sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fd1cdfddaebf557564a0e25d1d1581f7e6ecdeaa36a847a50183cc680c467b73 | R | 522 | 28 | suppressPackageStartupMessages({
library(yaml)
library(tidyr)
library(arrow)
library(dplyr)
library(multidplyr)
library(purrr)
library(forecast)
library(multidplyr)
library(parallel)
library(R.utils)
library(readr)
library(ggplot2)
library(sjmisc)
library(viridis)
library(foreach)
librar... |
48d7446cf5a84441d23c80f537df9a2ab98373e9043b29bb3ba6cd8cf167e703 | R | 525 | 15 | #' @title Computes polygenic scores as a genotype matrix multiplied by a matrix of weights
#' @description Note that this is a low-level command. For applying estimated
#' betas to new bfiles, refer to https://github.com/tshmak/lassosum#apply-validated-betas-to-new-data
pgs <- function(bfile, weights, ...) {
#' @rd... |
d587dfcc981521c9dc2db360564173454b3687732fa2e776ffd6cfc5ed0072c8 | R | 525 | 22 | #' Paracetamol spectrum
#'
#' A Raman spectrum of a paracetamol tablet.
#'
#' @name paracetamol
#' @docType data
#' @format The spectrum was acquired with a Renishaw InVia spectrometer from
#' 100 to 3200 cm^-1 in step scan mode. Thus the spectrum has several
#' overlapping wavelength regions.
#' @author C. Beleite... |
24d5154ccda1f03338eaf43b2331e6f548a0e86830c9816f2c396e149ca6e8da | R | 534 | 35 |
library(ggplot2)
csvPath = "./plots/Vascular_Endothelium_fix_Anatomical_Site,Age,Sex_rand_Donor_Harmony_Aligned_CoordsRegress_Protocadherin_PC_20_All_CellsFRIP=0.2_FRIT=0.05UMI=1000DL=0.5_k_20_peak_cds_MMresult/Vascular_Endothelium_SexM_Table.csv"
myCSV = read.csv(csvPath)
png('./plots/VascEndothPlot.png'... |
c54cdd3a8865f8c67f59e27738ae9030ae88f07eff35bfd31495f0aef00db53e | R | 535 | 9 | #!/usr/local/bin/Rscript
library(MetaCycle)
meta2d(infile = "metadata.csv", outdir = "metaout", filestyle = "csv", timepoints = "Line1", minper = 1080,
maxper = 1800, cycMethod = c("ARS", "JTK", "LS"),
analysisStrategy = "auto", outputFile = TRUE,
outIntegration = "both", adjustPhase = "predictedPe... |
98adbd04cc57297657c4b15d1f891e4477cd106159dcc19cf6ed7abf9882eca8 | R | 537 | 16 | setwd("/media/hansruedi/12TB_HD/Brain_region_final_12TB")
library(ggplot2)
library(Seurat)
pbmc <- readRDS(file = "Exc_raw_NP_ET.rds")
#Marker genes
DefaultAssay(pbmc)<-"RNA"
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
Idents(pbmc)<-"cell_type_high_resolution"
... |
8c554190c7e2dddf763669daf382a6f0cb589b3b213afd8802ad554ff43d7653 | R | 539 | 9 | # Load Libraries
library(ImpulseDE2)
source('/E-P-INs/Scripts/ImpulseDE2/ImpulseDE2.R')
# ImpulseDE2 Analysis
ImpulseDE2_RNASeq(comparisons = "/E-P-INs/RNASeq/ImpulseDE2/Comparisons/0hrs_3hrs_6hrs_12hrs_24hrs_48hrs_72hrs_RNASeq.ImpulseDE2_comparisons.csv",
conditions = "/E-P-INs/RNASeq/ImpulseDE2/Con... |
5705dc10cbf2cbadc5ceb569523867940120d08a71b6bd2f05107c9fa9574394 | R | 543 | 14 | retrieveFASTAQID <- function(sequences,
PE=FALSE)
{
if (PE) {
sequencesID <- as.character(ShortRead::id(sequences))
#sequencesID <- substr(ShortRead::id(sequences),1,width(ShortRead::id(sequences))) #Use for PE reads
sequencesID <- unlist(lapply(strsplit(sequencesID, " "),"[",1)) #Use for PE ... |
853ce4277be59f1e22d4567288dbca53da937f67473cdb8e0694321bcd569d18 | R | 543 | 22 | library(data.table)
library(dplyr)
### MetS
trait <- "MetS_lind"
file_in <- "leadSNP.MetS_lind.txt"
df <- fread(file_in, data.table= F, nThread = 1)
write.table(df$SNP,
paste0("leadSNP_list.", trait, ".txt"),
sep="\t", row.names = F, quote = F, col.names = F
)
### MetS
trait <- "MetS_walree"
... |
769b47d434cdf81b0175812dcfd5daa5026c84eba013982f05bfaec8ae56469f | R | 548 | 9 | # Load Libraries
library(ImpulseDE2)
source('/E-P-INs/Scripts/ImpulseDE2/ImpulseDE2.R')
# ImpulseDE2 Analysis
ImpulseDE2_ATACSeq(comparisons = "/E-P-INs/ATACSeq/ImpulseDE2/Comparisons/0hrs_3hrs_6hrs_12hrs_24hrs_48hrs_72hrs_ATACSeq.ImpulseDE2_comparisons.csv",
conditions = "/E-P-INs/ATACSeq/ImpulseDE... |
6948f26268ea96c1551fb4a5de8ccaef21fe8aaff7a74ae03dd4250929beb793 | R | 549 | 9 | # Load Libraries
library(ImpulseDE2)
source('/E-P-INs/Scripts/ImpulseDE2/ImpulseDE2.R')
# ImpulseDE2 Analysis
ImpulseDE2_H3K27ac(comparisons = "/E-P-INs/H3K27ac/ImpulseDE2/Comparisons/0hrs_3hrs_6hrs_12hrs_24hrs_48hrs_72hrs_H3K27ac.ImpulseDE2_comparisons.csv",
conditions = "/E-P-INs/H3K27ac/ImpulseDE... |
63951f00757c05bb79bbf8e05526a70de0cae36ea6d4aa255b795e7b4d9a673c | R | 550 | 15 | library(biomaRt)
ensembl = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
gene_ids <- read.csv("../../Dataset/mart_export.txt", header = TRUE, stringsAsFactors = FALSE)
# Extract the Ensembl IDs into a vector
ensembl_ids <- gene_ids$Gene.stable.ID
refseq_ids = getBM(attributes = c("ensembl_gene_id", "refseq_mr... |
be8b1a6dceb1ee038f3f0af3ae5504b8a8b24db881dceef09d42bd00d4bc0103 | R | 559 | 17 | splitvec.from.bfile <- function(bfile) {
