_header <- function(con, pos, end)
{
obj <- list()
repeat {
line <- con[pos, 1]
pos <- pos + 1 # advancing line to read
if(line == end) break
splitLine <- strsplit(line, ':', fixed = TRUE)[[1]]
name <- splitLine[1]
if (length(splitLine) > 1) {
if (substr(splitLine[2], 1, 1) == ' ') {
splitLine[2] <- substr(splitLine[2], 2, nchar(splitLine[2]))
}
contents <- paste(splitLine[2:length(splitLine)], collapse = ':')
} else {
contents <- NA
}
obj[[name]] <- contents
# 1 passed to this function sometimes; when it is, it forces it to read
# one line and parse
if (end == 1) break
}
#returns the object
return(list(data = obj, position = pos))
}
#' Read the initial meta data from the rdf file; this is the descriptor:pair
#' data up through the END_PACKAGE_PREAMBLE keyword. These are read once for
#' each rdf file and there is only one set of meta data regardless of the
#' number of traces.
#' @noRd
read_rdf_meta <- function(rdf.mat, rdf.obj)
{
rdf.tmp <- read_rdf_header(rdf.mat, rdf.obj$position, 'END_PACKAGE_PREAMBLE')
rdf.obj[['meta']] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
return(rdf.obj)
}
read_rdf_run <- function(rdf.mat, rdf.obj)
{
this.run <- length(rdf.obj$runs) + 1
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position,'END_RUN_PREAMBLE')
rdf.obj$runs[[this.run]] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
#time steps
nts <- as.integer(rdf.obj$runs[[this.run]]$time_steps)
#for non-mrm files
if (length(nts) == 0) {
nts <- as.integer(rdf.obj$runs[[this.run]]$timesteps)
}
rr <- rdf.obj$position:(rdf.obj$position + nts -1)
rdf.obj$runs[[this.run]][['times']] <- rdf.mat[rr, 1]
rdf.obj$position <- rdf.obj$position + nts
#Series
nob <- 0
repeat {
nob <- nob + 1
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 'END_SLOT_PREAMBLE')
rdf.obj$runs[[this.run]][['objects']][[nob]] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
# name the object after their object.slot name
obj.name <- rdf.obj$runs[[this.run]][['objects']][[nob]]$object_name
slot.name <- rdf.obj$runs[[this.run]][['objects']][[nob]]$slot_name
name <- paste(obj.name, slot.name, sep = '.')
names(rdf.obj$runs[[this.run]][['objects']])[nob] <- name
# read in the extr two header pieces
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 1)
rdf.obj$runs[[this.run]][['objects']][[nob]]$units <- rdf.tmp$data[[1]]
rdf.obj$position <- rdf.tmp$position
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 1)
rdf.obj$runs[[this.run]][['objects']][[nob]]$scale <- rdf.tmp$data[[1]]
rdf.obj$position <- rdf.tmp$position
# Figure out when the END_COLUMN keyword shows up
#rdf_tmp <- read_rdf_header(rdf.mat, rdf.obj$position, "END_COLUMN")
ec_pos <- Position(function(x) x > rdf.obj$position, rdf.obj$end_col_i)
ec_i <- rdf.obj$end_col_i[ec_pos] + 1
# remove the already used indeces so next Position call doesn't have to
# search for indeces that are already used
rdf.obj$end_col_i <- rdf.obj$end_col_i[
(ec_pos + 1):length(rdf.obj$end_col_i)
]
if (ec_i == rdf.obj$position + 2) {
# must be a scalar slot
row_nums <- rdf.obj$position
} else if (ec_i - rdf.obj$position - 1 == nts) {
row_nums <- rdf.obj$position:(ec_i - 2)
} else {
stop(
"rdf includes an unexpected number of data points.\n",
"`read.rdf()` expects the data entries to either be 1, or\n",
"the number of time steps."
)
}
rdf.obj$runs[[this.run]][['objects']][[nob]]$values <- as.numeric(
rdf.mat[row_nums, 1]
)
rdf.obj$position <- rdf.obj$position + length(row_nums)
#END_COLUMN,END_SLOT, table slots need support here
#dummy <- readLines(rdf.con,n=2) # just advances position by 2??
if (rdf.mat[rdf.obj$position+2,1] == 'END_RUN') {
rdf.obj$position <- rdf.obj$position + 3
break
} else {
rdf.obj$position <- rdf.obj$position + 2
}
}
return(rdf.obj)
}
#' Read an rdf file into R.
#'
#' `read.rdf()` reads an rdf file into R and formats it as a multi-level list
#' containing all of the metadata included in the rdf file. rdf files are
#' generated by RiverWare and are documented in the
#' [RiverWare documentation](http://riverware.org/PDF/RiverWare/documentation/).
#'
#' `read.rdf()`uses [data.table::fread()] to read in the file, which provides
#' performance benefits as compared to earlier versions of the function.
#'
#' `read.rdf2()` is deprecated and will be removed in a future release.
#'
#' @param iFile The input rdf file that will be read into R.
#' @param rdf Boolean; if `TRUE`, then an rdf object is returned. If `FALSE`,
#' then a character vector is returned.
#'
#' @return An rdf object or character vector.
#'
#' @examples
#' zz <- read_rdf(system.file(
#' 'extdata/Scenario/ISM1988_2014,2007Dems,IG,Most',
#' "KeySlots.rdf",
#' package = "RWDataPlyr"
#' ))
#'
#' @export
read.rdf <- function(iFile, rdf = TRUE)
{
check_rdf_file(iFile)
rdf.obj <- list()
# read entire file into memory
rdf.mat <- as.matrix(data.table::fread(
iFile,
sep = '\t',
header = FALSE,
data.table = FALSE
))
if (!rdf) {
return(rdf.mat)
}
rdf.obj$position <- 1 # initialize where to read from
rdf.obj <- read_rdf_meta(rdf.mat, rdf.obj)
rdf.obj$end_col_i <- which(rdf.mat == "END_COLUMN")
# Read each trace/run
for (i in 1:as.numeric(rdf.obj$meta$number_of_runs)) {
rdf.obj <- read_rdf_run(rdf.mat, rdf.obj)
}
rdf.obj$position <- NULL # remove position before returning
rdf.obj$end_col_i <- NULL
structure(
rdf.obj,
class = "rdf"
)
}
#' @describeIn read.rdf Deprecated version of `read.rdf()`
#' @export
read.rdf2 <- function(iFile)
{
.Deprecated("read.rdf")
read.rdf(iFile, rdf = TRUE)
}
#' @rdname read.rdf
#' @export
read_rdf <- read.rdf
check_rdf_file <- function(file)
{
if (tools::file_ext(file) != "rdf") {
stop(
file, " does not appear to be an rdf file.",
call. = FALSE
)
}
if (!file.exists(file)) {
stop(
file, " does not exist.",
call. = FALSE
)
}
invisible(file)
}
| /R/read_rdf.R | permissive | romainfrancois/RWDataPlyr | R | false | false | 6,407 | r |
read_rdf_header <- function(con, pos, end)
{
obj <- list()
repeat {
line <- con[pos, 1]
pos <- pos + 1 # advancing line to read
if(line == end) break
splitLine <- strsplit(line, ':', fixed = TRUE)[[1]]
name <- splitLine[1]
if (length(splitLine) > 1) {
if (substr(splitLine[2], 1, 1) == ' ') {
splitLine[2] <- substr(splitLine[2], 2, nchar(splitLine[2]))
}
contents <- paste(splitLine[2:length(splitLine)], collapse = ':')
} else {
contents <- NA
}
obj[[name]] <- contents
# 1 passed to this function sometimes; when it is, it forces it to read
# one line and parse
if (end == 1) break
}
#returns the object
return(list(data = obj, position = pos))
}
#' Read the initial meta data from the rdf file; this is the descriptor:pair
#' data up through the END_PACKAGE_PREAMBLE keyword. These are read once for
#' each rdf file and there is only one set of meta data regardless of the
#' number of traces.
