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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e429cfd514469db5f198e16c37d4ac77acbbbc5e
|
ee036beea6789336117c6d05b0c9a4c622bf65c4
|
/RCode/ts_3a.R
|
cfecc0c7da7fc1ea62cdfcaf56702cbe00803d25
|
[] |
no_license
|
ibrahim85/Thesis
|
17d69ca75b05cd1e4446564b89c90cf141e91696
|
87ec5a2f7d0341a4467a99e8f8096de08d0d6cb5
|
refs/heads/master
| 2020-03-22T18:14:07.821060
| 2014-09-03T06:44:27
| 2014-09-03T06:44:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,235
|
r
|
ts_3a.R
|
ts_3a <- function(Mkt, SLoss, MktName){
#
#
# Mkt: market data
# SLoss: stop loss
# MktName: market's name for print out
#
# Returns:
# results vector.
results <- createResultsVector(MktName, SLoss)
#browser()
Mkt$v <- as.numeric(Mkt$p)
lvl <- min(Mkt$v) + ((max(Mkt$v) - min(Mkt$v))/2)
# Trade Long
Mkt$Long <- ifelse(Mkt$v > lvl, Mkt$Close - Mkt$Open, NA)
results["LongPL"] <- round(sum(Mkt$Long, na.rm=TRUE))
#Adj for SLoss
if (SLoss < 0) {
Mkt$Long <- ifelse(Mkt$v > lvl,
ifelse((Mkt$Low-Mkt$Open) < SLoss, SLoss, Mkt$Long),
Mkt$Long)
results["LongPL"] <- round(sum(Mkt$Long, na.rm=TRUE))
}
# Trade Short
Mkt$Short <- ifelse(Mkt$v < lvl, Mkt$Open - Mkt$Close, NA)
results["ShortPL"] <- round(sum(Mkt$Short, na.rm=TRUE))
#Adj for SLoss
if (SLoss < 0){
Mkt$Short <- ifelse(Mkt$v < lvl,
ifelse((Mkt$Open-Mkt$High) < SLoss, SLoss, Mkt$Short),
Mkt$Short)
results["ShortPL"] <- round(sum(Mkt$Short, na.rm=TRUE))
}
Stats <- calcStats2(Mkt$Long)
results[5:7] <- Stats
Stats <- calcStats2(Mkt$Short)
results[8:10] <- Stats
return(results)
}
|
0d872f9c615faf99ecd65857981358a8e33d1847
|
051880099402393c9249d41526a5ac162f822f8d
|
/man/tg.sampleProblem.Rd
|
bf24409e8b8c098712d3e86910532e69d30007c5
|
[
"MIT"
] |
permissive
|
bbTomas/rPraat
|
cd2b309e39e0ee784be4d83a980da60946f4c822
|
4c516e1309377e370c7d05245f6a396b6d4d4b03
|
refs/heads/master
| 2021-12-13T19:32:38.439214
| 2021-12-09T18:42:48
| 2021-12-09T18:42:48
| 54,803,225
| 21
| 7
| null | null | null | null |
UTF-8
|
R
| false
| true
| 445
|
rd
|
tg.sampleProblem.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rpraat_sampleData.R
\name{tg.sampleProblem}
\alias{tg.sampleProblem}
\title{tg.sampleProblem}
\usage{
tg.sampleProblem()
}
\value{
TextGrid
}
\description{
Returns sample TextGrid with continuity problem.
}
\examples{
tg <- tg.sampleProblem()
tg2 <- tg.repairContinuity(tg)
tg2 <- tg.repairContinuity(tg2)
tg.plot(tg2)
}
\seealso{
\code{\link{tg.repairContinuity}}
}
|
f19528d0916d1c77841ba2d6a725119b67f4baeb
|
825aae59c1c325e658cee4b7b9dd6101328f733e
|
/plot1.R
|
b913fe4a24f35e0fcabd2291e624ee701e6492fd
|
[] |
no_license
|
prebrov/ExData_Project2
|
bc9071076f0c5e760350e8c2d91e94f5e7784542
|
a0659d994024d30dff8f72457739b9faf1475978
|
refs/heads/master
| 2021-01-22T09:27:05.836506
| 2014-09-21T19:04:38
| 2014-09-21T19:04:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 694
|
r
|
plot1.R
|
## Q1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.
## Read data sets
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
## Aggregate total emissions for each year
totalPerYear <- aggregate(Emissions ~ year, NEI, sum)
## Plot the line to see if emissions have decreased
png(filename = "plot1.png", width = 480, height = 480)
plot(totalPerYear, type = "l", col = "darkblue",
xlab = "Year", ylab = "Total Emissions",
main = "Total emissions (1999 - 2008)")
dev.off()
|
556220f30f91425d1a0e4c1c787e13899c27e32e
|
90c5c8a79cb01f1a2475f01f8c0e4ba539492956
|
/Scripts/R_Scripts/build_CMV_predictor.R
|
a33c638a1194d56df41b506f2bf74748a37f9d4b
|
[] |
no_license
|
JacobBergstedt/MIMETH
|
626725179fb37adf3853adafd19ccf33c4c1623a
|
c475440ee5bb3389fae72f1684d270641884ce0a
|
refs/heads/main
| 2023-04-15T03:18:12.731765
| 2022-08-23T13:36:50
| 2022-08-23T13:36:50
| 527,968,587
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,160
|
r
|
build_CMV_predictor.R
|
# Initialize --------------------------------------------------------------
library(tidyverse)
library(stabs)
library(glmnet)
library(parallel)
source("./Scripts/R_scripts/Libraries/functions_for_CMV_prediction.R")
# Load data ---------------------------------------------------------------
meth <- readRDS("./Data/RData/Methylation/MIMETH.minfi.MMatrix.noob_969.ComBat2.rds")
ss <- readRDS("./Data/RData/Methylation/Annotation/MIMETH.969_sample_sheet.rds")
meth <- meth[, ss$SentrixID]
colnames(meth) <- ss$SUBJID
meth <- t(meth)
covs <- data.frame(SUBJID = rownames(meth)) %>%
left_join(readRDS("./Data/RData/Environment/covariates_all_samples.rds") )
y <- covs$CMV_serostatus
# Define globals ----------------------------------------------------------
q <- 50
tol <- 2
alpha <- 0.95
n_rep <- 4
fold_list <- replicate(n = n_rep, sample.int(10, length(y), replace = TRUE), simplify = FALSE)
# Run cross validation ----------------------------------------------------
selection_runs <- map_dfr(fold_list, cv_stability_selection, meth, y, alpha = alpha, q = q, tol = tol)
saveRDS(selection_runs, "./Data/RData/CMV_estimation_accuracy_stabsel.rds")
|
6e16373a61b3e27ab9a2067a0ad0f831b02e2d33
|
5efdf8e274a4a34a4f73645a59bb67995b6e3f4d
|
/cachematrix.R
|
7e7534e291b2932fa6af6877dd9cf85d60fdad4a
|
[] |
no_license
|
mjgrav2001/ProgrammingAssignment2
|
c658587c679790e10bf66c76f76361e9ea39592a
|
12c6bf4c3a5393c095462b0ce0a02ad7775893db
|
refs/heads/master
| 2021-01-18T12:44:19.582237
| 2015-04-25T00:57:14
| 2015-04-25T00:57:14
| 34,548,283
| 0
| 0
| null | 2015-04-25T00:20:00
| 2015-04-25T00:20:00
| null |
UTF-8
|
R
| false
| false
| 1,927
|
r
|
cachematrix.R
|
## Caching the Inverse of a Matrix:
##
## This .R files contains two functions to compute the inverse of a given "matrix"
## but caching the inverse of the matrix rather than computing it repeatedly.
## The file contains following two functions:
##
## 1. makeCacheMatrix: This function creates a special "matrix" object
## that can cache its inverse.
## 2. cacheSolve: This function computes the inverse of the special "matrix"
## returned by makeCacheMatrix above. If the inverse has already been calculated
## (and the matrix has not changed), then the function cacheSolve retrieves
## the inverse from the cache. The inverse of a square matrix is done
## with the solve function in R.
## The function makeCacheMatrix creates a special "matrix",
## which is a list containing a function to
## a) set the values of the matrix (set)
## b) get the values of the matrix (get)
## c) set the values of the inverse of the matrix (setInv)
## d) get the values of the inverse of the matrix (getInv)
makeCacheMatrix <- function(x = matrix()) {
XI <- NULL
set <- function(Y) {
x <<- Y
XI <<- NULL
}
get <- function() x
setInv <- function(Xinv) XI <<- Xinv
getInv <- function() XI
list(set = set, get = get,
setInv = setInv,
getInv = getInv)
}
## The following function calculates the inverse of a "matrix" created
## with the above function. It first checks to see if the inverse of the matrix
## has already been calculated. If so, it gets the inverse from the cache
## and skips the computation. Otherwise, it calculates the inverse of the data
## and sets the value of the inverse in the cache via the setInv function.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
XI <- x$getInv()
if(!is.null(XI)) {
message("getting cached data")
return(XI)
}
data <- x$get()
XI <- solve(data, ...)
x$setInv(XI)
XI
}
|
be7070a1c8a1fb41e3c15a94bd6e7d80e36bf5a0
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/mdsstat/examples/test_as_row.Rd.R
|
5073657c970e580f215d256b2cbd69d58a9763e4
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 168
|
r
|
test_as_row.Rd.R
|
library(mdsstat)
### Name: test_as_row
### Title: Coerce mdsstat Test to 1-Row Data Frame
### Aliases: test_as_row
### ** Examples
test_as_row(prr(mds_ts[[3]]))
|
477a7255de3a954a00f43a8d7dbd385f65321b5a
|
3eace8d25635ebbc9c9d498def0f32aae85d7d88
|
/man/mc.calc.bca.Rd
|
5e5f0f21d265b34e21e486c3e7bbc3c3e94a2e49
|
[] |
no_license
|
piodag/mcr
|
4b7c10d61188e63afe1af5b8109e19bec8c59862
|
41b5f5b2c526b2da11568471d7101e965d4d0a30
|
refs/heads/main
| 2023-04-12T13:48:48.175023
| 2021-05-10T22:26:11
| 2021-05-10T22:26:11
| 360,986,672
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,186
|
rd
|
mc.calc.bca.Rd
|
\name{mc.calc.bca}
\alias{mc.calc.bca}
\title{Bias Corrected and Accelerated Resampling Confidence Interval}
\usage{
mc.calc.bca(Xboot, Xjack, xhat, alpha)
}
\arguments{
\item{Xboot}{vector of point estimates for bootstrap
samples. The i-th element contains point estimate of the
i-th bootstrap sample.}
\item{Xjack}{vector of point estimates for jackknife
samples. The i-th element contains point estimate of the
dataset without i-th observation.}
\item{xhat}{point estimate for the complete data set
(scalar).}
\item{alpha}{numeric value specifying the 100(1-alpha)\%
confidence level for the confidence interval (Default is
0.05).}
}
\value{
a list with elements \item{est}{point estimate for the
complete data set (xhat).} \item{CI}{confidence interval
for point estimate.}
}
\description{
Calculate resampling BCa confidence intervals for
intercept, slope or bias given a vector of bootstrap and
jackknife point estimates.
}
\references{
Carpenter, J., Bithell, J. (2000) Bootstrap confidence
intervals: when, which, what? A practical guide for medical
statisticians. \emph{Stat Med}, \bold{19 (9)}, 1141--1164.
}
|
7816cc68b30197a4a27a521888e43095a9e6d738
|
cfe01977ef19f9f5ae8e39d7835cf979c9b67901
|
/man/default_col_pal.Rd
|
c8f934bb57b24710122885a0acb3ec4914bd24a7
|
[
"MIT"
] |
permissive
|
InseeFr/disaggR
|
27aecbf65f4fc65e539e90720660902a561d7d0c
|
f6c88857c7beb0a4d5c990bb253ed050302ba8c3
|
refs/heads/master
| 2023-08-22T13:44:32.143164
| 2023-08-05T17:42:55
| 2023-08-05T17:42:55
| 238,296,894
| 15
| 4
|
NOASSERTION
| 2023-09-06T16:29:02
| 2020-02-04T20:14:08
|
R
|
UTF-8
|
R
| false
| true
| 361
|
rd
|
default_col_pal.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{default_col_pal}
\alias{default_col_pal}
\title{Default color palette}
\usage{
default_col_pal(object)
}
\description{
The default color palette for the graphics, inspired from the package
\pkg{scales} whose scales can also be used as alternatives.
}
\keyword{internal}
|
c55aa2c5d221527f538bcac82ba29c13b3e988d8
|
277dbb992966a549176e2b7f526715574b421440
|
/R_training/실습제출/신현정/lab_08.R
|
06021ff08e122e33483a7e1dcaf3b1ea9bd60594
|
[] |
no_license
|
BaeYS-marketing/R
|
58bc7f448d7486510218035a3e09d1dd562bca4b
|
03b500cb428eded36d7c65bd8b2ee3437a7f5ef1
|
refs/heads/master
| 2020-12-11T04:30:28.034460
| 2020-01-17T08:47:38
| 2020-01-17T08:47:38
| 227,819,378
| 0
| 0
| null | 2019-12-13T12:06:33
| 2019-12-13T10:56:18
|
C++
|
UTF-8
|
R
| false
| false
| 875
|
r
|
lab_08.R
|
#문제 1
mySum <-function(...)
#(1)
{
data = c(...)
oddSum= 0
evenSum = 0
print(class(data))
if(!is.numeric(data)){
return(NULL)
}else{
for(i in data){
if(i %% 2 == 0)
}else{
}
}
data = list(evenSum,)
}
return(data)
}
testwarn() <- function(x){
if(any(!NA))
}
return(min) ;
#(3)
mySum<- function(x){
if(all(is.na(x)))
return("NA를 최저값으로 변경하여 처리함!!")
else (is.na(NULL))
return("NULL")}
#문제 2
myExpr = function(x){
if(is.function(x)){
result = sample(1:45,6)
cat(result,"\n")
}
else{
stop("수행 안할꺼임!!")
}
return(x(result))
}
myExpr(max) # 함수 명을 넣어야함
#문제 4
d <- scan("data/iotest1.txt")
sort(d)
sort(d, decreasing = TRUE)
sum(d)
mean(d)
#문제 5
|
be3613f16524981778810b28e252c501137175b9
|
ced0e8a0e6e8c40b64424b786356c34ef9c81a70
|
/Rcode_clinicalDataNew/rubins_rule.R
|
d02cf69423b323cf9dd39c25c2cfdf1423b84a3d
|
[
"MIT"
] |
permissive
|
AnacletoLAB/DataAnalysisR
|
59bff492b3cc945aa42f01c1edf154323cd6df7e
|
8c09903be89a1199869f0408f35378df77a0ebdc
|
refs/heads/main
| 2023-03-22T09:37:57.271192
| 2021-03-17T18:46:06
| 2021-03-17T18:46:06
| 316,894,320
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,268
|
r
|
rubins_rule.R
|
rubin_rule_pool <- function(imp){
#https://thomasleeper.com/Rcourse/Tutorials/mi.html
# imp is a list with num_imputations elements
# each element is a dataframe/matrix where rows are the repetitions of the classifier and each column is
# related to a different value (either performance value or importance of the feature)
source(file.path('.', 'Utils_postprocess.R'))
num_imputations = length(imp)
# with the following function I get a matrix with
# num_imputation columns - one for each imputation run.
# each row is related to one of the values in the imp dataframe/matrix
# (essentially, for each value I compute the mean over all the repetitions of the algorithm)
mean_estimates <- sapply(imp, mean_on_columns)
#Now I compute the mean over all the imputations
grandm <- apply(mean_estimates,1,mean)
grandm
# now for each imputation, I compute the standard error of the classifiers' runs
ses <- sapply(imp, var_on_columns)
stderrs <- sapply(imp, se_on_columns)
#To get the standard error of our multiple imputation estimate,
#we need to combine the standard errors of each of our estimates,
#so that estimates we need to start by getting the SEs of each imputed vector:
#The within variance is the mean of the se for all the imputations
within <- apply(ses,1, mean)
within_se <- apply(stderrs,1, mean)
#To calculate the between-imputation VARIANCE,
#we calculate the sum of squared deviations of each imputed mean from the grand mean estimate:
# FOR EACH IMPUTATION COMPUTINE THE SAMPLE VARIANCE
between = within-within
for (nv in 1:nrow(mean_estimates)) {
between[nv] = sum((mean_estimates[nv,]-grandm[nv])^2)* (1/(num_imputations-1))
}
between_se = sqrt(between)/sqrt(num_imputations)
#Then we sum the within- and between-imputation variances (multiply the latter by a small correction):
# cat(between, '\n')
grandvar <- within + (1+1/num_imputations)*between
grandse <- within_se + (1+1/num_imputations)*between_se
return(list("mean" = grandm, "var" = grandvar, "se" = grandse, "between" = between, "within" = within))
}
mean_on_cols <- function(df_mat){
return(apply(df_mat, 2, mean))
}
|
59429d3261b29a2211f5f8a8f5392c030594d822
|
ae782ac681b8e5bbfc68561bb06c0f7786cad1a0
|
/simulation_random_arrangement_successes_failures.R
|
4827ec4df8959c6ca081913ffd7ed560a40c4812
|
[] |
no_license
|
SirRichter/R-code
|
dea56bd746b8ba8d087e53d244c07679eda2351c
|
719aca4ae5779f07463ae36a96e0887e9249db5d
|
refs/heads/master
| 2018-11-29T17:18:29.442930
| 2018-09-05T14:57:50
| 2018-09-05T14:57:50
| 117,899,338
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 566
|
r
|
simulation_random_arrangement_successes_failures.R
|
success <- 21
attempt <- 30
fail <- attempt - success
streak <- function()
{
success.pos <- sort(sample(1:attempt, success))
fail.pos <- sort((1:attempt)[-success.pos])
streak <- 0
prev <- success.pos[1]
for(i in success.pos[-1])
{
current <- i
if(prev == current-1)
{
streak <- streak + 1
}
prev <- current
}
prev <- fail.pos[1]
for(i in fail.pos[-1])
{
current <- i
if(prev == current-1)
{
streak <- streak + 1
}
prev <- current
}
return(streak/attempt)
}
|
74f45c8425f84efb41772de56bdb1de89320d82f
|
34a646deb8254171bd8e4882e3ea3c7e13fb63fb
|
/man/get_form_data.Rd
|
d97dc58a98a90401c7615b35788161dd84b26324
|
[
"MIT"
] |
permissive
|
mattmalcher/lift.tracker
|
0e516bddb9570f1cdb4c83780f3e1c3c804291d2
|
bfcdce90eb2dc9f97e5ce9a1107a7286ad192679
|
refs/heads/main
| 2023-02-05T11:33:10.629001
| 2020-10-13T10:45:22
| 2020-10-13T10:45:22
| 303,187,016
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 383
|
rd
|
get_form_data.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_form_data.R
\name{get_form_data}
\alias{get_form_data}
\title{Get Form Data}
\usage{
get_form_data(form_url)
}
\arguments{
\item{form_url}{\itemize{
\item the share link for the sheet which the google form drops
data into
}}
}
\value{
a dataframe of lift breakage data
}
\description{
Get Form Data
}
|
4f052962f0f61ec548127c2746febf28617ad29c
|
7e38971daf48e04414cb0c65facd38c98648142b
|
/R/cleanAQRQMJDaily.R
|
5e4b1c592daf3938205c61207456762fcef36ea6
|
[] |
no_license
|
yn1/FFAQR
|
338800ec7209a10b33d79630ae2c082d7df1f22c
|
c173309a9415f9d83130650b8c7e4f5132fabdf4
|
refs/heads/master
| 2021-01-02T23:02:57.359761
| 2015-02-06T21:49:02
| 2015-02-06T21:49:02
| 29,973,189
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 740
|
r
|
cleanAQRQMJDaily.R
|
#' Reads in, cleans, and subdivides daily QMJ data set in data folder.
#' Currently disabled, Java heap runs out of memory when reading xlsx.
cleanAQRQMJDaily <- function() {
temp <- tempfile()
QMJDaily <- "https://www.aqr.com/~/media/files/data-sets/quality-minus-junk-factors-daily.xlsx"
download.file(QMJDaily, temp, method = "curl")
# Imports QMJ data
AQRQMJFactorsDaily <- read.xlsx(temp, "QMJ Factors", startRow=19, colIndex=c(1:30))
row.names(AQRQMJFactorsDaily) <- NULL
names(AQRQMJFactorsDaily)[1] <- "Date"
AQRQMJFactorsDaily[,1] <- ymd(AQRQMJFactorsDaily[,1])
unlink(temp)
start <- system.file(package="FFAQR")
save(AQRQMJFactorsDaily, file=paste0(start, "/data/AQRQMJFactorsDaily.Rdata"))
}
|
3f64c49c2fa4bc456019de2b8bce019d975e3a28
|
9916af82f94b822f233475296447adb486db56c1
|
/R/cSimulator.R
|
156762e26efc86010ef6f24e908f1aeeb4c01807
|
[] |
no_license
|
saezlab/CellNOptR
|
865bc6cf866e00faf8718f1f61388fde7fbb58b8
|
4660813a35227bab86359d51f9f61a9e3deb0298
|
refs/heads/master
| 2022-05-20T06:58:25.273631
| 2022-05-11T08:59:33
| 2022-05-11T08:59:33
| 116,661,047
| 8
| 2
| null | 2022-03-21T13:28:13
| 2018-01-08T10:12:00
|
R
|
UTF-8
|
R
| false
| false
| 2,343
|
r
|
cSimulator.R
|
#
# This file is part of the CNO software
#
# Copyright (c) 2011-2012 - EMBL - European Bioinformatics Institute
#
# File author(s): CNO developers (cno-dev@ebi.ac.uk)
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# http://www.gnu.org/licenses/gpl-3.0.html
#
# CNO website: http://www.cellnopt.org
#
##############################################################################
# $Id$
cSimulator <- function(CNOlist, model, simList, indexList, mode=1) {
if (!is(CNOlist,"CNOlist")){
CNOlist = CellNOptR::CNOlist(CNOlist)
}
# check the structures
if(is.null(CNOlist@stimuli) || is.null(CNOlist@inhibitors)) {
stop("This function needs 'valueStimuli' and 'valueInhibitors' in CNOlist")
}
if(is.null(model$reacID) || is.null(model$namesSpecies)) {
stop("This function needs 'reacID' and 'namesSpecies' in model")
}
# variables
nStimuli <- as.integer(length(indexList$stimulated))
nInhibitors <- as.integer(length(indexList$inhibited))
nCond <- as.integer(dim(CNOlist@stimuli)[1])
nReacs <- as.integer(length(model$reacID))
nSpecies <- as.integer(length(model$namesSpecies))
nMaxInputs <- as.integer(dim(simList$finalCube)[2])
# simList
# used to be
# >>> finalCube = as.integer(as.vector(t(simList$finalCube))-1)
# but as.vector(t is slow and can be replaced by just as.integer albeit
# appropriate C modifications
finalCube = as.integer(simList$finalCube-1)
ixNeg = as.integer(simList$ixNeg)
ignoreCube = as.integer(simList$ignoreCube)
maxIx = as.integer(simList$maxIx-1)
# index
indexSignals <- as.integer(indexList$signals-1)
indexStimuli <- as.integer(indexList$stimulated-1)
indexInhibitors <- as.integer(indexList$inhibited-1)
nSignals <- length(indexSignals)
# cnolist
valueInhibitors <- as.integer(CNOlist@inhibitors)
valueStimuli <- as.integer(CNOlist@stimuli)
res = .Call("simulatorT1",
# variables
nStimuli,
nInhibitors,
nCond,
nReacs,
nSpecies,
nSignals,
nMaxInputs,
# simList
finalCube,
ixNeg,
ignoreCube,
maxIx,
# index
indexSignals,
indexStimuli,
indexInhibitors,
# cnolist
valueInhibitors,
valueStimuli,
as.integer(mode)
)
# should not be cut because it is used in simulateTN as an input
# res = res[,indexList$signals]
return(res)
}
|
9a307cb1df0b403439627a169cdb1784f9f5ca60
|
6f7ae9c734fda6fbd7338ce1af3945c65f609088
|
/02_create_main_analysis_datasets/04_compute_market_access/03a_woreda_traveltime_dataset.R
|
7ff8f68983a038cf367f5f1ee657ad25ea5382d4
|
[] |
no_license
|
mohammed-seid/Ethiopia-Corridors-IE
|
d9b16014f866d529f39720f434a5f5dda433f9eb
|
401836d71d60a81faded25a18c19a5f14e082701
|
refs/heads/master
| 2022-11-09T01:23:41.471170
| 2020-06-15T22:00:30
| 2020-06-15T22:00:30
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,703
|
r
|
03a_woreda_traveltime_dataset.R
|
# Travel Time
#source("~/Documents/Github/Ethiopia-Corridors-IE/Code/_ethiopia_ie_master.R")
SEP_ROAD_SHAPEFILES <- T # Use separate road shapefiles
RESOLUTION_KM <- 3
WALKING_SPEED <- 5
for(SEP_ROAD_SHAPEFILES in c(TRUE, FALSE)){
# Load Data --------------------------------------------------------------------
woreda_wgs84 <- readRDS(file.path(finaldata_file_path, DATASET_TYPE, "individual_datasets", "points.Rds"))
gpw <- raster(file.path(rawdata_file_path, "gpw-v4-population-density-2000", "gpw-v4-population-density_2000.tif"))
gpw <- gpw %>% crop(woreda_wgs84)
# Location with largest population with woreda ---------------------------------
woreda_points <- lapply(1:nrow(woreda_wgs84), function(i){
print(i)
gpw_i <- gpw %>%
crop(woreda_wgs84[i,]) %>%
mask(woreda_wgs84[i,])
df <- gpw_i %>% coordinates() %>% as.data.frame()
df$pop <- gpw_i[]
loc_df <- df[which.max(df$pop),] %>%
dplyr::select(x,y)
if(nrow(loc_df) %in% 0){
loc_df <- coordinates(woreda_wgs84[i,]) %>%
as.data.frame() %>%
dplyr::rename(x= V1,
y= V2)
}
return(loc_df)
}) %>% bind_rows()
woreda_points$uid <- woreda_wgs84$uid
coordinates(woreda_points) <- ~x+y
crs(woreda_points) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
# Reproject to Ethiopia Projection ---------------------------------------------
# Reproject to UTM. Better for distance calculations (eg, for setting grid cell size)
woreda_points <- spTransform(woreda_points, UTM_ETH)
woreda <- spTransform(woreda_wgs84, UTM_ETH)
# Crete Raster BaseLayer -------------------------------------------------------
r <- raster(xmn=woreda@bbox[1,1],
xmx=woreda@bbox[1,2],
ymn=woreda@bbox[2,1],
ymx=woreda@bbox[2,2],
crs=UTM_ETH,
resolution = RESOLUTION_KM*1000)
# Function for Travel Times ----------------------------------------------------
calc_travel_time <- function(year, woreda_points, SEP_ROAD_SHAPEFILES){
# If SEP_ROAD_SHAPEFILES=T, then "roads" is ignored, as loads roads within
# the function.
print(paste(year, "--------------------------------------------------------"))
#### Load/Prep Roads
if(SEP_ROAD_SHAPEFILES){
# If road isn't even (ie, odd), use previous year
if((year %% 2) %in% 0){
year_road <- year
} else{
year_road <- year - 1
}
# Load Roads
roads <- readOGR(dsn = file.path(project_file_path, "Data", "RawData", "RoadNetworkPanelDataV3_1996_2016_Revised"),
layer = paste0("All_Network_", year_road))
if("Speed2006a" %in% names(roads)) roads$Speed2006 <- roads$Speed2006a
} else{
roads <- readRDS(file.path(project_file_path, "Data", "FinalData", "roads", "RoadNetworkPanelData_1996_2016.Rds"))
year_road <- year
}
roads <- spTransform(roads, UTM_ETH)
speed_var <- paste0("Speed", year_road)
roads$SpeedYYYY <- roads[[speed_var]]
roads$SpeedYYYY[roads$SpeedYYYY %in% 0] <- WALKING_SPEED
#### Sort by Speed
# If multiple polylines interesect with a cell, velox uses the last polygon from
# the spatial polygons dataframe. Consequently, we sort by speeds from slowest to
# fastest so that velox uses the fastest speed.
roads <- roads[order(roads$SpeedYYYY),]
#### Rasterize
roads_r <- r
roads_r[] <- 0
roads_r_vx <- velox(roads_r)
roads_r_vx$rasterize(roads, field="SpeedYYYY", background=WALKING_SPEED) # background should be walking speed (5km/hr); https://en.wikipedia.org/wiki/Preferred_walking_speed
roads_r <- roads_r_vx$as.RasterLayer()
#### Make Transition Layer
# Roads is currently speed; calculate how long it takes to move across cell Now, values are the number
# of hours it takes to cross the cell.
roads_r[] <- RESOLUTION_KM/roads_r[]
cost_t <- transition(roads_r, function(x) 1/mean(x), directions=8)
#cost_t <- geoCorrection(cost_t, type="c")
#### Calculate Travel Time for Each Location
tt_df <- lapply(1:nrow(woreda_points), function(i){
if((i %% 10) %in% 0) print(i)
tt <- costDistance(cost_t,
woreda_points[i,],
woreda_points) %>% as.numeric()
tt <- tt * RESOLUTION_KM # to get more accurate travel time???? TODO
#### TESTING
#tt <- costDistance(cost_t,
# woreda_points[1,],
# woreda_points[100,]) %>% as.numeric()
#tt1 <- shortestPath(cost_t,
# woreda_points[1,],
# woreda_points[100,],
# output = "SpatialLines")
#coordinates(woreda_points[1,] %>% spTransform(CRS("+init=epsg:4326"))) %>% rev()
#coordinates(woreda_points[100,] %>% spTransform(CRS("+init=epsg:4326"))) %>% rev()
#plot(tt1)
#plot(roads_r,add=T)
#plot(tt1,add=T)
df_out <- data.frame(dest_uid = woreda_points$uid,
travel_time = tt)
df_out$orig_uid <- woreda_points$uid[i]
return(df_out)
}) %>% bind_rows
tt_df$year <- year
return(tt_df)
}
location_traveltimes <- lapply(1996:2016, calc_travel_time, woreda_points, SEP_ROAD_SHAPEFILES) %>%
bind_rows() %>%
as.data.table()
# Calculate Linear Distance ----------------------------------------------------
distance_df <- lapply(1:nrow(woreda_points), function(i){
if((i %% 100) %in% 0) print(i)
distance <- gDistance(woreda_points[i,],
woreda_points,
byid=T) %>%
as.vector()
df_out <- data.frame(dest_uid = woreda_points$uid,
distance = distance)
df_out$orig_uid <- woreda_points$uid[i]
return(df_out)
}) %>%
bind_rows %>%
as.data.table()
location_traveltimes <- merge(location_traveltimes, distance_df, by=c("orig_uid",
"dest_uid"))
# Export -----------------------------------------------------------------------
if(SEP_ROAD_SHAPEFILES){
out_add <- "_rdsep"
} else{
out_add <- ""
}
saveRDS(location_traveltimes, file.path(finaldata_file_path, DATASET_TYPE,
"individual_datasets",
paste0("woreda_traveltimes_distances",out_add,".Rds")))
}
|
18f23d87f61b709e03d499d50bb1aab16a58dbd8
|
d16d3a64c5707acf1faa5ab5db1ddf50f5bc48d6
|
/ACE2_analysis/3_dseq2.R
|
408dc70f892fbaaf3d56a6f5c0eb22fc6e2c2730
|
[] |
no_license
|
takeonaito/rnaseq
|
80782d7e0366f50021b4bdef2469bfa21f871de1
|
39ce4cc8d92d1d9384ce46987b1bf3dadc43548e
|
refs/heads/master
| 2021-03-04T22:41:43.103095
| 2020-04-08T22:44:18
| 2020-04-08T22:44:18
| 246,072,279
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,947
|
r
|
3_dseq2.R
|
library(ggrepel)
library(DESeq2)
library(BiocParallel)
library(tidyverse)
library(readr)
library(data.table)
library(readxl)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(ggbeeswarm)
# read necessary files (count, sample and serology)
count <- read_tsv("/home/takeo/rnaseq/WashU/data/all.gene_counts.xls")
sample <- read_xlsx("/home/takeo/rnaseq/WashU/data/WashU_BMI_RNAseq_IDlink.xlsx")
sample$Genetic_ID <- str_replace(sample$Genetic_ID,"10-0441/10-1045","10-0441")
serology<- read_tsv("/home/takeo/rnaseq/WashU/data/serology_updated_dalin.txt")
# read necessary files (disease type and phenotype)
location <- read_tsv("/home/takeo/rnaseq/WashU/data/cd_clean.txt")
disease <- read_xls("/home/takeo/rnaseq/WashU/data/Copy of Genetics 01_02_2019.xls")
colnames(disease) <- make.names(colnames(disease))
# merge disease type and phenotype data to target file
target <- sample %>%
dplyr::select(Genetic_ID,RNAseq_ID)
# exclude non CD and caucasian
target <- target %>% left_join(disease,by = c("Genetic_ID" = "Genetic.ID")) %>%
dplyr::filter(Race == "Caucasian")
target1 <- target %>%
left_join(location,by = c("Genetic_ID" = "genetic_id"))
target1$RNAseq_ID <- str_replace(target1$RNAseq_ID,"-","_")
# make RNAseq_ID in sample file match to count files.
target1$RNAseq_ID <- paste0("sample.",target1$RNAseq_ID) %>%
base::tolower()
# confirm complete match
table( target1$RNAseq_ID %in% colnames(count))
# make sampleTable which contain serology and Genetic ID
serology1 <- serology %>%
drop_na(Genetic.ID)
sampleTable <- target1 %>%
inner_join(serology1,by = c("Genetic_ID" = "Genetic.ID"))
## there is a duplication in cc -89( genetcid = 97-0329) --> ask dalin.
sampleTable <- sampleTable[-73,] # I excluded old data of 97-0329
# omit subjects whose serology are NA
sampleTable$hensuu <- as.numeric(sampleTable$Age.at.Collection)
sampleTable$hensuu <- as.factor(sampleTable$Gender)
sampleTable1 <- sampleTable %>%
drop_na(hensuu) %>%
dplyr::select(-Genetic_ID)
# extract count data whose serology data are available
kouho <- sampleTable1$RNAseq_ID
count1 <- count %>%
dplyr::select(ensembl_gene_id,kouho) %>%
as.data.frame()
row.names(count1) <- count1$ensembl_gene_id
count1 <- count1[,-1]
# confirm sample order match
identical(colnames(count1),sampleTable1$RNAseq_ID)
# make dds object for DESeq2
dds <- DESeqDataSetFromMatrix(countData = count1,
colData = sampleTable1,
design = ~hensuu )
keep <- rowSums(counts(dds)>10) > 10
dds <- dds[ keep, ]
nrow(dds)
# do variance stabilizing transformation (VST) for visualization of data.
vsd <- vst(dds, blind = FALSE)
# make PCA for visualizing data set.
plotPCA(vsd, intgroup = c("hensuu"))
pcaData <- plotPCA(vsd, intgroup = c("hensuu"), returnData = TRUE)
pcaData$order <- c(1:dim(vsd)[2])
p <- ggplot(pcaData, aes(x = PC1, y = PC2, color = hensuu)) +
geom_point(size =3)
p + geom_text_repel(data = pcaData,aes(label = order))
# exclude outliner CC_89
exclude <- which(row.names(colData(dds)) == "sample.cc_89")
dds <- dds[,-exclude]
# do variance stabilizing transformation (VST) for visualization of data after exclusion
vsd <- vst(dds, blind = FALSE)
# make PCA for visualizing data set after exclude outliner.
plotPCA(vsd, intgroup = c("hensuu"))
# do analysis of DEG --> this will take a few minutes
register(MulticoreParam(workers = 10))
dds1 <- DESeq(dds,parallel = TRUE,minReplicatesForReplace = Inf)
res <- results(dds1,alpha = 0.05,contrast=c("hensuu","F","M"))
res <- results(dds1,alpha = 0.05)
res$ensemble = row.names(res)
res$symbol <- mapIds(org.Hs.eg.db,
keys=row.names(res),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$entrez <- mapIds(org.Hs.eg.db,
keys=row.names(res),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
res %>%
data.frame(res) %>%
filter(ensemble == 'ENSG00000130234')
# subset only significant genes
resSig <- subset(res, padj < 0.05)
resSig <- subset(resSig,abs(log2FoldChange) > 1 )
resSig <- resSig[order(resSig$pvalue),]
resSig
# check the cooks distance of resSig
kouho <- which(row.names(res) %in% row.names(resSig))
round(apply(assays(dds1)[["cooks"]][kouho,],1,max),2)
# make plot of top hit gene
topGene <- rownames(res)[which.min(res$padj)]
geneCounts <- plotCounts(dds1, gene =topGene, intgroup = c("hensuu"),
returnData = T,normalized = T)
geneCounts <- plotCounts(dds1, gene ="ENSG00000130234", intgroup = c("hensuu"),
returnData = T,normalized = T)
ggplot(geneCounts, aes(x = hensuu, y = count,color = hensuu)) + scale_y_log10() +
geom_beeswarm(cex = 3)
ggplot(geneCounts,aes(x = hensuu, y = count)) +scale_y_log10() + geom_point()
mode(geneCounts)
|
8f3d23f9001aeb68a94a6dd0d29f817664816702
|
3a882c3eb6867a5ce5081747c9c538aec0d08705
|
/man/read.txt.Renishaw.Rd
|
695b3b26fabb58f39be21b934421849e54fc4e2d
|
[] |
no_license
|
cran/hyperSpec
|
02c327c0ea66014936de3af2cb188e9e30a4e6f7
|
4fc1e239f548e98f3a295e0521a2f99a5b84316d
|
refs/heads/master
| 2021-09-22T07:57:28.497828
| 2021-09-13T12:00:02
| 2021-09-13T12:00:02
| 17,696,713
| 3
| 10
| null | 2016-10-31T16:36:46
| 2014-03-13T05:00:53
|
R
|
UTF-8
|
R
| false
| true
| 2,540
|
rd
|
read.txt.Renishaw.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deprecated.R, R/read.txt.Renishaw.R
\name{scan.txt.Renishaw}
\alias{scan.txt.Renishaw}
\alias{scan.zip.Renishaw}
\alias{read.txt.Renishaw}
\alias{read.zip.Renishaw}
\title{import Raman measurements from Renishaw ASCII-files}
\usage{
scan.txt.Renishaw(...)
scan.zip.Renishaw(...)
read.txt.Renishaw(
file = stop("file is required"),
data = "xyspc",
nlines = 0,
nspc = NULL
)
read.zip.Renishaw(
file = stop("filename is required"),
txt.file = sub("[.]zip", ".txt", basename(file)),
...
)
}
\arguments{
\item{...}{Arguments for \code{read.txt.Renishaw}}
\item{file}{file name or connection}
\item{data}{type of file, one of "spc", "xyspc", "zspc", "depth", "ts", see
details.}
\item{nlines}{number of lines to read in each chunk, if 0 or less read
whole file at once.
\code{nlines} must cover at least one complete spectrum,i.e. \code{nlines}
must be at least the number of data points per spectrum. Reasonable
values start at \code{1e6}.}
\item{nspc}{number of spectra in the file}
\item{txt.file}{name of the .txt file in the .zip archive. Defaults to zip
file's name with suffix .txt instead of .zip}
}
\value{
the \code{hyperSpec} object
}
\description{
import Raman measurements from Renishaw (possibly compressed) .txt file.
}
\details{
The file may be of any file type that can be read by
\code{\link[base]{gzfile}} (i.e. text, or zipped by gzip, bzip2, xz or
lzma). .zip zipped files need to be read using \code{read.zip.Renishaw}.
Renishaw .wxd files are converted to .txt ASCII files by their batch
converter. They come in a "long" format with columns (y x | time | z)?
wavelength intensity. The first columns depend on the data type.
The corresponding possibilities for the \code{data} argument are:
\tabular{lll}{ \code{data} \tab columns \tab \cr \code{"spc"} \tab wl int
\tab single spectrum \cr \code{"zspc"}, \code{"depth"} \tab z wl int \tab
depth profile\cr \code{"ts"} \tab t wl int \tab time series\cr
\code{"xyspc"} \tab y x wl int \tab 2d map\cr }
This function allows reading very large ASCII files, but it does not work
on files with missing values (\code{NA}s are allowed).
If the file is so large that it sould be read in chunks and \code{nspc} is
not given, \code{read.txt.Renishaw} tries to guess it by using \code{wc}
(if installed).
}
\seealso{
\code{\link{read.txt.long}}, \code{\link{read.txt.wide}},
\code{\link[base]{scan}}
}
\author{
C. Beleites
}
\keyword{IO}
\keyword{file}
\keyword{internal}
|
cde8a7b882eb3dd2b5ef1823cdcd53a07a3a8171
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/chebpol/examples/chebcoef.Rd.R
|
1d240bfeacc06be555c83c69777040cc3549d436
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 238
|
r
|
chebcoef.Rd.R
|
library(chebpol)
### Name: chebcoef
### Title: Compute Chebyshev-coefficients given values on a Chebyshev grid
### Aliases: chebcoef
### ** Examples
## Coefficients for a 2x3x4 grid
a <- array(rnorm(24),dim=c(2,3,4))
chebcoef(a)
|
eb7e2ffbb6211b7dea202b78068e3676e7069fbb
|
0ce3453dd3ea67d3d162486b1b78427a41d163d4
|
/man/sea_ice_area.Rd
|
e4485107590970e368a3886c02c065e321d41ba2
|
[
"MIT"
] |
permissive
|
coolbutuseless/emphatic
|
0d79045a4893285d0cb5028a60c5ec772457e88a
|
32488dc0a91b7b461c6f3c592fdd1d2c1123b7dd
|
refs/heads/main
| 2023-09-01T12:58:08.954192
| 2023-08-30T05:59:10
| 2023-08-30T05:59:10
| 308,440,058
| 100
| 3
|
MIT
| 2020-12-17T22:53:18
| 2020-10-29T20:17:47
|
R
|
UTF-8
|
R
| false
| true
| 492
|
rd
|
sea_ice_area.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-sets.R
\docType{data}
\name{sea_ice_area}
\alias{sea_ice_area}
\title{Monthly Southern Sea Ice Area over the last 40 years}
\format{
Matrix of sea ice area, monthly from 1978 to 2020.
}
\source{
\url{ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/south/monthly/data/}
}
\usage{
sea_ice_area
}
\description{
From the 'National Snow and Ice Data Center' \url{https://nsidc.org/data/g02135}
}
\keyword{datasets}
|
7a99d08ebe73bfb859ac32e51aa2763db81adcf5
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/BIEN/examples/BIEN_metadata_citation.Rd.R
|
ba11ed8c8b0aa8648fd043c77a12ab84f8b810e8
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 444
|
r
|
BIEN_metadata_citation.Rd.R
|
library(BIEN)
### Name: BIEN_metadata_citation
### Title: Generate citations for data extracted from BIEN.
### Aliases: BIEN_metadata_citation
### ** Examples
## Not run:
##D BIEN_metadata_citation()#If you are referencing the phylogeny or range maps.
##D Xanthium_data<-BIEN_occurrence_species("Xanthium strumarium")
##D citations<-BIEN_metadata_citation(dataframe=Xanthium_data)#If you are referencing occurrence data
## End(Not run)
|
2047a5561a3c7c7d51847fd4c2bcf2b0d8772260
|
6e8d099d91bc467c36a8a5e3b609de0b44380603
|
/R/setTypes.R
|
f7f9ee829795f1d48b0bcf34e57c92eb8d3c60c4
|
[] |
no_license
|
Peder2911/Unfed_Gnostic
|
4fb8bcb46880c978cd913d4c44a0b381cb2e8edf
|
aac66c233195a02447474d1c4f970a62680c250e
|
refs/heads/master
| 2020-04-04T03:47:02.053555
| 2018-11-01T14:15:30
| 2018-11-01T14:15:30
| 155,725,757
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 579
|
r
|
setTypes.R
|
#' Set Types By Vector
#'
#' Set the data types of a data frame using a character vector.
#' The vector can be created by sapply(data,class)
#' @param df A data frame
#' @param types A character vector of length ncol(df)
#' @keywords types metaprogramming
#' @export
#' @importFrom magrittr "%>%"
#' @examples
#' setTypes(mtcars,rep('character',11))
setTypes <- function(df,types){
expressions <- sapply(types,function(x){
paste('as.',x,sep='')%>%
parse(text = .)