#' Function to create split vectors from the info in vectors of bfiles
#' @keywords internal
pvec <- attr(bfile, "p")
Pvec <- attr(bfile, "P")
if(is.null(pvec) || is.null(Pvec)) {
stop("Since v0.4.3 bfile should have the attributes 'p' and 'P' if it is a vector")
... |
dafbc87fefe12876344f7e893ca8e0222e3e142cf9aded075868cfff14adb6ac | R | 561 | 19 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
wd<-args[1]
pvalueString<-args[2]
replicateMean<-as.numeric(args[3])
replicateStdev<-as.numeric(args[4])
pvalues<-unlist(strsplit(pvalueString,","))
pvalues<-as.numeric(pvalues)
ksTest<-ks.test(pvalues,pnorm,mean=replicateMean,sd=replicateStdev,alterna... |
ee7df497f0479ec4892cc06b0508fce84d7157b6f4de381096e3a60550047411 | R | 561 | 14 | library(dplyr)
library(tidyr)
setwd('~/hail/hail-files/SV')
args = commandArgs(trailingOnly=TRUE)
cn <- args[1]
infile <- paste('SV-results/', cn, '-SV-inher-aggregated', sep ='')
results <- read.table(paste(infile, '.tsv', sep =''), header=TRUE, sep = '\t')
bed <- results %>% dplyr::select(SVloc, name)
bed <- bed %>... |
dc90ebf000ef82b9f3c64bdb15583b002036bfe050258c4347cec6c4453e833a | R | 566 | 15 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
designTTest <- function(ints, design) {
.Call(`_permFDP_designTTest`, ints, design)
}
permFDRAdjust <- function(expPs, threshold, design, intOnly, nPerms, nc, nt) {
.Call(`_permFDP_per... |
1585ccd481647b8ab9f99b6f00bd8ec4ce999ece4a4140f2bd68d4932e792f50 | R | 568 | 15 | f <- function(x, output) {
testor=rbind(c(as.numeric(x[2]),as.numeric(x[3])),c(as.numeric(x[4]),as.numeric(x[5])))
capture.output(fisher.test(testor,alternative="greater"), file = paste(x[1],"tests1.txt",sep="."))
foo=fisher.test(testor,alternative="greater")
capture.output(foo$estimate[1], file=paste(x[1],"FisherOR.tx... |
c3caa44d73ca8739a78f65fd8748cc40a307c3101dd5368199920559bce4829d | R | 583 | 15 | # Function that takes in full path to CSV file of FANC IDs and writes to a CSV
# file in the same folder the latest FANC IDs
# Expects no header for CSV file
csvFancIDsToLatest <- function(csvFilePath) {
# read in CSV file
csvData <- read.csv(csvFilePath,header=FALSE, colClasses='character')
# for each of th... |
bc54598da1e19e20809d24b3894f7a68b5a6b3207e4e79452b1c4913ed5c16d9 | R | 588 | 18 | #' @title Obtains the number of individuals in a PLINK bfile
#'
#' @param bfile Plink file stem
#' @return an integer with the number of rows
#' #@keywords internal
#' @export
nrow.bfile <- function(bfile) {
famfile <- paste0(bfile, ".fam")
if(!file.exists(famfile))
stop(paste0("Cannot find ", famfile))
return(... |
e3d0f8087235a4090ed9c7d13365e61154d65b4424a7672cc70f0b56a3cd98b7 | R | 592 | 22 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
cwd<-args[1]
inputFile<-args[2]
jobName<-args[3]
setwd(cwd)
data<-read.table(inputFile,header=T,sep="\t",comment.char="#")
dat.df <- data.frame(evr = data$evr,eigenvector = data$eigenvector)
# turn into percent
dat.df$evr<-dat.df$evr*100
pngfile<... |
e568b76ba5fb8bf8f4b1529abde841aaaff55a8d02ddb0f5049de2c7cda6bc33 | R | 592 | 24 | rm(list=ls())
b<- ("Z:/Shared/zhenzhen/VBN/handle")
savepath <- "Z:/Shared/zhenzhen/VBN/result/"
functionpath <-"Z:/Shared/zhenzhen/VBN"
functionname <-'gretna_heritability.R'
setwd(functionpath)
source(functionname)
library("R.matlab")
{
Files<-list.files("Z:/Shared/zhenzhen/VBN/handle")
ACEresults <- lis... |
0070ce5b0da9faf36a15ef3b2e52df659951733827b144475dc11881ab8a5d84 | R | 594 | 20 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
inputFile<-args[1]
inputFileName<-basename(inputFile)
myTable <- as.matrix(read.table(inputFile, header=TRUE, sep = "\t",row.names = 1,check.names=FALSE),check.names=FALSE)
pngfile<-paste(inputFileName,".png",sep='')
png(pngfile,height=800,width=800)
... |
409b2ce96c3b71d121b1fe8afdb52933ba12f557ece0518fd23f8c47cd26d85d | R | 595 | 15 | # Function that takes in full path to CSV file of FlyWire IDs and writes to a CSV
# file in the same folder the latest FlyWire IDs
# Expects no header for CSV file
csvFlywireIDsToLatest <- function(csvFilePath) {
# read in CSV file
csvData <- read.csv(csvFilePath,header=FALSE, colClasses='character')
# for e... |
ff06790ecb7f20869910f90d47977bd9242b708be99b153d910cbd33d3fbb20d | R | 595 | 19 | #' @title Obtains the number of column (SNPs) in a PLINK bfile
#'
#' @param bfile Plink file stem
#' @return an integer with the number of columns
#' #@keywords internal
#' @export
ncol.bfile <- function(bfile) {
bimfile <- paste0(bfile, ".bim")
if(!file.exists(bimfile))
stop(paste0("Cannot find ", bimfile))
... |
404596082db302e8b32a6f0bace2d405db65483100b5627257d2730b37f109ed | R | 597 | 24 | #' Quinine fluorescence spectra
#'
#' Fluorescence spectra of different dilutions of quinine forming a
#' calibration set.