#' @noRd
read_rdf_meta <- function(rdf.mat, rdf.obj)
{
rdf.tmp <- read_rdf_header(rdf.mat, rdf.obj$position, 'END_PACKAGE_PREAMBLE')
rdf.obj[['meta']] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
return(rdf.obj)
}
read_rdf_run <- function(rdf.mat, rdf.obj)
{
this.run <- length(rdf.obj$runs) + 1
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position,'END_RUN_PREAMBLE')
rdf.obj$runs[[this.run]] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
#time steps
nts <- as.integer(rdf.obj$runs[[this.run]]$time_steps)
#for non-mrm files
if (length(nts) == 0) {
nts <- as.integer(rdf.obj$runs[[this.run]]$timesteps)
}
rr <- rdf.obj$position:(rdf.obj$position + nts -1)
rdf.obj$runs[[this.run]][['times']] <- rdf.mat[rr, 1]
rdf.obj$position <- rdf.obj$position + nts
#Series
nob <- 0
repeat {
nob <- nob + 1
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 'END_SLOT_PREAMBLE')
rdf.obj$runs[[this.run]][['objects']][[nob]] <- rdf.tmp$data
rdf.obj$position <- rdf.tmp$position
# name the object after their object.slot name
obj.name <- rdf.obj$runs[[this.run]][['objects']][[nob]]$object_name
slot.name <- rdf.obj$runs[[this.run]][['objects']][[nob]]$slot_name
name <- paste(obj.name, slot.name, sep = '.')
names(rdf.obj$runs[[this.run]][['objects']])[nob] <- name
# read in the extr two header pieces
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 1)
rdf.obj$runs[[this.run]][['objects']][[nob]]$units <- rdf.tmp$data[[1]]
rdf.obj$position <- rdf.tmp$position
rdf.tmp <- read_rdf_header(rdf.mat,rdf.obj$position, 1)
rdf.obj$runs[[this.run]][['objects']][[nob]]$scale <- rdf.tmp$data[[1]]
rdf.obj$position <- rdf.tmp$position
# Figure out when the END_COLUMN keyword shows up
#rdf_tmp <- read_rdf_header(rdf.mat, rdf.obj$position, "END_COLUMN")
ec_pos <- Position(function(x) x > rdf.obj$position, rdf.obj$end_col_i)
ec_i <- rdf.obj$end_col_i[ec_pos] + 1
# remove the already used indeces so next Position call doesn't have to
# search for indeces that are already used
rdf.obj$end_col_i <- rdf.obj$end_col_i[
(ec_pos + 1):length(rdf.obj$end_col_i)
]
if (ec_i == rdf.obj$position + 2) {
# must be a scalar slot
row_nums <- rdf.obj$position
} else if (ec_i - rdf.obj$position - 1 == nts) {
row_nums <- rdf.obj$position:(ec_i - 2)
} else {
stop(
"rdf includes an unexpected number of data points.\n",
"`read.rdf()` expects the data entries to either be 1, or\n",
"the number of time steps."
)
}
rdf.obj$runs[[this.run]][['objects']][[nob]]$values <- as.numeric(
rdf.mat[row_nums, 1]
)
rdf.obj$position <- rdf.obj$position + length(row_nums)
#END_COLUMN,END_SLOT, table slots need support here
#dummy <- readLines(rdf.con,n=2) # just advances position by 2??
if (rdf.mat[rdf.obj$position+2,1] == 'END_RUN') {
rdf.obj$position <- rdf.obj$position + 3
break
} else {
rdf.obj$position <- rdf.obj$position + 2
}
}
return(rdf.obj)
}
#' Read an rdf file into R.
#'
#' `read.rdf()` reads an rdf file into R and formats it as a multi-level list
#' containing all of the metadata included in the rdf file. rdf files are
#' generated by RiverWare and are documented in the
#' [RiverWare documentation](http://riverware.org/PDF/RiverWare/documentation/).
#'
#' `read.rdf()`uses [data.table::fread()] to read in the file, which provides
#' performance benefits as compared to earlier versions of the function.
#'
#' `read.rdf2()` is deprecated and will be removed in a future release.
#'
#' @param iFile The input rdf file that will be read into R.
#' @param rdf Boolean; if `TRUE`, then an rdf object is returned. If `FALSE`,
#' then a character vector is returned.
#'
#' @return An rdf object or character vector.
#'
#' @examples
#' zz <- read_rdf(system.file(
#' 'extdata/Scenario/ISM1988_2014,2007Dems,IG,Most',
#' "KeySlots.rdf",
#' package = "RWDataPlyr"
#' ))
#'
#' @export
read.rdf <- function(iFile, rdf = TRUE)
{
check_rdf_file(iFile)
rdf.obj <- list()
# read entire file into memory
rdf.mat <- as.matrix(data.table::fread(
iFile,
sep = '\t',
header = FALSE,
data.table = FALSE
))
if (!rdf) {
return(rdf.mat)
}
rdf.obj$position <- 1 # initialize where to read from
rdf.obj <- read_rdf_meta(rdf.mat, rdf.obj)
rdf.obj$end_col_i <- which(rdf.mat == "END_COLUMN")
# Read each trace/run
for (i in 1:as.numeric(rdf.obj$meta$number_of_runs)) {
rdf.obj <- read_rdf_run(rdf.mat, rdf.obj)
}
rdf.obj$position <- NULL # remove position before returning
rdf.obj$end_col_i <- NULL
structure(
rdf.obj,
class = "rdf"
)
}
#' @describeIn read.rdf Deprecated version of `read.rdf()`
#' @export
read.rdf2 <- function(iFile)
{
.Deprecated("read.rdf")
read.rdf(iFile, rdf = TRUE)
}
#' @rdname read.rdf
#' @export
read_rdf <- read.rdf
check_rdf_file <- function(file)
{
if (tools::file_ext(file) != "rdf") {
stop(
file, " does not appear to be an rdf file.",
call. = FALSE
)
}
if (!file.exists(file)) {
stop(
file, " does not exist.",
call. = FALSE
)
}
invisible(file)
}
|
# Initialize environment
libraryBooks <- c("knitr", "tidyverse", "cowplot")
invisible(lapply(libraryBooks, require, character.only = TRUE)); rm(libraryBooks)
# scriptPath <- getwd()
scriptPath <- "~/Documents/GitHub/PHP2511_emotion_project/Analysis"
# Graph aesthetics
pnas_theme = theme_bw(base_size = 10) +
theme(text = element_text(size = 10), # Increase the font size
panel.grid = element_blank(),
axis.ticks = element_blank()) # remove x & y ticks
# Read in data
d1 <- read.csv(paste0(scriptPath, "/Data/Exp2Classifications.csv"),
header = TRUE, stringsAsFactors=F) %>%
select(-DBSCAN_type) %>%
mutate(sensations = replace(sensations, sensations=="Being conscious", "BeingConscious"),
sensations = replace(sensations, sensations=="Being dazzled", "BeingDazzled"),
sensations = replace(sensations, sensations=="Closeness (in social relations)",
"ClosenessInSocialRelations"),
sensations = replace(sensations, sensations=="Feeling nauseous", "FeelingNauseous"),
sensations = replace(sensations, sensations=="Feeling pain", "FeelingPain"),
sensations = replace(sensations, sensations=="Feeling touch", "FeelingTouch"),
sensations = replace(sensations, sensations=="Having cold", "HavingCold"),
sensations = replace(sensations, sensations=="Having fever", "HavingFever"),
sensations = replace(sensations, sensations=="Having flu", "HavingFlu"),
sensations = replace(sensations, sensations=="Having headache", "HavingHeadache"),
sensations = replace(sensations, sensations=="Having stomach flu", "HavingStomachFlu"),
sensations = replace(sensations, sensations=="Having toothache", "HavingToothache"),
sensations = replace(sensations, sensations=="Longing for", "LongingFor"),
sensations = replace(sensations, sensations=="Self-regulation", "SelfRegulation"),
sensations = replace(sensations, sensations=="Sexual arousal", "SexualArousal"),
sensations = replace(sensations, sensations=="Social exclusion", "SocialExclusion"),
sensations = replace(sensations, sensations=="Social longing", "SocialLonging"))
d2 <- read_csv(paste0(scriptPath, "/Data/Fig2_tSNE_coords.csv")) %>%
rename(xcoord = Var1, ycoord = Var2, sensations = Row)
d0 <- inner_join(d1, d2) %>%
select(-DBSCAN_labels) %>%
rename(dbscan = DBSCAN_class,
kmeans = KMEANS_class,
hc = HC_class) %>%
mutate(dbscan = as.character(dbscan),
dbscan = recode(dbscan,
"-1" = "Between",
"1" = "Negative Emotions",
"2" = "Positive Emotions",
"3" = "Illness",
"4" = "Cognition",
"5" = "Homeostasis"),
dbscan = factor(dbscan, levels=c("Between",
"Negative Emotions",
"Positive Emotions",
"Illness",
"Cognition",
"Homeostasis"))) %>%
mutate(kmeans = as.character(kmeans),
kmeans = recode(kmeans,
"1" = "Positive Emotions & Cognition",
"2" = "Illness",
"3" = "Negative Emotions",
"4" = "Between",
"5" = "Homeostasis"),
kmeans = factor(kmeans, levels=c("Between",
"Negative Emotions",
"Positive Emotions & Cognition",
"Illness",
"Homeostasis"))) %>%
mutate(hc = as.character(hc),