})
i = 1
for(e in expressions){
df[[i]] <- eval(e)(df[[i]])
i <- i + 1
}
df
}
|
682d60245ed92ad6f2d010f4593f68d795faa212
|
bfe324beb0c335272362e7514938a82c08a9cc40
|
/tests/testthat/test_calc_water_tax.R
|
d0c2e70b3b14a8f16b9ac76b9532d4b74201919e
|
[] |
no_license
|
jkmiller-wildlife/PrecipPackage
|
79157b7d394835f34a2999bdbaf380c74b09a7d0
|
070035c67dbdb496716c60e5d598b8315b7a4e3a
|
refs/heads/master
| 2020-06-01T12:07:27.458399
| 2019-06-16T03:43:21
| 2019-06-16T03:43:21
| 190,774,172
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 180
|
r
|
test_calc_water_tax.R
|
test_that(
"Tax is positive and higher than zero",
{
data(monthly_precip)
expect_true( calc_water_tax(monthly_precip, 100, "SANTA BARBARA", "OCT", 2012) > 0 )
}
)
|
66a640ef620d17507b83d958e8b4f676f8b2f82d
|
7db5e131633d086c7a30675857c114615a9efe6f
|
/man/reverseList.Rd
|
df3a5b2428de4f4bb8ff567fc776a6045c3f4911
|
[] |
no_license
|
guokai8/rcellmarker
|
969b13d4af8019fda574748064af4d72b77e58e6
|
32d3a12a4b84310a36f3eb645c7e364909257bd9
|
refs/heads/master
| 2022-05-04T00:48:55.315592
| 2022-03-30T17:27:52
| 2022-03-30T17:27:52
| 244,527,840
| 9
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 262
|
rd
|
reverseList.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{reverseList}
\alias{reverseList}
\title{reverse List}
\usage{
reverseList(lhs)
}
\arguments{
\item{lhs}{list with names}
}
\description{
reverse List
}
\author{
Kai Guo
}
|
23f3d400f67be1d0e7d8141b9d82f32d179ed289
|
1ff45a674ca54329a98451899d834a094e70b115
|
/asian-pacific-heritage/process-acs-data-not-used-yet.R
|
6226b7f5e5da562dccfd4d138221263cc89ee796
|
[] |
no_license
|
psrc/equity-data-tools
|
7642185d3c1d70e2ab71df489a9271c87356965c
|
d0bea587ec63731468c52895146fa3d7346ae321
|
refs/heads/main
| 2023-05-05T23:55:47.299847
| 2021-05-18T22:12:56
| 2021-05-18T22:12:56
| 359,532,801
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,761
|
r
|
process-acs-data-not-used-yet.R
|
# Inputs ------------------------------------------------------------------
library(tidycensus)
library(tidyverse)
Sys.setenv(CENSUS_KEY='c4780eb03010d73b7ae4e6894c1592375e545a21')
census_api_key('c4780eb03010d73b7ae4e6894c1592375e545a21')
acs <- "acs1"
yrs <- c(seq(2010,2019,1))
psrc.county <- c("53033","53035","53053","53061")
psrc.msa <- c("14740","42660")
pop.tbl <- "B03002"
pop.vars <- c("001","003","004","005","006","007","008","009","012")
inc.tbl <- "S1903"
inc.vars <- c("001","003","004","005","006","007","008","009","010")
edu.tbl <- "S1501"
edu.vars <- c("031","032","033","034","035","036","037","038","039",
"040","041","042","043","044","045","046","047","048","049",
"050","051","052","053","054")
ownership <- "S2502"
# Population ---------------------------------------------------------
acs.population <- NULL
census.tbl <- pop.tbl
keep.vars <- paste0(pop.tbl,"_",pop.vars)
total.var <- paste0(pop.tbl,"_001")
# Download the list of variables from the latest data year
variable.labels <- load_variables(max(yrs), acs, cache = TRUE) %>% rename(variable = name)
for (c.yr in yrs) {
# Download County Level Data
census.download <- get_acs(geography = "county", state="53", year=c.yr, survey = acs, table = census.tbl) %>%
mutate(NAME = gsub(", Washington", "", NAME)) %>%
filter(GEOID %in% psrc.county, variable %in% keep.vars)
# Get a region total from the county data
temp <- census.download %>%
select(variable, estimate, moe) %>%
group_by(variable) %>%
summarize(sumest = sum(estimate), summoe = moe_sum(moe, estimate)) %>%
rename(estimate=sumest, moe=summoe) %>%
mutate(GEOID="53033035053061", NAME="Region", year=c.yr)
# Calculate Total population by geography
totals <- temp %>% filter(variable==total.var) %>% select(NAME,estimate) %>% rename(total=estimate)
# Add totals and calculate share of total by race
temp <- left_join(temp, totals, by=c("NAME")) %>%
mutate(share=estimate/total) %>%
select(-total)
# Combine with other data years
if (is.null(acs.population)) {acs.population <- temp} else {acs.population <- bind_rows(list(acs.population, temp))}
rm(census.download,temp,totals)
}
# Add labels from the latest census data year downloaded and clean up labels
acs.population <- left_join(acs.population,variable.labels,by=c("variable")) %>%
mutate(concept="Population by Race", label = str_extract(label, "(?<=!!)[^!!]*$"), label = gsub(" alone", "", label), label = gsub(":", "", label)) %>%
rename(race=label) %>%
mutate(category="Population") %>%
mutate(race=gsub("White","White, not Hispanic or Latino",race))
# Median Income ------------------------------------------------------------------
acs.income <- NULL
census.tbl <- inc.tbl
total.var <- paste0(inc.tbl,"_C03_001")
# Download the list of variables from the latest data year
variable.labels <- load_variables(max(yrs), paste0(acs,"/subject"), cache = TRUE) %>% rename(variable = name)
for (c.yr in yrs) {
# Variables for income table changed in 2017 so make the keep lsit consistent depending on year
if (c.yr <2017) {
keep.vars <- paste0(inc.tbl,"_C02_",inc.vars)
} else {
keep.vars <- paste0(inc.tbl,"_C03_",inc.vars)
}
# Download Census Data by MSA for Median Income since we can't combine counties
census.download <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area", year=c.yr, survey = acs, table = census.tbl) %>%
mutate(NAME = gsub(", WA Metro Area", " MSA", NAME)) %>%
filter(GEOID %in% psrc.msa, variable %in% keep.vars)
# Variable names changed in 2017 so adjust pre-2017 variable names to match so the labels align correctly
if (c.yr <2017) {
census.download <- census.download %>%
mutate(variable = gsub("C02","C03",variable))
}
# Calculate Total population by geography
totals <- census.download %>% filter(variable==total.var) %>% select(NAME,estimate) %>% rename(total=estimate)
# Add totals and calculate share of total by race
temp <- left_join(census.download, totals, by=c("NAME")) %>%
mutate(share=estimate/total) %>%
select(-total)
# Combine with other data years
if (is.null(acs.income)) {acs.income <- temp} else {acs.income <- bind_rows(list(acs.income, temp))}
rm(census.download,temp,totals)
}
# Add labels from the latest census data year downloaded and clean up labels
acs.income <- left_join(acs.income,variable.labels,by=c("variable")) %>%
mutate(year=c.yr, concept="Median Income by Race") %>%
mutate(race=label) %>%
mutate(race = str_extract(race, "(?<=!!)[^!!]*$"), race = gsub("Households","Total",race), label="Median Income") %>%
rename(category=label)
# Education ---------------------------------------------------------
acs.education <- NULL
census.tbl <- edu.tbl
keep.vars <- paste0(edu.tbl,"_C01_",edu.vars)
# Download the list of variables from the latest data year
variable.labels <- load_variables(max(yrs), paste0(acs,"/subject"), cache = TRUE) %>% rename(variable = name)
for (c.yr in yrs) {
# Download County Level Data
census.download <- get_acs(geography = "county", state="53", year=c.yr, survey = acs, table = census.tbl) %>%
mutate(NAME = gsub(", Washington", "", NAME)) %>%
filter(GEOID %in% psrc.county, variable %in% keep.vars)
# Get a region total from the county data
temp <- census.download %>%
select(variable, estimate, moe) %>%
group_by(variable) %>%
summarize(sumest = sum(estimate), summoe = moe_sum(moe, estimate)) %>%
rename(estimate=sumest, moe=summoe) %>%
mutate(GEOID="53033035053061", NAME="Region", year=c.yr)
# Add Labels
temp <- left_join(temp,variable.labels,by=c("variable"))
# Calculate Total population by geography
totals <- temp %>% filter(!grepl("Bachelor's", label),!grepl("High school", label)) %>% select(NAME,estimate) %>% rename(total=estimate)
# Add totals and calculate share of total by race
temp <- left_join(temp, totals, by=c("NAME")) %>%
mutate(share=estimate/total) %>%
select(-total)
# Combine with other data years
if (is.null(acs.population)) {acs.population <- temp} else {acs.population <- bind_rows(list(acs.population, temp))}
rm(census.download,temp,totals)
}
# Add labels from the latest census data year downloaded and clean up labels
acs.population <- left_join(acs.population,variable.labels,by=c("variable")) %>%
mutate(concept="Population by Race", label = str_extract(label, "(?<=!!)[^!!]*$"), label = gsub(" alone", "", label), label = gsub(":", "", label)) %>%
rename(race=label) %>%
mutate(category="Population") %>%
mutate(race=gsub("White","White, not Hispanic or Latino",race))
|
6f208d4aba17a090405c66c02b05c936b2896949
|
a330829b1c70080a8a3b221b093e5dccfa71af90
|
/SDM_Project_HillsboroughCountyHomes.R
|
11ea2c492bdceead2d0fa8260e66944449127454
|
[] |
no_license
|
erichmccartney/SDM_Project_HillsboroughCountyRealestate
|
917c8954789877a7c1259b292ddec48cb4b4098a
|
3cbd36ac50e3c3fe86034255c50c2933eb060b84
|
refs/heads/main
| 2023-08-28T17:40:52.587923
| 2021-10-24T13:31:42
| 2021-10-24T13:31:42
| 363,657,217
| 0
| 0
| null | 2021-05-10T00:43:51
| 2021-05-02T13:26:38
|
R
|
UTF-8
|
R
| false
| false
| 9,114
|
r
|
SDM_Project_HillsboroughCountyHomes.R
|
#' SDM Project: Hillsborough County Real Estate
install.packages("rio")
install.packages("moments")
install.packages("car")
install.packages("readxl")
install.packages("corrplot")
install.packages("reshape2")
rm(list=ls())
library(rio)
library(readxl)
library(lattice)
library(dplyr)
library(ggplot2)
library(corrplot)
library(readxl)
library(openxlsx)
library(lubridate)
library(reshape2)
library(stargazer)
library(lme4)
library(survival)
library(PerformanceAnalytics)
setwd("~/GitHub/SDM_Project_HillsboroughCountyRealestate")
df <- read.csv("HillsboroughCountyData.csv")
str(df)
View(df)
#Feature engineering
df$BuildingAge = 2021 - df$YearBuilt
df$PricePerHeatedArea = df$JustValue/df$TotalHeatedAreaSqFt
df$HeatedAreaProportion = df$TotalHeatedAreaSqFt/(df$Acreage*43560)
df$LastSaleDate = as.Date(df$LastSaleDate, format = "%m/%d/%y" )
df$LengthOwnershipProportion = df$YearsSinceTurnover/df$BuildingAge
#' Data visualizations
hist(df$YearsSinceTurnover)
hist(log(df$YearsSinceTurnover)) # Misleading histogram: has different varieties
#DensityPlot
densityplot(~YearsSinceTurnover | Avg_GradePoint2019, data=df)
#Linear Regression
#Summary:
linearMod <- lm(YearsSinceTurnover ~ Avg_GradePoint2019 + Avg_GradePoint2018 +
Avg_GradePoint2017, data=df)
print(linearMod)
#' OLS model (pooled)
ols1 <- lm(YearsSinceTurnover ~ PropertyType*Neighborhood, data=df)
summary(ols1)
ols2 <- lm(YearsSinceTurnover ~ Avg_GradePoint2019 + Avg_GradePoint2018 +
Avg_GradePoint2017*LastSalePrice, data=df)
summary(ols2)
# Fixed Effects Model
fe1 <- lm(YearsSinceTurnover ~ Avg_GradePoint2019*LastSalePrice +
Avg_GradePoint2019*SchoolZipCodeGroup + as.factor(Neighborhood),
data=df)
summary(fe1)
confint(fe2)
fe2 <- lm(LastSalePrice ~ PropertyType*Neighborhood + SiteCity, data=df)
summary(fe2)
confint(fe2)
options(max.print = 60000)
stargazer(linearMod, ols1, ols2, fe1, fe2, type="text", single.row=TRUE)
# Random Effects Model
re <- lmer(LastSalePrice ~ Neighborhood*PropertyType + (1 | SiteZip),
data=df, REML=FALSE)
summary(re)
confint(re)
AIC(re)
fixef(re) # Magnitude of fixed effects
ranef(re) # Magnitude of random effects
coef(re) # Magnitude of total effects
ggplot(df, aes(x=LastSalePrice, y = PropertyType)) +
geom_bar(stat = "Identity", width = 0.10)
ggplot(df, aes(x= LastSalePrice, y = PropertyType, fill = Neighborhood)) +
geom_bar(stat = "Identity")
stargazer(ols1, ols2, fe1, fe2, re, type="text", single.row=TRUE)
AIC(ols1, ols2, fe1, fe2, re)
#Test for Assumptions
hist(ols1$res)
ols1$fit
hist(ols2$res)
ols2$fit
#ResidualPlot
plot(fe1$res ~ fe1$fit)
plot(fe2$res ~ fe2$fit)
#QQPlot
qqnorm(fe1$res)
qqline(fe1$res, col="red")
qqnorm(fe2$res)
qqline(fe2$res, col="red")
#Shapiro-Wilk's Test inconclusive sample size must be between 3 and 5000
shapiro.test(fe1$res)
shapiro.test(fe2$res)
# Group by neighborhood (unit of analysis)
neighborhood_df = df %>%
group_by(Neighborhood) %>%
summarize(stories_avg = mean(TotalStories, na.rm = TRUE),
bedrooms_avg = mean(TotalBedrooms, na.rm = TRUE),
bathrooms_avg = mean(TotalBathrooms, na.rm = TRUE),
building_age_avg = mean(BuildingAge, na.rm = TRUE),
price_avg = mean(PricePerHeatedArea, na.rm = TRUE),
heated_area_proportion_avg = mean(HeatedAreaProportion, na.rm = TRUE),
grade_point_2019 = mean(Avg_GradePoint2019, na.rm = TRUE),
minority_percentage = mean(Avg_Percentage.of.Minority.Students, na.rm=TRUE),
economically_disadvantaged_percentage =
mean(Avg_Percentage.of.Economically.Disadvanteged.Students, na.rm = TRUE),
length_of_ownership = mean(YearsSinceTurnover, na.rm=TRUE)
)
# Checking missing values
summary(neighborhood_df)
summary(df$TotalHeatedAreaSqFt)
df$TotalHeatedAreaSqFt == 0
# We discovered that 361 observations did not have values for TotalHeatedAreaSqFt
# and 1832 did not have values for Acreage and were dropped from the analysis
# It represents 5.6823% of our dataset
# It caused us to drop 2 neighborhoods
nrow(filter(df, Acreage == 0 | TotalHeatedAreaSqFt == 0))
nrow(filter(df, Acreage == 0 | TotalHeatedAreaSqFt == 0))/38945
df2 = filter(df, TotalHeatedAreaSqFt != 0)
df2 = filter(df2, Acreage != 0)
neighborhood_df2 = df2 %>%
group_by(Neighborhood) %>%
summarize(stories_avg = mean(TotalStories, na.rm = TRUE),
bedrooms_avg = mean(TotalBedrooms, na.rm = TRUE),
bathrooms_avg = mean(TotalBathrooms, na.rm = TRUE),
building_age_avg = mean(BuildingAge, na.rm = TRUE),
price_avg = mean(PricePerHeatedArea, na.rm = TRUE),
heated_area_proportion_avg = mean(HeatedAreaProportion, na.rm = TRUE),
grade_point_2019 = mean(Avg_GradePoint2019, na.rm = TRUE),
minority_percentage = mean(Avg_Percentage.of.Minority.Students, na.rm=TRUE),
economically_disadvantaged_percentage = mean(Avg_Percentage.of.Economically.Disadvanteged.Students, na.rm = TRUE),
length_of_ownership = mean(YearsSinceTurnover, na.rm=TRUE),
length_of_ownership_proportion = mean(LengthOwnershipProportion, na.rm=TRUE)
)
summary(neighborhood_df2)
# Create Visualizations
attach(neighborhood_df2)
par(mfrow=c(3,4))
hist(stories_avg)
hist(bedrooms_avg)
hist(bathrooms_avg)
hist(building_age_avg)
hist(price_avg)
hist(grade_point_2019)
hist(heated_area_proportion_avg)
hist(minority_percentage)
hist(economically_disadvantaged_percentage)
hist(length_of_ownership)
hist(length_of_ownership_proportion)
# Check for extremely high correlations
par(mfrow=c(1,1))
cor = cor(neighborhood_df2[,c(-1)])
cor
corrplot(cor, method = "circle")
# we found that the percentage of minority and economically disadvantage
# percentage are highly and negatively correlated to grade point, and so it will
# be dropped from our analysis to avoid multicollinearity
colnames(neighborhood_df2)
# Statistical Analysis
model1 = lm(length_of_ownership~stories_avg + bedrooms_avg + bathrooms_avg
+ price_avg + heated_area_proportion_avg + grade_point_2019
+ building_age_avg, neighborhood_df2)
model2 = lm(length_of_ownership~stories_avg + bedrooms_avg + bathrooms_avg
+ price_avg + heated_area_proportion_avg + grade_point_2019
+ length_of_ownership_proportion, neighborhood_df2)
model3 = lm(length_of_ownership~stories_avg + bedrooms_avg + bathrooms_avg
+ log(price_avg) + heated_area_proportion_avg + grade_point_2019
+ building_age_avg, neighborhood_df2)
summary(model1)
summary(model2)
summary(model3)
stargazer(model1, model2, model3, type="text")
#Linearity
par(mfrow=c(1,1))
par(mar=c(5.1,4.1,4.1,2.1))
plot(neighborhood_df2$length_of_ownership,model1$fitted.values,
pch=19,main="Length of Ownership Actuals v. Fitted")
abline(0,1,col="red",lwd=3)
#Normality
par(mar=c(5.1,4.1,4.1,2.1))
qqnorm(model1$residuals,pch=19,
main="Length of Ownership Normality Plot")
qqline(model1$residuals,lwd=3,col="red")
#Equality of Variances
par(mar=c(5.1,4.1,4.1,2.1))
plot(neighborhood_df2$length_of_ownership,rstandard(model1),
pch=19,main="Model 1 Residual Plot")
abline(0,0,col="red",lwd=3)
#It looks like we have some heteroskedasticety
model4 = lm(1+log(length_of_ownership)~stories_avg + bedrooms_avg + bathrooms_avg
+ log(price_avg) + heated_area_proportion_avg + grade_point_2019
+ building_age_avg, neighborhood_df2)
#Equality of Variances
par(mar=c(5.1,4.1,4.1,2.1))
plot(neighborhood_df2$length_of_ownership,rstandard(model4),
pch=19,main="Model 4 Residual Plot")
abline(0,0,col="red",lwd=3)
#Identifying high leverage points.
lev=hat(model.matrix(model1))
plot(lev,pch=19,ylim=c(0,.5), main="High leverage points")
abline(3*mean(lev),0,col="red",lwd=3)
neighborhood_df2[lev>(3*mean(lev)),] ##identifying which data points are 3 times higher than the mean leverage
neighborhood_df2[lev>(3*mean(lev)),1]
outliers = which(lev>(3*mean(lev)),1)
df_no_outliers = neighborhood_df2[-outliers,]
model5 = lm(length_of_ownership~stories_avg + bedrooms_avg + bathrooms_avg
+ price_avg + heated_area_proportion_avg + grade_point_2019
+ building_age_avg, df_no_outliers)
summary(model5)
par(mar=c(5.1,4.1,4.1,2.1))
plot(df_no_outliers$length_of_ownership,rstandard(model5),
pch=19,main="Model 5 Residual Plot")
abline(0,0,col="red",lwd=3)
model6 = lm(1+log(length_of_ownership)~stories_avg + bedrooms_avg + bathrooms_avg
+ price_avg + heated_area_proportion_avg + grade_point_2019
+ building_age_avg, df_no_outliers)
par(mar=c(5.1,4.1,4.1,2.1))
plot(df_no_outliers$length_of_ownership,rstandard(model6),
pch=19,main="Model 6 Residual Plot")
abline(0,0,col="red",lwd=3)
stargazer(model1, model2, model3, model4, model5, model6, type="text")
stargazer(ols2, model1, model2, type="text")
|
6f15eef9258b1c95edcd019951cb83520db8b29b
|
c200977129e98de598e665a37508d150f564d286
|
/get_allele_id_to_query.R
|
3312f56ab4250f818892d5efe663d8da16372766
|
[] |
no_license
|
jenjohnson7/CS702
|
ba9b6cf029462a47168b297ed41001e42dec2138
|
78647a03f3237851b8985212f63631849fdbe385
|
refs/heads/master
| 2020-03-11T15:04:14.432669
| 2018-05-21T13:12:39
| 2018-05-21T13:12:39
| 123,441,033
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 475
|
r
|
get_allele_id_to_query.R
|
library(tidyverse)
# get clinvar data
AD_clinvar_data <- read.delim('data/AD_clinvar_result.txt', header = TRUE)
AR_clinvar_data <- read.delim('data/AR_clinvar_result.txt', header = TRUE)
XLR_clinvar_data <- read.delim('data/XLR_clinvar_result.txt', header = TRUE)
#rowbind into total_data
total_data <- rbind(AD_clinvar_data, AR_clinvar_data, XLR_clinvar_data)
# list of allele_ids
write.table(total_data$AlleleID.s., "data/allele_ids_to_query.txt", row.names = FALSE)
|
780ac1d9b7570ef64a887a3b797549c6f69457f5
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/recluster/examples/recluster.group.col.Rd.R
|
c3d818b5873635061e2aad2bcab62bc018f74210
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 417
|
r
|
recluster.group.col.Rd.R
|
library(recluster)
### Name: recluster.group.col
### Title: Compute mean coordinate values and RGB colours.
### Aliases: recluster.group.col
### Keywords: cluster
### ** Examples
data(datamod)
sordiss<- recluster.dist(datamod,dist="sorensen")
points<-metaMDS(sordiss, center=TRUE)$points
col<-recluster.col(points)
group<-c(1,2,3,3,3,1,2,1,2)
ncol<-recluster.group.col(col,group)
recluster.plot.col(ncol$aggr)
|
3cd4223bd99d8fed8dc979d0288900d1a8ee733f
|
9822e0e83895f17a69d529c2c5a54097e2260af2
|
/rf_AirBNB.R
|
45018aaaec3ba36e5108e153499844cee67b0609
|
[] |
no_license
|
tboats/kaggle_AirBNB
|
e308a5938af514ea8793d48a71f27e31a298bb63
|
332378708251c136742d7e82f643287e3b80792b
|
refs/heads/master
| 2021-01-10T02:52:35.610412
| 2016-03-06T07:14:29
| 2016-03-06T07:14:29
| 49,351,586
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,433
|
r
|
rf_AirBNB.R
|
######################################################################
# Goal of script: create a random forest model on AirBNB data to predict country of visit
#
#
# date of start: 01/09/2016
#
#
######################################################################
## load libraries
library(plyr)
library(dplyr)
library(ggplot2)
library(reshape2)
library(caret)
library(lubridate)
library(randomForest)
#library(ROCR)
## load input from "exploration_AirBNB.R"
filename <- "dfTrain_sessionStats1.csv"
dfSessionStats <- read.csv(filename)
statCols <- c("sum", "mean", "sd", "max", "min")
dfSessionStats[dfSessionStats$N == 1, statCols] <- 0
dfSessionStats[dfSessionStats$N == 2, "sd"] <- 0
## load other data
dfCountries <- read.csv("../data/countries.csv")
## merge data sets
df <- full_join(x = dfSessionStats, y = dfCountries) #, by = c("country_destination"="country_destination")
# clean up the data formats
df$date_account_created <- ymd(df$date_account_created)
df$date_first_booking <- ymd(df$date_first_booking)
df$country_destination <- as.factor(df$country_destination)
df <- mutate(df, travel = as.factor(1.*(country_destination != "NDF")))
df <- mutate(df, age = as.numeric(age))
df$age[is.na(df$age)] <- 0
df$timestamp_first_active <- ymd_hms(df$timestamp_first_active)
colsExclude <- c("X", "id", "date_first_booking", "lat_destination", "lng_destination",
"distance_km", "destination_km2", "destination_language",
"language_levenshtein_distance", "travel") #,"timestamp_first_active"
goodCols <- !(names(df) %in% colsExclude)
#naCols <- names(which(colSums(is.na(df))>0))
#notNACols <- !unname(colSums(is.na(df))>0)
y1Cols <- (names(df) %in% "country_destination")
#travelCol <- (names(df) %in% "travel")
#####################################################################
## split into training, validation, and test sets
set.seed(1245)
trainFraction <- 0.7
trainIndex <- createDataPartition(df$country_destination, p = trainFraction, list = FALSE)
dfTrain1 <- df[trainIndex,goodCols] # & !y1Cols] & notNACols
dfTest1 <- df[-trainIndex,] # & !y1Cols] #
idTrain <- df[trainIndex, "id"]
idTest <- df[-trainIndex, "id"]
travel1Col <- (names(dfTrain1) %in% "travel")
y1Col <- (names(dfTrain1) %in% "country_destination")
####################################################################
## train random forest on travel/no travel
# tr <- na.omit(dfTrain)
# find best value of "mtry"
# Start the clock!
ptm <- proc.time()
# sample the full data set to check how long it will run
nsamp <- dim(dfTrain1)[1]#200 #10000 #
samp <- sample(1:dim(dfTrain1)[1], nsamp)
dfTrain1_s <- dfTrain1[samp,!travel1Col]
dfTrain1_s$country_destination <- as.factor(as.character(dfTrain1_s$country_destination))
y1Col_s <- (names(dfTrain1_s) %in% "country_destination")
#
computeMtry <- FALSE
if (computeMtry == TRUE){
bestMtry <- tuneRF(dfTrain1_s[,!y1Col_s], dfTrain1_s[,y1Col_s],
mtryStart = 5, ntreeTry = 100, stepFactor = 1.5, improve = 0.10)
m1 <- bestMtry[which(bestMtry[,"OOBError"] == min(bestMtry[,"OOBError"])), "mtry"]
} else {
m1 <- 4
}
ntree <- 100
#rf <- randomForest(dfTrain1_s[,!y1Col_s], dfTrain1_s[,y1Col_s], data = dfTrain1_s,
# use classwt to weight the classes
freq <- table(dfTrain1_s$country_destination)
wt <- unname(1/freq)
# impute missing ages
# cs <- dfTrain1_s[,!(names(dfTrain1_s) %in% c("date_account_created"))]
# dfTrain1_s.imputed <- rfImpute(country_destination ~ ., data = cs)
# dfTrain1_s.imputed <- mutate(dfTrain1_s.imputed, date_account_created = dfTrain1_s$date_account_created)
# dfTrain1_s.imputed <- dfTrain1_s
# dfTrain1_s.imputed$age[is.na(dfTrain1_s.imputed$age)] <- 0
# train random forest
rf <- randomForest(country_destination ~ ., data = dfTrain1_s,
mtry=m1,
classwt=wt,
ntree=ntree,
keep.forest = TRUE,
importance = TRUE) #, test = dfTest1
# plot the most important variables
varImpPlot(rf)
# how well does classifier perform on training set?
p1tr <- predict(rf, dfTrain1_s)
table(p1tr, dfTrain1_s$country_destination)
# how well does classifier perform on test set?
# impute age
# cs <- dfTest1[,!(names(dfTest1) %in% c("date_account_created"))]
# dfTest1.imputed <- rfImpute(country_destination ~ ., data = cs)
# dfTest1.imputed <- mutate(dfTest1.imputed, date_account_created = dfTest1$date_account_created)
p1prob <- predict(rf, dfTest1, type="prob")
p1 <- predict(rf, dfTest1)
table(p1, dfTest1$country_destination)
# Stop the clock
proc.time() - ptm
## create data frame to save predictions
p1df <- data.frame(country_destination = dfTest1$country_destination)
p1df <- cbind(p1df, data.frame(p1prob))
# head(p1df)
## generate top 5 predictions
p1prob_5 <- t(apply(p1prob, 1, predictionSort))
df_p1prob_5 <- data.frame(p1prob_5)
names(df_p1prob_5) <- c("C1", "C2", "C3", "C4", "C5") #names of top 5 countries
df1_p1prob_5_ans <- mutate(df_p1prob_5, country_destination = dfTest1$country_destination)
#evals <- t(apply(df1_p1prob_5[,1:5], 1, DCG, df1_p1prob_5[,6]))
evals <- (apply(df1_p1prob_5_ans, 1, DCG))
print(paste("mean DCG: ", mean(evals)))
######################################################################
## format for text output
dfOut <- mutate(df_p1prob_5, id=idTest)
dfOutm <- melt(dfOut, id = c("id"))
dfOutm <- with(dfOutm, dfOutm[order(id, variable),])
saveCols <- c("id", "value")
dfOutm <- dfOutm[,names(dfOutm) %in% saveCols]
source('E:/Dropbox/R/general/timestamp.R')
ts <- timestamp()
outputName <- paste("output_", ts, '.txt', sep="")
fileConn <- file(outputName)
# writeLines(c("id,country\n"), fileConn)
cat(c("id,country"), file=fileConn,sep="\n")
nLines <- dim(dfOutm)[1]
for (i in 1:100){
line <- paste(dfOutm[i,"id"], dfOutm[i,"value"],sep=",")
#writeLines(line, fileConn)
cat(line, file=outputName, sep="\n", append=TRUE)
if (i %% 1000 == 0){
print(paste("line ", i, sep=""))
}
}
close(fileConn)
# ####################################################################
# ## train random forest
# tr <- na.omit(dfTrain)
# tr$country_destination <- as.factor(as.character(tr$country_destination))
# rf <- randomForest(country_destination ~ ., data = tr,
# mtry=2,
# ntree=1000,
# keep.forest = TRUE,
# importance = TRUE,
# test = dfTest)
|
8550fe62ced39cac1c4422a6c52187b8dccd5414
|
befe41bcf631337c50bb65c50d9f4c6ab548e0a5
|
/server.R
|
0c545b04c73d7c35bf040a889693f479dfae0c91
|
[] |
no_license
|
Tutuchan/parisdata
|
ce2003ca8e1cb3740887c8b826bb44644efec787
|
1c9cbfd13a6e1de3f5c02fa5927598ba526ce01e
|
refs/heads/master
| 2021-01-10T15:21:12.491490
| 2015-09-30T13:04:40
| 2015-09-30T13:04:40
| 43,299,477
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,507
|
r
|
server.R
|
library(shinydashboard)
library(shiny)
source("global.R")
shinyServer(function(input, output, session) {
output$textTest <- renderPrint({
# spPolygons@data$insee[input$mainMap_shape_click$id]-100
})
output$mainMap <- renderLeaflet({
pal = colorNumeric("RdBu", dfNbAccs$n)
leaflet(spPolygons) %>%
addTiles() %>%
addProviderTiles("Acetate.terrain") %>%
addPolygons(stroke = TRUE, weight = 2, color = "black",
fillColor = "blue", smoothFactor = 0.2, fillOpacity = 0.6, popup = ~arrs, layerId = 1:20)
})
dataClick <- reactive({
validate(
need(!is.null(input$mainMap_shape_click), "Choisissez un arrondissement.")
)
arr = spPolygons@data$insee[input$mainMap_shape_click$id]-100
dfDataAccidents %>% filter(cp == arr)
})
output$plotNbAccMois <- renderPlot({
dfPlot <- dataClick() %>%
select(date, starts_with("vehic")) %>%
mutate(date = as.Date(date),
mois = format(date, "%m"),
annee = format(date, "%Y")) %>%
count(annee, mois) %>%
mutate(date = as.Date(paste(annee, mois, "01", sep = "/")))
ggplot(dfPlot, aes(date, n)) +
geom_bar(stat = "identity", fill = RColorBrewer::brewer.pal(4, "Paired")[2]) +
theme_linedraw() +
xlab("") + ylab("") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = date_breaks("month"),
labels = date_format("%m/%Y"))
})
})
|
d6431db5315b33693e9f1be406abcb0b880b26d4
|
e9bec00a92cfb7b91186d458288784b4464a07d3
|
/test_dplyr.R
|
70665c5da4df6cd88b7223d674e54c2b7c48e60f
|
[
"MIT"
] |
permissive
|
15210280436/thomas
|
4d6b296f8381f29d8a8df35a1b0d8d31e28cca4a
|
7010af4e5421decc965fb379ca810340a776c624
|
refs/heads/master
| 2020-04-22T20:46:58.318338
| 2019-02-15T06:08:27
| 2019-02-15T06:08:27
| 170,652,009
| 0
| 0
| null | 2019-02-14T08:05:13
| 2019-02-14T08:05:13
| null |
UTF-8
|
R
| false
| false
| 8,111
|
r
|
test_dplyr.R
|
library(tidyverse)
library(nycflights13)
library(ggplot2)
library(DBI)
library(modelr) #
library(charlatan)
library(stringr)
con <- DBI::dbConnect(RPostgreSQL::PostgreSQL(),
host = "127.0.0.1",
dbname = "octopus_susuan",
user = "wangjf",
password = "d4aSXN2P",
port = 5000)
student <- tbl(con,"o_student")
student_1 <- head(student,n=100) %>%
collect()
student_1
dbDisconnect(con)
mpg
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) +
geom_point(
mapping = aes(x = displ, y = hwy), color = "blue")
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy),color = "orange") +
facet_wrap(~ class,nrow = 3)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy),color = "orange") +
facet_grid(drv ~ cyl)
mpg %>%
distinct(cyl)
flights %>%
filter(month==1 & day==1,is.na(dep_time)) #is.na()选择有缺失值的
flights %>%
arrange(desc(is.na(dep_time)))
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
flights %>%
rename(year_1=year)
flights %>%
mutate(strptime(dep_time,'%H:%M:%S %Y')) #表示两个字段之间所有列
flights %>%
transmute(dep_time,hour=dep_time %/% 60,minit=dep_time %%60) #转变时间
flights %>%
select(-(year:dep_delay)) #表示不在两个字段之间所有列
flights_sml <- flights %>%
select(year:day,ends_with("delay"),distance,air_time) #ends_with结尾包含delay的lie
flights_sml %>%
mutate(gain=air_time-dep_delay,
speed=distance/air_time*60)
flights_sml %>%
transmute(gain=air_time-dep_delay, #transmute只保留增加的变量
speed=distance/air_time*60)
flights %>%
select(origin,tailnum,dep_time) %>%
group_by(origin,tailnum) %>%
arrange(desc(dep_time))
flights %>%
group_by(year,month,day) %>%
summarise(delay=mean(dep_delay,na.rm = TRUE)) #na.rm 去掉空值
student_1 %>%
filter(grade<8) %>%
ggplot(mapping=aes(x=grade,color=grade))+
geom_bar()
# geom_point and geom_smooth 配合使用，可以看到散点以及趋势
student_1 %>%
filter(grade<8) %>%
ggplot(mapping=aes(x=grade,y=province_id))+
geom_boxplot()
seq(1,10)
by_dest <- flights %>%
group_by(dest)
delay <- by_dest %>%
summarise(count=n(),dist=mean(distance,na.rm=TRUE),delay=mean(arr_delay,na.rm=TRUE))
delay <- filter(delay,count>20,dest!="HNL")
delay %>%
filter(dist<750) %>%
ggplot(mapping = aes(x=dist,y=delay))+
geom_point(aes(size=count),alpha=1/3)+
geom_smooth(se=FALSE)
flights %>%
group_by(tailnum) %>%
summarise(delay=mean(arr_delay,na.rm=TRUE),n=n()) %>%
ggplot(mapping=aes(x=n,y=delay))+
geom_point(alpha=1/10)
diamonds %>%
filter(carat<3) %>%
ggplot(mapping = aes(x=carat,color=cut))+
geom_freqpoly(binwidth=0.1)
diamonds %>% #直方图+密度图
filter(carat<3) %>%
ggplot(mapping = aes(x=carat,y=..density..))+
geom_histogram(binwidth=0.1)+
geom_density(alpha=.7)
flights %>%
select(origin,tailnum,dep_time) %>%
group_by(origin,tailnum) %>%
arrange(desc(dep_time))
flights %>%
group_by(year,month,day) %>%
summarise(delay=mean(dep_delay,na.rm = TRUE)) #na.rm 去掉空值
diamonds1 <- diamonds %>%
mutate(y_1=ifelse(y<3 | y>20,NA,y))
diamonds1 %>%
ggplot(mapping = aes(x=x,y=y_1))+
geom_point()
#看一下取消航班和未取消航班延误时间的样本量
flights %>%
mutate(
cancelled=is.na(dep_time),# is.na =true表示dep_time为空的取消航班标签。
sched_hour=sched_dep_time %/% 100,
sched_min=sched_dep_time %% 100,
sched_dep_time=sched_hour+sched_min/60
) %>%
group_by(cancelled) %>%
summarise(n=n())
#通过图形查看取消航班和未取消航班延误时间的差异，未取消航班远远多于取消航班数量，不能说明两者存在差异
flights %>%
mutate(
cancelled=is.na(dep_time),
sched_hour=sched_dep_time %/% 100,
sched_min=sched_dep_time %% 100,
sched_dep_time=sched_hour+sched_min/60
) %>%
ggplot(mapping = aes(x=sched_dep_time,color=cancelled))+
geom_freqpoly(binwidth=1/4)
#通过密度来看两个样本延误时间差异
flights %>%
mutate(
cancelled=is.na(dep_time),
sched_hour=sched_dep_time %/% 100,
sched_min=sched_dep_time %% 100,
sched_dep_time=sched_hour+sched_min/60
) %>%
ggplot(mapping = aes(x=sched_dep_time,y=..density..,color=cancelled))+
geom_freqpoly(binwidth=1/4)+
geom_density(alpha=.7)
flights %>%
mutate(
cancelled=is.na(dep_time),
sched_hour=sched_dep_time %/% 100,
sched_min=sched_dep_time %% 100,
sched_dep_time=sched_hour+sched_min/60
) %>%
ggplot(mapping = aes(x=origin,y=sched_dep_time))+
geom_boxplot()
diamonds %>%
count(color, cut) %>% #相当于groupby+summarise
ggplot(mapping = aes(x=color,y=cut,fill=n))+
geom_tile()#两个变量组合观测数量
diamonds %>%
ggplot(mapping = aes(x = x, y = y)) +
geom_point() +
coord_cartesian(xlim = c(4, 11), ylim = c(4, 11)) # 首先画散点图后发现x集中在4-11，集中在4-11，所以用coord_cartesian函数来限定一下，显得图形更直观
ggsave("diamonds.pdf") #保存到PDF，write_csv(diamonds, "diamonds.csv") 保存到csv
faithful %>%
ggplot(mapping = aes(x=eruptions,y=waiting))+
geom_point()
readxl::read_xls("trial_class.xls", col_names = FALSE) # col_names = FALSE不要将第一行作为列 标题
x1 <- "El Ni\xf1o was particularly bad this year"
x2 <- "\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd"
parse_character(x1, locale = locale(encoding = "Latin1"))
parse_character(x2, locale = locale(encoding = "Shift-JIS")) #处理字符编码
planes %>%
count(tailnum) %>%
filter(n>1)
#Generate dummy dataset
# set seed
set.seed(1212) #由于是随机data，所以需要seed来记录
# fake data
df <- data_frame(
name = ch_name(30),#ch_name(30) 随机创建30个假名字
country = rep(c("US", "UK", "CN"), 10) %>% sample(), #rep函数，限制随机重复次数
job = sample(ch_job(3), 30, replace = TRUE), #ch_job(3) 创建3个假job，sample随机打乱
spending = rnorm(30, mean = 100, sd = 20), #随机一组平均值为100，标准差为20的30个数据
item = sample(1:3, 30, replace = TRUE) #sample 随机1-3 出现30次
)
glimpse(df) #横向查看数据
# common tools
df %>%
filter(country=="US") %>%
mutate(per_item_spending=spending/item) %>%
group_by(job) %>%
summarise(total_spending=sum(spending),
max_item = max(item),
per_item_mu = mean(per_item_spending)
) %>%
arrange(total_spending)
# use select drop column
df %>%
#select(-country,-job)
#select(-c(country, job))
select(item, spending, name)
df %>% rename(person = name,
amount = spending,
quantity = item)
df %>% select(contains("ing")) # 选择包含ing列
df %>%
select(one_of("name", "item"))
# use function from other package
df %>%
# from stringr package
mutate(first_name = str_extract(name, "^\\w*"),
last_name = str_extract(name, "\\w*$")) %>%
select(contains("name"))
# using if-else
df %>%
mutate(one_item = ifelse(item == 1, "Yes", "No")) %>% # ifelse 加入判断
select(contains("item"))
# using case-when
df %>%
mutate(
spending_cat = case_when(
spending > 100 ~ "above 100",
spending > 50 ~ "above 50",
TRUE ~ "below 50"
)
) %>%
select(contains("spending"))
# filter multiple conditions
df %>%
filter(country == "CN", item != 1, spending >= 100) #多条件筛选
# find spending mean
df %>%
summarise(spending_mean = mean(spending),spending_sd=sd(spending))
# combine group by with filter or mutate
df %>%
group_by(job) %>%
filter(spending < mean(spending)) %>%
mutate(cumsum_spending = cumsum(spending))
k <- 2:10
k %>% map_dbl(sqrt)
# map() # 返回一个列表（list）
# map_lgl() # 返回一个逻辑型向量
# map_int() # 返回一个整数型向量
# map_dbl() # 返回双精度数值向量
# map_chr() # 返回字符串向量
|
c242f43d75b131c2326bf536cc327727899fd068
|
0a906cf8b1b7da2aea87de958e3662870df49727
|
/ggforce/inst/testfiles/enclose_points/libFuzzer_enclose_points/enclose_points_valgrind_files/1609955978-test.R
|
f5b798eae411a36083c7786640d198468ec8607a
|
[] |
no_license
|
akhikolla/updated-only-Issues
|
a85c887f0e1aae8a8dc358717d55b21678d04660
|
7d74489dfc7ddfec3955ae7891f15e920cad2e0c
|
refs/heads/master
| 2023-04-13T08:22:15.699449
| 2021-04-21T16:25:35
| 2021-04-21T16:25:35
| 360,232,775
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,511
|
r
|
1609955978-test.R
|
testlist <- list(id = c(60821L, -8748155L, 590633780L, 2051080192L, 0L, -8716289L, -16776961L, -1L, -1L, -13534598L, 2054881025L, 0L, 0L, 0L, 0L, 255L, 2054815744L, 0L), x = c(9.70418706716128e-101, 9.70418706716128e-101, -3.57143978277452e+250, -3.57143978277452e+250, -3.57143978277452e+250, -3.57143978277452e+250, -3.57143978277452e+250, 7.89897283195045e-317, 0, -8.73989987746428e+245, -3.57143978277452e+250, -3.57143978277452e+250, -3.57143978277452e+250, 3.60189371937265e-275, -3.57143978277452e+250, Inf, -3.57143978277452e+250, 1.20057951939423e-321, -3.57077350460498e+250, -3.57077349397652e+250, 3.24586890601023e-298, 3.30036915594569e-296, 9.6134979878195e+281, -1.16450119635426e+70, 1.01522932745225e-314, 2.5990303916246e-312, -5.82900159111767e+303, -8.77779432448941e+304, -5.48612657193497e+303, 2.59894765607829e-312, 2.40273209939742e-306, 2.1008587222514e-312, 7.11756791544715e-304, -8.50349230663022e+304, 1.01522935857838e-314, 4.17201344146859e-309, 7.56414503782948e-320, 9.61276248427429e+281, 6.02760087926321e-322, 0, -1.07927704458837e+304, 7.11756792194462e-304, 8.07404892631684e-315, -1.1031304526204e+217, NaN, 6.05127750601865e-307, 4.66602416025939e-299, NaN, NaN, 9.50322928411057e-314, 7.06416447240789e-304, -5.48612406882363e+303, 8.25679295903193e-317, 1.25986739689518e-321, -5.48612541614556e+303, 3.61247587838313e-67, 5.42745243716277e-315, 0, 0, 0), y = 2.01158338396807e+131)
result <- do.call(ggforce:::enclose_points,testlist)
str(result)
|
894bec73cae39486bdb2fb9689e5106bdd1a15b0
|
e3fd2e053b75918b8d39403dae93d735dbe47381
|
/setupcode.R
|
79154c4a1987a2c815c470aae7f5dd0cd0ed8f18
|
[] |
no_license
|
jeagleso/website
|
00bfbb85f4218a7b5d22f682d4e15f5c50498617
|
51b35fbfa09fa51a57ab41da48c62746d17e65eb
|
refs/heads/main
| 2023-04-20T14:17:20.126198
| 2021-05-01T16:30:22
| 2021-05-01T16:30:22
| 353,528,605
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 512
|
r
|
setupcode.R
|
# install packages
# install.packages(c("distill", "postcards", "fontawesome"))
# load packages
library(distill)
library(postcards)
library(fontawesome)
# create website files
distill::create_website(dir = ".",
title = "Jenna Eagleson",
gh_pages = TRUE)
# create postcard for homepage
distill::create_article(file = "postcard",
template = "jolla",
package = "postcards")
# create a theme
distill::create_theme()
|
29fc792577d6bfa728af923109b38a4a1ef0aaa3
|
7f72ac13d08fa64bfd8ac00f44784fef6060fec3
|
/RGtk2/man/gtkPaperSizeGetHeight.Rd
|
cb8f0a47e6a3deaa73801a5bff8beefde8379478
|
[] |
no_license
|
lawremi/RGtk2
|
d2412ccedf2d2bc12888618b42486f7e9cceee43
|
eb315232f75c3bed73bae9584510018293ba6b83
|
refs/heads/master
| 2023-03-05T01:13:14.484107
| 2023-02-25T15:19:06
| 2023-02-25T15:20:41
| 2,554,865
| 14
| 9
| null | 2023-02-06T21:28:56
| 2011-10-11T11:50:22
|
R
|
UTF-8
|
R
| false
| false
| 479
|
rd
|
gtkPaperSizeGetHeight.Rd
|
\alias{gtkPaperSizeGetHeight}
\name{gtkPaperSizeGetHeight}
\title{gtkPaperSizeGetHeight}
\description{Gets the paper height of the \code{\link{GtkPaperSize}}, in
units of \code{unit}.}
\usage{gtkPaperSizeGetHeight(object, unit)}
\arguments{
\item{\verb{object}}{a \code{\link{GtkPaperSize}} object}
\item{\verb{unit}}{the unit for the return value}
}
\details{Since 2.10}
\value{[numeric] the paper height}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
7a2f8ba3f96550e27ef3c12e8230c90b554dac9b
|
6cd15fd0e072741b5db8284ca20bf6534e495a20
|
/man/mlpca_b.Rd
|
0c39e54ccb3fe3f32450e26071ab12534903e55b
|
[
"MIT"
] |
permissive
|
renands/RMLPCA
|
fffbd18c502e2e3ccfafaa4be677159877cb831b
|
039d34002fe4b98688869184e5139a3b842bfa00
|
refs/heads/master
| 2023-05-09T07:34:03.769415
| 2021-05-31T19:22:13
| 2021-05-31T19:22:13
| 273,766,066
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,871
|
rd
|
mlpca_b.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mlpca_b.R
\name{mlpca_b}
\alias{mlpca_b}
\title{Maximum likelihood principal component analysis for mode B error conditions}
\usage{
mlpca_b(X, Xsd, p)
}
\arguments{
\item{X}{MxN matrix of measurements.}
\item{Xsd}{MxN matrix of measurements error standard deviations.}
\item{p}{Rank of the model's subspace, p must be than the minimum of M and N.}
}
\value{
The parameters returned are the results of SVD on the estimated
subspace. The quantity Ssq represents the sum of squares of weighted
residuals. All the results are nested in a list format.