#'
#' See the vignette: `vignette("flu", package = "hyperSpec")`
#'
#' @name flu
#' @docType data
#' @format The data set has 6 fluorescence emission spectra measured on
#' quinine concentrations... |
2146903c0070cbf6c698c524f1f8bf7e5dbe33a4f229d4679db62f316a128788 | R | 598 | 10 | test_that("run_spm_sepjobs final tracking reuses one terminal job status", {
spm_path <- source_tree_file("R", "spm_utils.R")
lines <- readLines(spm_path, warn = FALSE)
expect_true(any(grepl("if [ $job_failed -ne 0 ]; then", lines, fixed = TRUE)))
expect_true(any(grepl("job_status=\\\"FAILED\\\"", lines, fixed... |
50e9a579779b4249374e2f80d3544da056b48906c44badaeda50068f847d2fae | R | 602 | 18 | plot.validate.lassosum <- function(obj, legend.x="bottomright", log="x") {
#' @title Plot function for \code{validate.lassosum} objects
#' @export
t <- obj$validation.table
if(!any(is.finite(t$value))) return(invisible(NULL)) # If nothing to plot
plot(unlist(t$lambda),
unlist(t$value),
type="n... |
c29f11900b3a648df03b9bf7776f2fa74d6eb6fcba48328bd1e74763349bdf13 | R | 607 | 22 | library("tidyverse")
library("data.table")
setwd("./Step6.DEG/DEGs")
# Load the combined DEG files
DEGFileCombind <- readRDS("DEGFileCombind.rds")
DEG.tables <- list()
for (i in 1:length(DEGFileCombind)){
df <- DEGFileCombind[[i]]
deg.sum <- setDT(df)[, .(DEG.number = .N), by = "DEG"]
deg.sum <- drop_na(deg.su... |
df7c94e7a9596a557263c4cff2df0e11986e580b17d9ee3209e01a009ccce498 | R | 613 | 20 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
inputFile<-args[1]
jobName<-basename(inputFile)
tmpData<-read.table(inputFile,header=T,sep="\t")
pngfile<-paste(jobName,".png",sep='')
png(pngfile,height=800,width=800)
plot(tmpData$interactionDistance,tmpData$correlation,main="correlaton per distan... |
fd79fadddf5241fb76e750362eb734d62610e1f733d02b3f594ae8ce202c0618 | R | 617 | 28 |
## Identify PRS outliers
library(data.table)
in_dir <- "/path/to/prs_comb"
get_prs_n_files <- list.files(path=in_dir, pattern="*_eur.txt")
prs_ids <- gsub("_full_eur.txt", "", get_prs_n_files)
## Identify sample IDs passing global PGS outlier threshold
get_out <- lapply(prs_ids, function(prs_i) {
## Get PGS
get... |
1abb4a4206b227bba20867e03a09e3fe2780a2d8a5e17f0fe49494e2820af928 | R | 619 | 26 | require(testthat)
require(Cardinal)
context("findNeighbors")
test_that("findNeighbors", {
pos <- expand.grid(x=1:9, y=1:9)
pos <- PositionDataFrame(coord=pos)
nb <- findNeighbors(pos, r=1)
nb2 <- findNeighbors(pos, r=1, metric="euclidean")
nb3 <- findNeighbors(pos, r=2, metric="euclidean")
nb4 <- findNeighbor... |
9e98c3f86a52b3de186d57553cf061a8d06081a3fb782ecbc27bc3aec3f225d8 | R | 622 | 22 |
#' archstabms__format_dir
#'
#' Plot fitness versus growth rate.
#'
#' @param dir_suffix directory suffix (required)
#' @param stagenum stage number (required)
#' @param base_dir base directory (required)
#' @param no_digits number of digits to use for stage number
#'
#' @return Absolute directory character string
#' ... |
d2146990d01199fda445beaee0f715fab20cfd623d767c0ece2f05d1984a8bf6 | R | 636 | 15 | calculateScore <- function(observations, predictions){
TruePositive <- sum(observations & predictions)
FalsePositive <- sum(!observations & predictions)
TrueNegative <- sum(!observations & !predictions)
FalseNegative <- sum(observations & !predictions)
PositivePredictiveValue <- TruePositive / sum(predicti... |
23b53122d71a640a301c5934df809422ba8ae2822c9ac657355f1e57190f5cac | R | 643 | 13 | library(readr)
glm_multi_beta_binomial = read_file("inst/stan/glm_multi_beta_binomial.stan")
save(glm_multi_beta_binomial, file="data/glm_multi_beta_binomial.rda", compress = "xz")
glm_multi_beta_binomial_generate = read_file("inst/stan/glm_multi_beta_binomial_generate_date.stan")
save(glm_multi_beta_binomial_gener... |
2813960da3736b02b68893b987a56046b67bf788e9d2f436b546b6cb0f619f28 | R | 644 | 20 | # Function that takes in full path to CSV file of FANC IDs and writes to a
# specified folder all the change logs for those IDs
# Expects no header for CSV file
csvFancChangeLog <- function(csvFilePath, outPath) {
# read in CSV file
csvData <- read.csv(csvFilePath,header=FALSE, colClasses='character')
# set... |
cfedd601ba1a78f049b6b3cd8d22fe65de65c2cd3d9a4349218e12d848dd0b25 | R | 646 | 22 | # clear()
# Tim.load(Rplink)
# setwd(attachroot("~/WORK/myRpackages/lassosum/tests/"))
# load_all()
#
# library(data.table)
# bfile("../inst/data/refpanel")
# n <- nrow.bfile(.bfile)
# set.seed(1000)
# pheno <- rnorm(n)
# nfolds <- 3
# ss <- cp.plink.linear(bfile=.bfile, nfolds=nfolds, pheno=pheno)
#
# ld <- fread(".... |
d2efc7251096fb958c7c6a407d481c032d1511dcdb915b4fb0f65f09790e35dd | R | 671 | 21 | test_that("no data available", {
analyser = test_analyser()
no_df = NULL
expect_warning(analyser$setData(no_df), "No data available to generate plots.")