hc = recode(hc,
"1" = "Homeostasis",
"2" = "Cognition",
"3" = "Illness",
"4" = "Negative Emotions",
"5" = "Positive Emotions"),
hc = factor(hc, levels=c("Negative Emotions",
"Positive Emotions",
"Illness",
"Cognition",
"Homeostasis")))
feelcolors.dbscan <- c("#636363", "#4292c6", "#de2d26", "#756bb1", "#31a354", "#fdae6b")
feelcolors.kmeans <- c("#636363", "#4292c6", "#de2d26", "#756bb1", "#fdae6b")
feelcolors.hc <- c("#4292c6", "#de2d26", "#756bb1", "#31a354", "#fdae6b")
# DBSCAN
plot.dbscan <- ggplot(d0, aes(x=xcoord, y=ycoord, group=dbscan, color=dbscan)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="DBSCAN",
values=feelcolors.dbscan) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
# K-MEANS
plot.kmeans <- ggplot(d0, aes(x=xcoord, y=ycoord, group=kmeans, color=kmeans)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="K-Means",
values=feelcolors.kmeans) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
# HIERARCHICAL CLUSTERING
plot.hc <- ggplot(d0, aes(x=xcoord, y=ycoord, group=hc, color=hc)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="Hierarchical Clustering",
values=feelcolors.hc) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
plot_grid(plot.dbscan, plot.kmeans, plot.hc, ncol=1)
| /Analysis/ScratchCode/pt3_JYS_scratch.R | no_license | jpw3/PHP2511_emotion_project | R | false | false | 6,426 | r | # Initialize environment
libraryBooks <- c("knitr", "tidyverse", "cowplot")
invisible(lapply(libraryBooks, require, character.only = TRUE)); rm(libraryBooks)
# scriptPath <- getwd()
scriptPath <- "~/Documents/GitHub/PHP2511_emotion_project/Analysis"
# Graph aesthetics
pnas_theme = theme_bw(base_size = 10) +
theme(text = element_text(size = 10), # Increase the font size
panel.grid = element_blank(),
axis.ticks = element_blank()) # remove x & y ticks
# Read in data
d1 <- read.csv(paste0(scriptPath, "/Data/Exp2Classifications.csv"),
header = TRUE, stringsAsFactors=F) %>%
select(-DBSCAN_type) %>%
mutate(sensations = replace(sensations, sensations=="Being conscious", "BeingConscious"),
sensations = replace(sensations, sensations=="Being dazzled", "BeingDazzled"),
sensations = replace(sensations, sensations=="Closeness (in social relations)",
"ClosenessInSocialRelations"),
sensations = replace(sensations, sensations=="Feeling nauseous", "FeelingNauseous"),
sensations = replace(sensations, sensations=="Feeling pain", "FeelingPain"),
sensations = replace(sensations, sensations=="Feeling touch", "FeelingTouch"),
sensations = replace(sensations, sensations=="Having cold", "HavingCold"),
sensations = replace(sensations, sensations=="Having fever", "HavingFever"),
sensations = replace(sensations, sensations=="Having flu", "HavingFlu"),
sensations = replace(sensations, sensations=="Having headache", "HavingHeadache"),
sensations = replace(sensations, sensations=="Having stomach flu", "HavingStomachFlu"),
sensations = replace(sensations, sensations=="Having toothache", "HavingToothache"),
sensations = replace(sensations, sensations=="Longing for", "LongingFor"),
sensations = replace(sensations, sensations=="Self-regulation", "SelfRegulation"),
sensations = replace(sensations, sensations=="Sexual arousal", "SexualArousal"),
sensations = replace(sensations, sensations=="Social exclusion", "SocialExclusion"),
sensations = replace(sensations, sensations=="Social longing", "SocialLonging"))
d2 <- read_csv(paste0(scriptPath, "/Data/Fig2_tSNE_coords.csv")) %>%
rename(xcoord = Var1, ycoord = Var2, sensations = Row)
d0 <- inner_join(d1, d2) %>%
select(-DBSCAN_labels) %>%
rename(dbscan = DBSCAN_class,
kmeans = KMEANS_class,
hc = HC_class) %>%
mutate(dbscan = as.character(dbscan),
dbscan = recode(dbscan,
"-1" = "Between",
"1" = "Negative Emotions",
"2" = "Positive Emotions",
"3" = "Illness",
"4" = "Cognition",
"5" = "Homeostasis"),
dbscan = factor(dbscan, levels=c("Between",
"Negative Emotions",
"Positive Emotions",
"Illness",
"Cognition",
"Homeostasis"))) %>%
mutate(kmeans = as.character(kmeans),
kmeans = recode(kmeans,
"1" = "Positive Emotions & Cognition",
"2" = "Illness",
"3" = "Negative Emotions",
"4" = "Between",
"5" = "Homeostasis"),
kmeans = factor(kmeans, levels=c("Between",
"Negative Emotions",
"Positive Emotions & Cognition",
"Illness",
"Homeostasis"))) %>%
mutate(hc = as.character(hc),
hc = recode(hc,
"1" = "Homeostasis",
"2" = "Cognition",
"3" = "Illness",
"4" = "Negative Emotions",
"5" = "Positive Emotions"),
hc = factor(hc, levels=c("Negative Emotions",
"Positive Emotions",
"Illness",
"Cognition",
"Homeostasis")))
feelcolors.dbscan <- c("#636363", "#4292c6", "#de2d26", "#756bb1", "#31a354", "#fdae6b")
feelcolors.kmeans <- c("#636363", "#4292c6", "#de2d26", "#756bb1", "#fdae6b")
feelcolors.hc <- c("#4292c6", "#de2d26", "#756bb1", "#31a354", "#fdae6b")
# DBSCAN
plot.dbscan <- ggplot(d0, aes(x=xcoord, y=ycoord, group=dbscan, color=dbscan)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="DBSCAN",
values=feelcolors.dbscan) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
# K-MEANS
plot.kmeans <- ggplot(d0, aes(x=xcoord, y=ycoord, group=kmeans, color=kmeans)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="K-Means",
values=feelcolors.kmeans) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
# HIERARCHICAL CLUSTERING
plot.hc <- ggplot(d0, aes(x=xcoord, y=ycoord, group=hc, color=hc)) +
geom_point(size = 2) +
geom_label(label=d0$sensations, nudge_x=0.25, nudge_y=0.2,
size=2, show.legend=FALSE) +
scale_color_manual(name="Hierarchical Clustering",
values=feelcolors.hc) +
xlab("t-SNE1") +
ylab("t-SNE2") +
pnas_theme +
guides(color = guide_legend(override.aes = list(size=4))) +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text=element_blank(),
legend.position="bottom")
plot_grid(plot.dbscan, plot.kmeans, plot.hc, ncol=1)
|
#' Iowa Class E Liquor Sales Summary
#'
#' @description Monthly summary of the different Class E liquor sales in State of Iowa
#'
#' @details This dataset contains an aggregated view (aggregated by multiple attributes) of the sales data for Class E liquor. The dataset has been pre-processed to remove NULL values from the county variable. See vignette for more details
#'
#' @format a data frame with 10 variables.
#' \describe{
#' \item{year}{The year in which the sale occurred.}
#' \item{year_month}{This is an aggregated value indicating the month and year in YYYY-MM-DD format.}
#' \item{county}{The county in which the sale occurred.}
#' \item{population}{The population of the county of the year of sale as recorded by the US Census Bureau. NA values indicate no census data were found on the Iowa Data Portal.}
#' \item{type}{A high level grouping of the liquor. This was derived separately.}
#' \item{category}{A grouping variable used by the State of Iowa.}
#' \item{state_cost}{The cost (in US$) to the state to purchase the liquor from a vendor. Not adjusted for inflation.}
#' \item{state_revenue}{The revenue (in US$) the state earned from the sale of the liquor to retailers. Not adjusted for inflation.}
#' \item{bottles_sold}{The number of bottles sold by the state to a retailer.}
#' \item{volume}{The volume sold (in liters) by the state to a retailer.}
#' }
#'
#' @source State of Iowa Data \href{https://data.iowa.gov/resource/m3tr-qhgy.csv}{API}
#' @keywords datasets timeseries liquor revenue
#'
"liquor_sales" | /R/data.R | permissive | nikdata/ialiquor | R | false | false | 1,559 | r | #' Iowa Class E Liquor Sales Summary
#'
#' @description Monthly summary of the different Class E liquor sales in State of Iowa
#'
#' @details This dataset contains an aggregated view (aggregated by multiple attributes) of the sales data for Class E liquor. The dataset has been pre-processed to remove NULL values from the county variable. See vignette for more details
#'
#' @format a data frame with 10 variables.