}
\description{
Performs maximum likelihood principal components analysis for
mode B error conditions (independent errors, homoscedastic within a column).
Equivalent to perfoming PCA on data scaled by the error SD, but results are
rescaled to the original space.
}
\details{
The returned parameters, U, S and V, are analogs to the
truncated SVD solution, but have somewhat different properties since they
represent the MLPCA solution. In particular, the solutions for different
values of p are not necessarily nested (the rank 1 solution may not be in
the space of the rank 2 solution) and the eigenvectors do not necessarily
account for decreasing amounts of variance, since MLPCA is a subspace
modeling technique and not a variance modeling technique.
}
\examples{
library(RMLPCA)
data(data_clean)
data(data_error_b)
data(sds_b)
# data that you will usually have on hands
data_noisy <- data_clean + data_error_b
# run mlpca_b with rank p = 2
results <- RMLPCA::mlpca_b(
X = data_noisy,
Xsd = sds_b,
p = 2
)
# estimated clean dataset
data_cleaned_mlpca <- results$U \%*\% results$S \%*\% t(results$V)
}
\references{
Wentzell, P. D.
"Other topics in soft-modeling: maximum likelihood-based soft-modeling
methods." (2009): 507-558.
}
|
d15317af5c6172533dc950da58f7d0bf13b070eb
|
7bba3974e2c51fa744ae2c8e5fc5cc8d46bde10b
|
/SM7 - R_script_heatmaps.R
|
38d819674be6e935afb5c946ed3839c2a340ba5f
|
[] |
no_license
|
pochotustra/genomics_endophytes
|
07404aaa857f9d33dd3378ee448449fffdbde0c9
|
407c4e260dda6288b5da066a86acb32391e3a809
|
refs/heads/master
| 2020-05-16T21:09:32.077734
| 2015-03-25T13:26:14
| 2015-03-25T13:26:14
| 32,863,947
| 0
| 0
| null | null | null | null |
ISO-8859-1
|
R
| false
| false
| 5,535
|
r
|
SM7 - R_script_heatmaps.R
|
rm(list=ls(all=TRUE))
install.packages("XLConnect") #esto es para instalar un paquete que requiere para abrir el archivo de excel. Solo necesita correrlo la promera vez. Despues ya puede borrar esta linea
install.packages("plyr") # this is a new package you must installe the just the first time you use it.
setwd("C:/Users/lopezfernj/Desktop/heatmap") # esta linea es para decirle a R donde están sus archivos. Con los que va a trabajar. DOnde tiene las tablas con los datos.
require(XLConnect) # esto es para abrir el paquete que instaló. Tiene que crrer esta linea cada vez que abre R.
wb<-loadWorkbook("./434_vs_Erwinia amylovora ATCC 49946.xls")
wb2 = loadWorkbook("./434_vs_Erwinia billingiae Eb661.xls")
wb3 = loadWorkbook("./434_vs_Erwinia pyrifoliae Ep1_96.xls")
wb4 = loadWorkbook("./434_vs_Erwinia sp. Ejp617.xls")
data = readWorksheet(wb, sheet = 1, header = TRUE)[,c(2,3,4,5,7,9)]
new.table<-rbind(data)
head(new.table)
data2 = readWorksheet(wb2, sheet = 1, header = TRUE)[,c(2,3,4,5,7,9)]
new.table2<-rbind(data2)
head(new.table2)
data3 = readWorksheet(wb3, sheet = 1, header = TRUE)[,c(2,3,4,5,7,9)]
new.table3<-rbind(data3)
head(new.table3)
data4 = readWorksheet(wb4, sheet = 1, header = TRUE)[,c(2,3,4,5,7,9)]
new.table4<-rbind(data4)
head(new.table4)
#aquí va a general una lista de todos los posible "Role" de todas las tables
list.Roles<-c()
number_of_data<-c()
for (x in unique(new.table[,1])){
data_list = new.table[new.table$Category==x,4]
data_list2 = new.table2[new.table2$Category==x,4]
data_list3 = new.table3[new.table2$Category==x,4]
data_list4 = new.table4[new.table2$Category==x,4]
count_data<-c()
for (l in c(data_list, data_list2, data_list3, data_list4)){
if (!(l %in% list.Roles)){
list.Roles<-c(list.Roles, l)
count_data[length(count_data)+1]<-1
}
}
number_of_data[x]<-length(count_data)
}
Category_data<-c()
for (x in unique(new.table[,1])){
Category_data<-c(Category_data,rep(x,number_of_data[x]))
}
# aquí va a sacar el dato para cada "Role" en cada tabla por separado, por ejemplo aqui se va a trabajar la tabla 1, la cual yo llamé new.table
results_table1<-matrix(c(0,0), nrow=1,ncol=2)
colnames(results_table1)<-c("SS.active.A", "SS.active.B")
for (x in list.Roles){
if (x %in% new.table[,4]){
element<-which(new.table[,4]==x)[1]
activeResult<-new.table[element,c(5,6)]
}
else{
activeResult<-c("no", "no")
}
results_table1<-rbind(results_table1,activeResult)
}
# las siguientes tres lines cambian "yes" y "no" por "1" y "0"
library(plyr) # For the revalue() function
results_table1$SS.active.A <- revalue(results_table1$SS.active.A, c("yes"=1, "no"=0))
results_table1$SS.active.B <- revalue(results_table1$SS.active.B, c("yes"=1, "no"=0))
# Esto es lo mismo que antes, pero con la table 2. la cual yo llamé new.table2
results_table2<-matrix(c(0,0), nrow=1,ncol=2)
colnames(results_table2)<-c("SS.active.A", "SS.active.B")
for (x in list.Roles){
if (x %in% new.table2[,4]){
element<-which(new.table2[,4]==x)[1]
activeResult<-new.table2[element,c(5,6)]
}
else{
activeResult<-c("no", "no")
}
results_table2<-rbind(results_table2,activeResult)
}
# las siguientes tres lines cambian "yes" y "no" por "1" y "0"
results_table2$SS.active.A <- revalue(results_table2$SS.active.A, c("yes"=1, "no"=0))
results_table2$SS.active.B <- revalue(results_table2$SS.active.B, c("yes"=1, "no"=0))
# aquí va a sacar el dato para cada "Role" en cada tabla por separado, por ejemplo aqui se va a trabajar la tabla 1, la cual yo llamé new.table
results_table3<-matrix(c(0,0), nrow=1,ncol=2)
colnames(results_table3)<-c("SS.active.A", "SS.active.B")
for (x in list.Roles){
if (x %in% new.table[,4]){
element<-which(new.table[,4]==x)[1]
activeResult<-new.table[element,c(5,6)]
}
else{
activeResult<-c("no", "no")
}
results_table3<-rbind(results_table3,activeResult)
}
# las siguientes tres lines cambian "yes" y "no" por "1" y "0"
library(plyr) # For the revalue() function
results_table3$SS.active.A <- revalue(results_table3$SS.active.A, c("yes"=1, "no"=0))
results_table3$SS.active.B <- revalue(results_table3$SS.active.B, c("yes"=1, "no"=0))
# aquí va a sacar el dato para cada "Role" en cada tabla por separado, por ejemplo aqui se va a trabajar la tabla 1, la cual yo llamé new.table
results_table4<-matrix(c(0,0), nrow=1,ncol=2)
colnames(results_table4)<-c("SS.active.A", "SS.active.B")
for (x in list.Roles){
if (x %in% new.table[,4]){
element<-which(new.table[,4]==x)[1]
activeResult<-new.table[element,c(5,6)]
}
else{
activeResult<-c("no", "no")
}
results_table4<-rbind(results_table4,activeResult)
}
# las siguientes tres lines cambian "yes" y "no" por "1" y "0"
library(plyr) # For the revalue() function
results_table4$SS.active.A <- revalue(results_table4$SS.active.A, c("yes"=1, "no"=0))
results_table4$SS.active.B <- revalue(results_table4$SS.active.B, c("yes"=1, "no"=0))
# Esta linea ve a juntar lo de las dos tablas en una sola table
final_table<-cbind(Category=Category_data, Role=list.Roles, results_table1[-1,], results_table2[-1,], results_table3[-1,], results_table4[-1,])
head(final_table)
#esta ultima linea es para guardar la tabla final en un archivo de texto que puede abrir en excel
write.table(final_table, file = "Heatmap_Erwnia.txt", sep = "\t", row.names = F, col.names = T)
|
ae12163e43ceebb63cb864318ddd2750ccdd5767
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/QCAGUI/R/findTh.R
|
593e9ae669f908035de58285a8a8616b44d236c3
|
[] |
no_license
|
ingted/R-Examples
|
825440ce468ce608c4d73e2af4c0a0213b81c0fe
|
d0917dbaf698cb8bc0789db0c3ab07453016eab9
|
refs/heads/master
| 2020-04-14T12:29:22.336088
| 2016-07-21T14:01:14
| 2016-07-21T14:01:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 701
|
r
|
findTh.R
|
`findTh` <-
function(x, n = 1, hclustm = "ward.D2", distm = "canberra", ...) {
if (!isNamespaceLoaded("QCA")) {
requireNamespace("QCA", quietly = TRUE)
}
other.args <- list(...)
###
### ### backwards compatibility
###
if ("groups" %in% names(other.args)) {
n <- other.args$groups - 1
}
###
### ### backwards compatibility
###
x <- sort(x)
cutpoints <- cumsum(rle(cutree(hclust(dist(x, method = distm), method = hclustm), k = n + 1))[[1]])
values <- rep(NA, n)
for (i in seq(length(values))) {
values[i] <- mean(x[seq(cutpoints[i], cutpoints[i] + 1)])
}
return(values)
}
|
20ca84e3a554efc46edf0c31873e9507bc6e281b
|
cd521a577c838a89b9ea0bd0db03ed13588ee5b2
|
/server.R
|
f12b1afc2ceea035855ef3c96b51ffecb077d4c5
|
[] |
no_license
|
chessstats/motion
|
23490eb8644cf79aa1ab4bd7c93a67f5b5576de0
|
419aec8a82ee86291c7097f38baa8a5273457616
|
refs/heads/master
| 2021-01-10T13:35:05.155726
| 2016-02-16T05:26:54
| 2016-02-16T05:26:54
| 51,384,996
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,978
|
r
|
server.R
|
library(shiny)
library(plotly)
library(DT)
dt<-read.table("ratinghist.txt")
lists<-sprintf("%04d",dt$date)
load("players.dat")
names(players)<-paste("X",as.character(names(players)),sep="")
fideids<-names(players)
playernames<-as.character(players)
#cat("ids",fideids[1:5],"names",playernames[1:5])
dt$year=2000+dt$year/100
chartsize<<-800
startyear<<-2001
shinyServer(function(input, output, session){
drawchart<- function(){
output$trendPlot <- renderPlotly({
selp<-input$control
if(length(selp)==0) {
selp=c("X1503014","X2016192")
}
starti<-1
for(j in 1:nrow(dt)){
if(dt$year[j]>=startyear){
starti<-j
break
}
}
dteff<-dt[starti:nrow(dt),]
command<-paste("p<-plot_ly(dteff,type='line',x=year,y=",selp[1],",name='",players[[selp[1]]],"')",sep="")
#print(command)
eval(parse(text=command))
if(length(selp)>1) for(j in 2:length(selp)) {
command=paste("p<-add_trace(p,type='line',x=year,y=",selp[j],",name='",players[[selp[j]]],"')",sep="")
#print(command)
eval(parse(text=command))
}
p<-layout(p,height=round(chartsize/1.5),width=chartsize,xaxis=list(title=''),yaxis=list(title=''),margins=list(pad=10))
p
})
}
observeEvent(input$rplayers,{
#print("rplayers")
session$sendCustomMessage(type="setPlayers",message=list(fideids,playernames,lists))
})
observeEvent(input$rlist,{
#print("rlist")
i<-input$rlist+1
date<-dt$date[i]
dt2<-dt[,2:length(fideids)]
dt2<-dt2[,order(-dt2[i,])]
playersordered=players[colnames(dt2)]
ranklistnames=as.character(playersordered)
ranklistrtgs=as.character(dt2[i,])
ranklist<-data.frame(ranklistnames,ranklistrtgs)
#print("render")
dto<-datatable(ranklist)
output$dataTable<- renderDataTable(dto)
})
observeEvent(input$reqchart,{
cparams<-input$reqchart
setchartsize<-cparams[1]
setstartyear<-cparams[2]
if(setchartsize>0){
chartsize<<-setchartsize
}
if(setstartyear>0){
startyear<<-setstartyear
}
drawchart()
})
observeEvent(input$control,{
drawchart()
if(FALSE){
tablestr="<table id='mytable' class='display'><thead><tr><th>head</th></tr></thead><tbody><tr><td>body</td></tr></tbody>"
session$sendCustomMessage(type="setTable",message=list("tablecont",tablestr,"mytable"))
plotly='[{"x": [1, 2, 3, 4, 5], "y": [1, 2, 4, 8, 16] },{"x": [6, 2, 3, 4, 5], "y": [10, 3, 5, 7, 16] }]'
layout='{"margin": { "t": 0 } }'
session$sendCustomMessage(type="setPlot",message=list("plotlycont",plotly,layout))
svgcontent='<svg width="400" height="180"><rect x="50" y="20" rx="20" ry="20" width="150" height="150" style="fill:red;stroke:black;stroke-width:5;opacity:0.5" /></svg>'
session$sendCustomMessage(type="setInnerHTML",message=list("svgcont",svgcontent))
}
})
})
|
f1e70d60591d8ed3a780be7dc9599c7c2d47e16a
|
6aa307176ec4899e13015d4f20aa9a4fdaef46f7
|
/man/powermcpt.Rd
|
46c112a366b434258d2e626f51a7a369d9690e2e
|
[] |
no_license
|
cran/MCPAN
|
7a56bec761537a8bd3cf9cd66f1dd0b8d90bb680
|
dcfedcf90abc5df5974e72fcf9b84c487f937e98
|
refs/heads/master
| 2020-12-24T15:50:00.850274
| 2018-03-22T11:22:58
| 2018-03-22T11:22:58
| 17,691,867
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,963
|
rd
|
powermcpt.Rd
|
\name{powermcpt}
\alias{powermcpt}
\title{
Testversion. Power calculation for multiple contrast tests (1-way ANOVA model)
}
\description{
Testversion. Calculate the power of a multiple contrast tests of k means in a model with homogeneous Gaussian errors, using the function pmvt(mvtnorm) to calculate multivariate t probabilities. Different options of power defineition are
"global": the overall rejection probability (the probability that at the elementary null is rejected for at least one contrast, irrespective of being under the elementary null or alternative), "anypair": the probability to reject any of the elementary null hypotheses for those contrasts that are under the elmentary alternatives, "allpair": and the probability that all elementary nulls are rejected which are indeed under the elementary nulls. See Sections 'Details' and 'Warnings'!
}
\usage{
powermcpt(mu, n, sd, cmat = NULL, rhs=0, type = "Dunnett",
alternative = c("two.sided", "less", "greater"), alpha = 0.05,
ptype = c("global", "anypair", "allpair"), crit = NULL, ...)
}
\arguments{
\item{mu}{a numeric vector of expected values in the k treatment groups}
\item{n}{a numeric vector of sample sizes in the k treatment groups}
\item{sd}{a single numeric value, specifying the expected standard deviation of the residual error}
\item{cmat}{optional specification of a contrast matrix; if specified, it should have as many columns as there are groups in arguments \code{mu} and \code{n} and it should have at least 2 rows, if specified, argument \code{type} is ignored, if not specified, the contrast is determined by argument \code{type}}
\item{rhs}{numeric vector, specifying the right hand side of the hyptheses to test, defaults to 0, other specifications lead to tests of non-inferiority and superiority.}
\item{type}{ a single character string, naming one of the contrast types available in \code{contrMat(multcomp)}; argument is ignored if \code{cmat} is specified}
\item{alternative}{ a single character string, specifying the direction of the alternative hypothesis, one of \code{"two.sided","less","greater"}. Note that this argument governs how the multivariate t probabilities are evaluated as well as the computation of the critical value if none is specified (i.e. default \code{crit=NULL})}
\item{alpha}{ a single numeric value, familywise type I error to be controlled, is ignored if argument \code{crit} is specified}
\item{ptype}{ a single character string, naming the type of rejection probability to be computed; options are \code{"global"} for the global rejection probability, \code{"anypair"} for the rejection probability considering only those contrasts under the alternative, \code{"global"} for the probability that all elementary alternatives are rejected. }
\item{crit}{ a single numeric value to serve as equicoordinate critical point in the multiple test; if it is not specified, it is computed as a quantile of the multivariate t distribution based on the specifications in arguments \code{n}, \code{cmat} (or \code{type}); note that for alternatives \code{'two.sided'} and \code{'greater'}, \code{crit} should be a single positive value, while for alternative \code{'less'}, \code{crit} should be a single negative value. }
\item{\dots}{ further arguments, which are passed to the functions \code{qmvt} and \code{pmvt}, mainly to control the computation errors, see help \code{GenzBretz(mvtnorm)} for details}
}
\details{
In a homoscedastic Gaussian model with k possibly different means compared by (user-defined) multiple contrast tests, different types of rejection probabilities in the multiple testing problem can be computed.
Based on a central multivariate t distribution with df=sum(n)-k appropriate equicoordinate critical points for the test are computed, different critical points can be specified in \code{crit}
Computing probabilities of non-central multivariate t distributions \code{pmvt(mvtnorm)} one can calculate:
The global rejection probability (\code{power="global"}), i.e. the probability that at least one of the elementary null hypotheses is rejected, irrespective, whether this (or any contrast!) is under the corresponding elementary alternative). As a consequence this probability involves elementary type-II-errors for those contrasts which are under their elementary null hypothesis.
The probability to reject at least one of those elementary null hypotheses which are indeed under their corresponding elementary alternatives (\code{power="anypair"}). Technically, this is achieved by omitting those contrasts under the elementary null and compute the rejection probability for a given criticla value from a multivariate t distribution with reduced dimension. Note that for \code{'two-sided'} alternatives, type III-errors (rejection of the two-sided null in favor of the wrong direction) are included in the power.
The probability to reject all elementary null hypotheses which are indeed under their corresponding elementary alternatives (\code{power="allpair"}). Also here, for 'two-sided' alternatives type III-error contribute to the computed 'allpair power'. Note further that two-sided allpair power is simulated based on multivariate t random numbers.
}
\value{
A list consisting of the following items:
\item{power}{a numeric value the computed power, with estimated computational error as an attribute}
\item{mu}{the input vector of expected values of group means}
\item{n}{the input vector of group sample sizes}
\item{conexp}{a data frame containing the contrast matrix, the expected values of the contrasts given mu (expContrast), the right hand sides of the hypotheses (rhs, as input), the expected values of the test statistics corresponding to the contrasts and rhs, and a column of logical values indicating whether the corresponding contrasts was under the alternative (under HA)}
\item{crit}{a single numeric value, the critical value used for power computation}
\item{alternative}{a single character string, as input}
\item{ptype}{a single character string, as input}
\item{alpha}{a single numeric value, as input}
}
\references{
\emph{Genz A, Bretz F (1999):} Numerical computation of multivariate t-probabilities with application to power calculation of multiple contrasts. Journal of Statistical Computation and Simulation, 63, 4, 361-378.
\emph{Bretz F, Hothorn LA (2002):} Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data. Statistics in Medicine, 21, 22, 3325-3335.
\emph{Bretz F, Hayter AJ and Genz A (2001):} Critical point and power calculations for the studentized range test for generally correlated means. Journal of Statistical Computation and Simulation, 71, 2, 85-97.
\emph{Dilba G, Bretz F, Hothorn LA, Guiard V (2006):} Power and sample size computations in simultaneous tests for non-inferiority based on relative margins. Statistics in Medicien 25, 1131-1147.
}
\author{
Frank Schaarschmidt
}
\section{Warning}{This is a test version, which has roughly (but not for an extensive number of settings) been checked by simulation. Any reports of errors/odd behaviour/amendments are welcome.}
\examples{
powermcpt(mu=c(3,3,5,7), n=c(10,10,10,10), sd=2, type = "Dunnett",
alternative ="greater", ptype = "global")
powermcpt(mu=c(3,3,5,7), n=c(10,10,10,10), sd=2, type = "Williams",
alternative ="greater", ptype = "global")
powermcpt(mu=c(3,3,5,7), n=c(10,10,10,10), sd=2, type = "Dunnett",
alternative ="greater", ptype = "anypair")
powermcpt(mu=c(3,3,5,7), n=c(10,10,10,10), sd=2, type = "Williams",
alternative ="greater", ptype = "anypair")
powermcpt(mu=c(3,4,5,7), n=c(10,10,10,10), sd=2, type = "Dunnett",
alternative ="greater", ptype = "allpair")
powermcpt(mu=c(3,2,1,-1), n=c(10,10,10,10), sd=2, type = "Dunnett",
alternative ="greater", ptype = "allpair")
}
\keyword{htest}
\concept{power}
|
05cf05a83598b48f4043c9628f5e6d8d1f02a295
|
251c9dd59afa6d9ca96339d2b94eb72d6dd37179
|
/man/readDcpRectangle.Rd
|
90f7255ae88603c657ae01f4d735592e7ffcf90b
|
[] |
no_license
|
KUNJU-PITT/dChipIO
|
ebcd315e18e6a52b6cfb25dc9d47677c0bee3c0e
|
85080e5d289646f89c7ad96051cef3c155dfb7dd
|
refs/heads/master
| 2020-03-31T09:50:11.248755
| 2016-01-14T07:13:13
| 2016-01-14T07:13:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,113
|
rd
|
readDcpRectangle.Rd
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% readDcpRectangle.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{readDcpRectangle}
\alias{readDcpRectangle}
\title{Reads a spatial subset of probe-level data from a dChip DCP file}
\usage{
readDcpRectangle(filename, fields=c("rawIntensities", "normalizedIntensities"),
xrange=c(0, Inf), yrange=c(0, Inf), ..., asMatrix=TRUE)
}
\description{
Reads a spatial subset of probe-level data from a dChip DCP file.
}
\arguments{
\item{filename}{The pathname of the DCP file.}
\item{fields}{The cell fields to be read.}
\item{xrange}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length two giving the left
and right coordinates of the cells to be returned.}
\item{yrange}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length two giving the top
and bottom coordinates of the cells to be returned.}
\item{asMatrix}{If \code{\link[base:logical]{TRUE}}, the CEL data fields are returned as
matrices with element (1,1) corresponding to cell
(xrange[1],yrange[1]).}
\item{...}{Additional arguments passed to \code{\link{readDcp}}().}
}
\value{
A named \code{\link[base]{list}} CEL structure similar to what \code{\link{readDcp}}().
In addition, if \code{asMatrix} is \code{\link[base:logical]{TRUE}}, the CEL data fields
are returned as matrices, otherwise not.
}
\author{Henrik Bengtsson}
\examples{
path <- system.file("exData", package="dChipIO")
filename <- "Test3-1-121502.dcp"
pathname <- file.path(path, filename)
data <- readDcpRectangle(pathname)
layout(matrix(1:4, nrow=2, byrow=TRUE))
image(data$rawIntensities, main="Raw probe signals")
image(data$normalizedIntensities, main="Normalized probe signals")
}
\seealso{
The \code{\link{readDcp}}() method is used internally.
This method was inspired by \code{readCelRectangle()} of the
\pkg{affxparser} package.
}
\keyword{file}
\keyword{IO}
|
92fcd2cfd7af7565626d2eb2acf8afd88b92efa6
|
12a97000d7e61c7d5ddaaa05873ff98ebd90b34e
|
/man/fit_model.Rd
|
eb1803fa31eef762d3dd55260d272894771cad2b
|
[] |
no_license
|
ThierryO/testlme4
|
0900472348a12af209d8020ae83362b0974f1deb
|
d1ee848b719760a4896acbc5e35f69f6c1d80914
|
refs/heads/master
| 2020-04-06T04:30:37.865206
| 2015-03-23T16:42:08
| 2015-03-23T16:42:08
| 32,745,270
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 313
|
rd
|
fit_model.Rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/fit_model.R
\name{fit_model}
\alias{fit_model}
\title{Fit a poisson glmer}
\usage{
fit_model(formula, dataset)
}
\arguments{
\item{formula}{the glmer formula}
\item{dataset}{the dataset}
}
\description{
Fit a poisson glmer
}
|
8083738b8c02f6e27aa44f7fa151955127141186
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/genasis/examples/genplot.Rd.R
|
bced644e7bbcb452ea9b2ac6f1c68a3ce7e7865a
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,048
|
r
|
genplot.Rd.R
|
library(genasis)
### Name: genplot
### Title: Plot of concentration time series
### Aliases: genplot
### Keywords: genplot
### ** Examples
## Definition of simple data sources:
c1<-c(0.386,0.256,0.182,0.254)
c2<-"fluorene"
c3<-c("2013-05-01","2013-06-03","2013-07-05","2013-08-07")
c4<-c("2013-05-08","2013-06-10","2013-07-12","2013-08-14")
sample_genasis<-data.frame(c1,c2,c3,c4)
sample_openair<-data.frame(c4,c1)
colnames(sample_openair)=c("date","fluorene")
## Examples of different usages:
genplot(sample_openair,input="openair",pollutant="fluorene",distr="lnorm",
n=10,ci="gradient",col="black",col.points="red",pch=15)
genplot(sample_genasis,input="genasis",n=10,col="blue")
genplot(c1,c3,ci=FALSE,pch=1,main="fluorene")
## Use of example data from the package:
data(kosetice.pas.openair)
genplot(kosetice.pas.openair[,1:8],col="orange",il="ts",ci=FALSE)
data(kosetice.pas.genasis)
## Not run:
##D genplot(kosetice.pas.genasis[1:208,],input="genasis",
##D distr="lnorm",ci="gradient",col="orange")
## End(Not run)
|
5bc84bdfff86192626e3c05ab84f9d209f7918f9
|
2da2406aff1f6318cba7453db555c7ed4d2ea0d3
|
/man/undocumented.Rd
|
8a801cc3b816b6c577ed58b8d8b61da11f967a25
|
[] |
no_license
|
rpruim/fastR2
|
4efe9742f56fe7fcee0ede1c1ec1203abb312f34
|
d0fe0464ea6a6258b2414e4fcd59166eaf3103f8
|
refs/heads/main
| 2022-05-05T23:24:55.024994
| 2022-03-15T23:06:08
| 2022-03-15T23:06:08
| 3,821,177
| 11
| 8
| null | null | null | null |
UTF-8
|
R
| false
| true
| 437
|
rd
|
undocumented.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/undocumented.R
\name{undocumented}
\alias{undocumented}
\alias{grid.identify.points}
\alias{funvec}
\title{Undocumented functions}
\description{
These objects are undocumented.
}
\details{
Some are left-overs from a previous version of the book and package.
In other cases, the functions are of limited suitability for general use.
}
\author{
Randall Pruim
}
|
140cd41d4df5099e02c64e373212f235f7a3e3a5
|
05a9f722bfd91a75144ebf840f296f932b5baf20
|
/BrestNewlyn/keyWestACREannualSLP.R
|
5f4856d2ae3435395fff61f9b4a04812b10b7931
|
[] |
no_license
|
simonholgate/R-Scripts
|
05118e4e92118a506eaf29bf6fa9aca4ea3f9477
|
89ab9ee9da1bbce10f4dc9a422259dda64748689
|
refs/heads/master
| 2020-05-17T14:48:02.345740
| 2012-06-21T11:19:58
| 2012-06-21T11:19:58
| 4,738,138
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,180
|
r
|
keyWestACREannualSLP.R
|
##****************************************************************************************************##
## Calculate annual mean SLP for Key West and San Francisco area using ACRE SLP data
##
##****************************************************************************************************##
##****************************************************************************************************##
########################################################################################################
## Functions for use below ##
########################################################################################################
##****************************************************************************************************##
annual.2d.slp <- function(slpArray){
## Convert a 2D array of monthly data into a 2D annual array.
## There should be nstns columns and nmon rows
nstns <- dim(slpArray)[2]
nmon <- dim(slpArray)[1]
nyr <- nmon/12
slpAnnualArray <- array(NA, dim=c(nyr,nstns))
for(i in 1:nstns){
## Make a temporray vector of each station
temp <- slpArray[,i]
## Reshape to 3d array with nstns*nyr*12
dim(temp) <- c(12, nyr)
## Place the nyr column means from the temporary array into the column for stn i
slpAnnualArray[,i] <- colMeans(temp)
}
slpAnnualArray
}
##*******************************************************************************************************
#########################
## Non-functional part ##
#########################
#library(fields)
nyr<-138
slp.yrs <- c(1871:2008)
##*******************************************************************************************************
## Key West/San Francisco annual pressure
## Monthly data 2 stations. First two are Key West and San Francisco. Variable name is slpKeyWestStns.
load("~/diskx/polcoms/brestNewlyn/analysis/paper/keyWestEofACRE/keyWestACREslp.RData")
# Convert Pa to Mb
slpKeyWestStns <- slpKeyWestStns/100
nstns <- 2
slpAnnArray <- annual.2d.slp(slpKeyWestStns)
save(slpAnnArray, file="keyWestACREannualSLP.RData")
|
9f39f61b6d49cf8094a458f943ec5b6302ced7ed
|
1ea0969c88f299c5f97fd426cb0befe285adfc7e
|
/man/get_all_commits.Rd
|
ee71f03839be1aabdb57153bc1996d8002c8dd3e
|
[] |
no_license
|
chapmandu2/gitscraper
|
b1e180fe46fc01314a79e969d9e1bbe8b47fb7b5
|
2f6bad9a705d79071ef829deb6bfa93ef1e0ce23
|
refs/heads/master
| 2020-04-21T18:35:33.218985
| 2019-02-08T12:04:14
| 2019-02-08T12:04:14
| 169,775,658
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 322
|
rd
|
get_all_commits.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_all_commits.R
\name{get_all_commits}
\alias{get_all_commits}
\title{Get all commits}
\usage{
get_all_commits(git_repo)
}
\arguments{
\item{git_repo}{path to git repo}
}
\value{
data frame
}
\description{
Get all commits
}
\examples{
NULL
}
|
6c6bae60d8292f3208064c9e8f939f529a4a853d
|
fe6cc44c3444421c510ec380118e92deb55b1564
|
/class3-master/code.R
|
be0781817c6000577ab13049b1a6841aa62a2699
|
[] |
no_license
|
IlgizMurzakhanov/BDA
|
d5a9f0fe09f45a4fffd4eff256cc603eb6eabdc0
|
87cf6fbdde52fa53296488f269704e26fcb433a4
|
refs/heads/master
| 2021-04-08T05:41:47.741798
| 2015-12-19T19:50:10
| 2015-12-19T19:50:10
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,214
|
r
|
code.R
|
#data <- read_rdump('data_file.data.R')
#fit <- stan("model_file.stan", data = data)
#print(fit)
#plot(fit)
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
options(width = 160)
data <- read_rdump('normal1.data.R')
fit <- stan("normal.stan", data = data)
print(fit)
plot(fit)
data <- read_rdump('normal2.data.R')
fit <- stan(fit = fit, data = data)
print(fit) #Not enough data or add priors
fit <- stan("compiletime-error1.stan", data = data) #Needed semicolon
fit <- stan("compiletime-error2.stan", data = data) #Need to declare all variables before use
#real x[N, M]; x[1]: real[M]
#matrix[N, M] x; x[1]: vector[M]
#vector[N] x[M];
#All same dims
fit <- stan("compiletime-error3.stan", data = data) #Samples from Bernoulli are ints not reals
fit <- stan("runtime-error1.stan", data = read_rdump('runtime-error1.data.R')) #Y data is greater than 1
fit <- stan("runtime-error2.stan", data = read_rdump("runtime-error2.data.R")) #J is missing from data
fit <- stan("runtime-error3.stan", data = read_rdump("runtime-error3.data.R")) #Use sqrt(sigma) instead so not sampling wrong values
data <- read_rdump('normal1.data.R')
fit <- stan("normal2.stan", data = data)
|
75d622b731178ad5c092297008f910d7fe0d11ef
|
fefc5800250818b2a53e1211cb83723af03ec82b
|
/reveals_handmade.R
|
5d06baa0eb445be0e8741212514fc0fe12db085e
|
[] |
no_license
|
mtrachs/reveals_test
|
46c8b4968b251cd8ab4057035c25886672412d79
|
ee4088ad812c45e79a2cf43585b21c7d194008be
|
refs/heads/master
| 2020-03-19T08:17:00.630413
| 2018-06-05T15:33:17
| 2018-06-05T15:33:17
| 136,191,095
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,335
|
r
|
reveals_handmade.R
|
library(zipfR)
library(rioja)
setwd('~/Reveals_NEUS/')
#--------------------------------------------------------------------------------------------------------------]
#function to estimate species dependent depositional coefficient
Ki <- function(b, R, zmax=400000){
ul1 <- b*(zmax-R)^(1/8)
ul2 <- b*(zmax+R)^(1/8)
ul3 <- b*(2*R)^(1/8)
gamma_Ki <- Igamma(8, ul1, lower=TRUE) -
Igamma(8, ul2, lower=TRUE) +
Igamma(8, ul3, lower=TRUE)
return(4*pi*R/b^8*gamma_Ki)
}
#---------------------------------------------------------------------------------
#function to estimate vegetation proportion
REVEALS_gamma <- function(pollen,fall_speed,ppes,R,zmax,n,u,c){
#calculate parameter b later on used to estimate deposition coeffcient
b <- 4/sqrt(pi) * fall.speed/(n*u*c)
#species specific depositional coefficient
K_species <-
sapply(b,function(x){
Ki(x[[1]],R=R,zmax = zmax)
})
#eq (5) in Sugita (2007a)
weighted_pollen <- pollen/(ppes*K_species)
veg_proportion <- weighted_pollen/sum(weighted_pollen)
return(as.numeric(veg_proportion))
}
#------------------------------------------------------------------------------------------------------------
#load data
pollen <- read.csv("data/reveals_input.csv")
pollen <- pollen[-1]
names(pollen) <- unlist(strsplit(names(pollen),'[.]'))[seq(2,(2*ncol(pollen)),2)]
reveals.params <- read.csv('data/reveals_input_params_variable.csv')
taxa <- reveals.params$species
#fall speed
fall.speed <- reveals.params$fallspeed
names(fall.speed) <- taxa
fall.speed <- as.data.frame(t(fall.speed))
#ppe
ppes <- reveals.params$PPEs
names(ppes) <- taxa
fall.speed <- fall.speed[names(fall.speed)%in%names(pollen)]
ppes <- ppes[names(ppes)%in%names(pollen)]
#massive difference depending on use of meters or km
REVEALS_gamma(pollen,fall_speed,ppes,R=1,zmax=100,n=0.25,u=3,c=0.12)
REVEALS_gamma(pollen,fall_speed,ppes,R=1000,zmax=100000,n=0.25,u=3,c=0.12)
#----------------------------------------------------------------------------------------------------
#test sensitivity to maximum dispersal
distances <- c(seq(10,100,10),seq(200,1000,100))
sensitivity_reveals_gamma <-
sapply(distances,function(x){
REVEALS_gamma(pollen,fall_speed,ppes,R=1,zmax=x,n=0.25,u=3,c=0.12)
})
colnames(sensitivity_reveals_gamma) <- distances
#distances
dist(t(sqrt(sensitivity_reveals_gamma[,c("50","100","400")])))^2
paldist(t(sensitivity_reveals_gamma[,c("50","100","400")]))
#-----------------------------------------------------------------------------------------------------
# load new pollen data
#-----------------------------------------------------------------------------------------------------
load('~/workflow_stepps_calibration/vegetation/data_nb/prediction_13_taxa_6796_cells_120_knots_cal_pl_Ka_Kgamma_EPs_79_sites_final.rdata')
pollen <- y
colnames(pollen)[grep("Other",colnames(pollen))] <- c('Other_conifer','Other_hardwood')
pollen <- pollen[,colnames(pollen)%in%names(ppes)]
sensitivity_all <-
lapply(distances,function(distan){
pred_reveals <-
apply(pollen,1,function(x){
REVEALS_gamma(x,fall_speed,ppes,R=1,zmax=distan,n=0.25,u=3,c=0.12)
})
pred_reveals <- t(pred_reveals)
colnames(pred_reveals) <- colnames(pollen)
round(pred_reveals,3)
})
names(sensitivity_all) <- distances
|
3df9bbe5db4f5f443f36854beb8774e1e408b370
|
06fc7cc5aa8aae3ded43c73a854c99be37ed25be
|
/RandomVariable/man/draw_nogen.Rd
|
84a6c5c093e0740a4a260de4b08f03dd375d8b8c
|
[] |
no_license
|
emadsalehi/R-package
|
70c97acfa021f57c11365f3df9d74fb0bfd26003
|
1529223f72be9765d64bd46549fd0b924c25af40
|
refs/heads/master
| 2020-03-19T05:17:01.109380
| 2018-06-06T13:02:10
| 2018-06-06T13:02:10
| 135,916,615
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 266
|
rd
|
draw_nogen.Rd
|
\name{draw_nogen}
\alias{draw_nogen}
\title{draw density plot of normal random variable}
\usage{
draw_nogen(u, s)
}
\description{
draw density plot of normal random variable with mean u and variance s:
draw_nogen(u, s)
}
\examples{
draw_nogen(60, 16)
}
|
a71d7132bff0c031d32885198bd7c516c04095ef
|
cd58a7407a07c4b846465c489e9f7c5ff41fc002
|
/R/root_tree_in_outgroup.R
|
e38b35227f0007b07a6acd6419c30c00c4af3042
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
simeross/CuPhyR
|
e09c25c63bb5f8cf8800a87ecbafb971fbdca923
|
a69819cb48d6e5f5ed982ae9ad8bb85f6c1d9347
|
refs/heads/master
| 2022-04-13T06:49:33.264968
| 2020-03-31T08:38:19
| 2020-03-31T08:38:19
| 241,898,395
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,760
|
r
|
root_tree_in_outgroup.R
|
#' Root phylogenetic tree of a phyloseq object
#'
#' @description This funtion defines the leaf with the longest path as the root of the phylogenetic tree.
#' This makes results reproducible by avoiding the behaviour of some functions that would otherwise pick a
#' random leaf as the root of an unrooted phylogenetic tree.
#' Based on answers in https://github.com/joey711/phyloseq/issues/597. The function requires the packages
#' 'ape' and 'data.table' to be installed.
#' @author Simeon Rossmann
#' @seealso Discussion and answers in [related GitHub thread](https://github.com/joey711/phyloseq/issues/597)
#'
#' @param physeq a phyloseq object containing a phylogenetic tree to be rooted in an outgroup.
#'
#' @return a rooted phylogenetic tree.
#'
#' @examples
#' phyloseq::phy_tree(ps) <- root_tree_in_outgroup(physeq = ps)
#'
#'@export
root_tree_in_outgroup <- function(physeq = ps){
if(requireNamespace(c("ape", "data.table"), quietly = TRUE)){
phylo_tree <- phyloseq::phy_tree(physeq)
tips <- ape::Ntip(phylo_tree)
tree_data <- base::cbind(
data.table::data.table(phylo_tree$edge),
data.table::data.table(length = phylo_tree$edge.length))[1:tips,]
tree_data <- base::cbind(tree_data, data.table::data.table(id = phylo_tree$tip.label))
# longest terminal branch as outgroup
out_group <- dplyr::slice(tree_data, which.max(length)) %>%
select(id) %>%
as.character()
new_tree <- ape::root(phylo_tree, outgroup=out_group, resolve.root=TRUE)
message("Tree successfully rooted.")
}else{
stop("The function 'root_tree_in_outgroup' requires the packages 'ape' and 'data.table' to be installed. Please make sure those packages can be loaded.")
}
return(new_tree)
}
|
7fdea95ec3c5ac915a9cab15da9d54daca384a80
|
ba0d52a9447cc2cedcaacafd8349fc50a32363b5
|
/man/plotSurvGenderSeverity.Rd
|
701375eda5afa3238f16c8df1e0b051a43d44140
|
[
"CC0-1.0"
] |
permissive
|
robschick/tangled
|
49590a754531b8e50294abb4d86fcd9cc85d037c
|
e4c0e49fa87802dd39fba01dc4fba5cef25e7b31
|
refs/heads/master
| 2023-04-07T19:24:43.838552
| 2022-05-04T19:11:30
| 2022-05-04T19:11:30
| 33,547,111
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,303
|
rd
|
plotSurvGenderSeverity.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotSurvGenderSeverity.R
\name{plotSurvGenderSeverity}
\alias{plotSurvGenderSeverity}
\title{Plot of Kaplan-Meier survival curve for all entangled whales.}
\usage{
plotSurvGenderSeverity(kmlines, censTicks, yearEnd, increment, legendLabs)
}
\arguments{
\item{kmlines}{A list with \code{nboot} components that contains a data frame
in each component. The data frame is the KM curve based on samples
of death times for the presumed dead animals. The first component of the
list is the median estimate of survivorship. This will be used as the
main KM curve. The other components will be used to display uncertainty.}
\item{yearEnd}{A matrix where each row contains the estimated death times
for each animal. Times are in the columns from 1:bnt, which allows for
the animal to be alive at the time modelling end.}
\item{increment}{Scalar representing the temporal unit at which we're
showing survival.}
\item{legendLabs}{Character vector to be used in plotting the legend.}
\item{censticks}{A list with \code{nboot} components that contains a data frame
in each component. The data frame contains information on when the animal
is censored. The changes in eact iteration, and right now the function
is set up to just plot the censored marks from the most probable censored
year.}
}
\value{
A ggplot2 object that can be used to create the output plot
}
\description{
\code{plotSurvGenderSeverity} returns a plot of survivorship be severity and gender
}
\details{
This is a function that will build a \code{ggplot2} object that displays
the KM curve along with the times animals get censored. The median estimates
of survivorship and censored times are used to make the main line. This comes
from the first element of each list that is passed to the function.
The idea behind this function is to show the uncertainty in survivorship
that arises from the different estimates of death in each animal.