})
test_that("data available", {
analyser = test_analyser()
expect_equal(length(analyser$plots), 0)
expect_equal(length(analyser$plot_data), 0)
dfs = l... |
03cd96d1339f6c66cdc6c05cd6df6663497fb36eed2abbe60f5f0cb7e217825c | R | 676 | 25 | library(data.table)
library(dplyr)
# IV list
file_in_iv <- "MetS_exposure_IV_list.csv"
df_iv <- fread(file_in_iv, sep=",", data.table = F, nThread = 10)
iv_list <- df_iv$SNP
# MetS no-UKB GWAS
file_in <- ".MetS_noUKB_GWAS.csv"
df <- fread(file_in, sep=",", data.table = F, nThread = 20) %>%
filter(SNP %in% iv_lis... |
f7df3cdfa2115a45c05dda86077c8b7f2e64bb6f2949e943ebc0056d1f852598 | R | 677 | 23 | # clear()
# Tim.load(lassosum)
# Tim.load(Rplink)
# Tim.load(mysimtools)
# useWTCCC()
# pl <- cp.plink.linear(bfile=.bfile, chr=22, nfolds=2,
# pheno=rnorm(nrow.bfile(.bfile)))
#
# # Fake sumstats
# bim <- read.bim(.bfile)
# bim22 <- subset(bim, CHROM==22)
# n <- 1000
# samp <- sample(nrow(bim22... |
341da9efe43bac922150eed0e2eed12f8a50e6b0f184bfd3708905cd2601535f | R | 683 | 50 |
library("optparse")
source("/net/trapnell/vol1/home/readdf/trapLabDir/fibrosis/results/2020_12_29_SymSim_Power_Work/simFuncsDavidMod.R")
print("Starting Now")
option_list = list(
make_option(c("-i", "--indiv"), action="store", default=3, type="integer",
help="number of individuals per group"),
make_option(c("... |
a3e3978e3a6484dc82afb34067c8adaa192862268e1b4328142b868ce906b375 | R | 692 | 19 | # Tmisc()
# setwd0("~/WORK/myRpackages/lassosum/inst/data/")
# for(pop in c("EUR", "ASN", "AFR")) {
# # pop <- "EUR"
# bed <- read.table.tim(paste("Berisa", pop, "hg19", "bed", sep="."))
# l <- liftover(bed$chr, bed$start, bed$stop, from=19, to=38, bedformat = TRUE)
# chrs <- unique(l$chr)
# ll <- lapply(chrs... |
25abed2866c84dd5b20ff8d5acdbb97fbb6d423625a2c59e6b265be9d15430c6 | R | 697 | 22 | #!/usr/bin/env Rscript
#---------------------------
# R package check for SV-pipeline
# Talkowski Laboratory
#
# Harrison Brand and Joseph Glessner
# Update June 2017
#---------------------------
#Loads required packages; installs if necessary
RPackages <- c("caret", "fpc", "optparse", "plyr", "MASS","ROCR", "zoo","d... |
db2042492138a9832c16933ab7efd479f59b6872987a8efa7d4b8c32749bca22 | R | 703 | 31 | # install.packages("tidyverse")
# install.packages("lmSupport")
# install.packages("gmodels")
# install.packages("ez")
# install.packages("psych")
#--------------------------------------------#
# change dir
setwd("X:/07. Manuscripts/In preparation/Harnessing neural synchrony")
# load dataset
data2load = 'd... |
d4580769a1d227a13d121e926d075c0cf482a3e81575fcb87c86860a57579738 | R | 708 | 31 | #' @name DEPRECATED-pearson.dist
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Distance based on Pearson's \eqn{R^2}{R squared}
#'
#' @description
#'
#' This function is **deprecated**.
#' Use [dist_pearson()] instead.
#'
#' @param ... Passed to [dist_pearson()].