#' \describe{
#' \item{year}{The year in which the sale occurred.}
#' \item{year_month}{This is an aggregated value indicating the month and year in YYYY-MM-DD format.}
#' \item{county}{The county in which the sale occurred.}
#' \item{population}{The population of the county of the year of sale as recorded by the US Census Bureau. NA values indicate no census data were found on the Iowa Data Portal.}
#' \item{type}{A high level grouping of the liquor. This was derived separately.}
#' \item{category}{A grouping variable used by the State of Iowa.}
#' \item{state_cost}{The cost (in US$) to the state to purchase the liquor from a vendor. Not adjusted for inflation.}
#' \item{state_revenue}{The revenue (in US$) the state earned from the sale of the liquor to retailers. Not adjusted for inflation.}
#' \item{bottles_sold}{The number of bottles sold by the state to a retailer.}
#' \item{volume}{The volume sold (in liters) by the state to a retailer.}
#' }
#'
#' @source State of Iowa Data \href{https://data.iowa.gov/resource/m3tr-qhgy.csv}{API}
#' @keywords datasets timeseries liquor revenue
#'
"liquor_sales" |
#' geom_barH
geom_barh <-
function (mapping = NULL,
data = NULL,
stat = "count",
# stat = "barH",
position = "stack",
...,
width = NULL,
binwidth = NULL,
na.rm = FALSE,
orientation = NA,
hatch = 3,
show.legend = NA,
inherit.aes = TRUE) {
if (!is.null(binwidth)) {
warn("`geom_barh()` no longer has a `binwidth` parameter. Please use `geom_histogramh()` instead.")
return(geom_histogramh(mapping = mapping, data = data,
position = position, width = width, binwidth = binwidth, hatch = hatch,
..., na.rm = na.rm, show.legend = show.legend, inherit.aes = inherit.aes))
}
ggplot2::layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomBarH,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
width = width,
na.rm = na.rm,
orientation = orientation,
hatch = hatch,
...
)
)
}
# GeomBarH <- proto(GeomBarJ, {
# objname <- "barH"
#
# default_stat <- function(.) StatBinH
# default_aes <- function(.) aes(colour="black", fill="black", size=0.5, linetype=1, weight = 1, alpha = NA)
#
# draw_groups <- function(., data, scales, coordinates, ...) {
# GeomRectH$draw_groups(data, scales, coordinates, ...)
# }
#
# })
#' @format NULL
#' @usage NULL
#' @export
#' @include geom-rect.r
GeomBarH <- ggplot2::ggproto(
"GeomBarH",
GeomRectH,
required_aes = c("x", "y"),
non_missing_aes = c("xmin", "xmax", "ymin", "ymax"),
setup_params = function(data, params) {
params$flipped_aes <- has_flipped_aes(data, params)
params
},
extra_params = c("na.rm", "orientation"),
setup_data = function(data, params) {
data$flipped_aes <- params$flipped_aes
data <- flip_data(data, params$flipped_aes)
data$width <- data$width %||%
params$width %||% (resolution(data$x, FALSE) * 0.9)
# print(params)
# data$hatch = params$hatch
# print(data)
transform(
data,
ymin = pmin(y, 0),
ymax = pmax(y, 0),
xmin = x - width / 2,
xmax = x + width / 2,
width = NULL
)
},
draw_panel = function(self, data, panel_params, coord, width = NULL, flipped_aes = FALSE) {
# Hack to ensure that width is detected as a parameter
# data2 <- hatch2()
# print(data)
#print(coord)
ggplot2::ggproto_parent(GeomRectH, self)$draw_panel(data, panel_params, coord)
}
)
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
#' @include stat-.r
StatBarH <- ggplot2::ggproto(
"StatBarH",
ggplot2::Stat,
required_aes = "x",
default_aes = ggplot2::aes(y = ..count.., colour = "white", hatch = 3),
setup_params = function(data, params) {
if (!is.null(data$y) || !is.null(params$y)) {
stop("stat_barh() must not be used with a y aesthetic.", call. = FALSE)
}
# print(params)
params
},
compute_group = function(self, data, scales, width = NULL) {
x <- data$x
weight <- data$weight %||% rep(1, length(x))
width <- width %||% (resolution(x) * 0.9)
count <- as.numeric(tapply(weight, x, sum, na.rm = TRUE))
count[is.na(count)] <- 0
data.frame(
count = count,
prop = count / sum(abs(count)),
x = unique(x),
width = width,
hatch = hatch
)
}
)
| /R/geom-barh.R | no_license | joker8phoenix/statds | R | false | false | 3,501 | r | #' geom_barH
geom_barh <-
function (mapping = NULL,
data = NULL,
stat = "count",
# stat = "barH",
position = "stack",
...,
width = NULL,
binwidth = NULL,
na.rm = FALSE,
orientation = NA,
hatch = 3,
show.legend = NA,
inherit.aes = TRUE) {
if (!is.null(binwidth)) {
warn("`geom_barh()` no longer has a `binwidth` parameter. Please use `geom_histogramh()` instead.")
return(geom_histogramh(mapping = mapping, data = data,
position = position, width = width, binwidth = binwidth, hatch = hatch,
..., na.rm = na.rm, show.legend = show.legend, inherit.aes = inherit.aes))
}
ggplot2::layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomBarH,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
width = width,
na.rm = na.rm,
orientation = orientation,
hatch = hatch,
...
)
)
}
# GeomBarH <- proto(GeomBarJ, {
# objname <- "barH"
#
# default_stat <- function(.) StatBinH
# default_aes <- function(.) aes(colour="black", fill="black", size=0.5, linetype=1, weight = 1, alpha = NA)
#
# draw_groups <- function(., data, scales, coordinates, ...) {
# GeomRectH$draw_groups(data, scales, coordinates, ...)
# }
#
# })
#' @format NULL
#' @usage NULL
#' @export
#' @include geom-rect.r
GeomBarH <- ggplot2::ggproto(
"GeomBarH",
GeomRectH,
required_aes = c("x", "y"),
non_missing_aes = c("xmin", "xmax", "ymin", "ymax"),
setup_params = function(data, params) {
params$flipped_aes <- has_flipped_aes(data, params)
params
},
extra_params = c("na.rm", "orientation"),
setup_data = function(data, params) {
data$flipped_aes <- params$flipped_aes
data <- flip_data(data, params$flipped_aes)
data$width <- data$width %||%
params$width %||% (resolution(data$x, FALSE) * 0.9)
# print(params)
# data$hatch = params$hatch
# print(data)
transform(
data,
ymin = pmin(y, 0),
ymax = pmax(y, 0),
xmin = x - width / 2,
xmax = x + width / 2,
width = NULL
)
},
draw_panel = function(self, data, panel_params, coord, width = NULL, flipped_aes = FALSE) {
# Hack to ensure that width is detected as a parameter
# data2 <- hatch2()
# print(data)
#print(coord)
ggplot2::ggproto_parent(GeomRectH, self)$draw_panel(data, panel_params, coord)
}
)
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
#' @include stat-.r
StatBarH <- ggplot2::ggproto(
"StatBarH",
ggplot2::Stat,
required_aes = "x",
default_aes = ggplot2::aes(y = ..count.., colour = "white", hatch = 3),
setup_params = function(data, params) {
if (!is.null(data$y) || !is.null(params$y)) {
stop("stat_barh() must not be used with a y aesthetic.", call. = FALSE)
}
# print(params)
params
},
compute_group = function(self, data, scales, width = NULL) {
x <- data$x
weight <- data$weight %||% rep(1, length(x))
width <- width %||% (resolution(x) * 0.9)
count <- as.numeric(tapply(weight, x, sum, na.rm = TRUE))
count[is.na(count)] <- 0
data.frame(
count = count,
prop = count / sum(abs(count)),
x = unique(x),
width = width,
hatch = hatch
)
}
)
|
plotMapPoints<-function(area=area, xlim=c(19.5,29.5), ylim=c(34.8,41.8), col="grey60", fill=T,
type ="p", col_points="red",cex=0.5,
plotclr=c("pink1","red", "red4","black"), symbol.size=1.1){