The main difference from \code{plotSurv} is that this breaks out overall
survival and produces three lines for each of the three entanglement
categories as well as plotting facets for each gender
}
\examples{
\dontrun{
plotSurvGenderSeverity(kmlines, censTicks, 7)
}
}
|
6f8c6cb2e0604c0ff474d789c311f68ec2c169c1
|
e646416a1bbc302f73d2fdcbe78c5a8069e40fc8
|
/random_foodwebs/info_food_webs.R
|
fb2e5e63391444e6e8c1ba18a27bfdf9fdf60e72
|
[
"MIT"
] |
permissive
|
jusinowicz/info_theory_eco
|
c0ef0c0f94eca2df3b7308098f05b72233261c43
|
b0770b10464732aa32d13f46ba3c5ef958a74dcc
|
refs/heads/master
| 2022-05-28T19:34:50.642858
| 2022-05-05T17:37:19
| 2022-05-05T17:37:19
| 140,295,569
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 36,627
|
r
|
info_food_webs.R
|
#=============================================================================
# R code to create to explore the Information Theoretic properties of
# simple food webs. This creates a simple food web with an underlying dynamic
# model.
# 1. Food-web includes resource, herbivore, and predator:
# A. Resource is based on a consumer-resource model, with added predators
# 1. Competition between consumers and resources emerges from consumption
# 2. Parameters at each level can be made a function of temperature.
# B. Resources can be stochastic due to environmental fluctuations.
# C. Relative non-linearity allows 2 consumers per Resource
# 2. Generate a bunch of random food webs
# 3. Use information theory to track the resulting food-web structures.
# 4. This file has a lot of code for visualizing output of both the foodweb
# its information theoretic properties after the main loop.
#=============================================================================
#=============================================================================
# load libraries
#=============================================================================
library(deSolve)
library(fields)
source("../info_theory_functions/food_web_functions.R")
source("../info_theory_functions/info_theory_functions.R")
#=============================================================================
# Outer loop. Set the number of trials and determine how to generate
# combinations of species and parameters.
#=============================================================================
#Length and time steps of each model run
tend = 200
delta1 = 0.01
tl=tend/delta1
#The maximum block depth for dynamic info metrics (larger is more accurate, but
#slower and could cause crashing if too large)
k= 5
#Number of food webs to generate
nwebs = 20
#Output of each web
out1 = vector("list",nwebs)
#Converting the web to Rutledge's compartment model and calculating the information
#theoretic quantities: Shannon Entropy, Mutual Information, Conditional Entropy
rweb1 = vector("list",nwebs)
#Dynamic information metrics calculated from the (discretized) time series
di_web = vector("list",nwebs)
#Track the average transfer entropy and separable information between each pair of
#species as a way to build a network of information flow through the network.
te_web = vector("list",nwebs)
si_web = vector("list",nwebs)
#Random resources:
c = 0.1
amp = 1
res_R = c(amp,c)
for (w in 1:nwebs){
print(w)
#Assume 3 trophic levels unless otherwise specified.
nRsp = ceiling(runif(1)*30)
nCsp = ceiling(runif(1)*20)
nPsp = ceiling(runif(1)*10)
nspp = nRsp+nCsp+nPsp
#Randomly generate the species parameters for the model as well:
spp_prms = NULL
#Resource: Nearly identical resource dynamics:
spp_prms$rR = matrix(rnorm(nRsp,300,10), nRsp, 1) #intrinsic growth
spp_prms$Ki = matrix(rnorm(nRsp,500,10), nRsp, 1) #carrying capacity
#Consumers:
spp_prms$rC = matrix(rnorm(nCsp,.5,0.2), nCsp, 1) #intrisic growth
spp_prms$eFc = matrix(1,nCsp,nRsp) # just make the efficiency for everything 1 for now
spp_prms$muC = matrix(rnorm(nCsp,0.6,0.1), nCsp, 1) #mortality rates
#Consumption rates:
#Generate a hierarchy where each species predominantly feeds on particular resource.
dspp = abs((nCsp - nRsp))
hier1= seq(1/nRsp, (1-1/nRsp), length=nRsp)
spp_prms$cC = hier1
for( n in 1:nCsp) {
spp_prms$cC = cbind(spp_prms$cC, shifter(hier1,n))
}
spp_prms$cC = matrix(spp_prms$cC[1:nRsp,1:nCsp ],nRsp,nCsp)
#Predators:
spp_prms$rP = matrix(rnorm(nPsp,0.5,0.2), nPsp, 1) #intrisic growth
spp_prms$eFp = matrix(1,nPsp,nCsp) # just make the efficiency for everything 1 for now
spp_prms$muP = matrix(rnorm(nPsp,0.6,0.1), nPsp, 1) #mortality rates
#Consumption rates:
#Generate a hierarchy where each species predominantly feeds on particular resource.
dspp = ((nPsp - nCsp))
if(dspp<0){dspp = 0 }
hier1= seq(1/nCsp, (1-1/nCsp), length = nCsp)
spp_prms$cP = hier1
for( n in 1:nPsp) {
spp_prms$cP = cbind(spp_prms$cP, shifter(hier1,n))
}
spp_prms$cP = matrix(spp_prms$cP[1:nCsp,1:nPsp],nCsp,nPsp)
#=============================================================================
# Inner loop. Run the food web model, calculate information theoretic
# quantities.
#=============================================================================
#=============================================================================
# This function gives:
# out The time series for of population growth for each species in the web
# This can be set to just give the final 2 time steps of the web with
# "final = TRUE"
# spp_prms The parameters of all species in the food web
#=============================================================================
# tryCatch( {out1[w] = list(food_web_dynamics (spp_list = c(nRsp,nCsp,nPsp), spp_prms = spp_prms,
# tend, delta1, res_R = NULL,final = FALSE ))}, error = function(e){})
#Random resource fluctuations:
tryCatch( {out1[w] = list(food_web_dynamics (spp_list = c(nRsp,nCsp,nPsp), spp_prms = spp_prms,
tend, delta1, res_R = res_R) )
print( paste( "nRsp", sum(out1[[w]]$out[tl,1:nRsp]>1) ) )
print( paste( "nCsp", sum(out1[[w]]$out[tl,(nRsp+1):nCsp]>1) ) )
print( paste( "nPsp", sum(out1[[w]]$out[tl,(nCsp+1):nPsp]>1) ) )
# plot(out1[[w]]$out[,1], t="l", ylim = c(0, max(out1[[w]]$out[tl,],na.rm=T) ) )
# for(n in 2:nRsp){ lines(out1[[w]]$out[,n], col ="red") }
# for(n in (nRsp+1):(nCsp) ){ lines(out1[[w]]$out[,n], col ="blue") }
# for(n in (nCsp+1):(nPsp) ){ lines(out1[[w]]$out[,n]) }
}, error = function(e){})
#=============================================================================
# Information theoretic assessment of the foodweb.
#=============================================================================
#=============================================================================
# This section is as per Rutledge, Basore, and Mulholland 1976
#=============================================================================
## This code takes the ODEs and converts them to a biomass balance matrix and
## transition matrix.
## This version creates a compartment for each "event" where biomass is gained
## or loss. This includes birth, death, and "inefficiency" in the form of the
## way that biomass consumed translates to new population biomass.
#=============================================================================
# This function gives:
# Qi(t) Biomass proportion flow through a node at time t
# fij(t) Probability of biomass flow between i and j at t
# fijQi(t) Total biomass flowing from i to j at t
# sD Shannon entropy
# mI_mean Average mutual information
# mI_per Mutual information per interaction
# ce Conditional entropy
#=============================================================================
# rweb1[w] = list(rutledge_web( spp_list=c(nRsp,nCsp,nPsp), pop_ts = out1[[w]]$out[,2:(nspp+1)],
# spp_prms = out1[[w]]$spp_prms) )
#=============================================================================
# Information processing networks
#=============================================================================
## This code takes the population time-series counts output by the ODEs and
## calculates Excess Entropy, Active Information Storage, and Transfer Entropy.
## Each quantity is calculated at both the average and local level.
#=============================================================================
# This function gives:
# EE_mean Average mutual information per species
# AI_mean Average active information per species
# TE_mean Average transfer entropy per species
#
# EE_local Local mutual information per species
# AI_local Local active information per species
# TE_local Local transfer entropy per species
#=============================================================================
nt1 = 1
nt2 = tl
di_web[w] = list(get_info_dynamics(pop_ts = floor(out1[[w]]$out[nt1:tl,2:(nspp+1)]),
k=k,with_blocks=TRUE))
## This code takes the population time-series counts output by the ODEs and
## calculates the average Transfer Entropy from each species to every other
## species. The goal is to get an overview of the major information pathways
## in the web.
#=============================================================================
# This function gives:
# te_web Average transfer entropy per species as a pairwise matrix
#=============================================================================
te_web[w] = list( get_te_web( pop_ts = floor(out1[[w]]$out[nt1:tl,2:(nspp+1)]),
k=k) )
## This code takes the population time-series counts output by the ODEs and
## calculates the average Separable Information from each species to every other
## species. The goal is to get an overview of the major information pathways
## in the web.
#=============================================================================
# This function gives:
# si_web Average separable information per species as a pairwise matrix
#=============================================================================
si_web[w] = list( get_si_web( pop_ts = floor(out1[[w]]$out[nt1:tl,2:(nspp+1)]),
k=k) )
}
#=============================================================================
# Examine a particular food web more closely:
#=============================================================================
library(viridis)
library(fields)
library(igraph)
library(visNetwork)
w=1
#=============================================================================
#Export parameters into csv tables for easier reading.
# !!! Make sure to set the name of the excel file below!!!!
#=============================================================================
library(xlsx)
var_load = out1[[w]]$spp_prms[5] #These start at variable 5 and go to 14
write.xlsx(var_load, file="spp_prms_rweb1.xlsx", sheetName="sheet1", row.names=FALSE)
for (n in 6:14){
var_load = out1[[w]]$spp_prms[n]
sheet = paste("sheet",n-4, sep='')
write.xlsx(var_load, file="spp_prms_rweb1.xlsx", sheetName=sheet, append=TRUE,row.names=FALSE)
}
#=============================================================================
#Export the average information theoretic quantities into tables.
# !!! Make sure to set the name of the excel file below!!!!
#=============================================================================
library(xlsx)
var_load = di_web[[w]]$ee_means #These start at variable 5 and go to 14
write.xlsx(var_load, file="avg_dit_rweb1.xlsx", sheetName="sheet1", row.names=FALSE)
var_load = di_web[[w]]$ai_means #These start at variable 5 and go to 14
write.xlsx(var_load, file="avg_dit_rweb1.xlsx", sheetName="sheet2",append=TRUE,row.names=FALSE)
var_load = di_web[[w]]$te_means #These start at variable 5 and go to 14
write.xlsx(var_load, file="avg_dit_rweb1.xlsx", sheetName="sheet3",append=TRUE,row.names=FALSE)
var_load = di_web[[w]]$si_means #These start at variable 5 and go to 14
write.xlsx(var_load, file="avg_dit_rweb1.xlsx", sheetName="sheet4",append=TRUE,row.names=FALSE)
#=============================================================================
# Plot each of the average information theoretic metrics as a bar graph
#=============================================================================
fig.name = paste("average_dynamics_rweb1.pdf",sep="")
pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
layout.matrix=matrix(c(1:4), nrow = 2, ncol = 2)
layout(mat = layout.matrix,
heights = c(5,5), # Heights of the rows
widths = c(5,5)) # Widths of columns
#layout.show(4)
barplot(di_web[[w]]$ee_means,cex.lab =1.3, beside = TRUE,ylab="Bits of information", xlab = "")
abline(v =out1[[w]]$spp_prms$nRsp+1,col="red")
mtext("Resour", side=1, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(v =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp +1,col="blue" )
mtext("Consum", side=1, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Pred", side=1, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
barplot(di_web[[w]]$ai_means,cex.lab =1.3, beside = TRUE,ylab="Bits of information", xlab = "Species #")
abline(v =out1[[w]]$spp_prms$nRsp+1,col="red" )
mtext("Resour", side=1, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(v =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp +1,col="blue" )
mtext("Consum", side=1, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Pred", side=1, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
mtext("Average Information Storage", side = 3, line =4)
barplot(di_web[[w]]$te_means,cex.lab =1.3, beside = TRUE,ylab="", xlab = "")
abline(v =out1[[w]]$spp_prms$nRsp+1,col="red" )
mtext("Resour", side=1, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(v =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp +1,col="blue" )
mtext("Consum", side=1, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Pred", side=1, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
mtext("Average Information Transfer", side = 3, line = 2)
barplot(di_web[[w]]$si_means,cex.lab =1.3, beside = TRUE,ylab="", xlab = "Species #")
abline(v =out1[[w]]$spp_prms$nRsp+1,col="red" )
mtext("Resour", side=1, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(v =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp +1 ,col="blue" )
mtext("Consum", side=1, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Pred", side=1, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
mtext("Average Information Modification", side = 3, line = 2)
dev.off()
#=============================================================================
# Plot the population dynamics
#=============================================================================
out = out1[[w]]$out
nspp = out1[[w]]$spp_prms$nspp
nRsp = out1[[w]]$spp_prms$nRsp
nCsp = out1[[w]]$spp_prms$nCsp
nPsp = out1[[w]]$spp_prms$nPsp
tl = tend/delta1
par(mfrow=c(3,1))
#Resource species in RED
plot(out[,"1"],t="l",col="red",ylim = c(0,max(out[tl,2:(nRsp+1)],na.rm=T)))
for( n in 1:(nRsp) ) {
lines(out[,paste(n)],t="l",col="red")
}
#Consumer species in BLUE
plot(out[,paste(nRsp+2)],t="l",col="blue",ylim = c(0,max(out[tl,(nRsp+2):(nRsp+nCsp+1)],na.rm=T)))
for( n in ( (nRsp+1):(nRsp+nCsp) ) ) {
lines(out[,paste(n)],t="l",col="blue")
}
#Predator species in BLACK
plot(out[,paste(nRsp+nCsp+2)],t="l",ylim = c(0,max(out[tl,(nRsp+nCsp+2):(nspp+1)],na.rm=T)))
for( n in ((nRsp+nCsp+1):(nspp) ) ) {
lines(out[3900:4000,paste(n)],t="l")
}
#=============================================================================
# Plot the dynamic information metrics with time
#=============================================================================
#Local excess entropy
nt_use = dim(di_web[[w]]$ee_local)[1]
image.plot( 1:nt_use, 1:nspp, di_web[[w]]$ee_local, ylab="Species number", xlab="Time" )
abline(h =out1[[w]]$spp_prms$nRsp )
mtext("Resources", side=2, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(h =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )
mtext("Consumers", side=2, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Predators", side=2, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
#Local active information storage
nt_use = dim(di_web[[w]]$ai_local)[1]
image.plot( 1:nt_use, 1:nspp, di_web[[w]]$ai_local, ylab="Species number", xlab="Time" )
abline(h =out1[[w]]$spp_prms$nRsp )
mtext("Resources", side=2, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(h =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )
mtext("Consumers", side=2, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Predators", side=2, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
#Local transfer entropy
nt_use = dim(di_web[[w]]$te_local)[1]
image.plot( 1:nt_use, 1:nspp, di_web[[w]]$te_local, ylab="Species number", xlab="Time" )
abline(h =out1[[w]]$spp_prms$nRsp )
mtext("Resources", side=2, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(h =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )
mtext("Consumers", side=2, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Predators", side=2, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
#Local separable information
nt_use = dim(di_web[[w]]$si_local)[1]
image.plot( 1:nt_use, 1:nspp, di_web[[w]]$si_local, ylab="Species number", xlab="Time" )
abline(h =out1[[w]]$spp_prms$nRsp )
mtext("Resources", side=2, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(h =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )
mtext("Consumers", side=2, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Predators", side=2, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
#=============================================================================
# Network plots of information storage.
# The local exess entropy or active information could be used to show the
# dominant cycles involved in information storage...
#=============================================================================
#=============================================================================
# Network plots of information transfer.
# This uses the average Transfer Entropy between each species pair to create
# a directed network of information transfers.
#=============================================================================
###This shows the network, but only highlights the largest link between each
###node
#Pair down the graph by removing species that have essentially gone extinct
#from the system.
spp_use = (1:nspp)[out1[[w]]$out[10000,2:nspp]>1e-5]
te_web1 = te_web[[w]][spp_use,spp_use]
#Make an igraph object
te_gr = graph_from_adjacency_matrix(te_web1, mode="directed", weighted=T)
#Convert to VisNetwork list
te_visn = toVisNetworkData(te_gr)
te_visn$nodes$value = te_visn$nodes$id
#Copy column "weight" to new column "value" in list "edges"
te_visn$edges$value = te_visn$edges$weight
#Further prune links that are smaller than the 95% interval
m1 = mean(c(log(te_visn$edges$value)))
sd1 = sqrt(var(c(log(te_visn$edges$value))))
te_visn$edges =te_visn$edges[log(te_visn$edges$value) > (m1-sd1), ]
#Color code the nodes by trophic level
spp_colors= c( matrix("red",nRsp,1),matrix("blue",nCsp,1),
matrix("black",nPsp,1) )
spp_colors = spp_colors [spp_use]
te_visn$nodes$color = spp_colors
#Plot this as an HTML object
#Add arrows to show direction
#Add an option that when a node is clicked on only the "from" arrows are shown
visNetwork(te_visn$nodes, te_visn$edges) %>%
visEdges(arrows="to", arrowStrikethrough =FALSE ) %>%
visOptions(highlightNearest = list(enabled =TRUE, degree =0) )%>%
visIgraphLayout(layout = "layout_in_circle") %>%
#visSave(file="te_graph1p.html", selfcontained = FALSE, background = "white")
visExport( type = "pdf", name = "te_web_biggest_1")
######################################################
# Add information storage (AIS or EE) as a self-loop!#
######################################################
edges_tmp = data.frame(from = c(1:length(spp_use)), to =(1:length(spp_use)),weight =(1:length(spp_use)) )
edges_tmp$value = di_web[[1]]$ai_means[spp_use]
te_visn$edges=rbind(te_visn$edges,edges_tmp)
visNetwork(te_visn$nodes, te_visn$edges) %>%
visEdges(arrows="to", arrowStrikethrough =FALSE ) %>%
visOptions(highlightNearest = list(enabled =TRUE, degree =0) )%>%
visIgraphLayout(layout = "layout_in_circle") %>%
visSave(file="ai_te_graph1.html", selfcontained = FALSE, background = "white")
#visExport( type = "pdf", name = "te_web_biggest_1")
######################################
#Because transfer can be asymmetrical, make 2 different graphs showing direction
#of flows.
te_gr1 = graph_from_adjacency_matrix( (te_web[[w]]*lower.tri(te_web[[w]])), mode="directed", weighted=T)
te_gr2 = graph_from_adjacency_matrix( (te_web[[w]]*upper.tri(te_web[[w]])), mode="directed", weighted=T)
#Convert to VisNetwork list
te_visn1 = toVisNetworkData(te_gr1)
te_visn2 = toVisNetworkData(te_gr2)
#Copy column "weight" to new column "value" in list "edges"
te_visn1$edges$value = te_visn1$edges$weight
te_visn2$edges$value = te_visn2$edges$weight
#Color code the nodes by trophic level
te_visn1$nodes$color = c( matrix("red",nRsp,1),matrix("blue",nCsp,1),
matrix("black",nPsp,1) )
te_visn2$nodes$color = c( matrix("red",nRsp,1),matrix("blue",nCsp,1),
matrix("black",nPsp,1) )
#te_visn1$nodes$color = c( matrix(c("red","blue","black"),9,1) )
#te_visn2$nodes$color = c( matrix(c("red","blue","black"),9,1) )
#Plot this as an HTML object
#Add arrows to show direction:
te_visn1$edges$arrows = c(matrix("to",dim(te_visn1$edges)[1]))
te_visn2$edges$arrows = c(matrix("to",dim(te_visn2$edges)[1]))
visNetwork(te_visn1$nodes, te_visn1$edges) %>%
visIgraphLayout(layout = "layout_in_circle") %>%
visExport( type = "pdf", name = "te_web_clock_1")
visNetwork(te_visn2$nodes, te_visn2$edges) %>%
visIgraphLayout(layout = "layout_in_circle") %>%
visExport( type = "pdf", name = "te_web_clock_1")
#=============================================================================
# Network plots of information modification.
# This uses the average Separable Information between each species pair to create
# a directed network of information transfers.
#=============================================================================
###This shows the network, but only highlights the largest link between each
###node
#Pair down the graph by removing species that have essentially gone extinct
#from the system.
spp_use = (1:nspp)[out1[[w]]$out[10000,2:nspp]>1e-5]
si_web1 = si_web[[w]][spp_use,spp_use]
#Make an igraph object
si_gr = graph_from_adjacency_matrix(si_web1, mode="directed", weighted=T)
#Convert to VisNetwork list
si_visn = toVisNetworkData(si_gr)
si_visn$nodes$value = si_visn$nodes$id
#Copy column "weight" to new column "value" in list "edges"
si_visn$edges$value = si_visn$edges$weight
#Color code the nodes by trophic level
spp_colors= c( matrix("red",nRsp,1),matrix("blue",nCsp,1),
matrix("black",nPsp,1) )
spp_colors = spp_colors [spp_use]
si_visn$nodes$color = spp_colors
#Plot this as an HTML object
#Add arrows to show direction
#Add an option that when a node is clicked on only the "from" arrows are shown
visNetwork(si_visn$nodes, si_visn$edges) %>%
visEdges(arrows="to", arrowStrikethrough =FALSE ) %>%
visOptions(highlightNearest = list(enabled =TRUE, degree =0) )%>%
visIgraphLayout(layout = "layout_in_circle") %>%
visSave(file="si_graph1.html", selfcontained = FALSE, background = "white")
#visExport( type = "pdf", name = "si_web_biggest_1")
#=============================================================================
# Make combined plots of population and dynamic information metrics with time
#=============================================================================
#===========================================#
#plot1: Info storage (Excess Entropy or AIS)
#===========================================#
# fig.name = paste("dynamic_info_AIS_rweb1.pdf",sep="")
# pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
#When the figure is only over a subset of the time to show transient dynamics:
fig.name = paste("dynamic_info_AIS_rweb1_sub.pdf",sep="")
pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
layout.matrix=matrix(c(1:12), nrow = 6, ncol = 2)
layout(mat = layout.matrix,
heights = c(1.5, 3.5,1.5, 3.5, 1.5, 3.5,
1.5, 3.5,1.5, 3.5, 1.5, 3.5), # Heights of the rows
widths = c(12,1)) # Widths of columns
#layout.show(12)
#par(mfrow=c(2,1),mai= c( 0.0, 0.2, 0.0, 0.2), omi=c(0.5,0.75,0.5,0.75)) #,mai= c( 1, 0, 0.2, 0), omi=c(2,0.75,2,0.75))
###Common figure properties
t1 = 5840
nlevel = 64 #For viridis color scheme
#nt_use = dim(di_web[[w]]$ai_local)[1]
nt_use = 5940
rs1 = 450 #lower bound for Resource population plot
par(oma = c(3,2,3,3) )
#===========================================#
#===========================================#
###Predator species
par( mar = c(0.5,4,0,4) )
plot(out[t1:nt_use,paste(nRsp+nCsp+2)],t="l",ylim = c(0,max(out[t1:nt_use,(nRsp+nCsp+2):(nspp+1)],na.rm=T)),
ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ((nRsp+nCsp+1):(nspp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l")
}
mtext("Local Information Storage", side = 3, line = 0, outer = TRUE)
#Local excess entropy
#par( mar = c(2,4,0,4) )
# nt_use = dim(di_web[[w]]$ee_local)[1]
#image( 1:nt_use, 1:nCsp, di_web[[w]]$ee_local[,(nRsp+nCsp+1):(nspp)], ylab="Species number",
# xlab="Time",col=viridis(nlevel) )
#Local active information storage
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nPsp, di_web[[w]]$ai_local[t1:nt_use,(nRsp+nCsp+1):(nspp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3)
###Consumer species
par( mar = c(0.5,4,0,4) )
#Consumer species in BLUE
plot(out[t1:nt_use,paste(nRsp+2)],t="l",col="blue",ylim = c(0,max(out[t1:nt_use,(nRsp+2):(nRsp+nCsp+1)],na.rm=T))
, ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ( (nRsp+1):(nRsp+nCsp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="blue")
}
#Local excess entropy
#par( mar = c(2,4,0,4) )
# nt_use = dim(di_web[[w]]$ee_local)[1]
#image( 1:nt_use, 1:nCsp, di_web[[w]]$ee_local[,(nRsp+1):(nRsp+nCsp)], ylab="Species number",
# xlab="Time",col=viridis(nlevel) )
#Local active information storage
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nCsp, di_web[[w]]$ai_local[t1:nt_use,(nRsp+1):(nRsp+nCsp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),,cex.main=1.3,cex.lab=1.3 )
###Resource Species
par( mar = c(0.5,4,0,4) )
#Resource species in RED
plot(out[t1:nt_use,"1"],t="l",col="red",ylim = c(rs1,max(out[t1:nt_use,2:(nRsp+1)],na.rm=T)), ylab="Population", xlab="",
xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2, )
for( n in 1:(nRsp) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="red")
}
#Local excess entropy
#par( mar = c(2,4,0,4) )
# nt_use = dim(di_web[[w]]$ee_local)[1]
#image( 1:nt_use, 1:nRsp, di_web[[w]]$ee_local[,1:nRsp], ylab="Species number",
# xlab="Time",col=viridis(nlevel) )
#Local active information storage
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nRsp, di_web[[w]]$ai_local[t1:nt_use,1:nRsp], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3 )
###Plot color bars for image plots:
#Color bar 1
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$ai_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 2
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$ai_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 3
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$ai_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
dev.off()
#===========================================#
#plot2: Information transmission (TE)
#===========================================#
# fig.name = paste("dynamic_info_TE_rweb1.pdf",sep="")
# pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
#When the figure is only over a subset of the time to show transient dynamics:
fig.name = paste("dynamic_info_TE_rweb1_sub.pdf",sep="")
pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
layout.matrix=matrix(c(1:12), nrow = 6, ncol = 2)
layout(mat = layout.matrix,
heights = c(1.5, 3.5,1.5, 3.5, 1.5, 3.5,
1.5, 3.5,1.5, 3.5, 1.5, 3.5), # Heights of the rows
widths = c(12,1)) # Widths of columns
#layout.show(12)
#par(mfrow=c(2,1),mai= c( 0.0, 0.2, 0.0, 0.2), omi=c(0.5,0.75,0.5,0.75)) #,mai= c( 1, 0, 0.2, 0), omi=c(2,0.75,2,0.75))
###Common figure properties
nlevel = 64 #For viridis color
t1 = 5840
nlevel = 64 #For viridis color scheme
#nt_use = dim(di_web[[w]]$ai_local)[1]
nt_use = 5940
rs1 = 450 #lower bound for Resource population plot
par(oma = c(3,2,3,3) )
#===========================================#
#===========================================#
###Predator species
par( mar = c(0.5,4,0,4) )
plot(out[t1:nt_use,paste(nRsp+nCsp+2)],t="l",ylim = c(0,max(out[t1:nt_use,(nRsp+nCsp+2):(nspp+1)],na.rm=T)),
ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ((nRsp+nCsp+1):(nspp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l")
}
mtext("Local Transfer Entropy", side = 3, line = 0, outer = TRUE)
#Local Transfer Entropy
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nPsp, di_web[[w]]$te_local[t1:nt_use,(nRsp+nCsp+1):(nspp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3)
###Consumer species
par( mar = c(0.5,4,0,4) )
#Consumer species in BLUE
plot(out[t1:nt_use,paste(nRsp+2)],t="l",col="blue",ylim = c(0,max(out[t1:nt_use,(nRsp+2):(nRsp+nCsp+1)],na.rm=T))
, ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ( (nRsp+1):(nRsp+nCsp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="blue")
}
#Local transfer entropy
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nCsp, di_web[[w]]$te_local[t1:nt_use,(nRsp+1):(nRsp+nCsp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),,cex.main=1.3,cex.lab=1.3 )
###Resource Species
par( mar = c(0.5,4,0,4) )
#Resource species in RED
plot(out[1:tl,"1"],t="l",col="red",ylim = c(rs1,max(out[t1:nt_use,2:(nRsp+1)],na.rm=T)), ylab="Population", xlab="",
xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2, )
for( n in 1:(nRsp) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="red")
}
#local transfer entropy
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nRsp, di_web[[w]]$te_local[t1:nt_use,1:nRsp], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3 )
###Plot color bars for image plots:
#Color bar 1
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$te_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 2
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$te_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 3
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$te_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
dev.off()
#===========================================#
#plot3: Information modification (SI)
#===========================================#
# fig.name = paste("dynamic_info_SI_rweb1.pdf",sep="")
# pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
#When the figure is only over a subset of the time to show transient dynamics:
fig.name = paste("dynamic_info_SI_rweb1_sub.pdf",sep="")
pdf(file=fig.name, height=8, width=8, onefile=TRUE, family='Helvetica', pointsize=16)
layout.matrix=matrix(c(1:12), nrow = 6, ncol = 2)
layout(mat = layout.matrix,
heights = c(1.5, 3.5,1.5, 3.5, 1.5, 3.5,
1.5, 3.5,1.5, 3.5, 1.5, 3.5), # Heights of the rows
widths = c(12,1)) # Widths of columns
#layout.show(12)
#par(mfrow=c(2,1),mai= c( 0.0, 0.2, 0.0, 0.2), omi=c(0.5,0.75,0.5,0.75)) #,mai= c( 1, 0, 0.2, 0), omi=c(2,0.75,2,0.75))
###Common figure properties
nlevel = 64 #For viridis color
t1 = 5840
nlevel = 64 #For viridis color scheme
#nt_use = dim(di_web[[w]]$ai_local)[1]
nt_use = 5940
rs1 = 450 #lower bound for Resource population plot
par(oma = c(3,2,3,3) )
#===========================================#
#===========================================#
###Predator species
par( mar = c(0.5,4,0,4) )
plot(out[t1:nt_use,paste(nRsp+nCsp+2)],t="l",ylim = c(0,max(out[tl,(nRsp+nCsp+2):(nspp+1)],na.rm=T)),
ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ((nRsp+nCsp+1):(nspp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l")
}
mtext("Local Seprable Information", side = 3, line = 0, outer = TRUE)
#Local seprable informatio
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nPsp, di_web[[w]]$si_local[t1:nt_use,(nRsp+nCsp+1):(nspp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3)
###Consumer species
par( mar = c(0.5,4,0,4) )
#Consumer species in BLUE
plot(out[t1:nt_use,paste(nRsp+2)],t="l",col="blue",ylim = c(0,max(out[t1:nt_use,(nRsp+2):(nRsp+nCsp+1)],na.rm=T))
, ylab="Population", xlab="", xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2)
for( n in ( (nRsp+1):(nRsp+nCsp) ) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="blue")
}
#Local separable information
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nCsp, di_web[[w]]$si_local[t1:nt_use,(nRsp+1):(nRsp+nCsp)], ylab="Species #",
xlab="Time",col=viridis(nlevel),,cex.main=1.3,cex.lab=1.3 )
###Resource Species
par( mar = c(0.5,4,0,4) )
#Resource species in RED
plot(out[t1:nt_use,"1"],t="l",col="red",ylim = c(rs1,max(out[tl,2:(nRsp+1)],na.rm=T)), ylab="Population", xlab="",
xaxs="i", xaxt="n",yaxs="i",cex.main=1.2,cex.lab=1.2, )
for( n in 1:(nRsp) ) {
lines(out[t1:nt_use,paste(n)],t="l",col="red")
}
#Local separable information
par( mar = c(2,4,0,4) )
image( t1:nt_use, 1:nRsp, di_web[[w]]$si_local[t1:nt_use,1:nRsp], ylab="Species #",
xlab="Time",col=viridis(nlevel),cex.main=1.3,cex.lab=1.3 )
###Plot color bars for image plots:
#Color bar 1
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$si_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 2
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$si_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
#Color bar 3
par( mar = c(0.5,0.5,0.5,0.5) )
frame()
par( mar = c(3,0,0,2) )
var_dist = di_web[[w]]$si_local[t1:nt_use,(nRsp+nCsp+1):(nspp)]
image(1,(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
t(seq(min(var_dist),max(var_dist),max(var_dist)/nlevel)),
ylab="",xaxt='n',col=viridis(nlevel))
dev.off()
#===============================================================================
#===============================================================================
#===============================================================================
###Or plot a subset of the data:
nt1 = 5000
nt2 = tl-50
image.plot( nt1:nt2, 1:nspp, di_web[[w]]$ee_local[nt1:nt2,], ylab="Species number", xlab="Time" )
#Local active information storage
image.plot( nt1:nt2, 1:nspp, di_web[[w]]$ai_local[nt1:nt2,], ylab="Species number", xlab="Time" )
#Local transfer entropy
image.plot( nt1:nt2, 1:nspp, di_web[[w]]$te_local[nt1:nt2,], ylab="Species number", xlab="Time" )
#Local separable information
image.plot( nt1:nt2, 1:nspp, di_web[[w]]$si_local[nt1:nt2,], ylab="Species number", xlab="Time" )
abline(h =out1[[w]]$spp_prms$nRsp )
mtext("Resources", side=2, at = c( out1[[w]]$spp_prms$nRsp/2 ) )
abline(h =out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )
mtext("Consumers", side=2, at = c( (out1[[w]]$spp_prms$nCsp+out1[[w]]$spp_prms$nRsp )-(out1[[w]]$spp_prms$nCsp)/2 ) )
mtext("Predators", side=2, at = c( nspp-(out1[[w]]$spp_prms$nPsp)/2 ) )
out1[[w]]$out[10000,]>1e-5
#Generate quantities for the maximum entropy distribution, i.e. uniform:
pop_me = runif(nspp)
me_freq = pop_me/matrix(sum(pop_me),length(pop_me),1)
|
9c1c990cfa4dffa7fbe1b7a45b41c8b6e7b5b489
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/biogeo/examples/edat.Rd.R
|
33baa36668112e43aa008500b818a87bd4ad4fa5
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 202
|
r
|
edat.Rd.R
|
library(biogeo)
### Name: edat
### Title: Species collection records dataset and environmental variables
### data
### Aliases: edat
### Keywords: datasets
### ** Examples
data(edat)
head(edat)
|
99695970907a3682c3e333eddff2a71cec5280f9
|
283409d2a37155d58855bc9be3b78e0ad7cdacb8
|
/Assignment-1/5.R
|
7a3759b15e659430d9187dbbe15349b6ba4cd7a7
|
[] |
no_license
|
VivianeLovatel/Brasil_2019
|
ddca243336145336c94ce09ff97d1918bf67e95d
|
82128b52ed7fa47d343cfaccf6da698f44e9883a
|
refs/heads/master
| 2020-07-07T00:45:58.305182
| 2019-08-20T15:20:36
| 2019-08-20T15:20:36
| 203,190,340
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 166
|
r
|
5.R
|
numLions = 42
numTigers = 17
country ="South Africa"
a="is"
b ="The number of lions in"
c<-"The number of tigers in"
paste(b,country,a,numLions,c,country,a,numTigers)
|
20e9eecec8e25b8ed149eb6a6dc01010cb908e5c
|
541b8e18f977371bc002aa506489d6ab0dc6b165
|
/man/r18S_cov_tbl.Rd
|
c5040f3377fa1a2697c280416db730282f8da0b7
|
[] |
no_license
|
hesselberthlab/endoU
|
2c1953418eebac8f1bcca01bd6d64b59e67d0032
|
bc926ec7b7368ccab93916a19c8d2a241b3c4eee
|
refs/heads/master
| 2022-12-31T14:09:14.934835
| 2020-10-23T22:45:18
| 2020-10-23T22:45:18
| 151,328,734
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 364
|
rd
|
r18S_cov_tbl.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{r18S_cov_tbl}
\alias{r18S_cov_tbl}
\title{18S coverage table}
\format{An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 65736 rows and 7 columns.}
\usage{
r18S_cov_tbl
}
\description{
18S coverage table
}
\keyword{datasets}
|
7a0af2899b302027e26c3203dbf54125c46a77f0
|
9d9062f4972ca4eda10966882e8a35d7745b619c
|
/figures/defense/r-scripts/plotTraceUQPosterioTC216.R
|
708b4bdf644d22065489dbf83bab915f664258db
|
[] |
no_license
|
damar-wicaksono/wd41-thesis
|
651abda4d1565745b15993f9d3c1ebd349e83bcc
|
7af132b0d5755a702490970e64a8c2986bd00f45
|
refs/heads/master
| 2021-01-17T05:26:23.472518
| 2019-07-12T22:34:35
| 2019-07-12T22:34:35
| 60,852,652
| 5
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,954
|
r
|
plotTraceUQPosterioTC216.R
|
#
# title : plotTraceUQPosterioTC216.R
# purpose : R script to create plot of UQ propagation using posterior samples
# : for TC output
# : FEBA Test No. 216
# author : WD41, LRS/EPFL/PSI
# date : Jan. 2018
#
# Load required libraries -----------------------------------------------------
library(ggplot2)
# Global variables ------------------------------------------------------------
# FEBA Test No
feba_test <- 216
# Input filename
rds_tidy_prior_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/srs/febaTrans",
feba_test,
"-febaVars12Influential-srs_1000_12-tc-tidy.Rds")
# Graphic variables
fig_size <- c(9.75, 4.0)
# Make the plot ---------------------------------------------------------------
# w/ Bias
# Output filename
otpfullname <- paste0("./figures/plotTraceUQPosteriorAllDiscCenteredTC",
feba_test, ".png")
# Input filenames, posterior samples, correlated and independent
rds_tidy_corr_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/disc/centered/all-params/correlated/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllDiscCentered_1000_12-tc-tidy.RDs")
rds_tidy_ind_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/disc/centered/all-params/independent/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllDiscCenteredInd_1000_12-tc-tidy.RDs")
# Make the plot
source("./r-scripts/plotTraceUQPosteriorTC.R")
# w/o Bias
# Output filename
otpfullname <- paste0("./figures/plotTraceUQPosteriorAllNoDiscNoBCTC",
feba_test, ".png")
# Input filenames, posterior samples, correlated and independent
rds_tidy_corr_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/nodisc/not-centered/fix-bc/correlated/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllNoDiscNoBC_1000_12-tc-tidy.RDs")
rds_tidy_ind_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/nodisc/not-centered/fix-bc/independent/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllNoDiscNoBCInd_1000_12-tc-tidy.RDs")
# Make the plot
source("./r-scripts/plotTraceUQPosteriorTC.R")
# w/o Parameter 8 (dffbVIHTC)
# Output filename
otpfullname <- paste0(
"./figures/plotTraceUQPosteriorAllDiscCenteredNoParam8TC",
feba_test, ".png")
# Input filenames, posterior samples, correlated and independent
rds_tidy_corr_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/disc/centered/no-param8/correlated/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllDiscCenteredNoParam8_1000_12-tc-tidy.RDs")
rds_tidy_ind_fullname <- paste0(
"../../../wd41-thesis/figures/data-support/postpro/disc/centered/no-param8/independent/febaTrans",
feba_test,
"-febaVars12Influential-mcmcAllDiscCenteredNoParam8Ind_1000_12-tc-tidy.RDs")
# Make the plot
source("./r-scripts/plotTraceUQPosteriorTC.R")
|
eade0e4215ccc753881a36952489f5958d5d492f
|
37db44bf803a83936031efd79a3727f8c5c2ab51
|
/PK/server.R
|
a14fe31291029c8873b76fc53da2bf0372036d34
|
[] |
no_license
|
jeffwzhong1994/R-shiny-web
|
81b226d03c1b491c5ee4472f8b15507c12d4d967
|
b5c082de47593e2d3fdc6d8253cbc8bdf4d66729
|
refs/heads/master
| 2020-09-20T03:15:33.284097
| 2019-11-28T00:57:34
| 2019-11-28T00:57:34
| 224,364,866
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 717
|
r
|
server.R
|
server=function(input,output){
output$pk=renderDataTable({
library(dplyr)
library(lubridate)
data=read.csv("pk records.csv")
data$a.rtime=ymd_hms(data$a.rtime)-hours(16)
data$year = paste(year(data$a.rtime),"-",month(data$a.rtime))
data$year
data$a.rtime= as.Date(data$a.rtime)
data$a.rtime
summary(data)
data%>%
filter(a.uid == input$UID)%>%
filter(a.rtime==input$Date)%>%
mutate(PK_points=a.recvbeans*10)%>%
mutate(Opponent_PK_points=a.peerrecvbeans*10)%>%
select( time = a.rtime, uid=a.uid, PK_points, OpponentUID=a.peeruid, Opponent_PK_points)
})
}
app <- shinyApp(ui = ui, server = server)
runApp(app, host ="0.0.0.0", port = 80)
|
b5f22dfc2630d855b9f02170ee45be598d4381a5
|
6b955291e90d4097e13c3808523e2d20b3a71398
|
/man/Gini.Rd
|
94b6cc85bb3020052875d8743c9c48f707053a89
|
[] |
no_license
|
cran/shipunov
|
640f34408ae65c59a8fa655c23d01e5e86af38bc
|
8cd6acac881f048a17ddafcfc414a4894fa02f63
|
refs/heads/master
| 2023-03-16T23:19:10.341396
| 2023-02-05T13:42:56
| 2023-02-05T13:42:56
| 185,279,307
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,269
|
rd
|
Gini.Rd
|
\name{Gini}
\alias{Gini}
\title{Compute the simple Gini coefficient}
\description{
Computes the simple Gini coefficient of unequality
}
\usage{
Gini(x)
}
\arguments{
\item{x}{a numeric vector with non-negative elements}
}
\details{
Gini coefficient is a common measure of inequality. Here it presents only
for the convenience to have this calculation "outside" of social science
R packages (where it commonly presents). Please read elsewhere of its
meaning and uses.
Code is based on the 'reldist' package from Mark S. Handcock but
simplified to revome the using of weights (as a sideway result, it should
be slightly faster).
}
\value{
The Gini coefficient (number between 0 and 1).
}
\references{
\emph{Relative Distribution Methods in the Social Sciences}, by Mark S.
Handcock and Martina Morris, Springer-Verlag, Inc., New York, 1999. ISBN
0387987789.