#'
#' @export
pearson.dist <- function(...... |
22c802ef15b08363698906477f73a90d0f8fde7b33f4f1cd2531b59493bdd8b4 | R | 726 | 28 | library("tidyverse")
library("data.table")
setwd("./Step8.SubCluster.DEG/DEG.random")
FileList <- list.files(pattern = "Random.DEG.csv")
Celltypes <- gsub(".Random.DEG.csv", "", FileList)
DEGFileCombind <- list()
for (i in 1:length(Celltypes)){
dat <- read.csv(FileList[i], row.names = 1)
setDT(dat)[, fdr:= p.adj... |
fc0cb2e3065c56e1a0cb1c578d1725c4be7f15fbe615b232fafafd6396f5c956 | R | 726 | 33 | require(testthat)
require(Cardinal)
context("sliceImage")
test_that("sliceImage", {
path <- CardinalIO::exampleImzMLFile("continuous")
s <- readImzML(path)
s1 <- s
s2 <- s
runNames(s1) <- "run0"
runNames(s2) <- "run1"
s <- cbind(s1, s2)
mz <- mz(s)[1:2]
rs1 <- sliceImage(s, i=1)
rs2 <- sliceImage(s, i=1:2... |
584cff49a14cc03eb7c8ffbf92a8e1edcd3f476ffcb872bbad98b5b782145122 | R | 728 | 26 | train_ae <- function(torch_dl, torch_model, torch_optim,
#adv_model, adv_optim,
n_epochs_total) {
torch_model$train()
for (epoch in 1:n_epochs_total) {
loss_recorder <- 0
coro::loop(for (item in torch_dl) {
torch_optim$zero_grad()
outp... |
60daa88006ae0d361742e9dd78f127edc6791665337b685b940fac1bab5cb396 | R | 728 | 23 | ## HiC-Pro
## Copyright (c) 2015 Institut Curie
## Author(s): Nicolas Servant, Eric Viara
## Contact: nicolas.servant@curie.fr
## This software is distributed without any guarantee under the terms of the BSD-3 licence.
## See the LICENCE file for details
## Install R packages for HiC-Pro... |
d318abdc371beec55c42546361f53fb817ae60464543d7bc94d48c7f10ba23fe | R | 728 | 20 | test_that("lookup_* functions work correctly on a valid 4D NIfTI file", {
skip_if_not_installed("RNifti")
# Create synthetic 4D image with known dimensions
dims <- c(5, 6, 7, 8)
img_data <- array(rnorm(prod(dims)), dim = dims)
img <- RNifti::asNifti(img_data)
tmpfile <- tempfile(fileext = ".nii.gz")
RN... |
9f16391820b1e7671d1bf131c14140128fb2abd141177dfc9c2d1622beed06eb | R | 732 | 19 | # getPresynCSVsFlywire.r
#
# Function that generates CSV files of input neurons to neurons of interest and
# the number of synapses they make
#
getPresynCSVsFlywire <- function(rootid, thresh, cleftThresh, csvFilePath) {
thisID <- fafbseg::flywire_latestid(rootid)
theseInputs <- fafbseg::flywire_partner_summa... |
aaa0236c41d65643d37ac11fb321e6a7eb59aeeb5ca3f0e30215ff78aaa4cfec | R | 734 | 27 | require(GenomicSEM)
library(data.table)
NAME <- "MetS"
model <- "F1 =~ NA*BMI_GIANT18 + WC_Watanabe_UKB
F2 =~ NA*T2D_meta_FinR7_Mahajan22_MVP + FG_Lagou_MAGIC_noUKB + HYP_meta_FinR7_UKB
F3 =~ NA*HDL_rev_GLGC + TG_GLGC
MetS =~ NA*F1 + F2 + F3
MetS ~~ 1*MetS
F1 ~~ 1*F1
F2... |
e018dee9cd1264db105755c0fe83ccb41afa0f91f34ba0fd5cd3a760ec2d67e0 | R | 734 | 29 | library("tidyverse")
library("gprofiler2")
library("data.table")
library("BiocParallel")
# load the DEG datasets
setwd("./Projects/SNCSynuclein")
# load the DEG files
DEGFileCombind <- readRDS("Step6.DEG/DEGs/DEGFileCombind.rds")
Cluser <- names(DEGFileCombind)
DEGdf <- list()
for (i in 1:length(DEGFileCombind)){
... |
8ede75b61f0a16925dd1419f7da212b3600a5ee0e036e278594954bffa97a06f | R | 736 | 27 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_EVEN"
model <- "F1 =~ NA*BMI_GIANT18 + WC_Watanabe_UKB
F2 =~ NA*T2D_meta_FinR7_Mahajan22_MVP + FG_Lagou_MAGIC_noUKB + HYP_meta_FinR7_UKB
F3 =~ NA*HDL_rev_GLGC + TG_GLGC
MetS =~ NA*F1 + F2 + F3
MetS ~~ 1*MetS
F1 ~~ 1*F1
... |
fe8eaa655783ea55f63afe166f4ff4da4db7958aae942f774b54698a13c8e53c | R | 747 | 28 | # getNTpredFlywire.r
#
# Function that generates text files of neurotransmitter summary, given csv file
# of IDs (e.g. processedNeurons.csv from genPresynCSVsFlyWire.r)
#
getNTpredFlywire <- function(neuronsCSVpath, cleftThresh, outPath) {
# read in csv file
csvData <- read.csv(neuronsCSVpath,header=TRUE, colCla... |
6f8fd30e38f7b8f31a87d8a358dd8eaa258179283affee7b67241282f689a31a | R | 748 | 22 | #' @title Function to convert p-values to correlation via the t-statistic
#' @param n Sample size
#' @param p Vector of p-values
#' @param sign A vector giving the sign of the correlations (e.g. the log odds ratios)
#' @param min.n The minimum sample size to be considered a valid p-value
#' @return A vector of correlat... |
8f512ed137c29387f4ed8aad2796f6c28cdfd3e4710236b272fcfc767ddad678 | R | 748 | 29 | library("tidyverse")
library("data.table")
setwd("./Step6.DEG/DEGs")
FileList <- list.files(pattern = "Random.DEG.csv")
Celltypes <- str_split_fixed(FileList, "\\.", n = 4)[, 1]
DEGFileCombind <- list()
for (i in 1:length(Celltypes)){
dat <- read.csv(FileList[i], row.names = 1)
setDT(dat)[, fdr:= p.adjust(`Pr..C... |
4857c5ed6bc25ceb3fd8c36fc5e7ce5f61b27477ff41fee6e9fd5cf2a337de70 | R | 749 | 28 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_noUKB"
model <- "F1 =~ NA*BMI_GIANT15_noUKB + WC_GIANT_noUKB
F2 =~ NA*T2D_meta_FinR7_MVP_noUKB + FG_Lagou_MAGIC_noUKB + HYP_FinR7_noUKB
F3 =~ NA*HDL_rev_GLGC_noUKB + TG_GLGC_noUKB
MetS =~ NA*F1 + F2 + F3
MetS ~~ 1*MetS
F1 ~~... |
0bac532c3c79c6cc4b50eec402cc845c497105e296627411ff01638a9a897005 | R | 750 | 29 | #' Mark peak
#'
#' Marks location of the *first* spectrum at the data point closest to the
#' specified position on the current plot.