library(gstat)
library(mgcv)
library(maps)
library(mapdata)
library(RColorBrewer)
library(akima)
library(maptools)
library(classInt)
library(scales)
#area<-area[area$Abundance<5000,]
# map(database = "worldHires", xlim=xlim, ylim = ylim, resolution = 0, col=col, fill=fill)
#-------Display observations on a map------------
#let see the data we have
#pdf(name)
min(area$Abundance)
max(area$Abundance)
min(log(area$Abundance))
max(log(area$Abundance))
#Map the raw data - bubble plots. i want to view where is the most abandant area
plotvar <- area$Abundance
#plotvar <- log(area$Abundance)
#number of color is 4 (quite good choice)
#plotclr<-palette(gray(seq(0.8,0,len=4)))
#choose four different colors. wants red color for high values let's say
plotclr<-plotclr
nclr <- length(plotclr)
#display.brewer.all(n=NULL, type="all", select=NULL, exact.n=TRUE)# display palettes
#plotclr <- brewer.pal(nclr,"BuPu")
#plotclr <- brewer.pal(nclr,"Reds")
#plotclr <- plotclr[nclr:1] # reorder colors if appropriate
#max.symbol.size=3
#min.symbol.size=1
#class <- classIntervals(plotvar, style="equal",n=4)
#very important command. we ask R to get our dataset and difine different classes so that it will give different colors to different classes
#style is how to spit the data in my classes
class <- classIntervals(plotvar, n=nclr, style="quantile")
#let's view the classes
class
#the next is to put colors in the observations
colcode <- findColours(class, plotclr)
#size of the bullets
#symbol.size <- ((plotvar-min(plotvar))/(max(plotvar)-min(plotvar))*(max.symbol.size-min.symbol.size)+min.symbol.size)
map(database = "world", xlim=xlim, ylim = ylim, resolution = 0, col=col, fill=fill,bg="white",
xlab="Longitude",ylab="Latitude")
map.axes()
points(area$LON, area$LAT, pch=16, type="p",col=colcode,cex=symbol.size,xlab="Longitude",ylab="Latitude")
#plot(area$LON, area$LAT, pch=16, type="p",col="red",cex=symbol.size,xlab="Longitude",ylab="Latitude",xlim=c(22,29),ylim=c(35,41),axes=F)
#vazei to parathema
legend<-names(attr(colcode, "table"))
# legend2<-c(); i<-1;
# for(i in 1:length(legend)){
# legend2[i]<-
# paste("[",strsplit(legend[i],"[[:punct:]]")[[1]][2],",",
# strsplit(legend[i],"[[:punct:]]")[[1]][3],")",sep="")
# if(i==length(legend)) legend2[i]<-paste("[",strsplit(legend[i],"[[:punct:]]")[[1]][2],",",
# strsplit(legend[i],"[[:punct:]]")[[1]][3],"]",sep="")
# }
# legend("topright", legend=legend, fill=attr(colcode, "palette"), cex=1.5, bty="n")
text(34.5,41,"Turkey", cex = 1)
text(29.7,46.2,"Ukraine", cex = 1)
text(40.5,45.0,"Russia", cex = 1)
text(42.4,42.4,"Georgia", cex = 1)
text(27.7,43.6,"Bulgaria", cex = 1)
text(27.8,44.6,"Romania", cex = 1)
text(29,45.18,"Danube", cex = 2.0,font=2, col="#ADD8E6")
#text(34.3,45.4,"Crimea", cex = 1)
# title(input$mzrt[1])
#abline(44,0)
#abline(0,32)
#dev.off()
}
| /Dataset generation/Helper_plotmappoints.R | no_license | gbouzioto/chemical_polution | R | false | false | 3,419 | r | plotMapPoints<-function(area=area, xlim=c(19.5,29.5), ylim=c(34.8,41.8), col="grey60", fill=T,
type ="p", col_points="red",cex=0.5,
plotclr=c("pink1","red", "red4","black"), symbol.size=1.1){
library(gstat)
library(mgcv)
library(maps)
library(mapdata)
library(RColorBrewer)
library(akima)
library(maptools)
library(classInt)
library(scales)
#area<-area[area$Abundance<5000,]
# map(database = "worldHires", xlim=xlim, ylim = ylim, resolution = 0, col=col, fill=fill)
#-------Display observations on a map------------
#let see the data we have
#pdf(name)
min(area$Abundance)
max(area$Abundance)
min(log(area$Abundance))
max(log(area$Abundance))
#Map the raw data - bubble plots. i want to view where is the most abandant area
plotvar <- area$Abundance
#plotvar <- log(area$Abundance)
#number of color is 4 (quite good choice)
#plotclr<-palette(gray(seq(0.8,0,len=4)))
#choose four different colors. wants red color for high values let's say
plotclr<-plotclr
nclr <- length(plotclr)
#display.brewer.all(n=NULL, type="all", select=NULL, exact.n=TRUE)# display palettes
#plotclr <- brewer.pal(nclr,"BuPu")
#plotclr <- brewer.pal(nclr,"Reds")
#plotclr <- plotclr[nclr:1] # reorder colors if appropriate
#max.symbol.size=3
#min.symbol.size=1
#class <- classIntervals(plotvar, style="equal",n=4)
#very important command. we ask R to get our dataset and difine different classes so that it will give different colors to different classes
#style is how to spit the data in my classes
class <- classIntervals(plotvar, n=nclr, style="quantile")
#let's view the classes
class
#the next is to put colors in the observations
colcode <- findColours(class, plotclr)
#size of the bullets
#symbol.size <- ((plotvar-min(plotvar))/(max(plotvar)-min(plotvar))*(max.symbol.size-min.symbol.size)+min.symbol.size)
map(database = "world", xlim=xlim, ylim = ylim, resolution = 0, col=col, fill=fill,bg="white",
xlab="Longitude",ylab="Latitude")
map.axes()
points(area$LON, area$LAT, pch=16, type="p",col=colcode,cex=symbol.size,xlab="Longitude",ylab="Latitude")
#plot(area$LON, area$LAT, pch=16, type="p",col="red",cex=symbol.size,xlab="Longitude",ylab="Latitude",xlim=c(22,29),ylim=c(35,41),axes=F)
#vazei to parathema
legend<-names(attr(colcode, "table"))
# legend2<-c(); i<-1;
# for(i in 1:length(legend)){
# legend2[i]<-
# paste("[",strsplit(legend[i],"[[:punct:]]")[[1]][2],",",
# strsplit(legend[i],"[[:punct:]]")[[1]][3],")",sep="")
# if(i==length(legend)) legend2[i]<-paste("[",strsplit(legend[i],"[[:punct:]]")[[1]][2],",",
# strsplit(legend[i],"[[:punct:]]")[[1]][3],"]",sep="")
# }
# legend("topright", legend=legend, fill=attr(colcode, "palette"), cex=1.5, bty="n")
text(34.5,41,"Turkey", cex = 1)
text(29.7,46.2,"Ukraine", cex = 1)
text(40.5,45.0,"Russia", cex = 1)
text(42.4,42.4,"Georgia", cex = 1)
text(27.7,43.6,"Bulgaria", cex = 1)
text(27.8,44.6,"Romania", cex = 1)
text(29,45.18,"Danube", cex = 2.0,font=2, col="#ADD8E6")
#text(34.3,45.4,"Crimea", cex = 1)
# title(input$mzrt[1])
#abline(44,0)
#abline(0,32)
#dev.off()
}
|
\name{smooth.construct.tescv.smooth.spec}
\alias{smooth.construct.tescv.smooth.spec}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Tensor product smoothing constructor for a bivariate function concave
in the second covariate
}
\description{This is a special method function
for creating tensor product bivariate smooths concave in the second covariate which is built by the \code{mgcv} constructor function for smooth terms, \code{smooth.construct}.
It is constructed from a pair of single penalty
marginal smooths. This tensor product is specified by model terms such as \code{s(x1,x2,k=c(q1,q2),bs="tescv",m=c(2,2))},
where the basis for the first marginal smooth is specified in the second element of \code{bs}.
}
\usage{
\method{smooth.construct}{tescv.smooth.spec}(object, data, knots)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{A smooth specification object, generated by an \code{s} term in a GAM formula.}
\item{data}{A data frame or list containing the values of the elements of \code{object$term},
with names given by \code{object$term}.}
\item{knots}{An optional list containing the knots corresponding to \code{object$term}.