}
\author{Alexey Shipunov}
% \seealso{}
\examples{
salary <- c(21, 19, 27, 11, 102, 25, 21)
Gini(salary)
new.1000 <- sample((median(salary) - IQR(salary)) :
(median(salary) + IQR(salary)), 1000, replace=TRUE)
salary2 <- c(salary, new.1000)
Gini(salary2)
salary3 <- salary[-which.max(salary)]
salary3
Gini(salary3)
salary4 <- c(salary3, 1010)
salary4
Gini(salary4)
}
\keyword{univar}
|
34662d734a7fd2b67d6687211666ed3eb519f1a1
|
3ef1867d88291165d60c1189f84a27fb04ab1b7c
|
/exam/exam2_Q2.R
|
41a5536f448d5eb1e789840f12d467db3a80f51b
|
[] |
no_license
|
dalsgit/510
|
d319476b7ba1e2319c91c55f9d010749bb926a65
|
04af567cf4339edebd2d09ca8982183a59d01d3f
|
refs/heads/master
| 2021-01-19T09:03:52.182561
| 2017-05-15T20:11:40
| 2017-05-15T20:11:40
| 82,082,671
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 825
|
r
|
exam2_Q2.R
|
setwd("C:/study/psu/git/510/exam")
library(astsa)
y=ts(scan("e2q2.txt"))
plot(y,type="b")
diff1 = diff(y,1)
plot(diff1,type="b")
model = ts.intersect(y, lag1y=lag(y,-1))
x = model[,1]
P = model[,2]
c = -86 ## Threshold value
##Regression for values below the threshold
less = (P<c)
x1 = x[less]
P1 = P[less]
out1 = lm(x1~P1)
summary(out1)
##Regression for values above the threshold
greater = (P>=c)
x2 = x[greater]
P2 = P[greater]
out2 = lm(x2~P2)
summary(out2)
##Residuals
res1 = residuals(out1)
res2 = residuals(out2)
less[less==1]= res1
greater[greater==1] = res2
resid = less + greater
acf2(resid)
##Predicted values
less = (P<c)
greater = (P>=c)
fit1 = predict(out1)
fit2 = predict(out2)
less[less==1]= fit1
greater[greater==1] = fit2
fit = less + greater
plot(y, type="o")
lines(fit, col = "red", lty="dashed")
|
b397c046b66223ac297a1119571f15956cd1a9a7
|
22c8c61fd3f43093dba2ca6320804ec726c7a7e5
|
/1_linear_model_miRNA_mRNA.R
|
40c5c51b1a4ca0fa907989acff08c7c782b28bc6
|
[
"MIT"
] |
permissive
|
rwindsor1/miRNA_hallmarks_of_cancer
|
add8d92ba2f143ac282604377a25e355831f7733
|
0c6e504033ef3d79ef642fa8a8f339feb4f20ad6
|
refs/heads/master
| 2020-04-23T07:14:32.169467
| 2018-10-10T01:53:38
| 2018-10-10T01:53:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 17,928
|
r
|
1_linear_model_miRNA_mRNA.R
|
#linear model of miRNAs predicting various signature scores
library(RankProd)
library(reshape2)
library(penalized)
cancer_types_list <- list();
cancer_types_list[[1]] <- c('BRCA','UCEC','HNSC')
cancer_types_list[[2]] <- c('KIRC','LUAD','THCA')
cancer_types_list[[3]] <- c('PRAD','LUSC','OV')
cancer_types_list[[4]] <- c('STAD','BLCA','COAD')
cancer_types_list[[5]] <- c('LIHC','CESC','KIRP')
all_cancer_types <- melt(cancer_types_list)$value
#load the signatures
sig_fnames_list <- list();
sig_names_list <- list();
categories_of_sigs <- c('invasion','energetics','immortality','growth_suppressors','genome_instability','angiogenesis','apoptosis','proliferation','inflammation')
sig_fnames_list[['invasion']] <- c('HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.txt','invasiveness_gene_sig_entrez_marsan2014.txt')
sig_names_list[['invasion']] <- c('Hallmark: Epithelial Mesenchymal Transition','Invasiveness, Marsan 2014')
sig_fnames_list[['energetics']] <- c('HALLMARK_OXIDATIVE_PHOSPHORYLATION.txt','HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY.txt')
sig_names_list[['energetics']] <- c('Hallmark: Oxidative Phosphorylation','Hallmark: Reactive Oxygen Species Pathway')
sig_fnames_list[['immortality']] <- c('HALLMARK_G2M_CHECKPOINT.txt')
sig_names_list[['immortality']] <- c('Hallmark: G2M Checkpoint')
sig_fnames_list[['growth_suppressors']] <- c('HALLMARK_PI3K_AKT_MTOR_SIGNALING.txt','HALLMARK_XENOBIOTIC_METABOLISM.txt')
sig_names_list[['growth_suppressors']] <- c('Hallmark: PI3K AKT MTOR Signaling','Hallmark: Xenobiotic Metabolism')
sig_fnames_list[['genome_instability']] <- c('HALLMARK_DNA_REPAIR.txt','HALLMARK_P53_PATHWAY.txt')
sig_names_list[['genome_instability']] <- c('Hallmark: DNA Repair','Hallmark: p53 Pathway')
sig_fnames_list[['angiogenesis']] <- c('hypoxia_gene_sig_entrez_probes.txt','HALLMARK_ANGIOGENESIS.txt','HALLMARK_HYPOXIA.txt','angiogenesis_gene_sig_entrez_desmedt2008_pos.txt','Masiero2013angiogenesisENTREZ.txt')
sig_names_list[['angiogenesis']] <- c('Hypoxia, Buffa 2010','Hallmark: Angiogenesis','Hallmark: Hypoxia','Angiogenesis, Desmedt 2008','Angiogenesis, Masiero 2013')
sig_fnames_list[['apoptosis']] <- c('HALLMARK_APOPTOSIS.txt','apoptosis_gene_sig_entrez_desmedt2008_pos.txt')
sig_names_list[['apoptosis']] <- c('Hallmark: Apoptosis','Apoptosis, Desmedt 2008')
sig_fnames_list[['proliferation']] <-c('proliferation_gene_sig_entrez_desmedt2008_pos.txt','HALLMARK_KRAS_SIGNALING_UP.txt')
sig_names_list[['proliferation']] <-c('Proliferation, Desmedt 2008','Hallmark: KRAS Signaling Up')
sig_fnames_list[['inflammation']] <- c('HALLMARK_INFLAMMATORY_RESPONSE.txt','HALLMARK_IL2_STAT5_SIGNALING.txt','HALLMARK_IL6_JAK_STAT3_SIGNALING.txt','HALLMARK_TGF_BETA_SIGNALING.txt','HALLMARK_TNFA_SIGNALING_VIA_NFKB.txt','immune_gene_sig_entrez_desmedt2008_pos.txt')
sig_names_list[['inflammation']] <- c('Hallmark: Inflammatory Response','Hallmark: IL2 STAT5 Signaling','Hallmark: IL6 JAK STAT3 Signaling','Hallmark: TGF Beta Signaling','Hallmark: TNFa Signaling via NFKB','Immune, Desmedt 2008')
sigs_list_by_cat <- list();
for(sig_category in categories_of_sigs){
sigs_list_by_cat[[sig_category]] <- list();
for(i in 1:length(sig_fnames_list[[sig_category]])){
fname <- sig_fnames_list[[sig_category]][i]
genes = read.csv(paste0('gene_signatures/',fname), header=F, stringsAsFactors=F, colClasses = "character")
# print(genes)
sigs_list_by_cat[[sig_category]][[sig_names_list[[sig_category]][i]]]<- genes
}
}
#load the datasets
all_mRNA_datasets <- list();
for (cancer_type in all_cancer_types){
print(cancer_type)
if(cancer_type!='BRCA'){
fname_mrna <- paste0('../Reprocessed GDAC data/',cancer_type,'/mRNA/tumour/cleaned_mRNA.txt')
}else{
fname_mrna <- paste0('../Reprocessed GDAC data/',cancer_type,'/mRNA/tumour/cleaned_mRNA_ductal.txt')
}
all_mRNA_datasets[[cancer_type]] <- read.table(fname_mrna, sep='\t',stringsAsFactors = FALSE, header=TRUE,quote="")
colnames(all_mRNA_datasets[[cancer_type]]) <- gsub('[.]','-',colnames(all_mRNA_datasets[[cancer_type]]))
# want log2 data
all_mRNA_datasets[[cancer_type]] <- log2(all_mRNA_datasets[[cancer_type]]+1)
all_mRNA_datasets[[cancer_type]][!is.finite(as.matrix(all_mRNA_datasets[[cancer_type]]))] <- NA
}
#load the miRNA
all_miRNA_datasets <- list();
all_miRNA <- c()
for (cancer_type in all_cancer_types){
fname_miRNA <- paste0('../Reprocessed GDAC data/',cancer_type,'/miRNA/tumour/cleaned_miRNA_mature.txt')
all_miRNA_datasets[[cancer_type]] <- read.table(fname_miRNA, sep='\t',stringsAsFactors = FALSE, header=TRUE,quote="")
colnames(all_miRNA_datasets[[cancer_type]]) <- gsub('[.]','-',colnames(all_miRNA_datasets[[cancer_type]]))
all_miRNA <- unique(c(all_miRNA,rownames(all_miRNA_datasets[[cancer_type]])))
}
all_coeffs <- list();
all_rank_product_matrices <- list();
for (category in categories_of_sigs){
count <- 1
for (gene_sig in sigs_list_by_cat[[category]]){
sig_name <- sig_names_list[[category]][count]
print(sig_name)
all_coeffs[[sig_name]] <- matrix(0,nrow=length(all_miRNA),ncol=length(all_cancer_types))
row.names(all_coeffs[[sig_name]]) <- all_miRNA
colnames(all_coeffs[[sig_name]]) <- all_cancer_types
for (cancer_type in all_cancer_types){
print(cancer_type)
genes_present <- intersect(rownames(all_mRNA_datasets[[cancer_type]]),gene_sig$V1)
#compute and score the scores
scores <- apply(all_mRNA_datasets[[cancer_type]][genes_present,], 2, function(x) median(x,na.rm=T))
#cross-validated linear model
coeffs <- get_coefficients_pre_filter(cancer_type,scores)
#store the miRNA results
all_coeffs[[sig_name]][names(coeffs),cancer_type] <- coeffs
}
# #compute the rank-product matrix
# all_rank_product_matrices[[sig_name]] <- make_rank_prod_matrix(all_coeffs[[sig_name]])
count <- count + 1
}
}
#the above code is run on a server for each signature in parallel, and the files are saved into a folder
#called 'server_data.' Using this, we re-load everythng in R and then compute the overall rank prod matrices
#the following is the code to load in from all signatures the files from the code running on server
all_signatures <- melt(sig_names_list)$value
rank_prod_tables <- list();
RP_out_values <- list();
all_coeffs_tmp <- list();
for (sig_name in all_signatures){
load(paste0('server_data/all_coeffs_',sig_name,'.rda'))
all_coeffs_tmp[[sig_name]] <- all_coeffs[[sig_name]]
}
all_coeffs <- all_coeffs_tmp
#library(rankProd)
rank_prod_tables <- list();
RP_out_values <- list();
# for (category in categories_of_sigs){
# count <- 1
# for (gene_sig in sigs_list_by_cat[[category]]){
# sig_name <- sig_names_list[[category]][count]
#here we need to do the rankprod
for (sig_name in all_signatures){
all_coeffs[[sig_name]] <- all_coeffs[[sig_name]][which(rowSums(all_coeffs[[sig_name]]==0) < length(colnames(all_coeffs[[sig_name]]))),]
print(dim(all_coeffs[[sig_name]]))
RP.out <- RP(all_coeffs[[sig_name]],rep(1,15))
RP_out_values[[sig_name]] <- RP.out
rank_prod_tables[[sig_name]] <- topGene(RP.out,cutoff = 0.05,method="pfp",gene.names=rownames(all_coeffs[[sig_name]]))
# count <- count + 1
}
# }
#then save the outputs
save(file='rank_prod_output_pre_filtered.rda',RP_out_values)
save(file='rank_prod_tables_out_pre_filtered.rda',rank_prod_tables)
for (sig_name in all_signatures){
#for each sig let's save the heatmap of the miRNA coefficients to see whether the cancers act the same
all_coeffs_tmp_mod <- all_coeffs[[sig_name]][which(rowSums(all_coeffs[[sig_name]]!=0)!=0),]
gplots::heatmap.2( all_coeffs_tmp_mod,
col = gplots::colorpanel(100,"blue","white","red"),#gplots::colorpanel(100,"white","red"),#gplots::redgreen(100),#gplots::colorpanel(100,"blue","white","red"), #redgreen(100),#colorpanel(100,"red","yellow","green"),
trace = "none",
xlab = "Gene ID",
ylab="Gene ID",
na.color="grey",
#labRow=rownames(autocors),
#labCol=colnames(autocors),#gene_sig,
main = paste0("\n\n", sig_name),
dendrogram = "both",
#symbreaks = T,
Rowv = T,Colv=T ,key.xlab='Rho',key.ylab=NA, key.title=NA,margins=c(7,7),cexRow=0.15,cexCol=0.45)
dev.copy(pdf,paste0('miRNA_hmap_preFiltered_',sig_name,'.pdf'),width=12,height=12)
dev.off()
}
# #----------the following is to make a heatmap but for the miRNA that recur among cancer types for each signature themselves, not the families:
# all_sigs_miRNA_list <- list()
# for (sig_name in all_signatures){
# cur_miRNAs_list <- c()
# for (cancer_type in all_cancer_types){
# cur_miRNAs_list <- c(cur_miRNAs_list,rownames(all_coeffs[[sig_name]])[which(all_coeffs[[sig_name]][,cancer_type] < 0)])
# }
# print(table(cur_miRNAs_list))
# all_sigs_miRNA_list[[sig_name]] <- table(cur_miRNAs_list)
# } #counts frequency of the miRNA occurring across all cancer types as significant
# heatmap_matrix <- matrix(0, nrow=length(unique(melt(all_sigs_miRNA_list)[,1])),ncol=length(all_signatures))
# row.names(heatmap_matrix) <- unique(melt(all_sigs_miRNA_list)[,1])
# colnames(heatmap_matrix) <- all_signatures
# for (sig_name in all_signatures){
# heatmap_matrix[names(all_sigs_miRNA_list[[sig_name]]),sig_name] <- as.numeric(all_sigs_miRNA_list[[sig_name]])
# }
# gplots::heatmap.2( heatmap_matrix,
# col = gplots::colorpanel(100,"white","red"),#gplots::colorpanel(100,"white","red"),#gplots::redgreen(100),#gplots::colorpanel(100,"blue","white","red"), #redgreen(100),#colorpanel(100,"red","yellow","green"),
# trace = "none",
# # xlab = "Gene ID",
# # ylab="Gene ID",
# na.color="grey",
# #labRow=rownames(autocors),
# #labCol=colnames(autocors),#gene_sig,
# main = paste0("\n", "miRNA down freq \nof occurrence"),
# dendrogram = "both",
# #symbreaks = T,
# Rowv = T,Colv=T ,key.xlab='Rho',key.ylab=NA, key.title=NA,margins=c(7,7),cexRow=0.11,cexCol=0.35)
# dev.copy(pdf,paste0('miRNA_freq_preFiltered_DOWN_all_sigs.pdf'),width=12,height=12)
# dev.off()
# #--------------------------------------------------------------------------------------------------
get_coefficients <- function(cancer_type,scores){
#load in the miRNA data
#fname_miRNA <- paste0('../Reprocessed GDAC data/',cancer_type,'/miRNA/tumour/cleaned_miRNA_mature_log2.txt')
miRNA_data <- all_miRNA_datasets[[cancer_type]]#read.table(miRNA_fName, sep='\t',stringsAsFactors = FALSE, header=TRUE,quote="")
#colnames(miRNA_data) <- gsub('[.]','-',colnames(miRNA_data))
#take only common subset of miRNA and scores
common_colNames <- intersect(colnames(miRNA_data),names(scores))
#take just the common pieces
miRNA_data <- miRNA_data[,common_colNames]
scores <- scores[common_colNames]
#z-transform the scores
scores <- as.numeric(scores) - mean(as.numeric(scores))/sd(as.numeric(scores))
print(sum(is.na(scores)))
#expression filter for miRNA
expression_threshold <- 0.80 # means that at least 10% of samples must have a nonzero value of the mRNA
miRNA_data <-miRNA_data[which((rowSums(miRNA_data==0)) < ((1-expression_threshold) * length(colnames(miRNA_data)))),]
#remove NA values from miRNA data
# expression_threshold <- 0.5
# miRNA_data <-miRNA_data[which((rowSums(is.na(miRNA_data)==0)) < ((1-expression_threshold) * length(colnames(miRNA_data)))),]
# miRNA_data <-miRNA_data[which(rowSums(is.na(miRNA_data))==0),]
miRNA_data <- as.matrix(log2(miRNA_data))
miRNA_data[!(is.finite(miRNA_data))] <- NA
#z-transform the miRNA data
for (j in 1:length(rownames(miRNA_data))){
miRNA_data[j,] <- (as.numeric(miRNA_data[j,]) - mean(as.numeric(miRNA_data[j,])))/sd(as.numeric(miRNA_data[j,]))
}
print(paste0("mirna " , sum(is.na(miRNA_data))))
#penalised linear regression
new_df <- na.omit(t(rbind(scores,miRNA_data)))
colnames(new_df) <- c('scores',rownames(miRNA_data))
print(new_df[1:4,1:4])
lambda_2_values <- c(0, 0.01, 0.1,1,10,100)
max_likelihood <- -9999999999
for (lambda2_val in lambda_2_values){
cross_val_model <- optL1(response = new_df[,1],penalized = new_df[,2:length(colnames(new_df))], lambda2 = lambda2_val,data=as.data.frame(new_df),model="linear",fold=10,trace=F)#,trace=F,maxiter=1000,tol=.Machine$double.eps^0.23)
# cross_val_model <- optL2(response = all_sig_scores[,1],penalized = all_sig_scores[,2:length(colnames(all_sig_scores))], minlambda2 = 0,maxlambda2=100,data=all_sig_scores[,2:length(colnames(all_sig_scores))],model="linear",fold=10)#lambda2 = lambda2_val,data=all_sig_scores,model="linear",fold=10)
if ((cross_val_model$fullfit)@loglik > max_likelihood){
best_model <<- cross_val_model
best_lambda <- lambda2_val
}
}
miRNA_names_reported <- intersect(names(coef(best_model$fullfit)), rownames(miRNA_data))
#best_coef_matrix[rownames(miRNA_matrix)[i],mRNA_names_reported] <- coef(best_model$fullfit)[mRNA_names_reported]
#return the coefficients
coef(best_model$fullfit)[miRNA_names_reported]
}
get_coefficients_pre_filter <- function(cancer_type,scores){
#load in the miRNA data
#fname_miRNA <- paste0('../Reprocessed GDAC data/',cancer_type,'/miRNA/tumour/cleaned_miRNA_mature_log2.txt')
miRNA_data <- all_miRNA_datasets[[cancer_type]]#read.table(miRNA_fName, sep='\t',stringsAsFactors = FALSE, header=TRUE,quote="")
#colnames(miRNA_data) <- gsub('[.]','-',colnames(miRNA_data))
#take only common subset of miRNA and scores
common_colNames <- intersect(colnames(miRNA_data),names(scores))
#take just the common pieces
miRNA_data <- miRNA_data[,common_colNames]
scores <- scores[common_colNames]
#z-transform the scores
scores <- as.numeric(scores) - mean(as.numeric(scores))/sd(as.numeric(scores))
print(sum(is.na(scores)))
#expression filter for miRNA
expression_threshold <- 0.80 # means that at least 10% of samples must have a nonzero value of the mRNA
miRNA_data <-miRNA_data[which((rowSums(miRNA_data==0)) < ((1-expression_threshold) * length(colnames(miRNA_data)))),]
#remove NA values from miRNA data
# expression_threshold <- 0.5
# miRNA_data <-miRNA_data[which((rowSums(is.na(miRNA_data)==0)) < ((1-expression_threshold) * length(colnames(miRNA_data)))),]
# miRNA_data <-miRNA_data[which(rowSums(is.na(miRNA_data))==0),]
miRNA_data <- as.matrix(log2(miRNA_data))
miRNA_data[!(is.finite(miRNA_data))] <- NA
#z-transform the miRNA data
for (j in 1:length(rownames(miRNA_data))){
miRNA_data[j,] <- (as.numeric(miRNA_data[j,]) - mean(as.numeric(miRNA_data[j,])))/sd(as.numeric(miRNA_data[j,]))
}
print(paste0("mirna " , sum(is.na(miRNA_data))))
#first we need to subset the data into folds
new_df <- na.omit(t(rbind(scores,miRNA_data)))
colnames(new_df) <- c('scores',rownames(miRNA_data))
folds <- 10
nrows_combined_df <- 1:dim(new_df)[1]
best_overall_error <- 99999999
for (i in 0:(folds-1)){
new_df_subset <- as.data.frame(new_df[!(nrows_combined_df%%folds==i),]) #takes out the 1/nth row of the data set
#train the univaraite model
#put these as inputs to the penalized model
linear_models_miRNA <- matrix(,nrow=length(rownames(miRNA_data)),ncol=1)
row.names(linear_models_miRNA) <- rownames(miRNA_data)
for (j in 1:length(rownames(miRNA_data))){
univariate_data <- as.data.frame(cbind(new_df_subset[,1],new_df_subset[,(j+1)]))
colnames(univariate_data) <- c('sig_score','miRNA')
# print(univariate_data)
univariate_model <- lm(formula = sig_score ~ miRNA,data = univariate_data)
# print(summary(univariate_model))
linear_models_miRNA[j] <- (summary(univariate_model)$coefficients)[2,4]
#tmp_model <- coef(summary(coxph(Surv(as.numeric(combined_df_subsetted$times),as.numeric(combined_df_subsetted$events)) ~ combined_df_subsetted[,j+2])))
#cox_models_circRNA[j] <- tmp_model[5] #c(tmp_model[2],tmp_model[5])
}
#significant miRNAs are those w p < 0.2:
significant_miRNAs <- rownames(linear_models_miRNA)[which(linear_models_miRNA < 0.2 & !is.nan(linear_models_miRNA))]
# print("sig MiRNA")
# print(significant_miRNAs)
#penalised linear regression
# print(new_df_subset[1:4,1:4])
lambda_2_values <- c(0, 0.01, 0.1,1,10,100)
max_likelihood <- -9999999999
for (lambda2_val in lambda_2_values){
cross_val_model <- optL1(response = new_df_subset[,1],penalized = new_df_subset[,significant_miRNAs], lambda2 = lambda2_val,data=as.data.frame(new_df_subset),model="linear",fold=10,trace=F)#,trace=F,maxiter=1000,tol=.Machine$double.eps^0.23)
# cross_val_model <- optL2(response = all_sig_scores[,1],penalized = all_sig_scores[,2:length(colnames(all_sig_scores))], minlambda2 = 0,maxlambda2=100,data=all_sig_scores[,2:length(colnames(all_sig_scores))],model="linear",fold=10)#lambda2 = lambda2_val,data=all_sig_scores,model="linear",fold=10)
if ((cross_val_model$fullfit)@loglik > max_likelihood){
best_model <<- cross_val_model
best_lambda <- lambda2_val
}
}
#now that we know the best model, let's test it on the other 1/n of the data, and record the error
unused_df <- as.data.frame(new_df[(nrows_combined_df%%folds==i),])
current_predictions <- predict(best_model$fullfit, penalized=unused_df[,significant_miRNAs],data=unused_df)
cur_error <- norm((as.numeric(unused_df[,1]) - as.numeric(current_predictions)),type="2")
# print(cur_error)
if (cur_error < best_overall_error){
best_overall_error <- cur_error
best_overall_model <- best_model
best_overall_lambda <- best_lambda
}
}
miRNA_names_reported <- intersect(names(coef(best_overall_model$fullfit)), rownames(miRNA_data))
#best_coef_matrix[rownames(miRNA_matrix)[i],mRNA_names_reported] <- coef(best_model$fullfit)[mRNA_names_reported]
#return the coefficients
coef(best_overall_model$fullfit)[miRNA_names_reported]
}
|
5b9bf8a8d356d8ab6a96eeba958404572ce555c0
|
1cb097e8ead264823dac5e5e8821ebd6132da64d
|
/R/utils.R
|
5deb022b80ae47994062a0a2d62a0eaa40bb897e
|
[] |
no_license
|
impact-initiatives/koboAPI
|
2cd41c1655364ae8977e12a5ae4255f5908f5a2b
|
91b802a44edf92ba6d9eb192148d7267bce87b1b
|
refs/heads/master
| 2021-05-19T07:04:23.139014
| 2020-01-08T14:57:46
| 2020-01-08T14:57:46
| 251,577,490
| 0
| 1
| null | 2020-03-31T11:00:55
| 2020-03-31T11:00:54
| null |
UTF-8
|
R
| false
| false
| 488
|
r
|
utils.R
|
#' @name nullToNA
#' @rdname nullToNA
#' @title Replaces NULL values by NAs
#' @description Replaces NULL values by NAs
#' @param x Vector to be treated
#' @return Returns the vector with NULL replaced
#' @author Elliott Messeiller
#'
#' @export AddstartCol_SelectMultiple
nullToNA <- function(x) {
x[sapply(x, is.null)] <- NA
return(x)
}
replace_x <- function(x, replacement = NA_character_) {
if (length(x) == 0 || length(x[[1]]) == 0) {
replacement
} else {
x
}
}
|
c85c1756b40b634060fb71a7ba7ee614664c8077
|
e7ea72b1750bb43c6ec4ad9c12479cb9c93681ba
|
/week2/moneyball.R
|
a8db134964ea82824baec086862307bc99e4dff0
|
[] |
no_license
|
scholarly/ae
|
0ea867c7b53f5a396bc536df6950fc3e3888ad82
|
566ebcfd1419fba25698b7835554cbe46f308edf
|
refs/heads/master
| 2021-01-01T19:46:33.204250
| 2015-04-27T23:55:43
| 2015-04-27T23:55:43
| 31,639,688
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 782
|
r
|
moneyball.R
|
baseurl = "https://courses.edx.org/c4x/MITx/15.071x_2/asset/"
files = c( "baseball.csv")
get_data = function(url,local){
if(!file.exists(local)){
download.file(url,local,"curl")
}
read.csv(local)
}
data_dir = function(fname){
paste("data",fname,sep="/")
}
getm = function(file){
get_data(paste(baseurl,file,sep=""),data_dir(file))
}
bb = getm(files)
mb = subset(bb,Year<2002)
mb$RD = mb$RS - mb$RA
WinsReg = lm(W ~ RD, data=mb)
RunsReg = lm(RS ~ OBP + SLG, data=mb)
ORunsReg = lm(RA ~ OOBP + OSLG, data=mb)
teamRank = c(1,2,3,3,4,4,4,4,5,5)
wr12 =c(SFG=94,DET=88,NYY=95,STL=88,BAL=93,OAK=94,WSN=98,CIN=97,TEX=93,ATL=94)
wr13 = c(BRS=97,STL=97,LAD=92,DET=93,TBR=92,OAK=96,PTP=94,ATL=96,CLV=92,CIN=90)
print(cor(teamRank,wr12))
print(cor(teamRank,wr13))
|
3d2d73da9b69160e6c5122e8a2745b8a942620a4
|
860979ede989eec54b804e18c023a84f1b196ceb
|
/cachematrix.R
|
17966d1ffce838cba09eb5b222feceb3a2ebb0a2
|
[] |
no_license
|
oveedl/ProgrammingAssignment2
|
705366e8aac50d9f34b35f6045a99365ff43a356
|
c691d388b1a82a1dd6be98c28dbf8f35f60124f6
|
refs/heads/master
| 2021-01-14T11:25:52.433109
| 2015-10-18T08:21:15
| 2015-10-18T08:21:15
| 44,273,977
| 0
| 0
| null | 2015-10-14T20:18:52
| 2015-10-14T20:18:52
| null |
UTF-8
|
R
| false
| false
| 1,321
|
r
|
cachematrix.R
|
## This set of functions handles an object for storing a matrix and
## and its inverse. The inverse is only calculated when it is asked
## for, and then stored in a cache to avoid more computational work
## if it should be needed again.
## Create a CacheMatrix object containing a matrix and an empty
## placeholder for the inverse. Given an object created with
## xCache <- makeCacheMatrix(x)
## the matrix x can be extracted with xCache$get(),
## and it can be exchanged for another one with xCache$set(x).
makeCacheMatrix <- function(x = matrix()) {
invx <- NULL
set <- function(y) {
x <<- y
invx <<- NULL
}
get <- function() x
setinv <- function(theinv) invx <<- theinv
getinv <- function() invx
list(set = set, get = get,
setinv = setinv, getinv = getinv)
}
## Use this function to extract the inverse from a CacheMatrix
## object. If xCache is such an object, the inverse of the matrix
## inside is retrieved with invx <- cacheSolve(xCache). If the
## inverse is not present, it is automatically calculated and
## stored in xCache, before beeing returned.
cacheSolve <- function(x, ...) {
invx <- x$getinv()
if(!is.null(invx)) {
return(invx)
}
xmatrix <- x$get()
invx <- solve(xmatrix, ...)
x$setinv(invx)
invx
}
|
313bf207b976254ddd1155cde46e56f8ff057cb9
|
fafb9d8b9c02b4a5dc6bad0021107feb8abdbef6
|
/man/change_speed.Rd
|
268f8bb19f8cd6bc7641532e2b186f565418a3c4
|
[
"MIT"
] |
permissive
|
UBC-MDS/AudioFilters_R
|
e8bdf710796961bd9eef13ba99962ea6d5318259
|
ffd77322b38d889104692152d7ca941cd3cbd2de
|
refs/heads/master
| 2020-04-21T04:42:03.806263
| 2019-03-09T04:11:31
| 2019-03-09T04:11:31
| 169,320,277
| 1
| 2
|
MIT
| 2019-03-07T18:22:18
| 2019-02-05T22:02:43
|
R
|
UTF-8
|
R
| false
| true
| 587
|
rd
|
change_speed.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/change_speed.R
\name{change_speed}
\alias{change_speed}
\title{Change the playback speed of an audio signal}
\usage{
change_speed(input_signal, rate)
}
\arguments{
\item{input_signal}{numeric}
\item{rate}{numeric, desired rate of change to the speed.
To increase the speed, pass in a value greater than 1.0.
To decrease the speed, pass in a value between 0.0 and 1.0.}
}
\value{
numeric, vector representing the audio signal with changed speed.
}
\description{
Change the playback speed of an audio signal
}
|
8b30374a89723d3185be008a19e72c56841f685b
|
d4638aa62f44afebf5234eff1e6977b3c0738d3d
|
/man/market.api.process.Rd
|
e727cb82d424e9f60871a8590add6e448e785be7
|
[
"MIT"
] |
permissive
|
cran/Rbitcoin
|
549137db2d433674021ee54908e5882d0da2016e
|
a3f72a513fa20076daff1c1d8d4fdf9014e41243
|
refs/heads/master
| 2020-05-20T09:22:30.013880
| 2014-09-01T00:00:00
| 2014-09-01T00:00:00
| 17,693,080
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,930
|
rd
|
market.api.process.Rd
|
% Generated by roxygen2 (4.0.1): do not edit by hand
\name{market.api.process}
\alias{market.api.process}
\title{Process market API}
\usage{
market.api.process(market, currency_pair, action, req = list(), ...,
verbose = getOption("Rbitcoin.verbose", 0),
on.market.error = expression(stop(e[["message"]], call. = FALSE)),
on.error = expression(stop(e[["message"]], call. = FALSE)),
api.dict = NULL, raw.query.res = FALSE)
}
\arguments{
\item{market}{character, example: \code{'kraken'}.}
\item{currency_pair}{character vector of length 2, ex. \code{c(base = 'BTC', quote = 'EUR')}. Order does matter.}
\item{action}{character, defined process to get organized data.}
\item{req}{list with action details (price, amount, tid, oid, etc.) unified across the markets specific per action, see examples.}
\item{\dots}{objects to be passed to \code{\link{market.api.query}}
\itemize{
\item auth params: \code{key}, \code{secret}, \code{client_id} (last one used on bitstamp),
}}
\item{verbose}{integer. Rbitcoin processing messages, print to console if \code{verbose > 0}, each subfunction reduce \code{verbose} by 1. If missing then \code{getOption("Rbitcoin.verbose",0)} is used, by default \code{0}.}
\item{on.market.error}{expression to be evaluated on market level error. Rules specified in \code{\link{api.dict}}.}
\item{on.error}{expression to be evaluated on R level error related to \code{market.api.query}. For details read \code{\link{market.api.query}}.}
\item{api.dict}{data.table user custom API dictionary definition, if not provided function will use default Rbitcoin \code{\link{api.dict}}.}
\item{raw.query.res}{logical skip post-processing are return results only after \code{fromJSON} processing. Useful in case of change results structure from market API. It can always be manually post-processed as a workaround till the Rbitcoin update.}
}
\value{
Returned value depends on the \code{action} param. All actions will return market, currency pair (except \code{wallet} and \code{open_orders} which returns all currencies), R timestamp, market timestamp and below data (in case if market not provide particular data, it will result \code{NA} value):
\itemize{
\item \code{'ticker'} returns \code{data.table} with fields: \code{last}, \code{vwap}, \code{volume}, \code{ask}, \code{bid}.
\item \code{'wallet'} returns \code{data.table} with fields: \code{currency}, \code{amount}, \code{fee}.
\item \code{'order_book'} returns \code{list} with API call level attributes and sub elements \code{[['asks']]} and \code{[['bids']]} as \code{data.table} objects with order book including already calculated cumulative \code{amount}, \code{price} and \code{value}.
\item \code{'open_orders'} returns \code{data.table} with fields: \code{oid}, \code{type}, \code{price}, \code{amount}.
\item \code{'place_limit_order'} returns \code{data.table} with fields: \code{oid}, \code{type}, \code{price}, \code{amount}.
\item \code{'cancel_order'} returns \code{data.table} with fields: \code{oid}.
\item \code{'trades'} returns \code{list} with API call level attributes and sub element \code{[['trades']]} as \code{data.table} (ASC order) with fields: \code{date}, \code{price}, \code{amount}, \code{tid}, \code{type}.
}
}
\description{
Unified processing of API call according to API dictionary \code{\link{api.dict}}. Limited to markets and currency processing defined in \code{api.dict}, in case of currency pairs and methods not availble in dictionary use \code{\link{market.api.query}} directly. This function perform pre processing of request and post processing of API call results to unified structure across markets. It will result truncation of most (not common across the markets) attributes returned. If you need the full set of data returned by market's API you should use \code{\link{market.api.query}}.
}
\details{
To do not spam market's API, use \code{Sys.sleep(10)} between API calls.
}
\note{
The api dictionary was not fully tested, please follow the examples, if you find any bugs please report. Use only api dictionary \code{\link{api.dict}} from trusted source, in case if you use other \code{api.dict} it is advised to review pre-process, post-process and catch_market_error functions for markets and currency pairs you are going to use. Market level error handling might not fully work as not all markets returns API call status information.
}
\examples{
\dontrun{
# get ticker from market
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action='ticker')
# get ticker from all markets and combine
ticker_all <- rbindlist(list(
market.api.process(market = 'bitstamp', currency_pair = c('BTC', 'USD'), action='ticker')
,market.api.process(market = 'btce', currency_pair = c('LTC', 'USD'), action='ticker')
,{Sys.sleep(10);
market.api.process(market = 'btce', currency_pair = c('LTC', 'BTC'), action='ticker')}
,{Sys.sleep(10);
market.api.process(market = 'btce', currency_pair = c('NMC', 'BTC'), action='ticker')}
,market.api.process(market = 'kraken', currency_pair = c('BTC','EUR'), action='ticker')
,{Sys.sleep(10);
market.api.process(market = 'kraken', currency_pair = c('LTC','EUR'), action='ticker')}
,{Sys.sleep(10);
market.api.process(market = 'kraken', currency_pair = c('BTC','LTC'), action='ticker')}
))
print(ticker_all)
# get wallet from market
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'wallet',
key = '', secret = '')
# get wallet from all markets and combine
wallet_all <- rbindlist(list(
market.api.process(market = 'bitstamp', currency_pair = c('BTC', 'USD'), action = 'wallet',
client_id = '', key = '', secret = ''),
market.api.process(market = 'btce', currency_pair = c('LTC', 'USD'), action = 'wallet',
method = '', key = '', secret = ''),
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'wallet',
key = '', secret = '')
))
print(wallet_all)
# get order book from market
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'order_book')
# get open orders from market
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'open_orders',
key = '', secret = '')
# place limit order
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'place_limit_order',
req = list(type = 'sell', amount = 1, price = 8000), # sell 1 btc for 8000 eur
key = '', secret = '')
# cancel order
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'cancel_order,
req = list(oid = 'oid_from_open_orders'),
key = '', secret = '')
# get trades
market.api.process(market = 'kraken', currency_pair = c('BTC', 'EUR'), action = 'trades')
}
}
\seealso{
\code{\link{market.api.query}}
}
|
584fbe394dbdb8ced0e7c1e8fa52de6de982d857
|
6526ee470658c2f1d6837f7dc86a81a0fbdcffd5
|
/man/setPodsMatrix.mwIPM.Rd
|
8747d4d60998f9995ac616cf0142994b466f3bde
|
[] |
no_license
|
mdlama/milkweed
|
c7e8a24021a35eb6fbef13360400d2d4069b4649
|
b791c8b39802f33471f8e827f369afa47c06d6af
|
refs/heads/master
| 2023-09-06T03:00:45.554997
| 2022-09-14T15:25:58
| 2022-09-14T15:25:58
| 76,479,540
| 0
| 0
| null | 2021-09-21T19:04:44
| 2016-12-14T16:59:01
|
R
|
UTF-8
|
R
| false
| true
| 477
|
rd
|
setPodsMatrix.mwIPM.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mw_ipm.R
\name{setPodsMatrix.mwIPM}
\alias{setPodsMatrix.mwIPM}
\title{Create pods matrix.}
\usage{
\method{setPodsMatrix}{mwIPM}(obj, update = TRUE, perturb = rep(0, 4))
}
\arguments{
\item{obj}{A mwIPM model object.}
\item{update}{Update dependencies?}
\item{perturb}{Parameter perturbation vector for sensitivity analysis.}
}
\value{
A mwIPM model object.
}
\description{
Create pods matrix.
}
|
516e27e03cf2674661955a26536217ed968da41f
|
6c321997b2237e3432ebc89866e47c5636e8ccde
|
/man/stratifiedSamplingForCV.Rd
|
020b6b8cd1e59f3ae5f1abe60a61dd72f47b3ac8
|
[] |
no_license
|
cran/coca
|
e37d4a524d58e47400158ac4cfea0ea10570038e
|
2baeffda08df37be4aa3b0638f99e00869a49a37
|
refs/heads/master
| 2021-05-16T23:21:41.927083
| 2020-07-06T16:00:09
| 2020-07-06T16:00:09
| 250,513,558
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 645
|
rd
|
stratifiedSamplingForCV.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fill-moc.R
\name{stratifiedSamplingForCV}
\alias{stratifiedSamplingForCV}
\title{Divide data into 5 subsets using stratified sampling}
\usage{
stratifiedSamplingForCV(response)
}
\arguments{
\item{response}{Vector of categorical responses}
}
\value{
The function returns a vector of labels to assign each observation to
a different fold
}
\description{
This function is used to do stratified subsampling based on the
number of observations in each group in the response
}
\author{
Alessandra Cabassi \email{alessandra.cabassi@mrc-bsu.cam.ac.uk}
}
\keyword{internal}
|
67d3bec99b99b3d4407f9dd1199c4febb1ef612b
|
1d8ca36b20ffe9dc150803662434fe8e04c52b5d
|
/607/Projects/Porject 3/Data-607-Project-Three-Dan-Branch/Data-607-Project-Three-Dan-Branch/textmining 210322 2032.R
|
df8d450dd54be9f4d006617fbc0743e15c187069
|
[] |
no_license
|
zachsfr/Cuny-SPS
|
5842c51b7594b2e8da6f90125ce712ec78eed6e6
|
de707926e72996622f9eb63de38713f03b5f9db6
|
refs/heads/main
| 2023-05-27T14:08:29.826608
| 2021-06-07T19:27:21
| 2021-06-07T19:27:21
| 336,085,896
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,577
|
r
|
textmining 210322 2032.R
|
#This script takes atlanta, a folder of txt job descriptions for data scientists. It generates ds_skills_df, a dataframe. ds_skills_df has 1 row for each job listing in atlanta, and one column for each term in ds_skills_list. The value in each cell is the number of appearances of the column name in the listing.
library(tidyverse)
library(tm)
atlanta <- "C:/Users/dmosc/OneDrive/Documents/academic/CUNY SPS/DATA 607/Proj3/zachsfr project three/Data-607-Project-Three/atlanta"
find <- c("artificial intelligence","amazon web services","[^[[:alnum:]][Cc]\\#","[^[[:alnum:]][Cc]\\+\\+","computer science","computer vision","data analysis","data engineering","data wrangling","deep learning","large datasets","machine learning","natural language processing","neural networks","object oriented","project management","[^[[:alnum:]][Rr][^[[:alnum:]]","scikit-learn","software development","software engineering","time series")
repl <- c("ai","aws"," csharp"," cplusplus","computerscience","computervision","dataanalysis","dataengineering","datawrangling","deeplearning","largedatasets","machinelearning","nlp","neuralnetworks","oop","projectmanagement"," rrrr","scikitlearn","softwaredevelopment","softwareengineering","timeseries")
ds_skills_list <- c("ai","airflow","analysis","aws","azure","bigquery","c","caffe","caffe2","cassandra","communication","computerscience","computervision","cplusplus","csharp","d3","dataanalysis","dataengineering","datawrangling","databases","deeplearning","docker","excel","fintech","git","hadoop","hbase","hive","java","javascript","keras","kubernetes","largedatasets","linux","machinelearning","mathematics","matlab","mongodb","mysql","neuralnetworks","nlp","nosql","numpy","oop","pandas","perl","pig","projectmanagement","publications","python","pytorch","rrrr","sas","scala","scikitlearn","scipy","sklearn","softwaredevelopment","softwareengineering","spark","spss","sql","statistics","tableau","tensorflow","theano","timeseries","unix","visualization")
#Create corpus from Atlanta files#
atlanta_corpus <- VCorpus(DirSource(atlanta, encoding = "UTF-8"), readerControl = list(language = "en"))
#transform corpus#
atlanta_corpus <- tm_map(atlanta_corpus, removeWords, stopwords("english"))
atlanta_corpus <- tm_map(atlanta_corpus, stripWhitespace)
atlanta_corpus <- tm_map(atlanta_corpus, content_transformer(tolower))
#atlanta_corpus <- tm_map(atlanta_corpus, removePunctuation) so I can detect C#, C++
for (i in seq(length(find))) {
atlanta_corpus <- tm_map(atlanta_corpus, content_transformer(function(atlanta_corpus) gsub(atlanta_corpus, pattern = find[i], replacement = repl[i])))
}
atlanta_corpus <- tm_map(atlanta_corpus, removePunctuation) ###########
#build document_term dataframe#
document_term <- DocumentTermMatrix(atlanta_corpus)
document_term <- document_term %>%
as.matrix() %>%
as.data.frame()
#Find members of ds_skills_list in colnames(document_term)#
##PROBLEM: R is not in colnames(document_term)
ds_skills_in_document_term <- cbind(ds_skills_list, ds_skills_list %in% colnames(document_term))
ds_skills_in_document_term <- as.data.frame(ds_skills_in_document_term)
ds_skills_in_document_term <- ds_skills_in_document_term %>%
filter(V2 == "TRUE")
#build ds_skills_df dataframe#
ds_skills_df <- document_term %>%
select(ds_skills_in_document_term$ds_skills_list)
#tidy ds_skills_df#
ds_skills_df <- rownames_to_column(ds_skills_df)
ds_skills_df <- rename(ds_skills_df, "listing" = "rowname", "r" = "rrrr")
ds_skills_df <- ds_skills_df %>%
mutate("listing" = substr(listing,0,nchar(listing)-4))
|
8bfe96da1ef63ec3d112f099c41e56a9fa12c376
|
d746fef241f9a0e06ae48cc3b1fe72693c43d808
|
/ark_87287/d74s4s/d74s4s-018/rotated.r
|
9c2f9abf140f526bce22944a801a2a1f27067859
|
[
"MIT"
] |
permissive
|
ucd-library/wine-price-extraction
|
5abed5054a6e7704dcb401d728c1be2f53e05d78
|
c346e48b5cda8377335b66e4a1f57c013aa06f1f
|
refs/heads/master
| 2021-07-06T18:24:48.311848
| 2020-10-07T01:58:32
| 2020-10-07T01:58:32
| 144,317,559
| 5
| 0
| null | 2019-10-11T18:34:32
| 2018-08-10T18:00:02
|
JavaScript
|
UTF-8
|
R
| false
| false
| 195
|
r
|
rotated.r
|
r=0.46
https://sandbox.dams.library.ucdavis.edu/fcrepo/rest/collection/sherry-lehmann/catalogs/d74s4s/media/images/d74s4s-018/svc:tesseract/full/full/0.46/default.jpg Accept:application/hocr+xml
|
f91d7286520d167f58ac7538b510eb6cd726818a
|
e189d2945876e7b372d3081f4c3b4195cf443982
|
/man/show_samples.Rd
|
78f112112fc985a9ee69d63450fb1b8c4f931fcd
|
[
"Apache-2.0"
] |
permissive
|
Cdk29/fastai
|
1f7a50662ed6204846975395927fce750ff65198
|
974677ad9d63fd4fa642a62583a5ae8b1610947b
|
refs/heads/master
| 2023-04-14T09:00:08.682659
| 2021-04-30T12:18:58
| 2021-04-30T12:18:58
| 324,944,638
| 0
| 1
|
Apache-2.0
| 2021-04-21T08:59:47
| 2020-12-28T07:38:23
| null |
UTF-8
|
R
| false
| true
| 765
|
rd
|
show_samples.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/icevision_utils.R
\name{show_samples}
\alias{show_samples}
\title{Show_samples}
\usage{
show_samples(
dls,
idx,
class_map = NULL,
denormalize_fn = denormalize_imagenet(),
display_label = TRUE,
display_bbox = TRUE,
display_mask = TRUE,
ncols = 1,
figsize = NULL,
show = FALSE,
dpi = 100
)
}
\arguments{
\item{dls}{dataloader}
\item{idx}{image indices}
\item{class_map}{class_map}
\item{denormalize_fn}{denormalize_fn}
\item{display_label}{display_label}
\item{display_bbox}{display_bbox}
\item{display_mask}{display_mask}
\item{ncols}{ncols}
\item{figsize}{figsize}
\item{show}{show}
\item{dpi}{dots per inch}
}
\value{
None
}
\description{
Show_samples
}
|
1f15d8a0b220c92de942a2c6f762ffd945d01c58
|
b77b91dd5ee0f13a73c6225fabc7e588b953842b
|
/shared_functions/calculate_nndist_all_lg.R
|
23cc089ae6f7caa88ecf8f99bebc6acc68b7b0bf
|
[
"MIT"
] |
permissive
|
ksamuk/gene_flow_linkage
|
a1264979e28b61f09808f864d5fa6c75568147b0
|
6182c3d591a362407e624b3ba87403a307315f2d
|
refs/heads/master
| 2021-01-18T09:18:02.904770
| 2017-04-02T16:51:40
| 2017-04-02T16:51:40
| 47,041,898
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,471
|
r
|
calculate_nndist_all_lg.R
|
calculate_nndist_all_lg <- function (stats.file, num_permutations, trace = FALSE) {
## first, build null distributions of nndists for each linkage group:
nnd.stats <- list()
for (j in unique(stats.file$lg)){
if(trace){cat(paste0("LG ", j, "..."))}
# subset for lg j
stats.file.lg <- stats.file %>%
filter(stats.file$lg == j)
# the number of outliers on that lg
num.outliers <- stats.file.lg %>%
filter(!is.na(gen.pos)) %>%
select(fst.outlier) %>%
unlist %>%
sum(na.rm = TRUE)
if(trace){cat(paste0(num.outliers, " outliers."))}
if (num.outliers > 1){
# draw 10000 samples of num.outliers random loci, take the mean, and return the ecdf and mean
null.mean.nnds <- replicate(num_permutations, calculate.null.nnd(stats.file.lg, num.outliers))
# calculate the estimate mean null nndist
null.mean <- mean(null.mean.nnds, na.rm = TRUE)
null.ecdf <- ecdf(null.mean.nnds)
# calculate the empirical nndist for real outliers
site.sample <- stats.file.lg %>%
filter(!is.na(gen.pos)) %>%
filter(fst.outlier == TRUE) %>%
select(gen.pos) %>%
arrange(gen.pos) %>%
mutate(dist.1 = c(NA,diff(gen.pos))) %>%
mutate(dist.2 = c(diff(sort(gen.pos)),NA))
nn.dist <- rep(NA, length(site.sample$genpos))
for (k in 1:length(site.sample$gen.pos)){
if(!is.na(site.sample$dist.1[k]) & !is.na(site.sample$dist.2[k])){
nn.dist[k] <- min(c(site.sample$dist.1[k],site.sample$dist.2[k]))
}else if(is.na(site.sample$dist.1[k])){
nn.dist[k] <- site.sample$dist.2[k]
} else if(is.na(site.sample$dist.2[k])){
nn.dist[k] <- site.sample$dist.1[k]
}
}
empirical.mean.nnd <- mean(nn.dist, na.rm = TRUE)
#number of total loci
n.sites <- stats.file.lg %>% filter(!is.na(gen.pos)) %>% select(gen.pos) %>% unlist %>% length
nnd.stats[[j]] <- data.frame(lg = unique(stats.file.lg$lg),
n.sites = n.sites,
num.outliers = num.outliers,
nnd.mean.null = null.mean,
nnd.sd.null = sd(null.mean.nnds, na.rm = TRUE),
nnd.mean.emp = empirical.mean.nnd,
nnd.emp.percentile = null.ecdf(empirical.mean.nnd),
nnd.emp.zscore = (empirical.mean.nnd - null.mean)/sd(null.mean.nnds, na.rm = TRUE),
nnd.emp.pvalue = two_side_p(null.mean.nnds, empirical.mean.nnd))
}
}
return(do.call("rbind", nnd.stats))
}
|
a8aeb00dac6753c6cc2d6dac44d7d48bc07de9d6
|
318db4587504dba25316efb0f68ea49ec1279914
|
/DTRfunction_Feb2013.R
|
af16cca6b202fbee38e9271568ef8b9cc2345d0c
|
[] |
no_license
|
lbuckley/BuckleyetalFE2015
|
84b74299dab90145bb2e808dae150077edd54946
|
3e85de8db094600e980de19ee0f04ca52e354193
|
refs/heads/master
| 2016-09-03T00:55:14.116232
| 2015-10-28T20:04:08
| 2015-10-28T20:04:08
| 31,223,798
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,130
|
r
|
DTRfunction_Feb2013.R
|
# library( fields)
#library( evd)
#library( evdbayes)
#library( ismev)
library(chron) #convert dates
#library(gdata)
#library(maptools)
#library(spdep)
#Function to calculate Parton and Logan 1981 diurnal variation
#truncated sine wave to predict daytime temperature changes and an exponential function to predict nighttime temperatures
#Parameters for Colorado
alpha=1.86
gamma=2.20
beta= -0.17
#Wann 1985
#alpha= 2.59 #time difference between tx and noon
#beta= 1.55 #time difference between tx and sunrise
#gamma= 2.2 #decay parameter for rate of t change from sunset to tn
#PAtterson 1981 function from Wann 1985
Thour=function(Tmx, Tmn, Hr, tr, ts, alpha=1.86, beta=-0.17, gamma=2.20){
#Tmx= max temperature
#Tmn= min temperature
#Hr= hour of measurement (0-24)
l= ts-tr #daylength
tx= 0.5*(tr+ts)+alpha #time of maximum temperature
tn= tr+ beta #time of minimum temperature
#calculate temperature for nighttime hour
if( !(Hr>(tr+beta) & Hr<ts) ){
Tsn= Tmn+(Tmx-Tmn)*sin((pi*(ts-tr-beta))/(l+2*(alpha-beta)))
if(Hr<=(tr+beta)) Tas=Hr+24-ts
if(Hr>=ts) Tas=Hr-ts #time after sunset
T=Tmn+(Tsn-Tmn)*exp(-(gamma*Tas)/(24-l+beta))
}
#calculate temperature for daytime hour
if(Hr>(tr+beta) & Hr<ts){
T= Tmn+(Tmx-Tmn)*sin((pi*(Hr-tr-beta))/(l+2*(alpha-beta)))
}
return(T)
}
#---------------------
#PAtterson 1981 function from Wann 1985
#This function combines data together to make it easier to run across many rows
Thour.mat=function(Tmat, Hr, alpha=1.86, beta=-0.17, gamma=2.20){
#Tmx= max temperature
#Tmn= min temperature
#Hr= hour of measurement (0-24)
Tmx= Tmat[1]
Tmn= Tmat[2]
tr= Tmat[3]
ts= Tmat[4]
l= ts-tr #daylength
tx= 0.5*(tr+ts)+alpha #time of maximum temperature
tn= tr+ beta #time of minimum temperature
#calculate temperature for nighttime hour
if( !(Hr>(tr+beta) & Hr<ts) ){
Tsn= Tmn+(Tmx-Tmn)*sin((pi*(ts-tr-beta))/(l+2*(alpha-beta)))
if(Hr<=(tr+beta)) Tas=Hr+24-ts
if(Hr>=ts) Tas=Hr-ts #time after sunset
T=Tmn+(Tsn-Tmn)*exp(-(gamma*Tas)/(24-l+beta))
}
#calculate temperature for daytime hour
if(Hr>(tr+beta) & Hr<ts){
T= Tmn+(Tmx-Tmn)*sin((pi*(Hr-tr-beta))/(l+2*(alpha-beta)))
}
return(T)
}
|
cd3f4123681d668abe93aa7ddef6af1f38102751
|
9efa3d105b7323709cf9157226054e4bf91afeaa
|
/Scripts/Model/3d plotly.R
|
606e30e735cc98f3926c535fd939a2101bef0429
|
[] |
no_license
|
jachuR/WiFi-project
|
065c24b1d0a50cd53addfef7401de5a6449b4e68
|
6d3828323ec16963a78f94d25513b4d2e61fb052
|
refs/heads/master
| 2021-02-07T08:51:53.296147
| 2020-02-29T17:53:41
| 2020-02-29T17:53:41
| 244,005,436
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,067
|
r
|
3d plotly.R
|
map_errors_training <- readRDS(file = "Data/Results/FinnalTable_F_C.RDS")
#map_errors_training - można znaleźć w X1_floor_S
p <- plot_ly(map_errors_training %>% filter(FLOOR <2), x = ~LONGITUDE, y = ~LATITUDE, z = ~FLOOR,
marker = list(color = ~WAP310, colorscale = c('#FFE1A1', '#683531'), showscale = TRUE),
mode = 'markers', symbol = ~corect, symbols = c('x','circle','cross-dot'))%>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Weight'),
yaxis = list(title = 'Gross horsepower'),
zaxis = list(title = '1/4 mile time'))
)
p
p <- plot_ly(map_errors_training %>% filter(FLOOR <2), x = ~LONGITUDE, y = ~LATITUDE, z = ~FLOOR,
marker = list(color = ~WAP108, colorscale = c('#FFE1A1', '#683531'), showscale = TRUE),
mode = 'markers')%>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Weight'),
yaxis = list(title = 'Gross horsepower'),
zaxis = list(title = '1/4 mile time'))
)
p
|
92ac6ca1cbda4216e2801485d557c647f3ed70cb
|
135f8eed2aa58a1776c5b24a72aa95a50c81d3c4
|
/man/components.fbl_prophet.Rd
|
d2d5db8cd78a00dedaa31691c99f820cd5bcf976
|
[] |
no_license
|
mitchelloharawild/fable.prophet
|
f1ce81baafc923fa730fb584ffd9edf78b304780
|
6d3c4ac596dda43b7c457a62c59256b3fda59db8
|
refs/heads/master
| 2022-09-15T18:37:15.686104
| 2022-09-02T02:27:13
| 2022-09-02T02:27:13
| 162,971,323
| 59
| 9
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,267
|
rd
|
components.fbl_prophet.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model.R
\name{components.fbl_prophet}
\alias{components.fbl_prophet}
\title{Extract meaningful components}
\usage{
\method{components}{fbl_prophet}(object, ...)