#'
#' @param spc the `hyperSpec` object
#' @param xpos position of the peak(s) in current x-axis units
#' @param col color of the markers and text
#'
#'
#' @author R. Kiselev
#'
#' @conc... |
a70ad11f9f9b46c193e1ac861a5a2889f787223753a287abacb409b82c57be5a | R | 757 | 26 |
#' archstabms__mann_whitney_U_wrapper
#'
#' Mann-Whitney U test
#'
#' @param vals1 sample 1 (required)
#' @param vals2 sample 2 (required)
#'
#' @return named vector with effect size (AUC) and p-value obtained from coin package
#' @export
archstabms__mann_whitney_U_wrapper <- function(
vals1,
vals2
){
if(leng... |
000de6bdfb653c86abd746700356f67063d4b7fe3aed985a7ebf4e6e17a97321 | R | 758 | 35 |
#' archstabms__pvalue
#'
#' Obtain P-value from z test or t test
#'
#' @param av sample mean (required)
#' @param se sample standard error (required)
#' @param degreesFreedom an integer degrees of freedom (default:5)
#' @param mu population mean (default:0)
#' @param testType type of test: either "ztest" or "ttest" (d... |
5b6db069878ae87374a51de45819d8e36614b86747d1bb0f5a700cf2b693b7e7 | R | 759 | 28 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_noUKB_EVEN"
model <- "F1 =~ NA*BMI_GIANT15_noUKB + WC_GIANT_noUKB
F2 =~ NA*T2D_meta_FinR7_MVP_noUKB + FG_Lagou_MAGIC_noUKB + HYP_FinR7_noUKB
F3 =~ NA*HDL_rev_GLGC_noUKB + TG_GLGC_noUKB
MetS =~ NA*F1 + F2 + F3
MetS ~~ 1*MetS
... |
2458b5a4ff8a7ff54366bfc5cb7f2c390d94097a5a2169be974311e3a5005cc1 | R | 780 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*ceradsc+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="ceradsc.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*ceradsc+msex+educ+age,data=geno_last_joined)
capture.output(summa... |
49dbc9dd4762aee036d831efdaab02268f281c5d89b32ecf5d3de4f75d0c5759 | R | 780 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*caa_4gp+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="caa_4gp.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*caa_4gp+msex+educ+age,data=geno_last_joined)
capture.output(summa... |
c98026e627ce278190093f3daf053a1d3f4d87adeb5980e8f2809e345e2d16bc | R | 780 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*braaksc+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="braaksc.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*braaksc+msex+educ+age,data=geno_last_joined)
capture.output(summa... |
11040199013ac16532afffe7e64312e96874802b796b4fd38e51311758695920 | R | 789 | 31 | setwd('~/multiome')
library(ArchR)
library(parallel)
addArchRGenome("mm10")
proj <- loadArchRProject('/ArchR/clusters_shendure')
saveArchRProject(proj, outputDirectory = 'clusters_shendure_copy')
#####
mat <- getMatrixFromProject(proj, useMatrix = 'PeakMatrix')
mat_matrix <- mat@assays@data$PeakMatrix
rownames... |
03ab9b84873f5f74a504f2cdf0b03a7680c52b90e492e872b8db5eea12ae0703 | R | 792 | 19 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Brain_region_final")
library(ggplot2)
library(Seurat)
pbmc <- readRDS(file = "Ex_raw_highly_variable_mscores_integrated2_adjusted_umap.rds")
library(RColorBrewer)
# Define the number of colors you want
nb.cols <- 32
mycolors <- colorRa... |
bf9bfa76335384af33385cf69b588631417a14a9750f21cba8746650a2f47ad1 | R | 795 | 40 | library("readxl")
library("ggplot2")
library("ggpubr")
library("reshape2")
library("emmeans")
library("hrbrthemes")
library("umx")
library("interactions")
library("car")
library("dplyr")
library (tidyverse)
library(rstatix)
library(reshape)
library(datarium)
setwd("C:/Users/tup54227/Documents/GitHub/rf1-sra/stimuli/S... |
6bb0296e72ddd8c66a32f4ec6999386504a09869887a270ac55beaeb856e554c | R | 796 | 32 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
wd<-args[1]
name<-args[2]
setwd(wd)
inputfile<-paste(name,sep='')
data<-read.table(inputfile,header=F,sep="\t")
dist<-data[,3]
raw<-data[,4]
loess<-data[,5]
stdev<-data[,5]+data[,6]
y<-raw
y2<-y
y2<-sort(y2)
ysize<-length(y2)
ysize<-floor(ys... |
09dd7693dbaaa3561b68d3a8756d31240d29e1c365417deedbd165750642960c | R | 800 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*cvda_4gp2+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="cvda_4gp2.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*cvda_4gp2+msex+educ+age,data=geno_last_joined)
capture.output... |
f44990088b1bde228c0aecdceb67bf9034dc3c45c015063ad347c38898af3f10 | R | 800 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(nft+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(nft).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(nft+1)+msex+educ+age,data=geno_last_joined)
capture.outpu... |
b43d39e316054fdd69b3906ab1b669b47ffce5fc722e49ba01a5b9a82ae04402 | R | 806 | 27 | test_that("L1 spec drops emmeans fields when no model is available", {
mobj <- list(
level = 1L,
regressors = c("reg1"),
signals = c("sig1")
)
class(mobj) <- c("list", "l1_model_spec")
spec_list <- list(
diagonal = TRUE,
cond_means = "cond",
pairwise_diffs = "cond",
cell_means = TRU... |
df29e75d79594197167e9ea076d6f765219289a66d163f7346a91f317aae1191 | R | 812 | 34 | print("starting script")
library(arrow)
args <- commandArgs(trailingOnly = TRUE)
input_files <- head(args, -1)
print("Input files: ")
print(input_files)
output_file = tail(args, 1)
print(paste0("Output file: ", output_file))
print("Reading chunked rds files and combining them")
