sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
905b6d199408ddb72b35121de51f362182aa59a52a8d65364b244905c9ced1d1 | R | 12,036 | 264 | #' @title
#' Training/Leaning Step for Compressive Big Data Analytics - LONI PIPELINE
#'
#' @description
#' The CBDA.pipeline() function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This versi... |
3f263679f305a56cd13d23546b998e54db746360845c9909483a7a40a2fe0830 | R | 12,210 | 314 | context("Test cca")
## .cca is correct
test_that("(Basic) .cca works", {
set.seed(10)
n <- 20; p1 <- 50; p2 <- 40
mat_1 <- scale(MASS::mvrnorm(n = n, mu = rep(0,p1), Sigma = diag(p1)), center = T, scale = F)
mat_2 <- scale(MASS::mvrnorm(n = n, mu = rep(0,p2), Sigma = diag(p2)), center = T, scale = F)
svd... |
535dbded3f26db1564e232fe299aa8b49c539dd2449b16b9984261b50f1c9999 | R | 12,235 | 294 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(args)
print(label)
... |
9799eae27e9ba9aa7a297908daf5f5be4c1d350797a5d2a21d51b73144aa31ed | R | 12,291 | 247 | ---
title: "Tilted-CCA Simulation (3 cell types)"
output:
rmarkdown::html_vignette:
df_print: "kable"
vignette: >
%\VignetteIndexEntry{Tilted-CCA Simulation (3 cell types)}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
pkgdown:
as_is: true
---
```{r, include = FALSE}
knitr::opts_chunk$set... |
f60da5455aeaf688f6c5bcebe75eed9076d2fdacf6ac4f1a4c28b779a5c9d031 | R | 12,300 | 264 | #' @title
#' Training/Leaning Step for Compressive Big Data Analytics - LONI PIPELINE
#'
#' @description
#' The CBDA.pipeline() function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This... |
c5fde2fa80b2f23f76dff48fee4cda5cbb90a5ce3a97af196ff6b66f33e4babe | R | 12,323 | 300 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(label)
print(worksp... |
897c17900bb9b96cdbd38c2dfe5842aed0213752c142eedeb89937485f9d908e | R | 12,397 | 364 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
warning = FALSE,
message = FALSE
)
```
# pubmedR <img src="man/figur... |
ae77fea0d1e94984a1110977c380d7845f01509b1908e7f1d927f6871936e06d | R | 12,415 | 401 | ---
title: "Bayou - Brain Size ~ Body Size"
output: html_notebook
editor_options:
chunk_output_type: console
---
Setup for Bayou with Only BoM - 03.04.2019 - after adding in new diet categories and correcting for using DC inferred evolution rather than RDC on RDC analysis
Also updated SS, and ran everything togethe... |
00d290fdac3b5d910b14c76b83d0913288ae034f2e8792c8ed5f3ece15808cbe | R | 12,492 | 327 | # a tilt_perc near-0 means that most of the distinct information
# is in modality 1, not modality 2
.common_decomposition <- function(averaging_mat,
discretization_gridsize,
enforce_boundary,
fix_tilt_perc,
... |
af7e54c9c6f1089d4a3422cf609e3ece092927dae8f91de8dad287d650d8691e | R | 12,538 | 295 | ---
title: "SiT - Figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
ab0bd69ad81361c804ba653b393ec9c858e57f1d13787dec73d0f5511c1e6f2c | R | 12,686 | 292 | #' Computing cell enrichments (generic)
#'
#' @param input_obj \code{multiSVD} object, after using \code{tiltedCCA_decomposition()}
#' @param ... Additional arguments
#'
#' @export
postprocess_cell_enrichment <- function(input_obj, ...){UseMethod("postprocess_cell_enrichment")}
#' Computing cell enrichments for matrix... |
4071646b345c57b9576c09f3978fc84a5c4f679f0002a62cd65a08c08a085809 | R | 12,860 | 318 | # GLM Analysis
# Either TP, IF, or AF are tested as predictors for participants' responses
# Stimulus intensity, as an ordered factor, is included in an interaction with
# the respective learning model
library(tidyverse)
library(ggtext)
library(patchwork)
library(DescTools)
# Source functions
source("functions/funct... |
449b813f6686a139051a3b6ca3c2a5cd1355d30a96e3f9010eca28c71823c260 | R | 12,889 | 274 | ---