}
\arguments{
\item{object}{An estimated model.}
\item{...}{Unused.}
}
\value{
A \code{\link[fabletools:dable]{fabletools::dable()}} containing estimated states.
}
\description{
A prophet model consists of terms which are additively or multiplicatively
included in the model. Multiplicative terms are scaled proportionally to the
estimated trend, while additive terms are not.
}
\details{
Extracting a prophet model's components using this function allows you to
visualise the components in a similar way to \code{\link[prophet:prophet_plot_components]{prophet::prophet_plot_components()}}.
}
\examples{
\donttest{
if (requireNamespace("tsibbledata")) {
library(tsibble)
beer_components <- tsibbledata::aus_production \%>\%
model(
prophet = prophet(Beer ~ season("year", 4, type = "multiplicative"))
) \%>\%
components()
beer_components
autoplot(beer_components)
library(ggplot2)
library(lubridate)
beer_components \%>\%
ggplot(aes(x = quarter(Quarter), y = year, group = year(Quarter))) +
geom_line()
}
}
}
|
0c93f374b6d21e3dd4df3115766a0efa7aa02920
|
fab4e7ad290309d0028e8de349d262519b705ef0
|
/gene_constraints/plot/use_density.R
|
d8c44262c0e1b2433726f7d4f36d4037021091da
|
[] |
no_license
|
michaelbarton/michael-barton-thesis-figures
|
f4f3eb5937983b13c0b081cbc1b69d8228f436fd
|
7e0ad7de773cd5156209458b56ba3e2bc5915100
|
refs/heads/master
| 2016-09-11T04:09:10.956144
| 2009-08-17T15:42:57
| 2009-08-17T15:42:57
| 86,110
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 441
|
r
|
use_density.R
|
rm(list=ls())
library(lattice)
source('helpers/find_replace.R')
source('helpers/flux_data.R')
data <- flux_data()
data <- subset(data, value > -16)
plot <- densityplot(
~ value | setup,
groups = variable,
bw=1,
xlab="Absolute reaction flux (log.2)",
auto.key=TRUE,
data=data
)
postscript("results/use_density.eps",width=9,height=5,onefile=FALSE,horizontal=FALSE, paper = "special",colormodel="rgb")
print(plot)
graphics.off()
|
74f6f7787d01eb936fe90f614918c6b5de9df309
|
3ed96bb9a7e7e0ed668b2fc05d29311ac4756a5d
|
/clases/M1_clase2_r-base.R
|
7e32218e6811583922fb65ff7f9afe2907871236
|
[] |
no_license
|
JulioCursos/analisis_reproducible_iibp
|
ccababc0e55f205d0bee2e37e107063f37e36c56
|
5fb83e1e6e7908fab7873288a483e007da4248d5
|
refs/heads/main
| 2023-05-03T06:49:14.143767
| 2021-05-26T17:59:58
| 2021-05-26T17:59:58
| 371,774,381
| 0
| 0
| null | 2021-05-28T17:31:01
| 2021-05-28T17:31:00
| null |
UTF-8
|
R
| false
| false
| 7,480
|
r
|
M1_clase2_r-base.R
|
############-----------CLASE 2. R-BASE----------############
# CONTENIDO:
# 2.1. Directorio de trabajo/ Espacio de trabajo
# 2.2. R como calculadora
# 2.3. Objetos
# 2.4. Estilos para comentar, nombrar archivos, objetos
# 2.5. Tipos de datos (numericos, caracter, factor, logico),
# 2.6.Estructura de datos (vectores, matrices, data frames, listas y arrays)
# 2.7. indices, filtros, seleccionar
#### 2.1. Directorio de trabajo / Espacio de trabajo
getwd() # Ver el directorio de trabajo o en ingles "working directory"
setwd() # Establecer el directorio de trabajo
setwd("..") # sube al directorio que contiene el actual
setwd("c:/users/yo/proyecto") # ruta absoluta windows
setwd("home/yo/proyecto")
dir() # contenido del actual directorio de trabajo
list.files()
## volveremos a repasar cuando importemos datos
# 2.2. R como calculadora (En consola)
# operaciones arimeticas
2 + 2
4 - 2
12 * 3
24 / 3
3^2
sqrt(25)
2*3 + 4
2*(3+4)
#### 2.3. Objetos. R es un lenguaje orientado a objetos. Es decir, variables
# datos, funciones, resultados se guardan en la memoria activa de la compu en forma
# de "objetos" con un nombre especifico.
x = 2
y <- 4
x + y # se pueden hacer operaciones con los objetos
2 * y
a <- "mi nombre"
b <- "A"
# Instrucciones de asignacion
nombre_objeto <- valor
# agrupar expresiones
# punto y coma
x<- 2; y= 4; z= 6
# parentesis
(x <- 3)
#equivalente a
x <- 3
x
# llaves
{
x <- 3
y <- 2
x + y
}
#### 2.4. Estilos para comentar, nombrar archivos, objetos
NombreObjeto # Joroba de camello
nombre.objeto # Punto entre palabras
nombre_objeto # guion bajo
# No usar acentos
# No dejar espacios en blanco
# Mostrar ejemplo de Mayuscula-minuscula
#### 2.5. Tipos de datos
# Numerico (numeric). Con parte decimal o fracionaria
mi_altura_cm <- 170
mi_peso_kg <- 77.5
# Entero (integer). Sin una parte decimal o fraccionaria
## para especificar que es un entero hay que agregar una L
mi_edad <- 34L
class(mi_edad)
# Tambien llamados "double" o "float". Pero para fines practicos aca son todos numericos
# Cadena de caracteres (character, string)
mi_nombre <- "Julio" # siempre con comillas dobles o simples
class(mi_nombre)
nombre <- 'Julio'
class(nombre)
# Variables categoricas o factores (factor)
sexos <- c("M", "H","H", "M", "H")
class(sexos)
sexo_fac <- factor(sexos)# convertimos el vector a factorial
class(sexo_fac)
levels(sexo_fac) # vemos las categorias
levels(sexos)
# logicos. Valores booleanos
## < (menor a)
## > (mayor a)
## & (y)
## | (o)
## ! (no)
## == (es igual a)
## != (es distinto de)
a <- 2
b <- 4
a == b # a es igual a b?
a > b # a es mayor a b?
a != b # a es distinto de b?
(a < 3) & (b < 5) # a es a menor que 3 y b menor que 3?
(a < 1) | (b < 3) # a es a menor que 1 o b menor que 3?
#### 2.5. Estructura de datos (vectores, matrices, data frames, listas y arrays),
# Vectores
## Propiedades
# Tipo: numeric, character, logical
# Dimension: 1,la longitud
# atributos: metadatos
## vector numerico
c(1,2,3,4,5,6,7,8,9,10)
1:10 # secuencia
seq(10)# mismo que el anterior
rep(1, 10)# funcion repetir
# vector character
c("A", "B", "C", "D", "E")
c("perro", "gato", "gallina", "perro")
# vector logico
c(FALSE, TRUE, FALSE, FALSE, FALSE, TRUE)
# vector heterogeneo
c(2,"A", "B", TRUE, 3, 5, "Z")
## vectorizacion de operaciones
mi_vector <- c(1, 2, 3, 4, 5, 6)
mi_vector * 2 ; mi_vector + 2 # operadores aritmeticos
mi_vector < 4 # operador logico
mi_vector_nuevo <- c(mi_vector, "A", FALSE) # agregar un elemeno a un vector
class(mi_vector) # lo reconoce como character por que el tipo de datos mas flexible
# Funciones para inspeccionar datos
class()
is.vector()
length()
unique()
levels() # solo para factores
# Matrices. Vector de 2 dimensiones
## Solo puede contener un tipo de datos
# Argumentos funcion matrix()
#data es el vector que contiene los elementos que formaran parte de la matriz.
#nrow es el numero de filas.
#ncol es el numero de columnas.
#byrow es un valor logico. Si es TRUE el vector que pasamos ser?? ordenado por filas.
#dimnames nombres asignado a filas y columnas.
# crear una matriz con la funcion matrix
1:12
matrix(1:12)
matrix(data= 1:12, nrow = 3, ncol = 4)
matrix(data= 1:12, nrow = 4, ncol = 3)
# Arrays. La extension de un vector a mas de 2 dimensiones
# No se va a tratar en este curso
# funciones para inspeccionar una matriz
class()
dim()
# crear una matriz con cbind() o rbind
# cbind() para unir vectores, usando cada uno como una columna.
# rbind() para unir vectores, usando cada uno como un renglón.
vector_1 <- 1:4
vector_2 <- 5:8
vector_3 <- 9:12
vector_4 <- 13:16
matriz_cbind <- cbind( vector_1, vector_2, vector_3, vector_4)
matriz_rbind <- rbind(vector_1, vector_2, vector_3, vector_4)
# Dataframes. Estructura en 2 dimensiones rectangulares.
## Puede contener datos de diferentes tipos
mi_df <- data.frame(
"entero"= 1:5,
"factor"= c("a","b","c", "d","e"),
"numero"= c(2.3, 22, 23, 6.4, 5),
"cadena"= as.character(c("a","b","c", "d","e"))
)
mi_df
# funciones para inspeccionar un data frame
class()
dim()
ncol()
nrow()
length() # da el nro de columnas
names() # nombres de las variables
# Listas. Contiene objetos de cualquier clase(numero, caracteres, matrices, funciones, etc)
# una sola dimension, solo tiene largo
mi_lista <- list(1:9, "Pepe", pi, matrix(1:12, nrow = 4, ncol = 3))
mi_lista
#funciones para inspeccionar una lista
class(lista)
length(lista)
dim(lista)
str(lista)
#### 2.6. indices, filtros, seleccionar
##indexacion: identificacion de los elementos de objeto por medio de numero
# vector
x <- c(3, 5, 9, 13, "A", F, "C")
length(x)
x[1]
x[3:7]
# lista
lista <- list("A", c(2,4,5,4), matrix(1:12, ncol = 3, nrow = 4), FALSE)
length(lista)
lista[[1]]
lista[[4]]
# data.frame
library(MASS)
## inspeccionar primero
crabs
class(crabs)
dim(crabs)
nrow(crabs)
ncol(crabs)
str(crabs)
head(crabs)# primeras 6 filas
tail(crabs)# ultimas 6 filas
colnames(crabs)# nombre de las columnas
# seleccionar
## [] o $
# data[x,y] # x filas
# y, columnas
crabs[,2] # seleciono columna 2
crabs[,"sex"] # lo mismo pero por el nombre
crabs$sex #columna 2
crabs[1,] # selecciono fila 1
crabs[4,5] # elemento de la fila 4 columna 5
crabs[1:10, c("FL","CW")] # filas 1 al 10, variables "FL y "CW"
## Filtrar con algunos operadores logicos
# Solo los cangrejos azules, todas las columnas
crabs$sp == "B" # operacion logica
crabs[crabs$sp == "B",] # aplicado a un subconjunto
# Solo los cangrejos azules, columnas "RW", "FL"
crabs[crabs$sp == "B", c("RW", "FL")]
# cangrejos naranjas machos, todas las columnas
crabs$sp == "O" & crabs$sex == "M"# operacion logica
crabs[crabs$sp == "O" & crabs$sex == "M",]# aplicado a un filtro
# cangrejos lobulo frontal mayor a 10mm
crabs$FL > 10 # operacion logica
crabs[crabs$FL > 10,]# operacion aplicada a un filtro
# Ejercicio
# Seleccione las hembras de la variedad Azul con CL entre 35 y 40 mm
crabs$sp == "B" # sp azul
crabs$sex == "F"# sexo hembra
crabs$sex == "F" & crabs$sp == "B" # hembras de la variedad azul
crabs$CL >= 35 & crabs$CL <=40 # CL entre 35 y 40
crabs[(crabs$sex == "F" & crabs$sp == "B" & crabs$CL >= 35 & crabs$CL), ] # expresion completa
# o en dos pasos
#Paso 1. hembras de variedad azul
azul_hembra <- crabs[crabs$sex == "F" & crabs$sp == "B",]
# Paso 2. con el rango CL entre 35 y 40
azul_hembra[(azul_hembra$CL <=40 & azul_hembra$CL >= 35),]
|
20ad0fb2302eb8da3d95a62159ed069264ced541
|
888eb6041144ac34c7ed0d17684f856a4e3b95fd
|
/R/compare_designs.R
|
eb704e84900d254a884835bddb27b1c687b6c21a
|
[] |
no_license
|
reuning/DeclareDesign
|
c5ae645ae7e661469ff4c9a54f252c69619b2e51
|
b089b97397c6d95f334c129fdc0fb8fccb00d4d6
|
refs/heads/master
| 2023-08-19T19:30:45.244887
| 2021-10-17T13:11:15
| 2021-10-17T13:11:15
| 417,950,314
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,914
|
r
|
compare_designs.R
|
compare_partial <- function(FUN, DIFFFUN, is_data = FALSE){
if(is_data){
function(design1,
design2,
format = "ansi256",
mode = "auto",
pager = "off",
context = -1L,
rmd = FALSE) {
stopifnot(requireNamespace("diffobj"))
DIFFFUN <- get(DIFFFUN, getNamespace("diffobj"))
compare_design_internal(
FUN,
DIFFFUN,
design1,
design2,
format = format,
mode = mode,
pager = pager,
context = context,
rmd = rmd
)
}
} else{
function(design1,
design2,
format = "ansi256",
mode = "sidebyside",
pager = "off",
context = -1L,
rmd = FALSE) {
stopifnot(requireNamespace("diffobj"))
DIFFFUN <- get(DIFFFUN, getNamespace("diffobj"))
compare_design_internal(
FUN,
DIFFFUN,
design1,
design2,
format = format,
mode = mode,
pager = pager,
context = context,
rmd = rmd
)
}
}
}
#' Compare two designs
#'
#' @param design1 A design object, typically created using the + operator
#' @param design2 A design object, typically created using the + operator
#' @param format Format (in console or HTML) options from \code{diffobj::diffChr}
#' @param mode Mode options from \code{diffobj::diffChr}
#' @param pager Pager option from \code{diffobj::diffChr}
#' @param context Context option from \code{diffobj::diffChr} which sets the number of lines around differences that are printed. By default, all lines of the two objects are shown. To show only the lines that are different, set \code{context = 0}; to get one line around differences for context, set to 1.
#' @param rmd Set to \code{TRUE} use in Rmarkdown HTML output. NB: will not work with LaTeX, Word, or other .Rmd outputs.
#'
#' @examples
#'
#' design1 <- declare_model(N = 100, u = rnorm(N), potential_outcomes(Y ~ Z + u)) +
#' declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
#' declare_sampling(S = complete_rs(N, n = 75)) +
#' declare_assignment(Z = complete_ra(N, m = 50)) +
#' declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
#' declare_estimator(Y ~ Z, inquiry = "ATE")
#'
#' design2 <- declare_model(N = 200, U = rnorm(N),
#' potential_outcomes(Y ~ 0.5*Z + U)) +
#' declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
#' declare_sampling(S = complete_rs(N, n = 100)) +
#' declare_assignment(Z = complete_ra(N, m = 25)) +
#' declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
#' declare_estimator(Y ~ Z, model = lm_robust, inquiry = "ATE")
#'
#' compare_designs(design1, design2)
#' compare_design_code(design1, design2)
#' compare_design_summaries(design1, design2)
#' compare_design_data(design1, design2)
#' compare_design_estimates(design1, design2)
#' compare_design_inquiries(design1, design2)
#'
#' @name compare_functions
#' @rdname compare_functions
#' @export
compare_designs <- function(design1, design2, format = "ansi8", pager = "off", context = -1L, rmd = FALSE) {
compare_functions <-
list(code_comparison = compare_design_code,
data_comparison = compare_design_data,
estimands_comparison = compare_design_inquiries,
estimates_comparison = compare_design_estimates)
vals <-
lapply(compare_functions, function(fun)
fun(
design1,
design2,
format = format,
pager = pager,
context = context,
rmd = rmd
)
)
class(vals) <- "design_comparison"
vals
}
#' @export
print.design_comparison <- function(x, ...) {
cat("Research design comparison\n\n")
labels <- c("code_comparison" = "design code",
"data_comparison" = "draw_data(design)",
"estimands_comparison" = "draw_estimands(design)",
"estimates_comparison" = "draw_estimates(design)")
for(n in names(labels)) {
print_console_header(paste("Compare", labels[n]))
print(x[[n]])
}
}
#' @rdname compare_functions
#' @export
compare_design_code <- compare_partial(get_design_code, "diffObj")
#' @rdname compare_functions
#' @export
compare_design_summaries <- compare_partial(function(x) capture.output(summary(x)), "diffChr")
#' @rdname compare_functions
#' @export
compare_design_data <- compare_partial(draw_data, "diffObj")
#' @rdname compare_functions
#' @export
compare_design_estimates <- compare_partial(draw_estimates, "diffObj", is_data = TRUE)
#' @rdname compare_functions
#' @export
compare_design_inquiries <- compare_partial(draw_estimands, "diffObj", is_data = FALSE)
compare_design_internal <- function(FUN, DIFFFUN, design1, design2, format = "ansi256", mode = "sidebyside", pager = "off", context = -1L, rmd = FALSE){
check_design_class_single(design1)
check_design_class_single(design2)
seed <- .Random.seed
design1 <- FUN(design1)
set.seed(seed)
design2 <- FUN(design2)
if(rmd == TRUE) {
format <- "html"
style <- list(html.output = "diff.w.style")
} else {
style <- "auto"
}
diff_output <- structure(
DIFFFUN(
design1,
design2,
format = format,
mode = mode,
pager = pager,
context = context,
style = style
),
class = "Diff",
package = "diffobj"
)
if(rmd == TRUE) {
cat(as.character(diff_output))
} else {
diff_output
}
}
clean_call <- function(call) {
paste(sapply(deparse(call), trimws), collapse = " ")
}
get_design_code <- function(design){
if (is.null(attributes(design)$code)) {
sapply(design, function(x) clean_call(attr(x, "call")))
} else {
attributes(design)$code
}
}
print_console_header <- function(text) {
width <- options()$width
cat("\n\n#", text, paste(rep("-", width - nchar(text) - 2), collapse = ""), "\n\n")
}
|
a9528fc4e434e799cc3cee0e0fc5b993e20ff016
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/PivotalR/examples/null.data.Rd.R
|
d7197ac8dc7fe5f0cd884fe6bb85b0a5a17d5a30
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,219
|
r
|
null.data.Rd.R
|
library(PivotalR)
### Name: null.data
### Title: A Data Set with lots of 'NA' values
### Aliases: null.data
### Keywords: database data operation
### ** Examples
## Not run:
##D
##D
##D ## set up the database connection
##D ## Assume that .port is port number and .dbname is the database name
##D cid <- db.connect(port = .port, dbname = .dbname, verbose = FALSE)
##D
##D ## create a table from the example data.frame "abalone"
##D delete("null_data", conn.id = cid)
##D x <- as.db.data.frame(null.data, "null_data", conn.id = cid, verbose = FALSE)
##D
##D ## ERROR, because of NULL values
##D fit <- madlib.lm(sf_mrtg_pct_assets ~ ris_asset + lncrcd + lnauto +
##D lnconoth + lnconrp + intmsrfv + lnrenr1a + lnrenr2a +
##D lnrenr3a, data = x)
##D
##D ## select columns
##D y <- x[,c("sf_mrtg_pct_assets","ris_asset", "lncrcd","lnauto",
##D "lnconoth","lnconrp","intmsrfv","lnrenr1a","lnrenr2a",
##D "lnrenr3a")]
##D
##D dim(y)
##D
##D ## remove NULL values
##D for (i in 1:10) y <- y[!is.na(y[i]),]
##D
##D dim(y)
##D
##D fit <- madlib.lm(sf_mrtg_pct_assets ~ ., data = y)
##D
##D fit
##D
##D db.disconnect(cid, verbose = FALSE)
## End(Not run)
|
e6c2dcb36f1efa257cc05e9d6dafb9803272b1da
|
0f413fffdd3a6f7d740e64ff125875a0992deebc
|
/global.R
|
f38f72ca74ea66fb92614b55a5abaae68dcefd7e
|
[] |
no_license
|
CEREMA/appli_conso_espace_doubs
|
461820251a7df15cbc24b7984a7414b6c3d12de9
|
57fb3044dd08a45da1d364bfd6258f9292ff7d00
|
refs/heads/master
| 2021-12-06T11:46:40.058287
| 2021-06-16T15:15:11
| 2021-06-16T15:15:11
| 373,520,341
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,203
|
r
|
global.R
|
#
# APPLI DE VISUALISATION DES ENVELOPPES BATIES DU DOUBS
#
#
library(sf)
library(DBI)
library(rgdal)
library(RSQLite)
library(plotly)
library(leaflet)
library(leaflet.extras)
library(shiny)
library(data.table)
library(shinydashboard)
library(shinyWidgets)
library(RColorBrewer)
library(lwgeom)
library(classInt)
# LECTURE DES TABLES DEPUIS LE GEOPACKAGE -----------------------------------
conn <- dbConnect(RSQLite::SQLite(), dbname = "./data/bdd25.gpkg")
dcommunes <- st_as_sf(dbGetQuery(conn, "SELECT * FROM communes"))
dzonages <- dbReadTable(conn, "zonages")
dindic <- dbReadTable(conn, "indicateurs")
st_crs(dcommunes) = 4326
# quelques corrections dans la table des zonages
dzonages[dzonages$id_zone == "247000714",]$nom_zone <- "CC du Pays de Villersexel (partie Doubs)"
dzonages[dzonages$id_zone == "247000722",]$nom_zone <- "CC du Pays d'Héricourt (partie Doubs)"
dzonages[dzonages$id_zone == "242504496",]$nom_zone <- "CC du Plateau de Frasne et du Val de Drugeon"
dzonages[dzonages$id_zone == "pnrhj",]$nom_zone <- "PNR Haut Jura (partie Doubs)"
dzonages[dzonages$id_zone == "pnrph",]$nom_zone <- "PNR du Doubs Horloger"
dzonages[dzonages$id_zone == "200041887",]$nom_zone <- "CC du Val Marnaysien (partie Doubs)"
dzonages[dzonages$id_zone == "scot7",]$nom_zone <- "SCoT de l'Agglomération bisontine (partie Doubs)"
dzonages[dzonages$id_zone == "scot6",]$nom_zone <- "SCoT du Doubs Central"
dzonages <- dzonages %>% filter(id_zone != "scot8")
# liste déroulante pour choix des communes
choixcom <- dcommunes$insee_com
names(choixcom) <- dcommunes$nom_com
# liste déroulante pour choix des zonages d'étude
choixzone <- dzonages$id_zone
names(choixzone) <- dzonages$nom_zone
# année de référence par défaut
annee_t0 <- 1990
# calcul d'indicateurs supplémentaires
dcom <- dcommunes %>% select(nom_com, insee_com, surface)
dindic <- dindic %>%
dplyr::left_join(dcom) %>%
mutate(sartif = senv17 + senvnd,
partif = 1000000 * sartif / surface,
cos = 100*sbati / (senv17 + senvnd),
sartif_par_hab = 10000 * sartif / p17_pop,
occpot17 = p17_pop + p17_emplt + p17_rsec,
occpot12 = p12_pop + p12_emplt + p12_rsec,
occpot07 = p07_pop + p07_emplt + p07_rsec,
sartif_par_op = 10000 * sartif / occpot17,
sartif_evo_men = menhab1217
)
dindic <- st_as_sf(dindic)
# restructuration des données temporelles
dpop <- as.data.table(dindic) %>%
select(insee_com, p17_pop, p12_pop, p07_pop, d99_pop, d90_pop, d82_pop, d75_pop, d68_pop) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "population") %>%
mutate(annee = strtoi(substr(annee, 2, 3)) + 2000) %>%
mutate(annee = ifelse(annee > 2050, annee - 100, annee))
drsec <- as.data.table(dindic) %>%
select(insee_com, p17_rsec, p12_rsec, p07_rsec, d99_rsec, d90_rsec, d82_rsec, d75_rsec, d68_rsec) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "rsec") %>%
mutate(annee = strtoi(substr(annee, 2, 3)) + 2000) %>%
mutate(annee = ifelse(annee > 2050, annee - 100, annee))
dsenv <- as.data.table(dindic) %>%
select(insee_com, senv17, senv12, senv07, senv99, senv90, senv82, senv75, senv68, senvnd) %>%
mutate(senv17 = senv17 + senvnd) %>%
mutate(senv12 = senv12 + senvnd) %>%
mutate(senv07 = senv07 + senvnd) %>%
mutate(senv99 = senv99 + senvnd) %>%
mutate(senv90 = senv90 + senvnd) %>%
mutate(senv82 = senv82 + senvnd) %>%
mutate(senv75 = senv75 + senvnd) %>%
mutate(senv68 = senv68 + senvnd) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "stot") %>%
mutate(annee = strtoi(substr(annee, 5, 6)) + 2000) %>%
mutate(annee = ifelse(annee > 2050, annee - 100, annee))
demplt <- as.data.table(dindic) %>%
select(insee_com, p17_emplt, p12_emplt, p07_emplt) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "emplt") %>%
mutate(annee = strtoi(substr(annee, 2, 3)) + 2000)
docpot <- as.data.table(dindic) %>%
select(insee_com, occpot17, occpot12, occpot07) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "ocpot") %>%
mutate(annee = strtoi(substr(annee, 7, 8)) + 2000)
dmen <- as.data.table(dindic) %>%
select(insee_com, men12, men17) %>%
melt(id = c("insee_com"), variable.name = "annee", value.name = "men") %>%
mutate(annee = strtoi(substr(annee, 4, 5)) + 2000)
dtempo <- dpop %>%
dplyr::left_join(drsec, by = NULL, copy = FALSE) %>%
dplyr::left_join(dsenv, by = NULL, copy = FALSE) %>%
dplyr::left_join(demplt, by = NULL, copy = FALSE) %>%
dplyr::left_join(docpot, by = NULL, copy = FALSE) %>%
dplyr::left_join(dmen, by = NULL, copy = FALSE)
# couleurs pour les graphiques
col1 <- "#F58220" #orange
col2 <- "#268966" # pistache
col3 <- "#33475b" # bleu nuit
# dataframe nul
sfnul <- st_sf(surface = 0, datation = 0, geom = dcommunes$geom[1])
# années de référence
anneesref <- c(1968, 1975, 1982, 1990, 1999, 2007, 2012, 2017)
# nom des indicateurs
ind1 <- "surface artificialisée par le bâti en 2017"
ind2 <- "évolution de la surface artificialisée par le bâti"
ind3 <- "évolution relative de la surface artificialisée par le bâti"
ind4 <- "part de la surface communale artificialisée par le bâti en 2017"
ind5 <- "coefficient d'emprise au sol du bâti en 2017"
ind6 <- "surface artificialisée par habitant en 2017"
ind7 <- "surface artificialisée par occupant potentiel en 2017"
ind8 <- "nombre de nouveaux ménages par ha artificialisé pour l'habitat"
ind <- 1:8
names(ind) <- c(ind1, ind2, ind3, ind4, ind5, ind6, ind7, ind8)
vars <- c("sartif", "sartif_evo", "partif_evo", "partif", "cos", "sartif_par_hab", "sartif_par_op", "sartif_evo_men")
unites <- c(" ha", " ha", " %", " %", " %", " m2", " m2", " ménages")
# requetes pour tester le fonctionnement de l'appli
## com_date <- dbGetQuery(conn, "SELECT code_insee, datation, surface FROM env_date WHERE code_insee = '25001'")
## com_bati <- st_as_sf(dbGetQuery(conn, "SELECT * FROM bati WHERE insee_com = '25001'", crs = 4326))
|
d4994854947ea808e5ba005522882397f514935c
|
e0eba6a80fe2b5e346829f0d6f0c90d4748af58d
|
/data_creation.R
|
ca067558e38ed4f93244cf7f3a1be957747acb12
|
[] |
no_license
|
argyelan/MRI
|
ddcd33f077f6701e41c7a0d9471cfc37288b77aa
|
fdd3c05bc85f84eb22e28b3ec87a2f68c60dbe06
|
refs/heads/master
| 2020-06-20T21:28:23.542187
| 2019-07-26T16:25:12
| 2019-07-26T16:25:12
| 197,256,016
| 0
| 0
| null | 2019-07-26T16:25:13
| 2019-07-16T19:33:17
|
R
|
UTF-8
|
R
| false
| false
| 2,283
|
r
|
data_creation.R
|
#**************************************#
# Script to create the data file to be handled#
#**************************************#
#Edit file directories if needed
library(data.table)
library(corrplot)
library(dplyr)
file <- '/nethome/rkim/hcp_example/example3/Nodes.nii' #Import data
x <- paste0('fslstats ',file,' -R')
print(x)
range <- as.numeric(strsplit(system(x, intern = TRUE),' ')[[1]]) #Run bash command to find range of intensities
img4D <- '/nethome/rkim/hcp_example/example3/GSR_preprocessed_ses-22921_task-rest_acq-AP_run-01_bold_hp2000_clean.nii.gz'
x <- paste0('/nethome/rkim/Script/bin/corr_matrix.sh ',file,' ', img4D,' ', range[1],' ', range[2])
setwd('/nethome/rkim/Script/DataFolder/') #work directory where matrices will be stored
filenames <- list.files(full.names = FALSE)
sapply(filenames, unlink)
system(x) #Run bash script to create correlation matrices
print(x)
filenames <- list.files(full.names = FALSE) #Find all matrices
info = file.info(filenames)
good = rownames(info[info$size > 0, ]) #Keep only matrices that have values in them
dataFile <- do.call("cbind", lapply(good, read.csv, header = FALSE)) #Create the data file from the csv files
oldnames <- colnames(dataFile)
oldnames <- make.unique(oldnames, sep = ".")
colnames(dataFile) <- oldnames
dataFile <- data.table(dataFile)
#meanData <- dataFile[,lapply(.SD, mean)]
#dataWindow <- 100
#setwd('/nethome/rkim/Script/Pairwise Correlations/')
#sapply(paste('pair_corr', 1:(nrow(dataFile) - dataWindow + 1), '.csv', sep = ''), unlink)
#for (i in c(1:(nrow(dataFile) - dataWindow + 1)))
#{
#pair_corr <- cor(dataFile[c(i:(i + dataWindow - 1))]) #Find the pairwise correlation of data
#setwd('/nethome/rkim/Script/Pairwise Correlations/') #work directory where pairwise correlation vectors will be stored
#newPair <- pair_corr[upper.tri(pair_corr)]
#write.table(newPair, file = paste('pair_corr', sprintf('%03d',i), '.csv', sep = ''), row.names = FALSE, col.names = FALSE)
#}
#corrplot(pair_corr, method = "circle")
#pairNames <- list.files(full.names = FALSE)
#pairFile <- do.call("cbind", lapply(pairNames, read.csv, header = FALSE)) #Create the data file from the csv files
#finalCorr <- cor(pairFile)
#ind <- seq(2,ncol(pairFile),10)
#corrplot(finalCorr[ind,ind], method = "circle")
|
3d02ebf2f68406e1af2f3b1a2849323ce812fe85
|
d14bcd4679f0ffa43df5267a82544f098095f1d1
|
/inst/apps/figure2_5/server.R
|
31d74e052bea67befcfefd6daa579b36c584879b
|
[] |
no_license
|
anhnguyendepocen/SMRD
|
9e52aa72a5abe5274f9a8546475639d11f058c0d
|
c54fa017afca7f20255291c6363194673bc2435a
|
refs/heads/master
| 2022-12-15T12:29:11.165234
| 2020-09-10T13:23:59
| 2020-09-10T13:23:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 185
|
r
|
server.R
|
server = function(input, output, session) {
observeEvent(input$evalfig5, {
output$plotfig5 <- renderPlot({
return(isolate(eval(parse(text=input$fig5plot))))
})
})
|
96fd7e69352686539bbdc2bbcbff6453f61e5fe0
|
375b0780581873c3d5ec6035a5f1d4227f0f1bea
|
/tests/testthat/test.vig.R
|
18d6d27d82c4290b6c2431761d7025fad1f57cf5
|
[] |
no_license
|
cran/odds.converter
|
e7d5d3498410277e6eee1667324300c8f2e3efd8
|
335a2c09ec76b5f8b33ff0f8a7da4a9ebff4dbbd
|
refs/heads/master
| 2021-01-10T13:14:56.227086
| 2018-06-01T11:29:53
| 2018-06-01T11:29:53
| 36,883,159
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,488
|
r
|
test.vig.R
|
us <- c(-110, -120, -110, -100)
probs <- c(11/21, 6/11, 11/21, 1/2)
vigs <- c(1/22, 1/23)
# US input
expect_equal(odds.vig(us[1], us[3]), vigs[1])
expect_equal(odds.vig(us[c(1, 3)]), vigs[1])
expect_equal(odds.vig(us[c(1, 3, NA)]), NA_real_)
expect_equal(odds.vig(home = us[c(1:2, NA)],
away = us[c(3:4, NA)]),
c(vigs, NA_real_))
expect_equal(odds.vig(matrix(c(us[1:2], NA, us[3:4], NA), ncol = 2,
dimnames = list(paste0("gm", 1:3), c("h", "a")))),
c(gm1 = vigs[1], gm2 = vigs[2], gm3 = NA_real_))
expect_equal(odds.vig(data.frame(home = us[c(1:2, NA)],
away = us[c(3:4, NA)])),
c(vigs, NA_real_))
# Probability input
expect_equal(odds.vig(probs[1], probs[3], input = "prob"), vigs[1])
expect_equal(odds.vig(probs[c(1, 3)], input = "prob"), vigs[1])
expect_equal(odds.vig(probs[c(1, 3, NA)]), NA_real_)
expect_equal(odds.vig(home = probs[c(1:2, NA)],
away = probs[c(3:4, NA)], input = "prob"),
c(vigs, NA_real_))
expect_equal(odds.vig(matrix(probs[c(1:2, NA, 3:4, NA)], ncol = 2,
dimnames = list(paste0('gm', 1:3), c('h', 'a'))),
input = "prob"),
c(gm1 = vigs[1], gm2 = vigs[2], gm3 = NA_real_))
expect_equal(odds.vig(data.frame(home = probs[c(1:2, NA)],
away = probs[c(3:4, NA)]), input = "prob"),
c(vigs, NA_real_))
|
45f3c3e631345cfe5c9e11163c0fba49d9a624d0
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/DiagrammeR/examples/select_edges_by_node_id.Rd.R
|
b9df8ff21be25c0d5eb0091d991db8bfaf6cb7cf
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 848
|
r
|
select_edges_by_node_id.Rd.R
|
library(DiagrammeR)
### Name: select_edges_by_node_id
### Title: Select edges in a graph using node ID values
### Aliases: select_edges_by_node_id
### ** Examples
# Create a graph with 5 nodes
graph <-
create_graph() %>%
add_path(n = 5)
# Create a graph selection by selecting edges
# associated with nodes `1` and `2`
graph <-
graph %>%
select_edges_by_node_id(
nodes = 1:2)
# Get the selection of edges
graph %>%
get_selection()
# Perform another selection of edges, with nodes
# `1`, `2`, and `4`
graph <-
graph %>%
clear_selection() %>%
select_edges_by_node_id(
nodes = c(1, 2, 4))
# Get the selection of edges
graph %>%
get_selection()
# Get a fraction of the edges selected over all
# the edges in the graph
graph %>%
{
l <- get_selection(.) %>%
length(.)
e <- count_edges(.)
l/e
}
|
f81b1bd06f179e684783beca62086558e66e435d
|
9c54073b91052e69fcea27c7c7b685e0d12ae6d6
|
/CMPT318/Assignment1/Untitled.R
|
24412a0a058204bc45e7b1b40d108f64a046df39
|
[] |
no_license
|
yifanliu98/gitSFUBowen
|
b271b70867fa351717ce5fccda62d7fa974aafde
|
00a46d36bb78aa602ed278844ddef3409cf7204a
|
refs/heads/master
| 2022-02-24T07:57:11.145612
| 2019-09-19T05:45:48
| 2019-09-19T05:45:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 605
|
r
|
Untitled.R
|
dataset <- read.table("Dataset1.txt", header=TRUE, sep=",") #525600 with NA values
require( lubridate )
library("depmixS4")
dataset <- na.omit(dataset) #521860 with na values omitted.
dataset$Date <- as.POSIXlt(dataset$Date, na.rm = TRUE, format ="%d/%m/%y")$wday
sunday <- dataset[which(dataset$Date == 0),]
sunday$Time <- hms(sunday$Time)
sundayEvening <- sunday
sundayMorning <- sunday
sundayMorning <- subset(sundayMorning, as.numeric(Time) >= 28800 & as.numeric(Time) <= 43200)
sundayEvening <- subset(sundayEvening, (as.numeric(Time) >= 75600 & as.numeric(Time) < 86400) | as.numeric(Time) == 0)
|
d7db935686efede3e34c8bc7bb6a31da19199107
|
8944a932cf6b08d91e2975f4344598ebf15f03b2
|
/man/KmeansClust.Rd
|
5fbeab3222d1c3595fd28b874a175bc3750a2df1
|
[
"MIT"
] |
permissive
|
admurali/self
|
c830e8b486435f1503c2a3999035105c3dc0bf3d
|
112a723f6d388ac9323902407c88425d5ea38315
|
refs/heads/master
| 2020-05-30T23:24:11.664562
| 2016-12-15T07:53:04
| 2016-12-15T07:53:04
| 59,989,165
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,093
|
rd
|
KmeansClust.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kmeans_clustering.R
\name{KmeansClust}
\alias{KmeansClust}
\title{K-Means Clustering}
\usage{
KmeansClust(data, x, y, cluster, rstart = 1)
}
\arguments{
\item{data}{is the dataset containing the observations.}
\item{x}{is a reference to a column in the dataset that is could be the independent variable.}
\item{y}{is a reference to another column in the dataset that could be the dependent variable.}
\item{cluster}{is the number of clusters to perform k-means operation on.}
\item{rstart}{is how many random starting cluster assignments to try before choosing the one with the lowest within cluster variation}
}
\value{
Returns a list that contains values corresponding to the cluster number, and other details.
}
\description{
K-Means Clustering
}
\details{
Subsets given data using column names or number and performs k-means clustering on the subset data with error handling.
}
\examples{
print(KmeansClustList(iris, 'Sepal.Length', "Sepal.Width", 3))
}
\author{
Adithya Murali
}
\seealso{
\code{kmeans}
}
|
3fe203018d6a8ac74cb870e6acf7bd4cfacb00c6
|
a7f19a71d2bfb2fc3294d3aaae3be1aef6aafe3b
|
/Econometria/Trabajo_final.R
|
c3d24d19fed35337e75058260926684b4f301838
|
[] |
no_license
|
ArturoGon/Master
|
d3fe89e50964a51cc43cffe255e853f45e881a8e
|
322a8d9c36d9bb4ff81591885f219d0e03056b0c
|
refs/heads/main
| 2023-06-02T08:06:13.664904
| 2021-06-14T09:54:30
| 2021-06-14T09:54:30
| 376,586,518
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 25,291
|
r
|
Trabajo_final.R
|
rm(list=ls())
cat("\014")
library(wooldridge)
library(ISLR)
library(leaps)
library(stats)
library(ggcorrplot)
library(RColorBrewer)
library(tidyverse)
library(glmnet)
library(pls)
library(caret)
library(MLmetrics)
library(car)
library(selectiveInference)
library(covTest)
library(hdm)
attach(discrim)
# Variable independiente psoda
set.seed(44)
##### a)
discrim <-na.omit(discrim)
#Eliminamos las vaiables chain y state porque ya estan en forma binaria en otras variables.