for (file in input_files) {
# if... |
812a11c6a7343a5b74c36f82b4e05933e9c362d09418fbd445cbed16db757ada | R | 814 | 23 | library(tidyverse)
library(scibet)
library(Seurat)
library(scMCA)
seurat_data <- readRDS("scDART_integrated_seurat.rds")
raw_count_data <- as.data.frame(seurat_data$RNA@counts)
cell_to_sample <- seurat_data@meta.data$orig.ident
# table(cell_to_sample)
cell_to_cluster <- seurat_data@active.ident
result <- scMCA(scdata... |
c6822787f32d7e6492dd3ebd72994e673eec16a06745d074da8a6b9a0437850e | R | 816 | 30 | load_matrix <- function(path){
expr_matrix = read.table(path,sep=',',header=T,row.names=1)
expr_matrix = as.matrix(expr_matrix)
return(expr_matrix)
}
load_pathway <- function(path){
gSet=getGmt(path)
return(gSet)
}
pathway_scoring <- function(gSet, mat_gene){
cells_rankings <- AUCell_buildRank... |
39216985e7111074122e5d80d5fa9bc407c5c96b3578ad02bb792a8281923892 | R | 818 | 24 | log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("readr"))
suppressPackageStartupMessages(library("purrr"))
suppressPackageStartupMessages(library("tidyr"))
message(paste("log is", log))
# colDat... |
87db07181d6cf77cbfc57c336b2952fc86e1dce2af779d991294babe9940e36a | R | 818 | 18 | #This function conducts RLE normalization of RNA-seq dataset
RLE_normalization <- function(df, reads_thresh=10, percentage_thresh=0.5, constant=10^6){
library(edgeR)
#A) Remove unexpressed genes (for RNA-seq)
r <- apply(df, 1, function(x){sum(x>=reads_thresh)})>=ncol(df)*percentage_thresh
... |
a194a8ff8f9b0c3fa5660e1ae12d2def25b6a100da28e2770a03d0eefeda74bc | R | 820 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(gpath+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(gpath).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(gpath+1)+msex+educ+age,data=geno_last_joined)
capture... |
c96fa7e9c58ed0f5918bfe1edd3898423cb8ce6d49e1c9acb095875a9ea936f1 | R | 820 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*niareagansc+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="niareagansc.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*niareagansc+msex+educ+age,data=geno_last_joined)
capture.... |
e6b50666fdf03b6ade7a89d20da7820e11d6bca4948d08cc53f66b82cf9c0e2c | R | 821 | 21 | source("R/setup.R")
# read the configuration file
config <- yaml::read_yaml('config/config.yaml')
# check that all forecasts exist as expected
.check_expected_vs_forecasted(datasets = c("test", "eicu"))
# store error metrics without grouping by hours_since_admission
.store_error_metrics(targets = config$targets,
... |
32824fdf169a5dde2f8fc89e94571b233c7dbe8ec1bf72a77506d531c818ff41 | R | 823 | 35 | library("Seurat")
library("tidyverse")
setwd("./Projects/SNCSynuclein")
# Intergrated datasets at the individual level
Seurat.SNC.ind <- readRDS("Step3.Clustering/Seurat.SNC.Ind.rds")
# Dimplot by individual
DimPlot(Seurat.SNC.ind, label = T, split.by = "orig.ident") &
NoLegend()
# Featureplot
FeaturePlot(Seurat.... |
0f9b437c0db99d22733fc83d6a02c1ac6339302e2dba510657170e092fd624b0 | R | 830 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(plaq_d+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(plaq_d).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(plaq_d+1)+msex+educ+age,data=geno_last_joined)
capt... |
68655e1fbc57ee9c472c743fc2ed74ebd8a20b79c6f30156db8c7af921e62c7a | R | 830 | 34 |
#' archstabms__plot_fitness_replicates_cor
#'
#' Plot fitness replicate correlations.
#'
#' @param input_dt list of folder paths to fitness data (required)
#' @param output_file output file (required)
#' @param colour_scheme colour scheme file (required)
#'
#' @return Nothing
#' @export
#' @import data.table
archstabm... |
ce25deb6cd9feb40e4bd98d495d76315707746993fb40eebdddf9447b69cbb43 | R | 830 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(plaq_n+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(plaq_n).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(plaq_n+1)+msex+educ+age,data=geno_last_joined)
capt... |
ca76f1f8f53ad70f9d9eeb0f8fffde4cfb1980b1b7a3f9d0cad5c326694516e4 | R | 832 | 34 |
#' archstabms__get_ambiguity_code
#'
#' Get IUPAC ambiguity codes.
#'
#' @param input_nucs vector of nucleotides (required)
#'
#' @return ambiguity code
#' @export
archstabms__get_ambiguity_code <- function(input_nucs){
nuc_codes <- list(
"A" = "A",
"C" = "C",
"G" = "G",
"T" = "T",
"R" = c("A", ... |
70d9c8ebbbe342c246533b4c6b4a6b1fe57f7be05c1cdbfb80eff3cb9206e83f | R | 838 | 30 | ---
title: "Untitled"
author: "Kaitlyn Hair"
date: "2023-07-21"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R... |
3947c09525811988b4060f34f9acf2611e0400157e59eef7459cf865db645831 | R | 840 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(amyloid+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(amyloid).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(amyloid+1)+msex+educ+age,data=geno_last_joined)
c... |
5d4b3750ac80084a2f77b96bc96963cc78b2283c73177722648180eb441004f0 | R | 840 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(tangles+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(tangles).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(tangles+1)+msex+educ+age,data=geno_last_joined)
c... |
4c8f133a66c418b7190a96c692b799cf2e39e05548519974181a162ca0383884 | R | 843 | 31 | # Test script for sccomp issue #229