If it is \code{NULL} then the knot locations are generated automatically.}
}
%\details{
%% ~~ If necessary, more details than the description above ~~
%}
\value{An object of class \code{"tescv.smooth"}. In addition to the usual
elements of a smooth class documented under \code{smooth.construct} of the \code{mgcv} library, this object contains:
\item{p.ident}{A vector of 0's and 1's for model parameter identification:
1's indicate parameters which will be exponentiated, 0's - otherwise.}
\item{Zc}{A matrix of identifiability constraints.}
%\item{margin.bs}{ }
}
\references{
Pya, N. and Wood, S.N. (2015) Shape constrained additive models. Statistics and Computing, 25(3), 543-559
}
\author{
Natalya Pya <nat.pya@gmail.com>
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{smooth.construct.temicv.smooth.spec}}
\code{\link{smooth.construct.temicx.smooth.spec}}
\code{\link{smooth.construct.tedecv.smooth.spec}}
\code{\link{smooth.construct.tedecx.smooth.spec}}
\code{\link{smooth.construct.tescx.smooth.spec}}
}
\examples{
\dontrun{
## tensor product `tescv' example
## simulating data...
set.seed(5)
n <- 30
x1 <- sort(runif(n))
x2 <- sort(2*runif(n)-1)
f1 <- matrix(0,n,n)
for (i in 1:n) for (j in 1:n)
f1[i,j] <- sin(2*x1[i]) - 4*x2[j]^2
f1 <- as.vector(t(f1))
f <- (f1-min(f1))/(max(f1)-min(f1))
y <- f+rnorm(length(f))*0.1
x11 <- matrix(0,n,n)
x11[,1:n] <- x1
x11 <- as.vector(t(x11))
x22 <- rep(x2,n)
dat <- list(x1=x11,x2=x22,y=y)
## fit model ...
b <- scam(y~s(x1,x2,k=c(10,10),bs="tescv",m=2),
family=gaussian(), data=dat)
## plot results ...
par(mfrow=c(2,2),mar=c(4,4,2,2))
plot(b,se=TRUE)
plot(b,pers=TRUE, theta = 50, phi = 20)
plot(y,b$fitted.values,xlab="Simulated data",ylab="Fitted data")
x11()
vis.scam(b, theta = 50, phi = 20)
## plotting the truth...
x11()
x1 <- seq(min(x1),max(x1),length.out=30)
x2 <- seq(min(x2),max(x2),length.out=30)
f1 <- matrix(0,n,n)
for (i in 1:n) for (j in 1:n) f1[i,j] <- sin(2*x1[i]) - 4*x2[j]^2
persp(x1,x2,f1,theta = 50, phi = 20)
}
}
\keyword{models} \keyword{regression}%-- one or more ..
| /man/smooth.construct.tescv.smooth.spec.Rd | no_license | cran/scam | R | false | false | 3,575 | rd | \name{smooth.construct.tescv.smooth.spec}
\alias{smooth.construct.tescv.smooth.spec}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Tensor product smoothing constructor for a bivariate function concave
in the second covariate
}
\description{This is a special method function
for creating tensor product bivariate smooths concave in the second covariate which is built by the \code{mgcv} constructor function for smooth terms, \code{smooth.construct}.
It is constructed from a pair of single penalty
marginal smooths. This tensor product is specified by model terms such as \code{s(x1,x2,k=c(q1,q2),bs="tescv",m=c(2,2))},
where the basis for the first marginal smooth is specified in the second element of \code{bs}.
}
\usage{
\method{smooth.construct}{tescv.smooth.spec}(object, data, knots)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{A smooth specification object, generated by an \code{s} term in a GAM formula.}
\item{data}{A data frame or list containing the values of the elements of \code{object$term},
with names given by \code{object$term}.}
\item{knots}{An optional list containing the knots corresponding to \code{object$term}.
If it is \code{NULL} then the knot locations are generated automatically.}
}
%\details{
%% ~~ If necessary, more details than the description above ~~
%}
\value{An object of class \code{"tescv.smooth"}. In addition to the usual
elements of a smooth class documented under \code{smooth.construct} of the \code{mgcv} library, this object contains:
\item{p.ident}{A vector of 0's and 1's for model parameter identification:
1's indicate parameters which will be exponentiated, 0's - otherwise.}
\item{Zc}{A matrix of identifiability constraints.}
%\item{margin.bs}{ }
}
\references{
Pya, N. and Wood, S.N. (2015) Shape constrained additive models. Statistics and Computing, 25(3), 543-559
}
\author{
Natalya Pya <nat.pya@gmail.com>
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{smooth.construct.temicv.smooth.spec}}
\code{\link{smooth.construct.temicx.smooth.spec}}
\code{\link{smooth.construct.tedecv.smooth.spec}}
\code{\link{smooth.construct.tedecx.smooth.spec}}
\code{\link{smooth.construct.tescx.smooth.spec}}
}
\examples{
\dontrun{
## tensor product `tescv' example
## simulating data...
set.seed(5)
n <- 30
x1 <- sort(runif(n))
x2 <- sort(2*runif(n)-1)
f1 <- matrix(0,n,n)
for (i in 1:n) for (j in 1:n)
f1[i,j] <- sin(2*x1[i]) - 4*x2[j]^2
f1 <- as.vector(t(f1))
f <- (f1-min(f1))/(max(f1)-min(f1))
y <- f+rnorm(length(f))*0.1
x11 <- matrix(0,n,n)
x11[,1:n] <- x1
x11 <- as.vector(t(x11))
x22 <- rep(x2,n)
dat <- list(x1=x11,x2=x22,y=y)
## fit model ...
b <- scam(y~s(x1,x2,k=c(10,10),bs="tescv",m=2),
family=gaussian(), data=dat)
## plot results ...
par(mfrow=c(2,2),mar=c(4,4,2,2))
plot(b,se=TRUE)
plot(b,pers=TRUE, theta = 50, phi = 20)
plot(y,b$fitted.values,xlab="Simulated data",ylab="Fitted data")
x11()
vis.scam(b, theta = 50, phi = 20)
## plotting the truth...
x11()
x1 <- seq(min(x1),max(x1),length.out=30)
x2 <- seq(min(x2),max(x2),length.out=30)
f1 <- matrix(0,n,n)
for (i in 1:n) for (j in 1:n) f1[i,j] <- sin(2*x1[i]) - 4*x2[j]^2
persp(x1,x2,f1,theta = 50, phi = 20)
}
}
\keyword{models} \keyword{regression}%-- one or more ..
|
## Data can be found at this link, was originally accessed on August 10, 2014
## https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
## File was then unzipped on the desktop
## Read data into R
data <- read.table("/Users/Mario/Desktop/household_power_consumption.txt",sep=";",header=TRUE)
## Give the columns their appropriate names
names(data) <- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
## Create new column, DateTime, with formatted date and time
data$DateTime <- paste(data$Date,data$Time)
data$DateTime <- strptime(data$DateTime, format="%d/%m/%Y %T")
## Subset the dates we want (this could have been done sooner to save time)
data2 <- subset(data, (as.Date(data$DateTime)=="2007-02-01" | as.Date(data$DateTime)=="2007-02-02"))
## Correct some data types (for some reason, just using as.numeric led to some errors)
## We will not be using columns 1 and 2 ("Date" and "Time") again so those can be left alone
data2$Global_active_power <- as.numeric(as.character(data2$Global_active_power))
data2$Global_reactive_power <- as.numeric(as.character(data2$Global_reactive_power))
data2$Voltage <- as.numeric(as.character(data2$Voltage))
data2$Global_intensity <- as.numeric(as.character(data2$Global_intensity))
data2$Sub_metering_1 <- as.numeric(as.character(data2$Sub_metering_1))
data2$Sub_metering_2 <- as.numeric(as.character(data2$Sub_metering_2))
data2$Sub_metering_3 <- as.numeric(as.character(data2$Sub_metering_3))
### --------------------------------------------------------------------------###
### Now we make the FOURTH plot and save it
### --------------------------------------------------------------------------###
png("/Users/Mario/ExData_Plotting1/plot4.png", width=480, height=480)
par(mfrow=c(2,2))
## Plot A
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Global_active_power,
type="l",
ylab="Global Active Power",
xlab="",
lwd=1.25, ## Make a little thicker
cex.lab=1)
## ---------------------------------------------------------------------------------
## Plot B
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Voltage,
xlab="datetime",
ylab="Voltage",
type="l",
lwd=1.25) ## make lines a tad thicker
## ---------------------------------------------------------------------------------
## Plot C
## ---------------------------------------------------------------------------------
## Add the first set of points, Sub_metering_1, and put in the x and y axis labels
plot(x=data2$DateTime,
y=data2$Sub_metering_1,
type="l",
col="black",
xlab="",
ylab="Energy sub metering")
## Add in the second set of points, Sub_metering_2
points(x=data2$DateTime,
y=data2$Sub_metering_2,
type="l",
col="red")
## Add in the third set of points, Sub_metering_3
points(x=data2$DateTime,
y=data2$Sub_metering_3,
type="l",
col="blue")
## Add legend
legend("topright",
col=c("black","red","blue"),
legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),
lwd=c(2,2,2), ## make it a little darker so it looks better
bty="n", ## Remove that border!!!