#Eliminamos las variables hrsopen2, pentree2, wagest2, nregs2, psoda2, pfries2, nmgrs2, emp2 por estar medidas en otra fecha.
discrim <- dplyr::select(discrim, -c(state, chain, lpsoda, hrsopen2, pentree2, wagest2, nregs2, psoda2, pfries2, nmgrs2, emp2))
#Para hacer la correlación, quitamos las variables compown, NJ, BK, KFC, RR, lpfries, lhseval, lincome y ldensity
datos_cor <- dplyr::select(discrim, -c(compown, county, NJ, BK, KFC, RR, lpfries, lhseval, lincome, ldensity))
ggcorrplot(cor(datos_cor), hc.order = TRUE, type = "lower",
lab = TRUE, lab_size = 1.5)
# Quitamos las variables prppov, hseval, prpncar por tener alta correlación
# Eliminamos la variable county ya que es una etiqueta con 310 niveles.
discrim <- dplyr::select(discrim, -c(prppov, county, hseval, prpncar,lhseval))
# Seleccionamos muestra de entrenamiento y test
muestra <- sample(1:nrow(discrim), round(0.79*nrow(discrim), 0))
entrenamiento <- discrim[muestra, ]
test <- discrim[-muestra, ]
reg_1 = lm(psoda ~ .,
data = entrenamiento)
summary(reg_1)
pred = predict(reg_1, newdata = test)
error_reg_1 <- sqrt(MSE(y_pred = pred,y_true = test$psoda))
error_reg_1
##### b)
#Esta es la funcion para predecir los regsubsets
predict.regsubsets=function(object, newdata, id,...){
form=as.formula(object$call[[2]])
mat=model.matrix(form, newdata)
coefi=coef(object, id=id)
mat[, names(coefi)]%*%coefi
}
k = 10
folds = sample(1:k,nrow(entrenamiento),replace=TRUE)
cv_error_sub_10=matrix(NA,k,(ncol(entrenamiento)-1), dimnames=list(NULL, paste(1:(ncol(entrenamiento)-1))))
for(j in 1:k){
reg_full_10 = regsubsets(psoda ~., data=entrenamiento[folds != j,], nvmax = (ncol(entrenamiento)-1))
for (i in 1:(ncol(entrenamiento)-1)){
pred = predict.regsubsets(reg_full_10, entrenamiento[folds == j, ], id = i)
cv_error_sub_10[j, i] = mean((entrenamiento$psoda[folds == j] - pred)^2)
}
}
cv_error_media_10 = apply(cv_error_sub_10 ,2,mean)
cv_error_media_10
plot(cv_error_media_10,pch=19,type="b", xlab="Numero de variables", ylab="Error CV")
points(which.min(cv_error_media_10),cv_error_media_10[which.min(cv_error_media_10)], col="red",cex=2,pch=18)
mejor_reg=regsubsets (psoda~.,data=entrenamiento , nvmax=(ncol(entrenamiento)-1))
coef(mejor_reg ,which.min(cv_error_media_10))
reg_sub_10 =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1))
pred_reg_sub_10 = predict.regsubsets(reg_sub_10, newdata = test, id=which.min(cv_error_media_10))
error_mss_sub_10 <- sqrt(mean((test$psoda - pred_reg_sub_10)^2))
error_mss_sub_10
# Regla codo
codo_sub_10 = sd(cv_error_media_10)
which.max(cv_error_media_10 - codo_sub_10 <= min(cv_error_media_10))
reg_sub_10_codo =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1))
pred_reg_sub_10_codo = predict.regsubsets(reg_sub_10_codo, newdata = test, id=which.max(cv_error_media_10 - codo_sub_10 <= min(cv_error_media_10)))
error_mss_sub_10_codo <- sqrt(mean((test$psoda - pred_reg_sub_10_codo)^2))
error_mss_sub_10_codo
##### c)
cv_error_sub_for_10=matrix(NA,k,(ncol(entrenamiento)-1),
dimnames=list(NULL, paste(1:(ncol(entrenamiento)-1))))
for(j in 1:k){
reg_for_10=regsubsets(psoda~.,data=entrenamiento[folds != j,], nvmax= (ncol(entrenamiento)-1),
method= "forward")
for (i in 1:(ncol(entrenamiento)-1)){
pred = predict.regsubsets(reg_for_10, entrenamiento[folds == j, ], id = i)
cv_error_sub_for_10[j, i] = mean((entrenamiento$psoda[folds == j] - pred)^2)
}
}
cv_error_for_media_10 = apply(cv_error_sub_for_10 ,2,mean)
cv_error_for_media_10
plot(cv_error_for_media_10,pch=19,type="b", xlab="Numero de variables", ylab="Error CV")
points(which.min(cv_error_for_media_10),cv_error_for_media_10[which.min(cv_error_for_media_10)], col="red",cex=2,pch=18)
mejor_reg=regsubsets (psoda~.,data=entrenamiento , nvmax=(ncol(entrenamiento)-1), method= "forward")
coef(mejor_reg ,which.min(cv_error_for_media_10))
reg_for_10 =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1), method= "forward")
pred_reg_for_10 = predict.regsubsets(reg_for_10, newdata = test, id=which.min(cv_error_for_media_10))
error_mss_for_10 <- sqrt(mean((test$psoda - pred_reg_for_10)^2))
error_mss_for_10
# Regla codo
codo_for_10 = sd(cv_error_for_media_10)
which.max(cv_error_for_media_10 - codo_for_10 <= min(cv_error_for_media_10))
reg_for_10_codo =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1))
pred_reg_for_10_codo = predict.regsubsets(reg_for_10_codo, newdata = test, id=which.max(cv_error_for_media_10 - codo_for_10 <= min(cv_error_for_media_10)))
error_mss_for_10_codo <- sqrt(mean((test$psoda - pred_reg_for_10_codo)^2))
error_mss_for_10_codo
##### d)
#Mejor selección de conjuntos
k = 5
folds = sample(1:k,nrow(entrenamiento),replace=TRUE)
cv_error_sub_5=matrix(NA,k,(ncol(entrenamiento)-1), dimnames=list(NULL, paste(1:(ncol(entrenamiento)-1))))
for(j in 1:k){
reg_full_5 = regsubsets(psoda ~., data=entrenamiento[folds != j,], nvmax = (ncol(entrenamiento)-1))
for (i in 1:(ncol(entrenamiento)-1)){
pred = predict.regsubsets(reg_full_5, entrenamiento[folds == j, ], id = i)
cv_error_sub_5[j, i] = mean((entrenamiento$psoda[folds == j] - pred)^2)
}
}
cv_error_media_5 = apply(cv_error_sub_5 ,2,mean)
cv_error_media_5
plot(cv_error_media_5,pch=19,type="b", xlab="Numero de variables", ylab="Error CV")
points(which.min(cv_error_media_5),cv_error_media_5[which.min(cv_error_media_5)], col="red",cex=2,pch=18)
mejor_reg=regsubsets (psoda~.,data=entrenamiento , nvmax=(ncol(entrenamiento)-1))
coef(mejor_reg ,which.min(cv_error_media_5))
reg_sub_5 =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1))
pred_reg_sub_5 = predict.regsubsets(reg_sub_5, newdata = test, id=which.min(cv_error_media_5))
error_mss_sub_5 <- sqrt(mean((test$psoda - pred_reg_sub_5)^2))
error_mss_sub_5
# Regla codo
codo_sub_5 = sd(cv_error_media_5)
which.max(cv_error_media_5 - codo_sub_5 <= min(cv_error_media_5))
#Selección por pasos hacia adelante
cv_error_sub_for_5=matrix(NA,k,(ncol(entrenamiento)-1), dimnames=list(NULL, paste(1:(ncol(entrenamiento)-1))))
for(j in 1:k){
reg_for_5=regsubsets(psoda~.,data=entrenamiento[folds != j,], nvmax= (ncol(entrenamiento)-1),method= "forward")
for (i in 1:(ncol(entrenamiento)-1)){
pred = predict.regsubsets(reg_for_5, entrenamiento[folds == j, ], id = i)
cv_error_sub_for_5[j, i] = mean((entrenamiento$psoda[folds == j] - pred)^2)
}
}
cv_error_for_media_5 = apply(cv_error_sub_for_5 ,2,mean)
cv_error_for_media_5
plot(cv_error_for_media_5,pch=19,type="b", xlab="Numero de variables", ylab="Error CV")
points(which.min(cv_error_for_media_5),cv_error_for_media_5[which.min(cv_error_for_media_5)], col="red",cex=2,pch=18)
mejor_reg=regsubsets (psoda~.,data=entrenamiento , nvmax=(ncol(entrenamiento)-1), method= "forward")
coef(mejor_reg ,which.min(cv_error_for_media_5))
reg_for_5 =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1), method= "forward")
pred_reg_for_5 = predict.regsubsets(reg_for_5, newdata = test, id=which.min(cv_error_for_media_5))
error_mss_for_5 <- sqrt(mean((test$psoda - pred_reg_for_5)^2))
error_mss_for_5
# Regla codo
codo_for_5 = sd(cv_error_for_media_5)
which.max(cv_error_for_media_5 - codo_for_5 <= min(cv_error_for_media_5))
reg_for_5_codo =regsubsets(psoda~.,data= entrenamiento,nvmax=(ncol(entrenamiento)-1))
pred_reg_for_5_codo = predict.regsubsets(reg_for_5_codo, newdata = test, id=which.max(cv_error_for_media_5 - codo_for_5 <= min(cv_error_for_media_5)))
error_mss_for_5_codo <- sqrt(mean((test$psoda - pred_reg_for_5_codo)^2))
error_mss_for_5_codo
##### e)
tabla <- data.frame("Regresión" = c("Minimos cuadrados ordinarios",
"Seleccion de subconjuntos CV 10",
"Seleccion por pasos hacia adelante CV 10",
"Seleccion de subconjuntos CV 5",
"Seleccion por pasos hacia adelante CV 5" ),
"Error_prueba" = c(error_reg_1,
error_mss_sub_10,
error_mss_for_10,
error_mss_sub_5,
error_mss_for_5_codo),
"Número_variables" = c(" ",which.min(cv_error_media_10),
which.min(cv_error_for_media_10),
which.min(cv_error_media_5),
which.min(cv_error_for_media_5)))
tabla
##### f)
# Lo miramos por Bonferroni-Holm
reg_lineal <- lm(psoda ~pfries + pentree +prpblck +NJ+ BK + RR, data= entrenamiento)
summary(reg_lineal)
p = c(3.75*10^(-16), 0.000513, 0.017370, 0.000329, 1.11*10^(-8), 6.72*10^(-8)) #p-valores de la regresion
# Los que sean TRUE los seleccionamos.
p <= 0.05/length(p)
# Eliminamos la variable prpblack
reg_nueva <- lm(psoda ~pfries + pentree +NJ+ BK + RR, data= entrenamiento)
pred = predict(reg_nueva, newdata = test)
error_reg_nueva <- sqrt(MSE(y_pred = pred,y_true = test$psoda))
error_reg_nueva
# El error es mayor que antes
##### g)
matriz_entrenamiento = model.matrix(psoda~., data=entrenamiento)[,-1]
matriz_test = model.matrix(psoda~., data=test)[,-1]
grid = 10^seq(4, -2, length=100)
modelo_ridge_10 = cv.glmnet(matriz_entrenamiento, entrenamiento$psoda, alpha=0, lambda=grid, nfolds = 10)
mejor_lambda_ridge_10 = modelo_ridge_10$lambda.min
mejor_lambda_ridge_10
plot(modelo_ridge_10)
modelo_ridge_10_l=glmnet(matriz_entrenamiento,entrenamiento$psoda,alpha=0,lambda=grid, thresh = 1e-12)
prediccion_ridge_10 = predict(modelo_ridge_10_l, newx=matriz_test, s=mejor_lambda_ridge_10)
a = sqrt(mean((test$psoda - prediccion_ridge_10)^2))
a
# Regla del codo
lambda_codo_ridge_10 <- modelo_ridge_10$lambda.1se
lambda_codo_ridge_10
prediccion_ridge_10_2=predict(modelo_ridge_10_l,s=lambda_codo_ridge_10,newx=matriz_test)
error.ridge.2 <- sqrt(mean((prediccion_ridge_10_2-test$psoda )^2))
error.ridge.2
##### h)
modelo_LASSO_10= cv.glmnet(matriz_entrenamiento, entrenamiento$psoda, alpha=1, lambda=grid, nfolds = 10)
mejor_lambda_LASSO_10 = modelo_LASSO_10$lambda.min
mejor_lambda_LASSO_10
plot(modelo_LASSO_10)
modelo_LASSO_10_l=glmnet(matriz_entrenamiento,entrenamiento$psoda,alpha=1,lambda=grid, thresh = 1e-12)
prediccion_LASSO_10 = predict(modelo_LASSO_10_l, newx=matriz_test, s=mejor_lambda_LASSO_10)
b = sqrt(mean((test$psoda - prediccion_LASSO_10)^2))
b
# Regla del codo
lambda_codo_LASSO_10 <- modelo_LASSO_10$lambda.1se
lambda_codo_LASSO_10
prediccion_LASSO_10_2=predict(modelo_LASSO_10_l,s=lambda_codo_LASSO_10,newx=matriz_test)
error.LASSO.2 <- sqrt(mean((prediccion_LASSO_10_2-test$psoda )^2))
error.LASSO.2
##### i)
# Ridge CV-5
modelo_ridge_5 = cv.glmnet(matriz_entrenamiento, entrenamiento$psoda, alpha=0, lambda=grid, nfolds = 5)
mejor_lambda_ridge_5 = modelo_ridge_5$lambda.min
mejor_lambda_ridge_5
modelo_ridge_5_l=glmnet(matriz_entrenamiento,entrenamiento$psoda,alpha=0,lambda=grid, thresh = 1e-12)
prediccion_ridge_5 = predict(modelo_ridge_5_l, newx=matriz_test, s=mejor_lambda_ridge_5)
c =sqrt(mean((test$psoda - prediccion_ridge_5)^2))
c
# Regla del codo
lambda_codo_ridge_5 <- modelo_ridge_5$lambda.1se
lambda_codo_ridge_5
prediccion_ridge_5_2=predict(modelo_ridge_5_l,s=lambda_codo_ridge_5,newx=matriz_test)
error.ridge.2 <- sqrt(mean((prediccion_ridge_5_2-test$psoda )^2))
error.ridge.2
# LASSO CV-5
modelo_LASSO_5= cv.glmnet(matriz_entrenamiento, entrenamiento$psoda, alpha=1, lambda=grid, nfolds = 5)
mejor_lambda_LASSO_5 = modelo_LASSO_5$lambda.min
mejor_lambda_LASSO_5
modelo_LASSO_5_l=glmnet(matriz_entrenamiento,entrenamiento$psoda,alpha=1,lambda=grid, thresh = 1e-12)
prediccion_LASSO_5 = predict(modelo_LASSO_5_l, newx=matriz_test, s=mejor_lambda_LASSO_5)
d =sqrt(mean((test$psoda - prediccion_LASSO_5)^2))
d
# Regla del codo
lambda_codo_LASSO_5 <- modelo_LASSO_5$lambda.1se
lambda_codo_LASSO_5
prediccion_LASSO_5_2=predict(modelo_LASSO_5_l,s=lambda_codo_LASSO_5,newx=matriz_test)
error.LASSO.2 <- sqrt(mean((prediccion_LASSO_5_2-test$psoda )^2))
error.LASSO.2
##### j)
acp=pcr(psoda~., data=entrenamiento,scale=TRUE, validation="CV")
# CV_10
acp_cv_10 <- crossval(acp, segments = 10)
summary(acp_cv_10, what = "validation")
acp_pred_10_cv=predict(acp,newdata=test,ncomp=13)
error_acp_10_cv<- sqrt(mean((acp_pred_10_cv - test$psoda)^2))
error_acp_10_cv
# Regla del codo
regla_codo_10 <- selectNcomp(acp, method = "onesigma", plot = TRUE, validation = "CV",
segments = 10)
regla_codo_10
acp_pred_10_codo=predict(acp,newdata=test,ncomp=regla_codo_10)
error_acp_10_codo <- sqrt(mean((acp_pred_10_codo - test$psoda)^2))
error_acp_10_codo
#CV_5
acp_cv_5 <- crossval(acp, segments = 5)
summary(acp_cv_5, what = "validation")
acp_pred_5_cv=predict(acp,newdata=test,ncomp=13)
error_acp_5_cv<- sqrt(mean((acp_pred_5_cv - test$psoda)^2))
error_acp_5_cv
# Regla del codo
regla_codo_5 <- selectNcomp(acp, method = "onesigma", plot = TRUE, validation = "CV",
segments = 5)
regla_codo_5
acp_pred_5_codo=predict(acp,newdata=test,ncomp=regla_codo_5)
error_acp_5_codo <- sqrt(mean((acp_pred_5_codo - test$psoda)^2))
error_acp_5_codo
##### k)
pls=plsr(psoda~., data=entrenamiento ,scale=TRUE, validation="CV")
# PLS CV 10
pls_cv_10 <- crossval(pls, segments = 10)
summary(pls_cv_10, what = "validation")
pls_pred_10_cv=predict(pls,newdata=matriz_test,ncomp=3)
error_pls_10 <- sqrt(mean((pls_pred_10_cv - test$psoda)^2))
error_pls_10
codo_pls_10 <- selectNcomp(pls, method = "onesigma", plot = TRUE, validation = "CV",
segments = 10)
codo_pls_10
pls_pred_10_codo=predict(pls,newdata=matriz_test,ncomp=codo_pls_10)
error_pls_10_codo <- sqrt(mean((pls_pred_10_codo - test$psoda)^2))
error_pls_10_codo
# PLS CV 5
pls_cv_5 <- crossval(pls, segments = 5)
plot(RMSEP(pls_cv_5), legendpos="topright")
summary(pls_cv_5, what = "validation")
pls_pred_5_cv=predict(pls,newdata=matriz_test,ncomp=2)
error_pls_5 <- sqrt(mean((pls_pred_5_cv - test$psoda)^2))
error_pls_5
codo_pls_5 <- selectNcomp(pls, method = "onesigma", plot = TRUE, validation = "CV",
segments = 5)
codo_pls_5
pls_pred_5_codo=predict(pls,newdata=matriz_test,ncomp=codo_pls_5)
error_pls_5_codo <- sqrt(mean((pls_pred_5_codo - test$psoda)^2))
error_pls_5_codo
# Randomization
codo_pls_random <- selectNcomp(pls, method = "randomization", plot = TRUE)
codo_pls_random
pls_pred_random <- predict(pls,newdata=matriz_test,ncomp=codo_pls_random)
error_pls_random <- sqrt(mean((pls_pred_random - test$psoda)^2))
error_pls_random
##### l)
lambda_grid <- 10^seq(2,-2, length = 100)
alpha_grid <- seq(0,1, by = 0.05)
Control <- trainControl(method = "cv", number = 10)
buscar_grid <- expand.grid(alpha = alpha_grid, lambda = lambda_grid)
entrenamiento_modelo <- train(psoda~., data = entrenamiento, method = "glmnet",
tuneGrid = buscar_grid, trControl = Control,
tuneLength = 10,
standardize = TRUE, maxit = 1000000)
best_tune_EN_10 <- entrenamiento_modelo$bestTune
entrenamiento_modelo$bestTune
plot(entrenamiento_modelo)
modelo_glmnet <- entrenamiento_modelo$finalModel
coef(modelo_glmnet, s = entrenamiento_modelo$bestTune$lambda)
mejor_modelo <- glmnet(matriz_entrenamiento,entrenamiento$psoda, alpha=entrenamiento_modelo$bestTune$alpha,
lambda = entrenamiento_modelo$bestTune$lambda, thresh = 1e-12)
coef(mejor_modelo, s = entrenamiento_modelo$bestTune$lambda)
cbind(coef(mejor_modelo, s = entrenamiento_modelo$bestTune$lambda),
coef(modelo_glmnet,
s = entrenamiento_modelo$bestTune$lambda))
pred_LASSO_elastic_10 <- predict(mejor_modelo,s=entrenamiento_modelo$bestTune$lambda,newx=matriz_test)
error_pred_LASSO_elastic_10 <- sqrt(mean((pred_LASSO_elastic_10 - test$psoda)^2))
error_pred_LASSO_elastic_10
##### m)
Control <- trainControl(method = "cv", number = 5)
buscar_grid <- expand.grid(alpha = alpha_grid, lambda = lambda_grid)
entrenamiento_modelo <- train(psoda~., data = entrenamiento, method = "glmnet",
tuneGrid = buscar_grid, trControl = Control,
tuneLength = 10,
standardize = TRUE, maxit = 1000000)
best_tune_EN_5 <- entrenamiento_modelo$bestTune
entrenamiento_modelo$bestTune
plot(entrenamiento_modelo)
modelo_glmnet <- entrenamiento_modelo$finalModel
coef(modelo_glmnet, s = entrenamiento_modelo$bestTune$lambda)
mejor_modelo <- glmnet(matriz_entrenamiento,entrenamiento$psoda, alpha=entrenamiento_modelo$bestTune$alpha,
lambda = entrenamiento_modelo$bestTune$lambda, thresh = 1e-12)
coef(mejor_modelo, s = entrenamiento_modelo$bestTune$lambda)
cbind(coef(mejor_modelo, s = entrenamiento_modelo$bestTune$lambda),
coef(modelo_glmnet,
s = entrenamiento_modelo$bestTune$lambda))
pred_LASSO_elastic_5 <- predict(mejor_modelo,s=entrenamiento_modelo$bestTune$lambda,newx=matriz_test)
error_pred_LASSO_elastic_5 <- sqrt(mean((pred_LASSO_elastic_5 - test$psoda)^2))
error_pred_LASSO_elastic_5
##### n)
# Ridge con cv10
n = nrow(matriz_entrenamiento)
beta_ridge_10 = coef(modelo_ridge_10_l, s=mejor_lambda_ridge_10/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_ridge_10 = fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda ,beta_ridge_10, mejor_lambda_ridge_10/n)
out_ridge_10
# Eliminar todas las variables menos la 17,18,20
matriz_entrenamiento_nueva <- matriz_entrenamiento[,c(17,18,20)]
matriz_test_nueva <- matriz_test[,c(17,18,20)]
grid=10^seq(4,-2, length =100)
cv.mod=cv.glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=0,lambda=grid, nfolds = 10)
plot(cv.mod)
mejorlambda_1=cv.mod$lambda.min
mejorlambda_1
mod=glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=0,lambda=grid)
pred=predict(mod,s=mejorlambda_1 ,newx=matriz_test_nueva)
error_1 <- sqrt(mean((pred-test$psoda )^2))
error_1
# LASSO con cv10
beta_LASSO_10 = coef(modelo_LASSO_10_l, s=mejor_lambda_LASSO_10/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_LASSO_10 <- fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda, beta_LASSO_10 ,mejor_lambda_LASSO_10/n)
out_LASSO_10
# Eliminar todas las variables menos la 17,18,20
grid=10^seq(4,-2, length =100)
cv.mod=cv.glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=1,lambda=grid, nfolds = 10)
plot(cv.mod)
mejorlambda_2=cv.mod$lambda.min
mejorlambda_2
mod=glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=1,lambda=grid)
pred=predict(mod,s=mejorlambda_2 ,newx=matriz_test_nueva)
error_2 <- sqrt(mean((pred-test$psoda )^2))
error_2
# Ridge con cv5
beta_ridge_5 = coef(modelo_ridge_5_l, s=mejor_lambda_ridge_5/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_ridge_5 = fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda ,beta_ridge_5, mejor_lambda_ridge_5/n)
out_ridge_5
# Eliminar todas las variables menos la 17,18,20
grid=10^seq(4,-2, length =100)
cv.mod=cv.glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=0,lambda=grid, nfolds = 5)
plot(cv.mod)
mejorlambda_3=cv.mod$lambda.min
mejorlambda_3
mod=glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=0,lambda=grid)
pred=predict(mod,s=mejorlambda_3 ,newx=matriz_test_nueva)
error_3 <- sqrt(mean((pred-test$psoda )^2))
error_3
# LASSO con cv5
beta_LASSO_5 = coef(modelo_LASSO_5_l, s=mejor_lambda_LASSO_5/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_LASSO_5 <- fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda, beta_LASSO_5 ,mejor_lambda_LASSO_5/n)
out_LASSO_5
# Eliminar todas las variables menos la 17,18,20
grid=10^seq(4,-2, length =100)
cv.mod=cv.glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=1,lambda=grid, nfolds = 5)
plot(cv.mod)
mejorlambda_4=cv.mod$lambda.min
mejorlambda_4
mod=glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda,alpha=1,lambda=grid)
pred=predict(mod,s=mejorlambda_4 ,newx=matriz_test_nueva)
error_4 <- sqrt(mean((pred-test$psoda )^2))
error_4
# LASSO with Elastic Net con cv10
beta_LASSO_EN_10 = coef(mejor_modelo, s=best_tune_EN_10$lambda/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_LASSO_EN_10 <- fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda, beta_LASSO_EN_10 ,best_tune_EN_10$lambda/n)
out_LASSO_EN_10
# Eliminar todas las variables menos la 17,18,20 que son la 18,19 y 21 en el data set entrenamiento
entrenamiento_nuevo <- entrenamiento[,c(1,18,19,21)]
Control <- trainControl(method = "cv", number = 10)
buscar_grid <- expand.grid(alpha = alpha_grid, lambda = lambda_grid)
entrenamiento_modelo <- train(psoda~., data = entrenamiento_nuevo, method = "glmnet",
tuneGrid = buscar_grid, trControl = Control,
tuneLength = 10,
standardize = TRUE, maxit = 1000000)
entrenamiento_modelo$bestTune
modelo_glmnet <- entrenamiento_modelo$finalModel
coef(modelo_glmnet, s = entrenamiento_modelo$bestTune$lambda)
mejor_modelo <- glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda, alpha=entrenamiento_modelo$bestTune$alpha,
lambda = entrenamiento_modelo$bestTune$lambda, thresh = 1e-12)
pred_LASSO_elastic_1 <- predict(mejor_modelo,s=entrenamiento_modelo$bestTune$lambda,newx=matriz_test_nueva)
error_pred_LASSO_elastic_1 <- sqrt(mean((pred_LASSO_elastic_1 - test$psoda)^2))
error_pred_LASSO_elastic_1
# LASSO with Elastic Net con cv 5
beta_LASSO_EN_5 = coef(mejor_modelo, s=best_tune_EN_5$lambda/n, exact=TRUE, x = matriz_entrenamiento, y = entrenamiento$psoda)[-1]
out_LASSO_EN_5 <- fixedLassoInf(matriz_entrenamiento,entrenamiento$psoda, beta_LASSO_EN_5 ,best_tune_EN_5$lambda/n)
out_LASSO_EN_5
# Eliminar todas las variables menos la 17,18,20
Control <- trainControl(method = "cv", number = 5)
buscar_grid <- expand.grid(alpha = alpha_grid, lambda = lambda_grid)
entrenamiento_modelo <- train(psoda~., data = entrenamiento_nuevo, method = "glmnet",
tuneGrid = buscar_grid, trControl = Control,
tuneLength = 10,
standardize = TRUE, maxit = 1000000)
entrenamiento_modelo$bestTune
modelo_glmnet <- entrenamiento_modelo$finalModel
coef(modelo_glmnet, s = entrenamiento_modelo$bestTune$lambda)
mejor_modelo <- glmnet(matriz_entrenamiento_nueva,entrenamiento$psoda, alpha=entrenamiento_modelo$bestTune$alpha,
lambda = entrenamiento_modelo$bestTune$lambda, thresh = 1e-12)
pred_LASSO_elastic_2 <- predict(mejor_modelo,s=entrenamiento_modelo$bestTune$lambda,newx=matriz_test_nueva)
error_pred_LASSO_elastic_2 <- sqrt(mean((pred_LASSO_elastic_2 - test$psoda)^2))
error_pred_LASSO_elastic_2
##### o)
# Penalización independiente
post_lasso_reg_indep = rlasso(entrenamiento$psoda~matriz_entrenamiento,post=TRUE, X.dependent.lambda = FALSE)
print(post_lasso_reg_indep, all=FALSE)
yhat_postlasso_new_indep = predict(post_lasso_reg_indep, newdata=matriz_test)
error_postlasso_indep <- sqrt(mean((yhat_postlasso_new_indep - test$psoda )^2))
error_postlasso_indep
# Penalización dependiente
post_lasso_reg_dep = rlasso(entrenamiento$psoda~matriz_entrenamiento,post=TRUE, X.dependent.lambda = TRUE)
print(post_lasso_reg_dep, all=FALSE)
yhat_postlasso_new_dep = predict(post_lasso_reg_dep, newdata=matriz_test)
error_postlasso_dep <- sqrt(mean((yhat_postlasso_new_dep - test$psoda )^2))
error_postlasso_dep
##### p)
# Ambos modelos nos dan las mismas variables y los mismos coeficientes por lo que es igual para ambos.
lasso.effect = rlassoEffects(x=matriz_entrenamiento, y=entrenamiento$psoda, index=c("pfries", "NJ"), post = TRUE, )
print(lasso.effect)
summary(lasso.effect)
confint(lasso.effect, level=0.95, joint=TRUE)
plot(lasso.effect, main="Confidence Intervals")
##### q) escrito en el pdf
##### r)
pls_cv_5$coefficient
|
d5209c496715bd41d7897702ef6e4466091e796d
|
22f93d06424cbeeb1343623c20b1256aae2a08df
|
/classwork1/R-intro1_classwork.R
|
cbebf5fc92bc125eba0c38190cf43c7844cebf0e
|
[] |
no_license
|
normall777/MyDataAccessMethods
|
fedfc6c7814ec75a8cc7561d32186d1e2209203d
|
4c78903f2a5962082693f92303c0ddcbfd1079b9
|
refs/heads/master
| 2020-03-29T07:13:21.849549
| 2018-12-17T22:22:46
| 2018-12-17T22:22:46
| 149,657,373
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,441
|
r
|
R-intro1_classwork.R
|
1234+4567
29-45
325/25
56*12
11*11
111*111
1111111*1111111
options(digits=14)
options(width=40)
5:32
1:10
(1:10)+3
(1:10)-3
(1:10)*3
(1:10)^2
(1:10)^3
3:4
31 %% 7
31 %/% 7
7*4 + 3
x <-c(4, 1, 8, 9)
y <-c(6, 2, 4, 3)
plot(x,y);
lines(x, y)
x <- 1:10; y <- x^2;
plot(x,y)
plot(x,y);
lines(x,y)
learn <-c("stats" = 15, "math"= 10,"programming" = 30, "attempts" = 45)
pie(learn)
barplot(learn)
Z <- rnorm(1000)# 1000 standard normal random variates
hist(Z, prob = TRUE, main = "Гистограмма относительной частоты",
sub = "Плотность распределения")
curve(dnorm(x), from = -3, to = 3, add = TRUE, col = "blue")
#---------
11111111*11111111
11111111*1111111?.
#Error in `?`(11111111 * 1111111, .) :
#нет документации на тип ‘12345677654321’ и раздел ‘.’ (или ошибка в обработке помощи)
#Ошибка в постановке вопросительного знака и точки
a <- c(3,7,12,15,20)
b <- c(2,5,8,11,15)
S <- a*b
plot(a,S);
lines(a,S)
plot(b,S);
lines(b,S)
plot(a,b)
vasya <- c("Математика" = 40, "Английский"=40, "Физическая культура"=10, "Программирование"=150)
pie(vasya)
drinks <- rnorm(5, mean = 450, sd = 4)
drinks
drinks > 455
drinks <- rnorm(10000, mean = 450, sd = 4)
spent <- sum(drinks>455)
spent
#1014/10000 ~ 10%
|
184ac0fbe82633047d548e5f99c961d4d2ca77c1
|
60f1af254960315177c12f81558260d747582dc1
|
/codes/R/get_genebody_from_gtf.R
|
1254961d2fa584da7eca8c93258cccaa3a965ba4
|
[] |
no_license
|
15101538237ren/tcga
|
4f1dd60ca524aa770a72380fec4081e0aeba02c6
|
be81c473c15aa669a704598600f1d21e7df0b1a6
|
refs/heads/master
| 2021-09-07T16:20:42.789796
| 2018-02-26T01:36:11
| 2018-02-26T01:36:11
| 113,286,135
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 858
|
r
|
get_genebody_from_gtf.R
|
library(GenomicFeatures)
data_path = "~/PycharmProjects/tcga_raw_data/GRCh38/"
out_path = "~/PycharmProjects/tcga/global_files/"
txdb <- makeTxDbFromGFF(paste(data_path, "Homo_sapiens.GRCh38.90.gtf",sep = ""), format="gtf")
genes <- genes(txdb)
gene_df <- as.data.frame(genes)
ensembl_ids<-gene_df[,6]
library(biomaRt)
mart<-useMart("ensembl")
mart<- useDataset("hsapiens_gene_ensembl", mart)
attributs_df<- as.data.frame(listAttributes(mart)) #list all available attributes
genes_table <- getBM(filters= "ensembl_gene_id", attributes= c("ensembl_gene_id", "hgnc_symbol", "description"), values= ensembl_ids, mart= mart)
merged_gene_df<-merge(gene_df, genes_table, by.x="gene_id", by.y="ensembl_gene_id")
df_out <- merged_gene_df[ ,c(7,2,3,4,6)]
write.table(df_out, file= paste(out_path, "human_gene_bodys.tsv",sep = ""), col.names=F, row.names=F, sep="\t")
|
2b53022345283a6c5526dc6e1c8dee03dea0758d
|
f9e5ae04eae16761374e5c92f69db4a50f4fb34e
|
/R/NetworkView.R
|
7f1f05da0049df6f37dc804a4f4df1ef7fab21f3
|
[
"Apache-2.0"
] |
permissive
|
PriceLab/TrenaViz
|
f09ddc1ff6fc0e824890c833b55cced05e67a90a
|
b88f6785ed6efa398863d697fa83d64201059061
|
refs/heads/master
| 2021-07-22T15:40:47.936451
| 2020-04-29T18:29:42
| 2020-04-29T18:29:42
| 159,706,619
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 19,618
|
r
|
NetworkView.R
|
#' import shiny
#' import cyjShiny
#' import TrenaProject
#' import graph
#' @name NetworkView
#' @rdname NetworkView
#' @aliases NetworkView
#------------------------------------------------------------------------------------------------------------------------
# library(TrenaProject)
# library(cyjShiny)
#------------------------------------------------------------------------------------------------------------------------
.NetworkView <- setClass("NetworkView",
representation = representation(
quiet="logical",
targetGene="character",
tss="numeric",
tbl.model="data.frame",
tbl.regulatoryRegions="data.frame",
state="environment")
)
#------------------------------------------------------------------------------------------------------------------------
setGeneric('getGraph', signature='obj', function(obj) standardGeneric('getGraph'))
#------------------------------------------------------------------------------------------------------------------------
setMethod('getGraph', 'NetworkView',
function(obj){
tbl.nodes <- data.frame(id=c("A", "B", "C"),
type=c("kinase", "TF", "glycoprotein"),
lfc=c(1, 1, 1),
count=c(0, 0, 0),
stringsAsFactors=FALSE)
tbl.edges <- data.frame(source=c("A", "B", "C"),
target=c("B", "C", "A"),
interaction=c("phosphorylates", "synthetic lethal", "unknown"),
stringsAsFactors=FALSE)
graph.json <- dataFramesToJSON(tbl.edges, tbl.nodes)
targetGene <- obj@targetGene
tbl.model <- obj@tbl.model
tbl.reg <- obj@tbl.regulatoryRegions
tss <- obj@tss
g <- .geneRegulatoryModelToGraph(targetGene, tss, tbl.model, tbl.reg)
g <- .addGeneModelLayout(g, xPos.span=1500)
g
})
#------------------------------------------------------------------------------------------------------------------------
#' Create an NetworkView object
#'
#' @description
#' a shiny app
#'
#' @rdname NetworkView
#'
#' @param organism A character string, one of the supported species names: hsapiens, mmuscuulus
#' @param genome A character string, one of the supported genome builds: hg38, mm10
#' @param quiet A logical indicating whether or not the Trena object should print output
#'
#' @return An object of the NetworkView class
#'
#' @export
#'
NetworkView <- function(targetGene, tss, tbl.model, tbl.regulatoryRegions, quiet=TRUE)
{
state <- new.env(parent=emptyenv())
.NetworkView(targetGene=targetGene,
tss=tss,
tbl.model=tbl.model,
tbl.regulatoryRegions=tbl.regulatoryRegions,
state=state,
quiet=quiet)
} # NetworkView
#------------------------------------------------------------------------------------------------------------------------
setMethod("show", "NetworkView",
function(object){
cat(paste("a NetworkView object from the TrenaViz package:", "\n"))
cat(sprintf(" targetGene: %s\n", obj@targetGene))
cat(sprintf(" tss: %s\n", obj@tss))
cat(sprintf(" tbl.model: %d rows, %d columns\n", nrow(obj@tbl.model), ncol(obj@tbl.model)))
cat(sprintf(" tbl.regulatoryRegions: %d rows, %d columns\n", nrow(obj@tbl.regulatoryRegions),
ncol(obj@tbl.regulatoryRegions)))
})
#------------------------------------------------------------------------------------------------------------------------
#' create and return the control-rich UI
#'
#' @rdname createPage
#' @aliases createPage
#'
#' @param obj An object of class NetworkView
#'
#' @export
#'
setMethod("createPage", "NetworkView",
function(obj) {
fluidPage(id="networkViewPageContent",
fluidRow(
actionButton(inputId="fitNetworkButton", label="Fit"),
actionButton(inputId="fitSelectedNodesButton", label="Fit Selection"),
actionButton(inputId="removeNetworkButton", label="Remove Graph"),
actionButton(inputId="genomicLayoutButton", label="GenomicLayout")
),
fluidRow(column(width=12, cyjShinyOutput('cyjShiny')))
)
#cyjShinyOutput('cyjShiny', height=400)
})
#------------------------------------------------------------------------------------------------------------------------
#' display the page
#'
#' @rdname displayPage
#' @aliases displayPage
#'
#' @param obj An object of class NetworkView
#' @param tf character string, the geneSymbol name of the transcription factor
#'
#' @export
#'
setMethod("displayPage", "NetworkView",
function(obj){
printf("NetworkView displayPage")
removeUI(selector="#networkViewPageContent", immediate=TRUE)
insertUI(selector="#networkViewPage", where="beforeEnd", createPage(obj), immediate=TRUE)
#js$cyjSetupResize();
js$cyjShinySetWidth();
later(function(){fit(session, 300)}, 1000)
})
#------------------------------------------------------------------------------------------------------------------------
#' add shiny event handlers
#'
#' @rdname addEventHandlers
#' @aliases addEventHandlers
#'
#' @param obj An object of class NetworkView
#' @param session a Shiny session object
#' @param input a Shiny input object
#' @param output a Shiny output object
#'
#' @export
#'
setMethod("addEventHandlers", "NetworkView",
function(obj, session, input, output){
printf("--- NetworkView::addEventHandlers")
obj@state$session <- session
obj@state$input <- input
obj@state$output <- output
observeEvent(input$fitNetworkButton, ignoreInit=TRUE, {
fit(session, 80)
})
observeEvent(input$fitSelectedNodesButton, ignoreInit=TRUE, {
fitSelected(session, 80)
})
observeEvent(input$removeNetworkButton, ignoreInit=TRUE, {
removeGraph(session)
})
observeEvent(input$genomicLayoutButton, ignoreInit=TRUE, {
setNodePositions(session, obj@state$tbl.pos)
})
observeEvent(input$viewNetworkButton, ignoreInit=FALSE, {
printf("view network")
updateTabItems(session, "sidebarMenu", selected="networkViewTab")
# displayPage(obj)
xyz <- "observing viewNetworkButton"
output$cyjShiny <- renderCyjShiny({
printf("--- renderCyjShiny, triggered by viewNetworkButton")
style.file <- system.file(package="TrenaViz", "extdata", "trenaModelStyle.js")
g <- getGraph(obj)
obj@state$g <- g
print(g)
graph.json <- graphNELtoJSON(g)
xPos <- nodeData(g, attr="xPos")
yPos <- nodeData(g, attr="yPos")
tbl.pos <- data.frame(id=names(xPos), x=as.numeric(xPos), y=as.numeric(yPos), stringsAsFactors=FALSE)
obj@state$tbl.pos <- tbl.pos
cyjShiny(graph.json, layoutName="cola", styleFile=style.file, width=1000, height=1000)
})
})
}) # addEventHandlers
#------------------------------------------------------------------------------------------------------------------------
# by example:
#
# the incoming tbl.model presents these challenges:
#
# gene betaLasso lassoPValue pearsonCoeff rfScore betaRidge spearmanCoeff bindingSites
# 6 E2F3 0 7.124847e-07 0.8683105 2.936714 0.04945335 0.8149973 NA
# 45 HOXC13 0 3.987483e-02 -0.8640875 2.457541 -0.01531601 -0.7659080 NA
# 97 ZNF263 0 6.236969e-01 0.9003067 2.134046 0.04104303 0.6360153 NA
# 70 PRDM4 0 1.000000e+00 0.8984506 1.900193 0.03627523 0.7405583 NA
#
# and for which we want these results (first 4 rows only)
#
# tf pearson spearman betaLasso randomForest
# 6 E2F3 0.8683105 0.8149973 0 2.936714
# 45 HOXC13 -0.8640875 -0.7659080 0 2.457541
# 97 ZNF263 0.9003067 0.6360153 0 2.134046
# 70 PRDM4 0.8984506 0.7405583 0 1.900193
.standardizeModelTable <- function(tbl.model)
{
required.colNames <- c("tf", "pearson", "spearman", "betaLasso", "randomForest")
colnames.in <- tolower(colnames(tbl.model))
gene.col <- grep("^gene$", colnames.in)
if(length(gene.col) > 0)
colnames(tbl.model)[gene.col] <- "tf"
pearson.col <- grep("pearson", colnames.in)
if(length(pearson.col) > 0)
colnames(tbl.model)[pearson.col] <- "pearson"
spearman.col <- grep("spearman", colnames.in)
if(length(spearman.col) > 0)
colnames(tbl.model)[spearman.col] <- "spearman"
betaLasso.col <- grep("betalasso", colnames.in)
if(length(betaLasso.col) > 0)
colnames(tbl.model)[betaLasso.col] <- "betaLasso"
rf.1.col <- grep("forest", colnames.in)
rf.2.col <- grep("rfscore", colnames.in)
if(length(rf.1.col) > 0)
colnames(tbl.model)[rf.1.col] <- "randomForest"
if(length(rf.2.col) > 0)
colnames(tbl.model)[rf.2.col] <- "randomForest"
tbl.out <- tbl.model[, required.colNames]
tbl.out
} # .standardizeModelTable
#------------------------------------------------------------------------------------------------------------------------
# by example:
#
# one instance of the incoming tbl.reg presents these challenges:
#
# motifName loc fp_start fp_end type name length strand sample_id method provenance score1 score2 score3 score4 score5 score6 chrom database shortMotif geneSymbol pubmedID organism source
# Hsapiens-HOCOMOCOv10-CLOCK_HUMAN.H10MO.D chr3:128077447-128077466 128077441 128077451 motif.in.footprint Hsapiens-HOCOMOCOv10-CLOCK_HUMAN.H10MO.D 20 + ENCSR000EMT HINT lymphoblast_hint_16.minid 12 13.02250 1.81e-05 NA NA NA chr3 lymphoblast_hint_16 CLOCK_HUMAN.H10MO.D CLOCK 26586801 Hsapiens MotifDb
# Hsapiens-HOCOMOCOv10-PURA_HUMAN.H10MO.D chr3:128417965-128417981 128417972 128417989 motif.in.footprint Hsapiens-HOCOMOCOv10-PURA_HUMAN.H10MO.D 17 - ENCSR000EJK HINT lymphoblast_hint_20.minid 12 12.04400 3.25e-05 NA NA NA chr3 lymphoblast_hint_20 PURA_HUMAN.H10MO.D PURA 26586801 Hsapiens MotifDb
# Hsapiens-jaspar2016-HOXC11-MA0651.1 chr3:128617604-128617614 128617598 128617621 motif.in.footprint Hsapiens-jaspar2016-HOXC11-MA0651.1 11 - ENCSR000DBZ HINT lymphoblast_hint_20.minid 32 11.10000 7.99e-05 NA NA NA chr3 lymphoblast_hint_20 MA0651.1 HOXC11 24194598 Hsapiens MotifDb
# Hsapiens-jaspar2016-SP4-MA0685.1 chr3:128487237-128487253 128487253 128487267 motif.in.footprint Hsapiens-jaspar2016-SP4-MA0685.1 17 - ENCSR000EJE HINT lymphoblast_hint_16.minid 24 4.01124 8.85e-05 NA NA NA chr3 lymphoblast_hint_16 MA0685.1 SP4 24194598 Hsapiens MotifDb
# Hsapiens-jaspar2016-ZBTB7A-MA0750.1 chr3:128617856-128617867 128617847 128617892 motif.in.footprint Hsapiens-jaspar2016-ZBTB7A-MA0750.1 12 + ENCSR000DCA HINT lymphoblast_hint_16.minid 20 15.76400 2.28e-06 NA NA NA chr3 lymphoblast_hint_16 MA0750.1 ZBTB7A 24194598 Hsapiens MotifDb
#
# from which we wish to extract:
#
# chrom start end tf motif
# 1 chr3 128483072 128483461 MAZ Hsapiens-HOCOMOCOv10-MAZ_HUMAN.H10MO.A
# 2 chr3 128483072 128483461 SP4 Hsapiens-HOCOMOCOv10-SP4_HUMAN.H10MO.D
# 3 chr3 128483072 128483461 SP2 Hsapiens-HOCOMOCOv10-SP2_HUMAN.H10MO.C
# 4 chr3 128483072 128483461 SP3 Hsapiens-HOCOMOCOv10-SP3_HUMAN.H10MO.B
# 5 chr3 128483072 128483461 SP3 Hsapiens-SwissRegulon-SP3.SwissRegulon
# 6 chr3 128483072 128483461 SP1 Hsapiens-HOCOMOCOv10-SP1_HUMAN.H10MO.C
#
# and we want
# chrom start end name distance motifName
# chr3 128483072 128483461 MAZ Hsapiens-HOCOMOCOv10-MAZ_HUMAN.H10MO.A
#
# regRegions.names <- unlist(lapply(1:nrow(tbl.reg), function(i){
# distance.from.tss <- tbl.reg$distance.from.tss[i]
# region.size <- nchar(tbl.reg$match[i])
# motif.name <- tbl.reg$motifName[i]
# if(distance.from.tss < 0)
# sprintf("%s.fp.downstream.%05d.L%d.%s", targetGene, abs(distance.from.tss), region.size, motif.name)
# else
# sprintf("%s.fp.upstream.%05d.L%d.%s", targetGene, abs(distance.from.tss), region.size, motif.name)
# }))
#
# tbl.reg$regionName <- regRegions.names
# all.nodes <- unique(c(targetGene, tfs, regRegions.names))
# g <- addNode(all.nodes, g)
#
# nodeData(g, targetGene, "type") <- "targetGene"
# nodeData(g, tfs, "type") <- "TF"
# nodeData(g, regRegions.names, "type") <- "regulatoryRegion"
# nodeData(g, all.nodes, "label") <- all.nodes
# nodeData(g, regRegions.names, "label") <- tbl.reg$motifName
# nodeData(g, regRegions.names, "distance") <- tbl.reg$distance
# nodeData(g, regRegions.names, "motif") <- tbl.reg$motifName
#
.standardizeRegulatoryRegionsTable <- function(tbl.reg, targetGene, tss)
{
locs <- lapply(tbl.reg$loc, parseChromLocString)
tbl.rough <- do.call(rbind, lapply(locs, as.data.frame))
tbl.rough$chrom <- as.character(tbl.rough$chrom)
tbl.rough <- cbind(tbl.rough, tbl.reg[, c("motifName", "fp_start", "fp_end", "geneSymbol")])
make.name <- function(tss, start, tf){
distance.from.tss <- tss - start
sprintf("%s:%d:%s", targetGene, distance.from.tss, tf)
}
regulatory.region.names <- unlist(lapply(1:nrow(tbl.reg),
function(i) make.name(tss, tbl.rough$fp_start[i], tbl.rough$geneSymbol[i])))
tbl.rough$name <- regulatory.region.names
tbl.rough$distance <- tss - tbl.rough$fp_start
tbl.rough$targetGene <- targetGene
tbl.out <- tbl.rough[, c("chrom", "fp_start", "fp_end", "distance", "name", "targetGene", "geneSymbol", "motifName")]
colnames(tbl.out) <- c("chrom", "start", "end", "distance", "name", "targetGene", "tf", "motif")
rownames(tbl.out) <- NULL
tbl.out
} # .standardizeRegulatoryRegionsTable
#------------------------------------------------------------------------------------------------------------------------
.geneRegulatoryModelToGraph <- function(targetGene, tss, tbl.model, tbl.reg)
{
xyz <- ".geneRegulatoryModelToGraph"
tbl.model <- .standardizeModelTable(tbl.model)
tbl.reg <- .standardizeRegulatoryRegionsTable(tbl.reg, targetGene, tss)
required.geneModelColumnNames <- c("tf", "pearson", "spearman", "betaLasso", "randomForest")
required.regulatoryRegionsColumnNames <- c("chrom", "start", "end", "distance", "name", "targetGene", "tf", "motif")
stopifnot(all(required.geneModelColumnNames %in% colnames(tbl.model)))
stopifnot(all(required.regulatoryRegionsColumnNames %in% colnames(tbl.reg)))
printf("genes: %d, %d occurences of %d motifs", length(tbl.model$tf), length(tbl.reg$motif),
length(unique(tbl.reg$motif)))
g <- graphNEL(edgemode = "directed")
nodeDataDefaults(g, attr = "type") <- "undefined" # targetGene, tf, footprint
nodeDataDefaults(g, attr = "label") <- "default node label"
nodeDataDefaults(g, attr = "distance") <- 0
nodeDataDefaults(g, attr = "pearson") <- 0
nodeDataDefaults(g, attr = "randomForest") <- 0
nodeDataDefaults(g, attr = "betaLasso") <- 0
nodeDataDefaults(g, attr = "motif") <- ""
nodeDataDefaults(g, attr = "xPos") <- 0
nodeDataDefaults(g, attr = "yPos") <- 0
edgeDataDefaults(g, attr = "edgeType") <- "undefined"