# -------------------------------------------------
# This script loads the toy dataset that was causing
# the reframe/full_join error in issue #229 and runs
# the exact analysis pipeline described in the GitHub
# ticket to confirm that the bug is now resolved.
#
# Usage (from R cons... |
db7af5f148bfebdbf2e7e95a874baadf98de41e4f38dd8db09a18376feaaa6e0 | R | 850 | 29 | # clear() # Takes time to load so don't restart often
# setwd(attachroot("/WORK/Projects/validation/simulations/BBsim/data/sim_1000/sumstats"))
# load("ss_0.1_0.5_2000_.1.RData")
#
# load_all(attachroot("/WORK/Projects/validation/lassosum/."))
# ss.list <- result[[1]][[2]][1:2]
#
# test <- cp.lassosum(ss.list, trace=... |
dcee55da53809a0068ec817c803523bc5b32795fa9930fd5e869f0a320cd9bbd | R | 850 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*arteriol_scler+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="arteriol_scler.output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*arteriol_scler+msex+educ+age,data=geno_last_joined)... |
94176d525cb0522777a92b612038d59ea23e6a8ea022be0d45fc7b9a9000051f | R | 853 | 33 |
# Function -------------------------------------------------------------------
#' @name DEPRECATED-isample
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Return vector of row indices
#'
#' @description
#'
#' This function is **deprecated**.
#' Use `sample(..., index = TRUE)` instead.
#'
#' @param ... Argu... |
a393371da24343459493c2106e05360df18f2d09d92897097238e2d633ba4831 | R | 853 | 26 | # ***************
# 0b-preprocessing.r
# Initial preprocessing of the data.
source('Analysis/util.r')
library(Seurat)
library(dplyr)
library(magrittr)
x@meta.data <- x@meta.data
group_by(orig.ident) %>%
mutate(keep = ifelse(nCount_RNA < quantile(nCount_RNA, 0.99) & nFeature_RNA < quantile(nFeature_RNA, 0.99), TRUE... |
9e25ea2047db6d5d8e2dfd7a0fe9f868509103b13895bbec69b52f34c8b4bc03 | R | 859 | 21 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Brain_region_final")
library(Seurat)
pbmc=readRDS("Ast_integrated2.rds")
DefaultAssay(pbmc)<-"RNA"
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
addition=read.table("Ast_Metadata2_addition_c... |
8c67d73e9f599f9827ec68c26be3436adfa3129da334e8bae061b595a5d90a58 | R | 860 | 33 |
# Function -------------------------------------------------------------------
#' @name DEPRECATED-chk.hy
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Validate `hyperSpec` objects
#'
#' @description
#'
#' This function is **deprecated**.
#' Use [assert_hyperSpec()] or [is_hyperSpec()].
#'
#' @param obje... |
e9b13391e126007628e853dab422b69bd68b5216644185c3a850177fe480efeb | R | 860 | 21 | setup_future <- function(object, plan=c('multicore', 'multisession'), workers=4) {
plan = match.arg(plan)
library(future)
options(future.globals.maxSize = object.size(object)*10)
options(future.resolve.recursive = Inf)
options(future.wait.timeout = 10)
plan(strategy=plan, workers=workers)
rlog::log_info(p... |
8a1cabaee302a32a294afc7657e6219de2bc227d6e25ea2a37625a86005a874b | R | 863 | 29 |
#' archstabms__create_dir
#'
#' Create results folder for analysis script plots and saved objects.
#'
#' @param archstabms_dir directory path string (required)
#' @param execute whether or not the system command will be executed (required)
#' @param message message string (optional, default: NULL i.e. no message displ... |
37190ff37a743780aa1d65bbf0413b7613a363336b85a6d0b37cf0c499a4cdac | R | 866 | 33 | require(testthat)
require(Cardinal)
context("colocalized")
test_that("colocalized", {
set.seed(1, kind="L'Ecuyer-CMRG")
s <- simulateImage(preset=2, dim=c(10L, 10L),
centroided=TRUE)
s$class <- makeFactor(circle=s$circle, square=s$square,
bg=!s$circle & !s$square)
co <- colocalized(s, i=1:2)
co1 <- colocal... |
837561f52547e86510f93245093789aa33866e8856d7acca1d058da300ce147d | R | 870 | 11 | geno_last_joined<-read.csv(file="../geno_hema_joined.uniq.last.within3years.csv")
m<-lm(anemia_who~rs679515_dosage*log(hspath_typ+1)+msex+educ+age,data=geno_last_joined)
capture.output(summary(m),file="log(hspath_typ).output.txt",append = F)
m<-lm(hemacrit~rs679515_dosage*log(hspath_typ+1)+msex+educ+age,data=geno_last_... |
3fa283d339f48f9e383d898b6bdd34cf314ddff65386ce15c8be1dc2e8b5d6be | R | 875 | 35 | #' Laser emission dataset
#'
#' A time series of an unstable laser emission.
#'
#' @seealso
#' See the vignette `vignette("laser", package = "hyperSpec")`.
#'
#' @name laser
#' @docType data
#' @format The data set consists of 84 laser emission spectra measured during
#' 95 min. Each spectrum has 36 data points in t... |
f7226fba75aed68764a869c9335db33651923d30b1a62bad78d0b393cf53c988 | R | 880 | 23 | test_that("choose_glm_set skips confirmation prompt when model names are explicit", {
gpa <- create_mock_gpa(include_l1_models = TRUE, include_l2_models = TRUE, include_l3_models = TRUE)
gpa$multi_run <- TRUE
lg <- lgr::get_logger("glm_pipeline/run_glm_pipeline")
choose_glm_set_local <- fmri.pipeline:::choose_... |
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