cex=1)
## ---------------------------------------------------------------------------------
## Plot D
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Global_reactive_power,
xlab="datetime",
ylab="Global_reactive_power",
type="l",
lwd=1.2, ## Make lines a little thicker
cex=.1)
## ---------------------------------------------------------------------------------
dev.off()
| /plot4.R | no_license | ibanmd/ExData_Plotting1 | R | false | false | 3,880 | r | ## Data can be found at this link, was originally accessed on August 10, 2014
## https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
## File was then unzipped on the desktop
## Read data into R
data <- read.table("/Users/Mario/Desktop/household_power_consumption.txt",sep=";",header=TRUE)
## Give the columns their appropriate names
names(data) <- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
## Create new column, DateTime, with formatted date and time
data$DateTime <- paste(data$Date,data$Time)
data$DateTime <- strptime(data$DateTime, format="%d/%m/%Y %T")
## Subset the dates we want (this could have been done sooner to save time)
data2 <- subset(data, (as.Date(data$DateTime)=="2007-02-01" | as.Date(data$DateTime)=="2007-02-02"))
## Correct some data types (for some reason, just using as.numeric led to some errors)
## We will not be using columns 1 and 2 ("Date" and "Time") again so those can be left alone
data2$Global_active_power <- as.numeric(as.character(data2$Global_active_power))
data2$Global_reactive_power <- as.numeric(as.character(data2$Global_reactive_power))
data2$Voltage <- as.numeric(as.character(data2$Voltage))
data2$Global_intensity <- as.numeric(as.character(data2$Global_intensity))
data2$Sub_metering_1 <- as.numeric(as.character(data2$Sub_metering_1))
data2$Sub_metering_2 <- as.numeric(as.character(data2$Sub_metering_2))
data2$Sub_metering_3 <- as.numeric(as.character(data2$Sub_metering_3))
### --------------------------------------------------------------------------###
### Now we make the FOURTH plot and save it
### --------------------------------------------------------------------------###
png("/Users/Mario/ExData_Plotting1/plot4.png", width=480, height=480)
par(mfrow=c(2,2))
## Plot A
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Global_active_power,
type="l",
ylab="Global Active Power",
xlab="",
lwd=1.25, ## Make a little thicker
cex.lab=1)
## ---------------------------------------------------------------------------------
## Plot B
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Voltage,
xlab="datetime",
ylab="Voltage",
type="l",
lwd=1.25) ## make lines a tad thicker
## ---------------------------------------------------------------------------------
## Plot C
## ---------------------------------------------------------------------------------
## Add the first set of points, Sub_metering_1, and put in the x and y axis labels
plot(x=data2$DateTime,
y=data2$Sub_metering_1,
type="l",
col="black",
xlab="",
ylab="Energy sub metering")
## Add in the second set of points, Sub_metering_2
points(x=data2$DateTime,
y=data2$Sub_metering_2,
type="l",
col="red")
## Add in the third set of points, Sub_metering_3
points(x=data2$DateTime,
y=data2$Sub_metering_3,
type="l",
col="blue")
## Add legend
legend("topright",
col=c("black","red","blue"),
legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),
lwd=c(2,2,2), ## make it a little darker so it looks better
bty="n", ## Remove that border!!!
cex=1)
## ---------------------------------------------------------------------------------
## Plot D
## ---------------------------------------------------------------------------------
plot(x=data2$DateTime,
y=data2$Global_reactive_power,
xlab="datetime",
ylab="Global_reactive_power",
type="l",
lwd=1.2, ## Make lines a little thicker
cex=.1)
## ---------------------------------------------------------------------------------
dev.off()
|
# Load data
source("load_data.R")
# Open PNG device
png("plot3.png")
# Create Plot
plot(hpcSubset$DateTime,
hpcSubset$Sub_metering_1,
type="l",
xlab="",
ylab="Energy sub metering")
lines(hpcSubset$DateTime,
hpcSubset$Sub_metering_2,
col="red")
lines(hpcSubset$DateTime,
hpcSubset$Sub_metering_3,
col="blue")
legend("topright",
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
col=c("black", "red", "blue"),
lty="solid")
# Close PNG device
dev.off()
message("Plot 3 created successfully.") | /plot3.r | no_license | TowkayNew/ExData_Plotting1 | R | false | false | 577 | r | # Load data
source("load_data.R")
# Open PNG device
png("plot3.png")
# Create Plot
plot(hpcSubset$DateTime,
hpcSubset$Sub_metering_1,
type="l",
xlab="",
ylab="Energy sub metering")
lines(hpcSubset$DateTime,
hpcSubset$Sub_metering_2,
col="red")
lines(hpcSubset$DateTime,
hpcSubset$Sub_metering_3,
col="blue")
legend("topright",
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
col=c("black", "red", "blue"),
lty="solid")
# Close PNG device
dev.off()
message("Plot 3 created successfully.") |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/locate_site.R
\name{locate_site}
\alias{locate_site}
\title{Find a site folder on ScienceBase}
\arguments{
\item{site_name}{the site ID, e.g. "nwis_02322688", whose folder you want}
\item{format}{character indicating whether the folder should be returned as
an ID or as a full URL}
\item{by}{character indicating how to search for the item: using tags ("tag",
the default and recommended option), by scanning the parent directory for
the desired title ("dir"), or both in combination ("either")?}
\item{browser}{logical. Should the URL be opened in a browser?}
}
\description{
Find a site folder on ScienceBase
}
\examples{
\dontrun{
locate_site("nwis_02322688", format="url")
locate_site(c("nwis_02322688", "nwis_03259813", "nwis_04024000"))
locate_site("nwis_notasite", format="url")
testthat::expect_error(locate_site("notasite", format="url"))
}
}
| /man/locate_site.Rd | permissive | ehstanley/powstreams | R | false | true | 936 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/locate_site.R
\name{locate_site}
\alias{locate_site}
\title{Find a site folder on ScienceBase}
\arguments{
\item{site_name}{the site ID, e.g. "nwis_02322688", whose folder you want}
\item{format}{character indicating whether the folder should be returned as
an ID or as a full URL}
\item{by}{character indicating how to search for the item: using tags ("tag",
the default and recommended option), by scanning the parent directory for
the desired title ("dir"), or both in combination ("either")?}
\item{browser}{logical. Should the URL be opened in a browser?}
}
\description{
Find a site folder on ScienceBase
}
\examples{
\dontrun{
locate_site("nwis_02322688", format="url")
locate_site(c("nwis_02322688", "nwis_03259813", "nwis_04024000"))
locate_site("nwis_notasite", format="url")
testthat::expect_error(locate_site("notasite", format="url"))
}
}
|
x=seq(1, 10, len=1)
y=40*2 + rnorm(10,0,5)
plot(x,y)
summary(x)
mean(x)
| /analysis.R | no_license | camilamtl/TOTALJUNK | R | false | false | 72 | r | x=seq(1, 10, len=1)
y=40*2 + rnorm(10,0,5)
plot(x,y)
summary(x)
mean(x)
|
\name{GetAssignment}
\alias{GetAssignment}
\alias{GetAssignments}
\alias{assignment}
\alias{assignments}
\title{Get Assignment(s)}
\description{Get an assignment or multiple assignments for one or more HITs (or a HITType) as a dataframe.}
\usage{
GetAssignment( assignment = NULL, hit = NULL, hit.type = NULL, status = NULL,
return.all = FALSE, pagenumber = "1", pagesize = "10",
sortproperty = "SubmitTime", sortdirection = "Ascending",
response.group = NULL, keypair = credentials(), print = TRUE, browser = FALSE,
log.requests = TRUE, sandbox = FALSE, return.assignment.dataframe = TRUE)
}
\arguments{
\item{assignment}{An optional character string specifying the AssignmentId of an assignment to return.}
\item{hit}{An optional character string specifying the HITId whose assignments are to be returned, or a vector of character strings specifying multiple HITIds all of whose assignments are to be returned.}
\item{hit.type}{An optional character string specifying the HITTypeId of one or more HITs whose assignments are to be returned.}
\item{status}{An optional character string (of \dQuote{Approved},\dQuote{Rejected},\dQuote{Submitted}), specifying whether only a subset of assignments should be returned. If \code{NULL}, all assignments are returned (the default). Only applies when \code{hit} or \code{hit.type} are specified; ignored otherwise.}
\item{return.all}{If \code{TRUE}, all available assignments are returned. Otherwise, only assignments falling within the specified \code{pagenumber} and \code{pagesize} search results are returned.}
\item{pagenumber}{An optional character string indicating which page of search results should be returned (only appropriate when specifying a single HITId). Most users can ignore this.}
\item{pagesize}{An optional character string indicating how many search results should be returned by each request (only appropriate when specifying a single HITId), between 1 and 100. Most users can ignore this.}
\item{sortproperty}{One of \dQuote{AcceptTime}, \dQuote{SubmitTime}, \dQuote{AssignmentStatus}. Ignored if \code{return.all=TRUE}. Most users can ignore this.}
\item{sortdirection}{Either \dQuote{Ascending} or \dQuote{Descending}. Ignored if \code{return.all=TRUE}. Most users can ignore this.}
\item{response.group}{An optional character string (or vector of character strings) specifying what details to return. If \code{assignment} is specified, \code{response.group} can include any of \dQuote{Request}, \dQuote{Minimal}, \dQuote{AssignmentFeedback}, \dQuote{HITDetail}, and/or \dQuote{HITQuestion}. If \code{hit} or \code{hit.type} is specified, \code{response.group} can include \dQuote{Request}, \dQuote{Minimal}, and/or \dQuote{AssignmentFeedback}. For more information, see \url{http://docs.aws.amazon.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_CommonParametersArticle.html}.}
\item{keypair}{A two-item character vector containing an AWS Access Key ID in the first position and the corresponding Secret Access Key in the second position. Set default with \code{\link{credentials}}.}
\item{print}{Optionally print the results of the API request to the standard output. Default is \code{TRUE}.}
\item{browser}{Optionally open the request in the default web browser, rather than opening in R. Default is \code{FALSE}.}
\item{log.requests}{A logical specifying whether API requests should be logged. Default is \code{TRUE}. See \code{\link{readlogfile}} for details.}
\item{sandbox}{Optionally execute the request in the MTurk sandbox rather than the live server. Default is \code{FALSE}.}
\item{return.assignment.dataframe}{A logical specifying whether the Assignment dataframe should be returned. Default is \code{TRUE}.}
}
\details{This function returns the requested assignments. The function must specify an AssignmentId xor a HITId xor a HITTypeId. If an AssignmentId is specified, only that assignment is returned. If a HIT or HITType is specified, default behavior is to return all assignments through a series of sequential (but invisible) API calls meaning that returning large numbers of assignments (or assignments for a large number of HITs in a single request) may be time consuming.