tfs <- tbl.model$tf
all.nodes <- unique(c(targetGene, tfs, tbl.reg$name))
g <- addNode(all.nodes, g)
nodeData(g, targetGene, "type") <- "targetGene"
nodeData(g, tfs, "type") <- "TF"
nodeData(g, tbl.reg$name, "type") <- "regulatoryRegion"
nodeData(g, all.nodes, "label") <- all.nodes
xyz <- "NetworkView assiging graph node data"
nodeData(g, tbl.reg$name, "label") <- tbl.reg$motif
nodeData(g, tbl.reg$name, "distance") <- tbl.reg$distance
nodeData(g, tbl.reg$name, "motif") <- tbl.reg$motifName
nodeData(g, tfs, "pearson") <- tbl.model$pearson
nodeData(g, tfs, "betaLasso") <- tbl.model$betaLasso
nodeData(g, tfs, "randomForest") <- tbl.model$randomForest
g <- addEdge(tbl.reg$tf, tbl.reg$name, g)
edgeData(g, tbl.reg$tf, tbl.reg$name, "edgeType") <- "bindsTo"
g <- graph::addEdge(tbl.reg$name, targetGene, g)
edgeData(g, tbl.reg$name, targetGene, "edgeType") <- "regulatorySiteFor"
g
} # .geneRegulatoryModelToGraph
#------------------------------------------------------------------------------------------------------------------------
.addGeneModelLayout <- function(g, xPos.span=1500)
{
all.distances <- sort(unique(unlist(nodeData(g, attr='distance'), use.names=FALSE)))
print(all.distances)
fp.nodes <- nodes(g)[which(unlist(nodeData(g, attr="type"), use.names=FALSE) == "regulatoryRegion")]
tf.nodes <- nodes(g)[which(unlist(nodeData(g, attr="type"), use.names=FALSE) == "TF")]
targetGene.nodes <- nodes(g)[which(unlist(nodeData(g, attr="type"), use.names=FALSE) == "targetGene")]
# add in a zero in case all of the footprints are up or downstream of the 0 coordinate, the TSS
span.endpoints <- range(c(0, as.numeric(nodeData(g, fp.nodes, attr="distance"))))
span <- max(span.endpoints) - min(span.endpoints)
footprintLayoutFactor <- 1
printf("initial: span: %d footprintLayoutFactor: %f", span, footprintLayoutFactor)
footprintLayoutFactor <- xPos.span/span
#if(span < 600) #
# footprintLayoutFactor <- 600/span
#if(span > 1000)
# footprintLayoutFactor <- span/1000
printf("corrected: span: %d footprintLayoutFactor: %f", span, footprintLayoutFactor)
xPos <- as.numeric(nodeData(g, fp.nodes, attr="distance")) * footprintLayoutFactor
yPos <- 0
nodeData(g, fp.nodes, "xPos") <- xPos
nodeData(g, fp.nodes, "yPos") <- yPos
adjusted.span.endpoints <- range(c(0, as.numeric(nodeData(g, fp.nodes, attr="xPos"))))
printf("raw span of footprints: %d footprintLayoutFactor: %f new span: %8.0f",
span, footprintLayoutFactor, abs(max(adjusted.span.endpoints) - min(adjusted.span.endpoints)))
tfs <- names(which(nodeData(g, attr="type") == "TF"))
for(tf in tfs){
footprint.neighbors <- edges(g)[[tf]]
if(length(footprint.neighbors) > 0){
footprint.positions <- as.integer(nodeData(g, footprint.neighbors, attr="xPos"))
new.xPos <- mean(footprint.positions)
if(is.na(new.xPos)) browser()
if(is.nan(new.xPos)) browser()
#printf("%8s: %5d", tf, new.xPos)
}
else{
new.xPos <- 0
}
nodeData(g, tf, "yPos") <- sample(300:1200, 1)
nodeData(g, tf, "xPos") <- new.xPos
} # for tf
nodeData(g, targetGene.nodes, "xPos") <- 0
nodeData(g, targetGene.nodes, "yPos") <- -200
g
} # .addGeneModelLayout
#------------------------------------------------------------------------------------------------------------------------
|
f63252f044500e005838ad11d22162144d216d8a
|
a87349aeb0fe7ec8264f159731c413ab4f9bded5
|
/project/etl/model.R
|
f822bf8122ecf566bc3255e81a399104999c3a3f
|
[] |
no_license
|
ivan-rivera/EndOfMe
|
08bd5598206624cdf5b0d59eb9caa6e9cdde6f00
|
fdddd2b4ac8fc9b01a5f5f8497c4e4ed25fa0b57
|
refs/heads/master
| 2021-08-28T03:41:12.151848
| 2021-08-09T18:30:41
| 2021-08-09T18:30:41
| 207,624,691
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,013
|
r
|
model.R
|
# ================================
# Modelling
# ================================
#' Build and evaluate models
#'
#' @param sleep_collection list with processed sleep datasets
#' @param response_vars vector of strings that currently supports the default values only
#' @param recent_data_for_validation boolean, if true then the last N days (determined by the global parameter prop_for_model_validation in settings.R) are used for validation, if false, then validation data is picked randomly out of the entire dataset
#'
#' @return a list of datasets
generate_predictions <- function(
sleep_collection,
response_vars = c("sleep_rating_next", "time_asleep_next"),
recent_data_for_validation = FALSE # if we dont have much data, then recent records might be skewed towards either good or bad nights as there seems to be some autocorrelation in the sleep series
){
# NOTE: right now this function is optimized specifically for 2 response variables
# in a sense that it is designed to be used to generate graphics
predictor_vars <- sleep_collection[["modelling"]][["data"]] %>% select(
-one_of(
c(
sleep_collection[["modelling"]][["variables"]]$id,
sleep_collection[["modelling"]][["variables"]]$exclusions
)
)
) %>% colnames
model_results <- list(
"performance" = tibble(),
"variables" = tibble(),
"predictions" = tibble()
)
# generate a model for each response variable
for(v in response_vars){
print(sprintf("processing response variable %s...", v))
model_fitting_data <- sleep_collection[["modelling"]][["data"]] %>%
filter(!is.na(!!rlang::parse_expr(v)))
if(recent_data_for_validation){
days_for_model_validation <- floor(
n_distinct(sleep_collection[["modelling"]][["data"]]$sleep_date) * prop_for_model_validation
)
fitting_collection <- list(
"fitting" = model_fitting_data %>%
filter(sleep_date < max(sleep_date) - days_for_model_validation),
"validation" = model_fitting_data %>%
filter(sleep_date >= max(sleep_date) - days_for_model_validation) %>%
filter(!is.na(!!rlang::parse_expr(v)))
)
} else {
staging_dates <- model_fitting_data %>%
filter(!is.na(!!rlang::parse_expr(v))) %>%
sample_frac(prop_for_model_validation) %>%
pull(sleep_date)
fitting_collection <- list(
"fitting" = model_fitting_data %>% filter(!sleep_date %in% staging_dates),
"validation" = model_fitting_data %>% filter(sleep_date %in% staging_dates)
)
}
model_data <- list(
"fitting" = list(
"predictors" = fitting_collection[["fitting"]] %>% select(one_of(predictor_vars)),
"response" = fitting_collection[["fitting"]] %>% pull(v)
),
"validation" = list(
"predictors" = fitting_collection[["validation"]] %>% select(one_of(predictor_vars)),
"response" = fitting_collection[["validation"]] %>% pull(v)
),
"prediction" = list(
"predictors" = sleep_collection[["modelling"]][["data"]] %>%
filter(is.na(!!rlang::parse_expr(v))) %>%
select(one_of(predictor_vars))
)
)
target_model <- caret::train(
x=as.data.frame(model_data[["fitting"]][["predictors"]]),
y=model_data[["fitting"]][["response"]],
method="xgbLinear",
metric="RMSE",
tuneLength=20,
preProcOptions=list(method=c("center", "scale")),
trControl=caret::trainControl(
method = "boot",
number = 10,
search = "random",
verboseIter = FALSE
)
)
validation_results <- tibble(
response = v,
actual = model_data[["validation"]][["response"]],
predicted = predict(
target_model,
model_data[["validation"]][["predictors"]]
)
)
var_importance <- varImp(target_model)$importance %>%
as.data.frame() %>%
rownames_to_column() %>%
rename(
"variable" = rowname,
"importance" = Overall
) %>%
filter(importance > 0) %>%
mutate(
response = v,
importance = importance / 100,
variable = gsub("_", " ", variable)
# variable = ifelse(
# grepl("before", variable),
# variable,
# paste0(variable, " yesterday")
# )
)
prediction_results <- tibble(
variable = v,
prediction_date = sleep_collection[["modelling"]][["data"]] %>%
filter(is.na(!!rlang::parse_expr(v))) %>%
pull(sleep_collection[["modelling"]][["variables"]]$id),
prediction = predict(
target_model,
model_data[["prediction"]][["predictors"]]
)
)
model_results[["performance"]] %<>% rbind(validation_results)
model_results[["variables"]] %<>% rbind(var_importance)
model_results[["predictions"]] %<>% rbind(prediction_results)
}
model_results
}
|
499dca05810e503030cb313c585804b231af3a58
|
a3b61b2926f9cf93af8fd7f774c2ca1323f56e12
|
/R_scripts/DE_genes_bar_chart.R
|
a998774f0723a3081155bb19a89d1f8b750a462f
|
[] |
no_license
|
kerrimalone/AlvMac
|
77542b414e655bff2a84fc4e3fc93a753277a0df
|
dfbb9acffd90c7d3d24615232ba780ab6e0b6bab
|
refs/heads/master
| 2021-01-11T13:54:36.028650
| 2017-06-20T13:58:35
| 2017-06-20T13:58:35
| 94,886,756
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,221
|
r
|
DE_genes_bar_chart.R
|
###############################
# Load required packages #
##############################
library("ggplot2")
#http://stackoverflow.com/questions/38268741/geom-bar-ggplot2-stacked-grouped-bar-plot-with-positive-and-negative-values-p
###############################
# Read in and manipulate data #
##############################
# Set working directory and load any previously saved data
setwd("/Users/Kerri/Google Drive/Postdoc UCD /Alv mac work/EdgeR")
#Create vectors with desirable variables for graphing
Time.vec<-c(rep("02hr",4),rep("06hr",4),rep("24hr",4),rep("48hr",4))
Condition.vec<-c("MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB")
Variable.vec<-c(rep("Up",2),rep("Down",2),rep("Up",2),rep("Down",2),rep("Up",2),rep("Down",2),
rep("Up",2),rep("Down",2))
Variable.condition.vec<-c("MB_up","TB_up","MB_down","TB_down","MB_up","TB_up","MB_down","TB_down",
"MB_up","TB_up","MB_down","TB_down","MB_up","TB_up","MB_down","TB_down")
#Going to count how many genes are up and down so,
#set up empty vector with blank entries to store the gene counts
values.vec<-rep("x",16)
#Read in DE gene data for each timepoint and treatment and subset
#FDR < 0.05 and log2FC > 1
TB_2hr<-read.csv("FDR_0.05_logFC_DE_TB_2H.txt",sep="\t",header=TRUE)
head(TB_2hr)
dim(TB_2hr)
TB_2hr<-na.omit(TB_2hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(TB_2hr)
TB_2hr_logFC<-as.vector(TB_2hr["logFC"])
#counting up and down regged genes based on logFC values
#save result in a particular entry in the blank vector values.vec
values.vec[2] <-sum(TB_2hr_logFC > 1)
values.vec[4] <-sum(TB_2hr_logFC < 1)
#Repeat for all timepoints and treatments
bovis_2hr<-read.csv("FDR_0.05_logFC_DE_MB_2H.txt",sep="\t",header=TRUE)
head(bovis_2hr)
dim(bovis_2hr)
bovis_2hr<-na.omit(bovis_2hr)
dim(bovis_2hr)
bovis_2hr_logFC<-as.vector(bovis_2hr["logFC"])
values.vec[1] <-sum(bovis_2hr_logFC > 1)
values.vec[3] <-sum(bovis_2hr_logFC < 1)
bovis_6hr<-read.csv("FDR_0.05_logFC_DE_MB_6H.txt",sep="\t",header=TRUE)
head(bovis_6hr)
dim(bovis_6hr)
bovis_6hr<-na.omit(bovis_6hr)
dim(bovis_6hr)
bovis_6hr_logFC<-as.vector(bovis_6hr["logFC"])
values.vec[5] <-sum(bovis_6hr_logFC > 1)
values.vec[7] <-sum(bovis_6hr_logFC < 1)
TB_6hr<-read.csv("FDR_0.05_logFC_DE_TB_6H.txt",sep="\t",header=TRUE)
head(TB_6hr)
dim(TB_6hr)
TB_6hr<-na.omit(TB_6hr)
dim(TB_6hr)
TB_6hr_logFC<-as.vector(TB_6hr["logFC"])
values.vec[6] <-sum(TB_6hr_logFC > 1)
values.vec[8] <-sum(TB_6hr_logFC < 1)
bovis_24hr<-read.csv("FDR_0.05_logFC_DE_MB_24H.txt",sep="\t",header=TRUE)
head(bovis_24hr)
dim(bovis_24hr)
bovis_24hr<-na.omit(bovis_24hr)
dim(bovis_24hr)
bovis_24hr_logFC<-as.vector(bovis_24hr["logFC"])
values.vec[9] <-sum(bovis_24hr_logFC > 1)
values.vec[11] <-sum(bovis_24hr_logFC < 1)
TB_24hr<-read.csv("FDR_0.05_logFC_DE_TB_24H.txt",sep="\t",header=TRUE)
head(TB_24hr)
dim(TB_24hr)
TB_24hr<-na.omit(TB_24hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
rownames(TB_24hr)<-TB_24hr[,1]
dim(TB_24hr)
TB_24hr_logFC<-as.vector(TB_24hr["logFC"])
values.vec[10] <-sum(TB_24hr_logFC > 1)
values.vec[12] <-sum(TB_24hr_logFC < 1)
bovis_48hr<-read.csv("FDR_0.05_logFC_DE_MB_48H.txt",sep="\t",header=TRUE)
head(bovis_48hr)
dim(bovis_48hr)
bovis_48hr<-na.omit(bovis_48hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(bovis_48hr)
bovis_48hr_logFC<-as.vector(bovis_48hr["logFC"])
values.vec[13] <-sum(bovis_48hr_logFC > 1)
values.vec[15] <-sum(bovis_48hr_logFC < 1)
TB_48hr<-read.csv("FDR_0.05_logFC_DE_TB_48H.txt",sep="\t",header=TRUE)
head(TB_48hr)
dim(TB_48hr)
TB_48hr<-na.omit(TB_48hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(TB_48hr)
TB_48hr_logFC<-as.vector(TB_48hr["logFC"])
values.vec[14] <-sum(TB_48hr_logFC > 1)
values.vec[16] <-sum(TB_48hr_logFC < 1)
values.vec
#create a new df to store all of the above info with desired variables
bar_data.raw<-data.frame(a=character(),b=character(),c=character(),d=numeric(), e=character())
bar_data<-rbind(bar_data.raw, data.frame(a=Time.vec, b=Condition.vec, c=Variable.vec, d=as.numeric(values.vec), e=Variable.condition.vec))
colnames(bar_data)<-c("Time","Condition","Variable","Value","Variable.condition")
#Make custom labels for legend of graph to include both italicised and plain text
label_1<-expression(paste(italic("M. bovis")," up"))
label_2<-expression(paste(italic("M. tuberculosis")," up"))
label_3<-expression(paste(italic("M. bovis")," down"))
label_4<-expression(paste(italic("M. tuberculosis")," down"))
#########
# Plot #
#########
q<-ggplot(bar_data, aes(Time), ylim(-1300:1300)) +
geom_bar(data = subset(bar_data, Variable == "Up"),
aes(y = Value, fill = Variable.condition), stat = "identity", position = "dodge",colour="black",size=0.4) +
scale_fill_manual(values=c("#75a5e5","#323cd3","#f7c0cb","#bc2944","#b5b1b2","#605e5f"),
name=" ",
breaks=c("MB_up", "TB_up", "MB_down", "TB_down"), #define the
#breaks so that you can relabel
labels=c(label_1,label_2,label_3,label_4)) +
geom_bar(data = subset(bar_data, Variable == "Down"), #colours are bovis up, tb up, bovis down, tb down
aes(y = -Value, fill = Variable.condition), stat = "identity", position = "dodge",colour="black",size=0.4) +
geom_hline(yintercept = 0,colour = "black") +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
legend.text.align = 0) #aligning the legend labels to legend boxes
q +
geom_text(data = subset(bar_data, Variable == "Up"),
aes(Time, Value, group=Condition, label=Value),
position = position_dodge(width=0.9), vjust = -0.25, size=4) +
geom_text(data = subset(bar_data, Variable == "Down"),
aes(Time, -Value, group=Condition, label=Value),
position = position_dodge(width=0.9), vjust = 1.25, size=4) +
coord_cartesian(ylim = c(-1300, 1300)) +
scale_x_discrete(name="Time post-infection", breaks=c("02hr","06hr","24hr","48hr"),
labels=c("2hr","6hr","24hr","48hr")) + #getting rid of the 0 in 02hr and 06hr
scale_y_continuous("Number of differentially expressed genes") +
theme(legend.text=element_text(size=9),legend.key.size=unit(0.4,"cm")) + #changing size of legend
theme(axis.title.x=element_text(size=11)) +
theme(axis.title.y=element_text(size=11)) +
theme(legend.position="bottom", legend.box = "horizontal") + #horizontal legend at bottom of graph
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black", size=0.2))
#######################################
# Read in and manipulate data for FDR #
#######################################
setwd("/Users/Kerri/Google Drive/Postdoc UCD /Alv mac work/EdgeR")
#Create vectors with desirable variables for graphing
Time.vec<-c(rep("02hr",4),rep("06hr",4),rep("24hr",4),rep("48hr",4))
Condition.vec<-c("MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB","MB","TB")
Variable.vec<-c(rep("Up",2),rep("Down",2),rep("Up",2),rep("Down",2),rep("Up",2),rep("Down",2),
rep("Up",2),rep("Down",2))
Variable.condition.vec<-c("MB_up","TB_up","MB_down","TB_down","MB_up","TB_up","MB_down","TB_down",
"MB_up","TB_up","MB_down","TB_down","MB_up","TB_up","MB_down","TB_down")
#Going to count how many genes are up and down so,
#set up empty vector with blank entries to store the gene counts
values.vec<-rep("x",16)
#Read in DE gene data for each timepoint and treatment and subset
TB_2hr<-read.csv("FDR_0.05_DE_TB_2H.txt",sep="\t",header=TRUE)
head(TB_2hr)
dim(TB_2hr)
TB_2hr<-na.omit(TB_2hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(TB_2hr)
TB_2hr<-as.vector(TB_2hr["logFC"])
#counting up and down regged genes based on FDR values
#save result in a particular entry in the blank vector values.vec
values.vec[2] <-sum(TB_2hr > 0)
values.vec[4] <-sum(TB_2hr < 0)
#Repeat for all timepoints and treatments
bovis_2hr<-read.csv("FDR_0.05_DE_MB_2H.txt",sep="\t",header=TRUE)
head(bovis_2hr)
dim(bovis_2hr)
bovis_2hr<-na.omit(bovis_2hr)
dim(bovis_2hr)
bovis_2hr<-as.vector(bovis_2hr["logFC"])
values.vec[1] <-sum(bovis_2hr > 0)
values.vec[3] <-sum(bovis_2hr < 0)
bovis_6hr<-read.csv("FDR_0.05_DE_MB_6H.txt",sep="\t",header=TRUE)
head(bovis_6hr)
dim(bovis_6hr)
bovis_6hr<-na.omit(bovis_6hr)
dim(bovis_6hr)
bovis_6hr<-as.vector(bovis_6hr["logFC"])
values.vec[5] <-sum(bovis_6hr > 0)
values.vec[7] <-sum(bovis_6hr < 0)
TB_6hr<-read.csv("FDR_0.05_DE_TB_6H.txt",sep="\t",header=TRUE)
head(TB_6hr)
dim(TB_6hr)
TB_6hr<-na.omit(TB_6hr)
dim(TB_6hr)
TB_6hr<-as.vector(TB_6hr["logFC"])
values.vec[6] <-sum(TB_6hr > 0)
values.vec[8] <-sum(TB_6hr < 0)
bovis_24hr<-read.csv("FDR_0.05_DE_MB_24H.txt",sep="\t",header=TRUE)
head(bovis_24hr)
dim(bovis_24hr)
bovis_24hr<-na.omit(bovis_24hr)
dim(bovis_24hr)
bovis_24hr<-as.vector(bovis_24hr["logFC"])
values.vec[9] <-sum(bovis_24hr > 0)
values.vec[11] <-sum(bovis_24hr < 0)
TB_24hr<-read.csv("FDR_0.05_DE_TB_24H.txt",sep="\t",header=TRUE)
head(TB_24hr)
dim(TB_24hr)
TB_24hr<-na.omit(TB_24hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
rownames(TB_24hr)<-TB_24hr[,1]
dim(TB_24hr)
TB_24hr<-as.vector(TB_24hr["logFC"])
values.vec[10] <-sum(TB_24hr > 0)
values.vec[12] <-sum(TB_24hr < 0)
bovis_48hr<-read.csv("FDR_0.05_DE_MB_48H.txt",sep="\t",header=TRUE)
head(bovis_48hr)
dim(bovis_48hr)
bovis_48hr<-na.omit(bovis_48hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(bovis_48hr)
bovis_48hr<-as.vector(bovis_48hr["logFC"])
values.vec[13] <-sum(bovis_48hr > 0)
values.vec[15] <-sum(bovis_48hr < 0)
TB_48hr<-read.csv("FDR_0.05_DE_TB_48H.txt",sep="\t",header=TRUE)
head(TB_48hr)
dim(TB_48hr)
TB_48hr<-na.omit(TB_48hr) #NA rows correspond to ncRNAs and thus do not have gene symbols. Need to be removed for venn.
dim(TB_48hr)
TB_48hr<-as.vector(TB_48hr["logFC"])
values.vec[14] <-sum(TB_48hr > 0)
values.vec[16] <-sum(TB_48hr < 0)
values.vec
#create a new df to store all of the above info with desired variables
bar_data.raw<-data.frame(a=character(),b=character(),c=character(),d=numeric(), e=character())
bar_data<-rbind(bar_data.raw, data.frame(a=Time.vec, b=Condition.vec, c=Variable.vec, d=as.numeric(values.vec), e=Variable.condition.vec))
colnames(bar_data)<-c("Time","Condition","Variable","Value","Variable.condition")
#########
# Plot #
#########
#Make custom labels for legend of graph to include both italicised and plain text
label_1<-expression(paste(italic("M. bovis")," up"))
label_2<-expression(paste(italic("M. tuberculosis")," up"))
label_3<-expression(paste(italic("M. bovis")," down"))
label_4<-expression(paste(italic("M. tuberculosis")," down"))
q<-ggplot(bar_data, aes(Time), ylim(-4000:4000)) +
geom_bar(data = subset(bar_data, Variable == "Up"),
aes(y = Value, fill = Variable.condition), stat = "identity", position = "dodge",colour="black",size=0.4) +
scale_fill_manual(values=c("#75a5e5","#323cd3","#f7c0cb","#bc2944","#b5b1b2","#605e5f"),
name=" ",
breaks=c("MB_up", "TB_up", "MB_down", "TB_down"), #define the
#breaks so that you can relabel
labels=c(label_1,label_2,label_3,label_4)) +
geom_bar(data = subset(bar_data, Variable == "Down"), #colours are bovis up, tb up, bovis down, tb down
aes(y = -Value, fill = Variable.condition), stat = "identity", position = "dodge",colour="black",size=0.4) +
geom_hline(yintercept = 0,colour = "black") +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
legend.text.align = 0) #aligning the legend labels to legend boxes
q +
geom_text(data = subset(bar_data, Variable == "Up"),
aes(Time, Value, group=Condition, label=Value),
position = position_dodge(width=0.9), vjust = -0.25, size=4) +
geom_text(data = subset(bar_data, Variable == "Down"),
aes(Time, -Value, group=Condition, label=Value),
position = position_dodge(width=0.9), vjust = 1.25, size=4) +
coord_cartesian(ylim = c(-4000, 4000)) +
scale_x_discrete(name="Time post-infection", breaks=c("02hr","06hr","24hr","48hr"),
labels=c("2hr","6hr","24hr","48hr")) + #getting rid of the 0 in 02hr and 06hr
scale_y_continuous("Number of differentially expressed genes") +
theme(legend.text=element_text(size=9),legend.key.size=unit(0.4,"cm")) + #changing size of legend
theme(axis.title.x=element_text(size=11)) +
theme(axis.title.y=element_text(size=11)) +
theme(legend.position="bottom", legend.box = "horizontal") + #horizontal legend at bottom of graph
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black", size=0.2))
|
f9da7f61beb6afc5eaceab48bcbd69d28559b81d
|
efcf5fd4b6137da8e32d69c73de74d806d5db630
|
/R/getAuthenticationKey.R
|
c3e64dbf2a43565a8c8959b8d3a8751824fc4201
|
[] |
no_license
|
bestdan/telegramr
|
49c785a3fbb8457b90c253ac15ea7d538b9357f6
|
dcf617e29f99b87de2b075111daf2a4e06099fb9
|
refs/heads/master
| 2021-08-19T17:30:10.508546
| 2017-11-27T03:03:14
| 2017-11-27T03:04:19
| 112,140,698
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 476
|
r
|
getAuthenticationKey.R
|
#' @name getAuthenticationKey
#' @title getAuthenticationKey
#' @description Get a local authentication key for interacting with Telegram
#' @param bot The yaml block which corresponds to your key.
#' @importFrom yaml yaml.load_file
#' @examples
#' \dontrun{
# res <- getAuthenticationKey(bot="life_tasker")
#' }
getAuthenticationKey <- function(file_path = "~/src/telegram_credentials.yaml", bot){
token <- yaml::yaml.load_file(file_path)[[bot]]$token
return(token)
}
|
3eab1135b9dbeed95f24f7d4fc38fee05fd11f32
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/cocorresp/R/coca.formula.R
|
01ade955390c3dc7b5a6cd01054346a0f969b690
|
[] |
no_license
|
ingted/R-Examples
|
825440ce468ce608c4d73e2af4c0a0213b81c0fe
|
d0917dbaf698cb8bc0789db0c3ab07453016eab9
|
refs/heads/master
| 2020-04-14T12:29:22.336088
| 2016-07-21T14:01:14
| 2016-07-21T14:01:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,717
|
r
|
coca.formula.R
|
"coca.formula" <-
function(formula, data, method = c("predictive", "symmetric"),
reg.method = c("simpls", "eigen"), weights = NULL,
n.axes = NULL, symmetric = FALSE, ...)
{
parseFormula <- function (formula, data)
{
Terms <- terms(formula, "Condition", data = data)
flapart <- fla <- formula <- formula(Terms, width.cutoff = 500)
specdata <- formula[[2]]
Yresponse <- as.matrix(eval(specdata, data, parent.frame()))
formula[[2]] <- NULL
Ypredictors <- eval(formula[[2]], data, parent.frame())
#if(class(Ypredictors) == "data.frame")
# {
# return(list(Yresponse = Yresponse, Ypredictors = Ypredictors))
# } else {
if (formula[[2]] == "1" || formula[[2]] == "0")
Ypredictors <- NULL
else {
mf <- model.frame(formula, data, na.action = na.fail)
Ypredictors <- model.matrix(formula, mf)
if (any(colnames(Ypredictors) == "(Intercept)")) {
xint <- which(colnames(Ypredictors) == "(Intercept)")
Ypredictors <- Ypredictors[, -xint, drop = FALSE]
}
}
#}
list(Yresponse = Yresponse, Ypredictors = Ypredictors)
}
if (missing(data))
data <- parent.frame()
dat <- parseFormula(formula, data)
x <- dat$Ypredictors
y <- dat$Yresponse
nam.dat <- list(namY = deparse(formula[[2]], width.cutoff = 500),
namX = deparse(formula[[3]], width.cutoff = 500))
if (nam.dat$namX == ".")
nam.dat$namX <- deparse(substitute(data))
if(any(rowSums(y) <= 0 ))
stop("all row sums must be >0 in data matrix y")
if(any((csum <- colSums(y)) <= 0 )) {
y <- y[, csum > 0, drop = FALSE]
message("some species contain no data and were removed from data matrix y\n")
}
if(any(rowSums(x) <= 0 ))
stop("all row sums must be >0 in data matrix x")
if(any((csum <- colSums(x)) <= 0 )) {
x <- x[, csum > 0, drop = FALSE]
message("some species contain no data and were removed from data matrix x\n")
}
method <- match.arg(method)
if(method == "predictive")
{
reg.method <- match.arg(reg.method)
retval <- switch(reg.method,
simpls = predcoca.simpls(y, x, R0 = weights,
n.axes = n.axes, nam.dat),
eigen = predcoca.eigen(y, x, R0 = weights,
n.axes = n.axes, nam.dat))
} else {
retval <- symcoca(y, x, n.axes = n.axes, R0 = weights,
symmetric = symmetric, nam.dat)
}
retval
}
|
d7e9097491cb2cd44e26f6a354d1b6b748809cb5
|
910060d06b6c929bd49be80e2d2044e4e691fc0a
|
/R/R Scripts/Bioestadistica-R_03_Tratamiento_de_datos_en_R.r
|
e278d6b584bafa2ec71044f1c346d7de724c9338
|
[] |
no_license
|
Rodrigo-MP/Masterbioinfo
|
701b28e7cdbf4f28ce6b2d7d6d3aa6ca7433f8ff
|
272d9a99b4e7dd4b14241e94c4bee7f9ccdbd985
|
refs/heads/master
| 2020-08-31T01:17:21.541780
| 2019-11-19T19:13:14
| 2019-11-19T19:13:14
| 218,543,989
| 0
| 0
| null | null | null | null |
ISO-8859-1
|
R
| false
| false
| 4,784
|
r
|
Bioestadistica-R_03_Tratamiento_de_datos_en_R.r
|
################################################################################
################################################################################
## CURSO: Bioestadística con R - Máster de Bioinformática
##
## Autor: Jesús Herranz
##
## Sesión 03: Tratamiento de datos con R
##
################################################################################
################################################################################
################################################################################
## Data frames
################################################################################
df <- data.frame( ID=c(1,3,4,5,8,9,10,11), edad=c(34,46,23,19,23,11,14,34),
sexo=c("H","M","M","M","H","M","H","M") )
df
df$edad
mean(df$edad)
df[ , c("edad","sexo")]
df[ , 2:3]
names(df)
df$tratamiento <- c(0,0,0,1,1,0,1,0)
df
## Funciones con data frames
dim(df)
head(df)
tail(df)
names(df)
names(df)[2]
## Orden merge
df1 <- data.frame( ID =c(1,2,3,4), edad=c(12,34,44,54) )
df2 <- data.frame( ID2=c(1,2,3,4), hta=c(0,1,0,1) )
df3 <- merge(df1, df2, by.x="ID", by.y="ID2" )
df3
df2 <- data.frame( ID2=c(1,2,4,5), hta=c(0,1,0,1) )
df3 <- merge(df1, df2, by.x="ID", by.y="ID2" )
df3
df3 <- merge(df1, df2, by.x="ID", by.y="ID2", all=T )
df3
df3 <- merge(df1, df2, by.x="ID", by.y="ID2", all.x=T )
df3
df3 <- merge(df1, df2, by.x="ID", by.y="ID2", all.y=T )
df3
## Uniendo filas con rbind
df1 <- data.frame( ID =c(1,2,3,4), edad=c(12,34,44,54) )
df2 <- data.frame( ID =c(5,6), edad=c(42,28) )
df3 <- rbind(df1, df2)
df3
################################################################################
## Factores
################################################################################
df <- data.frame( ID=c(1,3,4,5,8,9,10,11), edad=c(34,46,23,19,23,11,14,34),
sexo=c("H","M","M","M","H","M","H","M") )
df$tratamiento <- c(0,0,0,1,1,0,1,0)
is.factor(df$edad)
is.factor(df$sexo)
is.factor(df$tratamiento)
df$sexo
df$tratamiento
levels(df$tratamiento)
df$tratamiento <- as.factor(df$tratamiento)
df$tratamiento
levels(df$tratamiento)
#########################
## Algunos problemas con Factores
x <- c(0,0,0,0,0,1,1,1,1,1,1,1,1,1,2,2,2,2)
x<-factor(x)
table(x)
levels(x)
## Asigna valores 1,2,3 .... a las categorías ordenadas
as.integer(x)
x.num <- as.integer( as.character(x) ) ## Truco
x.num
## Guarda los niveles siempre
x [ x == 2 ] <- 1
x
table(x)
x = as.factor ( as.character( x ))
x
################################################################################
## Lectura de ficheros
################################################################################
f1 <- read.table(file="C://Bioestadistica con R/Ficheros para importar/Ejemplo 1.txt",
header=T)
dim(f1)
head(f1)
f2 <- read.csv(file="C://Bioestadistica con R/Datos/Bajo peso al nacer.csv", sep=";")
dim(f2)
head(f2)
################################################################################
## Importación de ficheros de otros paquetes estadísticos
################################################################################
library(foreign)
library(xlsx)
#######################
## SPSS
f3<-read.spss("C://Bioestadistica con R/Ficheros para importar/Ejemplo 3.sav",
to.data.frame=TRUE)
head(f3)
#######################
## STATA
f4<-read.dta("C://Bioestadistica con R/Ficheros para importar/Ejemplo 4.dta")
head(f4)
#######################
## EXCEL - 2 hojas
f5<-read.xlsx ( "C://Bioestadistica con R/Ficheros para importar/Ejemplo 5.xlsx",
sheetIndex=1 )
head(f5)
f6<-read.xlsx ( "C://Bioestadistica con R/Ficheros para importar/Ejemplo 5.xlsx",
sheetIndex=2 )
head(f6)
################################################################################
## Ficheros de salida
################################################################################
## Salvamos a fichero de texto un dataframe
write.table ( f4, "C://Bioestadistica con R/Temp/Ejemplo 4.txt",
quote=FALSE , sep="\t", col.names=TRUE, row.names=FALSE)
## Se crea un fichero de salida
FileOut <- file("C://Bioestadistica con R/Temp/Resultados 4.csv", "w")
cat ( "Variable;Media;SD;Median", file=FileOut, sep="\n")
cat ( paste ("Edad", mean(f4$edad, na.rm=T), sd(f4$edad, na.rm=T),
median(f4$edad, na.rm=T), sep=";")
, file=FileOut, sep="\n")
close(FileOut)
|
589daf830190a5543fa76a51301ecd6e7805bd09
|
54b4976030ae6a42e10282c8f41609ef266721c9
|
/R/ecd-package.R
|
0f11f539b91b4b3f44e8cfea0b21b10296083ee1
|
[] |
no_license
|
cran/ecd
|
b1be437b407e20c34d65bcf7dbee467a9556b4c1
|
18f3650d6dff442ee46ed7fed108f35c4a4199b9
|
refs/heads/master
| 2022-05-18T20:24:56.375378
| 2022-05-09T20:10:02
| 2022-05-09T20:10:02
| 48,670,406
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,516
|
r
|
ecd-package.R
|
#' ecd: A package for the stable lambda distribution family.
#'
#' The ecd package provides the core classes and functions for the stable lambda distribution family.
#' The stable lambda distribution is implemented in \code{\link{dsl}} section.
#' The lambda distribution uses the \code{ecld} namespace. SGED is considered part of ecld.
#' (See \code{\link{ecld-class}} for definition.)
#' The original elliptic lambda distribution uses the generic methods or \code{ecd} namespace.
#' (See \code{\link{ecd-class}} for definition.)
#' The option pricing API uses the \code{ecop} namespace.
#' (See \code{\link{ecop-class}} for definition.)
#' Most helper utilities are named under either \code{ecd} or \code{ecld}.
#'
#' @author Stephen H-T. Lihn
#'
#' @docType package
#' @name ecd-package
#' @import xts methods polynom graphics moments stabledist parallel yaml RSQLite
#'
#' @seealso The two main classes are \code{\link{ecd-class}} and \code{\link{ecld-class}}
#'
NULL
# Some areas of this package require multi-core capability
cores <- switch( Sys.info()[['sysname']],
Windows = 1,
Linux = parallel::detectCores(),
Darwin = parallel::detectCores(),
parallel::detectCores()
)
if (is.null(getOption("mc.cores"))) {
options("mc.cores"=cores)
}
# MPFR default settings
if (is.null(getOption("ecd.precBits"))) {
options("ecd.precBits"=120L)
}
# MPFR default Inf conversion, number of sigma as replacement for +/- Inf
# for integrateR and imgf
.ecd.mpfr.N.sigma <- 300
# end
|
359bc16c0415e60950691c7072c0b49c4e6841c6
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/provenance/examples/minsorting.Rd.R
|
b556e77007bc20e588046873e54807f80f492f0c
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 351
|
r
|
minsorting.Rd.R
|
library(provenance)
### Name: minsorting
### Title: Assess settling equivalence of detrital components
### Aliases: minsorting
### ** Examples
data(endmembers,densities)
distribution <- minsorting(endmembers,densities,sname='ophiolite',phi=2,
sigmaphi=1,medium="seawater",by=0.05)
plot(distribution,cumulative=FALSE)
|
ac012aed3fd295150526110c53e2f6c3d246c68c
|
cbf60188ccba4933635055f23c97af3f409e42aa
|
/Danica_Ex04_01.R
|
df2c1f2926e6768975f73e96ca6fff72278ec772
|
[] |
no_license
|
dshipley2/Learning_R
|
3890d4e09718b8a1ec8b7f168359c413135459b3
|
b7ed062a7be68242a621f8858b503be5e6905d89
|
refs/heads/master
| 2020-04-28T06:53:47.198218
| 2019-03-12T22:08:34
| 2019-03-12T22:08:34
| 175,074,072
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,651
|
r
|
Danica_Ex04_01.R
|
# Up and running with R
# Ex04_01
# Recoding Variables
# Use dataset "social_network.csv" which records the
# gender and age of 202 online survey respondents
# along with their preferred social networking sites
# and an estimate of how many times they log in per week
# Create data frame "sn" from CSV file w/ headers
sn <- read.csv("C:/Users/Danica_Shipley/Desktop/social_network.csv", header = T)
# Install and load "psch" package
install.packages("psych")
library("psych")
# Original Variable Times
hist(sn$Times)
describe(sn$Times) # Normal skewness is 0, so above 10 is really high. Normal kurtosis is 0, so 120 is really high
# z-scores
# Use built-in function "scale"
times.z <- scale(sn$Times)
hist(times.z)
describe(times.z)
# log - When you have outliers on the high side, taking the log can help
times.ln0 <- log(sn$Times)
hist(times.ln0)
describe(times.ln0) # produces weird results for this data set because there are 0's in the data set
# Add 1 to data set to avoid the undefined logs for 0 tims
times.ln1 <- log(sn$Times + 1)
hist(times.ln1)
describe(times.ln1)
# Ranking (forces a nearly uniformed distribution by assigning ordinal variables)
times.rank <- rank(sn$Times)
hist(times.rank)
describe(times.rank)
# ties.method = c(average, first, random, max, min)
times.rankr <- rank(sn$Times, ties.method = "random") # flatens out the distribution
hist(times.rankr)
describe(times.rankr)
# Dicotomizing (use carefully, because you lose information in the process)
times.gt1 <- ifelse(sn$Times > 1, 1, 0) # Dichotimized based on if they logged in more than 1 times a week
times.gt1
|
9852eb9bebcb63bb3a54b337c812fb026605a27e
|
72d9009d19e92b721d5cc0e8f8045e1145921130
|
/heuristicsmineR/man/print.dependency_matrix.Rd
|
157a3f1ba47e052fe087743ed191b1e836198ccf
|
[] |
no_license
|
akhikolla/TestedPackages-NoIssues
|
be46c49c0836b3f0cf60e247087089868adf7a62
|
eb8d498cc132def615c090941bc172e17fdce267
|
refs/heads/master
| 2023-03-01T09:10:17.227119
| 2021-01-25T19:44:44
| 2021-01-25T19:44:44
| 332,027,727
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 436
|
rd
|
print.dependency_matrix.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/print.dependency_matrix.R
\name{print.dependency_matrix}
\alias{print.dependency_matrix}
\title{Generic print function for a dependency matrix}
\usage{
\method{print}{dependency_matrix}(x, ...)
}
\arguments{
\item{x}{dependency matrix object}
\item{...}{Additional Arguments}
}
\description{
Generic print function for a dependency matrix
}
|
69aac8e8f87e603c18b15bfb179080fe958176fc
|
6a4f552946002eb86443f39bd9887c91f1608c1f
|
/R/ageAccel.R
|
59861569e3af40e9cab1345dfe68f03f586caffb
|
[] |
no_license
|
RichardJActon/DNAmAgeMini
|
20f01399205d4dc94283583d1eee61a4c21dd610
|
0a443edece74cdd68a95442b910d2d2222115a43
|
refs/heads/master
| 2020-04-21T14:09:45.905735
| 2019-02-08T16:41:30
| 2019-02-08T16:41:30
| 169,625,124
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 270
|
r
|
ageAccel.R
|
# age acceleration
#' ageAccel
#' @export
ageAccel <- function(pred,chron) {
if (!(is.numeric(pred) & is.vector(pred))){ warning("pred is not a numeric vector")}
if (!(is.numeric(chron) & is.vector(chron))){ warning("chron is not a numeric vector")}
pred - chron
}
|
40ced25eb6aa148466cb0ef40d34b2ab6510677c
|
aae743a14d850d2eb3daa1bf573ff082af5aa5cc
|
/man-roxygen/RDclass.R
|
816f0e433acb2d7c66ba50b6a7d42758cf591b7b
|
[] |
no_license
|
mdroste/RDHonest
|
5d8f8890d7195db7044c91d3d5d37742c4b7a084
|
c471e3cfa1533807dc988e15bbecaa83d85846a4
|
refs/heads/master
| 2020-06-15T13:28:13.308470
| 2019-06-24T17:50:39
| 2019-06-24T17:50:39
| 195,312,954
| 1
| 0
| null | 2019-07-05T00:05:11
| 2019-07-05T00:05:10
| null |
UTF-8
|
R
| false
| false
| 177
|
r
|
RDclass.R
|
#' @param M Bound on second derivative of the conditional mean function.
#' @param sclass Smoothness class, either \code{"T"} for Taylor or
#' \code{"H"} for Hölder class.
|
208d10ab6f11106b18ed87bb28af8501bc6deea1
|
a5dc2f5e2cb3ecd3ab28fac55d6c11d13064e5f7
|
/R-package/quantmod/man/refit_quantile_genlasso.Rd
|
794a845a9aca1090ce440cb6c03c03e864a06f0d
|
[] |
no_license
|
elray1/quantmod
|
b1fdb1f8cac954a0e3309aee5d2271b62ade1761
|
e6d3dff57ae7564e0d8ff98cf0654332d9b6794f
|
refs/heads/master
| 2022-10-10T08:28:50.427163
| 2020-06-11T14:58:33
| 2020-06-11T14:58:33
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,304
|
rd
|
refit_quantile_genlasso.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cv_quantile_genlasso.R
\name{refit_quantile_genlasso}
\alias{refit_quantile_genlasso}
\title{Refit function for cv_quantile_genlasso object}
\usage{
refit_quantile_genlasso(obj, x, y, d, tau_new = c(0.01, 0.025, seq(0.05,
0.95, by = 0.05), 0.975, 0.99), weights = NULL, no_pen_rows = NULL,
intercept = TRUE, standardize = TRUE, noncross = FALSE,
x0 = NULL, lp_solver = NULL, time_limit = NULL,
warm_starts = NULL, params = NULL, transform = NULL,
inv_trans = NULL, jitter = NULL, verbose = FALSE)
}
\arguments{
\item{obj}{The \code{cv_quantile_genlasso} object to start from.}
\item{x}{Matrix of predictors.}
\item{y}{Vector of responses.}
\item{d}{Matrix defining the generalized lasso penalty.}
\item{tau_new}{Vector of new quantile levels at which to fit new
solutions. Default is a sequence of 23 quantile levels from 0.01 to 0.99.}
\item{noncross}{Should noncrossing constraints be applied? These force the
estimated quantiles to be properly ordered across all quantile levels being
considered. The default is FALSE. If TRUE, then noncrossing constraints are
applied to the estimated quantiles at all points specified by the next
argument \code{x0}.}
\item{x0}{Matrix of points used to define the noncrossing
constraints. Default is NULL, which means that we consider noncrossing
constraints at the training points \code{x}.}
\item{verbose}{Should progress be printed out to the console? Default is
FALSE.}
}
\value{
A \code{quantile_genlasso} object, with solutions at quantile levels
\code{tau_new}.
}
\description{
Refit generalized lasso solutions at a new set of quantile levels, given
an existing \code{cv_quantile_genlasso} object.
}
\details{
This function simply infers, for each quantile level in
\code{tau_new}, a (very) roughly-CV-optimal tuning parameter value, then
calls \code{quantile_genlasso} at the new quantile levels and corresponding
tuning parameter values. If not specified, the arguments \code{weights},
\code{no_pen_rows}, \code{intercept}, \code{standardize}, \code{lp_solver},
\code{time_limit}, \code{warm_starts}, \code{params}, \code{transform},
\code{inv_transorm}, \code{jitter} are all inherited from the given
\code{cv_quantile_genlasso} object.
}
|
f670371efc1cdbc5b184c103bb492180c711fde6
|
92e597e4ffc9b52cfb6b512734fb10c255543d26
|
/man/hsDelEmptyCols.Rd
|
09403276a8bf50cf00a92e9557e176122ee03b08
|
[
"MIT"
] |
permissive
|
KWB-R/kwb.utils
|
3b978dba2a86a01d3c11fee1fbcb965dd15a710d
|
0930eaeb9303cd9359892c1403226a73060eed5b
|
refs/heads/master
| 2023-05-12T15:26:14.529039
| 2023-04-21T04:28:29
| 2023-04-21T04:28:29
| 60,531,844
| 9
| 1
|
MIT
| 2023-04-21T04:28:30
| 2016-06-06T13:52:43
|
R
|
UTF-8
|
R
| false
| true
| 883
|
rd
|
hsDelEmptyCols.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/column.R
\name{hsDelEmptyCols}
\alias{hsDelEmptyCols}
\title{Delete empty Columns of Data Frame}
\usage{
hsDelEmptyCols(dataFrame, FUN = function(x) all(is.na(x)), drop = FALSE)
}
\arguments{
\item{dataFrame}{data frame of which empty columns (NA in all rows) are to be removed}
\item{FUN}{function to be applied to each column to decide whether the column
is empty or not. Default: \code{function(x) all(is.na(x))}}
\item{drop}{if \code{TRUE} (the default is \code{FALSE}) one dimension is
dropped (a vector is returned instead of a data frame) in case that all but
one columns are removed.}
}
\value{
copy of input data frame but with all empty columns removed
}
\description{
Returns data frame in which all empty columns (NA in all rows) are removed
}
\seealso{
\code{\link{removeEmptyColumns}}
}
|
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