\code{GetAssignments()}, \code{assignment()}, and \code{assignments()} are aliases.
}
\value{Optionally a dataframe containing Assignment data, including workers responses to any questions specified in the \code{question} parameter of the \code{CreateHIT} function.}
\references{
\href{http://docs.amazonwebservices.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_GetAssignmentOperation.html}{API Reference: GetAssignment}
\href{http://docs.amazonwebservices.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_GetAssignmentsForHITOperation.html}{API Reference: GetAssignmentsForHIT }
}
\author{Thomas J. Leeper}
%\note{}
\seealso{
\code{\link{GetHIT}}
\code{\link{ApproveAssignment}}
\code{\link{ApproveAllAssignments}}
\code{\link{RejectAssignment}}
}
\examples{
\dontrun{
GetAssignment(assignments="26XXH0JPPSI23H54YVG7BKLEXAMPLE")
GetAssignment(hit="2MQB727M0IGF304GJ16S1F4VE3AYDQ",return.all=TRUE)
GetAssignment(hit.type="2FFNCWYB49F9BBJWA4SJUNST5OFSOW",return.all=FALSE,pagenumber="1",pagesize="50")
}
}
\keyword{Assignments} | /man/GetAssignment.Rd | no_license | SolomonMg/MTurkR | R | false | false | 5,309 | rd | \name{GetAssignment}
\alias{GetAssignment}
\alias{GetAssignments}
\alias{assignment}
\alias{assignments}
\title{Get Assignment(s)}
\description{Get an assignment or multiple assignments for one or more HITs (or a HITType) as a dataframe.}
\usage{
GetAssignment( assignment = NULL, hit = NULL, hit.type = NULL, status = NULL,
return.all = FALSE, pagenumber = "1", pagesize = "10",
sortproperty = "SubmitTime", sortdirection = "Ascending",
response.group = NULL, keypair = credentials(), print = TRUE, browser = FALSE,
log.requests = TRUE, sandbox = FALSE, return.assignment.dataframe = TRUE)
}
\arguments{
\item{assignment}{An optional character string specifying the AssignmentId of an assignment to return.}
\item{hit}{An optional character string specifying the HITId whose assignments are to be returned, or a vector of character strings specifying multiple HITIds all of whose assignments are to be returned.}
\item{hit.type}{An optional character string specifying the HITTypeId of one or more HITs whose assignments are to be returned.}
\item{status}{An optional character string (of \dQuote{Approved},\dQuote{Rejected},\dQuote{Submitted}), specifying whether only a subset of assignments should be returned. If \code{NULL}, all assignments are returned (the default). Only applies when \code{hit} or \code{hit.type} are specified; ignored otherwise.}
\item{return.all}{If \code{TRUE}, all available assignments are returned. Otherwise, only assignments falling within the specified \code{pagenumber} and \code{pagesize} search results are returned.}
\item{pagenumber}{An optional character string indicating which page of search results should be returned (only appropriate when specifying a single HITId). Most users can ignore this.}
\item{pagesize}{An optional character string indicating how many search results should be returned by each request (only appropriate when specifying a single HITId), between 1 and 100. Most users can ignore this.}
\item{sortproperty}{One of \dQuote{AcceptTime}, \dQuote{SubmitTime}, \dQuote{AssignmentStatus}. Ignored if \code{return.all=TRUE}. Most users can ignore this.}
\item{sortdirection}{Either \dQuote{Ascending} or \dQuote{Descending}. Ignored if \code{return.all=TRUE}. Most users can ignore this.}
\item{response.group}{An optional character string (or vector of character strings) specifying what details to return. If \code{assignment} is specified, \code{response.group} can include any of \dQuote{Request}, \dQuote{Minimal}, \dQuote{AssignmentFeedback}, \dQuote{HITDetail}, and/or \dQuote{HITQuestion}. If \code{hit} or \code{hit.type} is specified, \code{response.group} can include \dQuote{Request}, \dQuote{Minimal}, and/or \dQuote{AssignmentFeedback}. For more information, see \url{http://docs.aws.amazon.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_CommonParametersArticle.html}.}
\item{keypair}{A two-item character vector containing an AWS Access Key ID in the first position and the corresponding Secret Access Key in the second position. Set default with \code{\link{credentials}}.}
\item{print}{Optionally print the results of the API request to the standard output. Default is \code{TRUE}.}
\item{browser}{Optionally open the request in the default web browser, rather than opening in R. Default is \code{FALSE}.}
\item{log.requests}{A logical specifying whether API requests should be logged. Default is \code{TRUE}. See \code{\link{readlogfile}} for details.}
\item{sandbox}{Optionally execute the request in the MTurk sandbox rather than the live server. Default is \code{FALSE}.}
\item{return.assignment.dataframe}{A logical specifying whether the Assignment dataframe should be returned. Default is \code{TRUE}.}
}
\details{This function returns the requested assignments. The function must specify an AssignmentId xor a HITId xor a HITTypeId. If an AssignmentId is specified, only that assignment is returned. If a HIT or HITType is specified, default behavior is to return all assignments through a series of sequential (but invisible) API calls meaning that returning large numbers of assignments (or assignments for a large number of HITs in a single request) may be time consuming.
\code{GetAssignments()}, \code{assignment()}, and \code{assignments()} are aliases.
}
\value{Optionally a dataframe containing Assignment data, including workers responses to any questions specified in the \code{question} parameter of the \code{CreateHIT} function.}
\references{
\href{http://docs.amazonwebservices.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_GetAssignmentOperation.html}{API Reference: GetAssignment}
\href{http://docs.amazonwebservices.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_GetAssignmentsForHITOperation.html}{API Reference: GetAssignmentsForHIT }
}
\author{Thomas J. Leeper}
%\note{}
\seealso{
\code{\link{GetHIT}}
\code{\link{ApproveAssignment}}
\code{\link{ApproveAllAssignments}}
\code{\link{RejectAssignment}}
}
\examples{
\dontrun{
GetAssignment(assignments="26XXH0JPPSI23H54YVG7BKLEXAMPLE")
GetAssignment(hit="2MQB727M0IGF304GJ16S1F4VE3AYDQ",return.all=TRUE)
GetAssignment(hit.type="2FFNCWYB49F9BBJWA4SJUNST5OFSOW",return.all=FALSE,pagenumber="1",pagesize="50")
}
}
\keyword{Assignments} |
\name{clear_cache}
\alias{clear_cache}
\title{Clear file cache.}
\usage{
clear_cache()
}
\description{
Clear file cache.
}
\keyword{internal}
| /man/clear_cache.Rd | no_license | andrie/devtools | R | false | false | 147 | rd | \name{clear_cache}
\alias{clear_cache}
\title{Clear file cache.}
\usage{
clear_cache()
}
\description{
Clear file cache.
}
\keyword{internal}
|
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