title: "CBDA-SL and Knockoff Filter Results on the Null Dataset - n=300 p=100, 9000 jobs x 10 experiments - MSE and Accuracy as ranking metrics for CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chun... |
2449aa60d1c14fb0de2f003d4122b32a1436ee6e54efd1143c57f8688be3abc8 | R | 12,925 | 274 | ---
title: "CBDA-SL and Knockoff Filter Results on the Null Dataset - n=300 p=900, 9000 jobs x 10 experiments - MSE and Accuracy as ranking metrics for CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chun... |
e49e05a85bb0423adbe5837993d0a6e30b3f937f9d7164c1d8d8b710e5e00092 | R | 12,965 | 306 | # Data Pre-Processing Psychometric Analysis Visual
library(tidyverse)
source("functions/CountConditionalProbabilitiesExtended.R")
load("processed_data/allValues/allValuesVisual.Rda")
df_visual <- allValuesVisual
# Function: Extract Psychometric Shift
extractPsychometricShift <- function(x){
y <- x |> select(st... |
3013e25123d3e29ba1e09ae4529a4bfce08ffdad5eb6ea34c4089f7a6d57940d | R | 12,969 | 249 | # Plot all Likelihood Plots
library(tidyverse)
library(patchwork)
library(ggpubr)
library(rstatix)
### === LOAD DATA === ###
load("processed_data/Results/df_LLhood_absolute_vs_Stim.Rdata")
load("processed_data/Results/df_LLhood_sig_absolute_vs_Stim.Rdata")
# Relevel factor
dfabs_LLhood_vs_Stim$level <- factor(dfab... |
0508aaadf43518ab19c344f13a1150491e6d802e29f01f623201cefd9fe9a388 | R | 13,033 | 276 | ---
title: "CBDA-SL and Knockoff Filter Results on the Null Dataset - n=300 p=1500, 9000 jobs x 8 experiments - MSE and Accuracy as ranking metrics for CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chun... |
c22abf1d5f9dc1d131f71fe1c4d530ec2d90f21387fffe1f4c023c1ab06fde23 | R | 13,090 | 342 | ---
title: "melas_wnn_analysis"
author: "Nate Mullin"
date: "1/11/2023"
output: html_document
---
```{r}
library(Seurat)
library(Signac)
library(tidyverse)
library(ggpubr)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(ggrepel)
library(RColorBrewer)
library(scCustomize)
```
################... |
b48917a928404b9500afa759497020b743fbcfc9baa57440b9b48ff5657cdadb | R | 13,098 | 275 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=900, 9000 jobs x 6 experiment - MSE and Accuracy as ranking metrics for CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_ch... |
060aa7b9273afe8fec1c32b935fe1c2a76040e6835b70054aa5e424a73d7b279 | R | 13,137 | 275 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=1500, 9000 jobs x 8 experiment - MSE and Accuracy as ranking metrics for CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_c... |
eb1d8c20904ce657bd9a7aaab258b32f80c380c368c1d0a34760c3ff8b348c69 | R | 13,254 | 409 | #!/usr/bin/env Rscript
#######################################################################
# crispro_graphs.R
# Draws score graphs above 1-D representation of annotations
#######################################################################
library(ggplot2)
library(readr)
library(dplyr)
library(egg)
libr... |
85a5ac3ffa447135bb9fe6ea2b807e0382a67a344b3c7154f593015b081d276a | R | 13,280 | 398 | ---
title: "Cluster Annotation and differential expression"
author: "Bismark Appiah"
date: "15 December 2021"
output: html_document
---
```{r setup, include=FALSE}
# Set root directory for markdown file
knitr::opts_knit$set(root.dir = 'your-directory-here')
knitr::opts_chunk$set(echo = TRUE)
```
```{r ... |
f87729bdebb66458696b5e88e0f1450107f155a05a08ec896580e0f4d6996caa | R | 13,309 | 292 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
## VERY IMPORTANT -- SCRIPT 2 [FOR THE PIPELINE WORKFLOW]
## This next step... |
e7a27d1f0ebdd06a715300d83b190f1aca8efb37622e962eb306adab4fecd50b | R | 13,344 | 295 | library(mixtools)
library(tidyverse)
library(lubridate)
#___________________________________________________________________________----
#CPOD DATA: ECHOLOCATION BUZZES ----
# 1.Extract Echolocation Buzzes from CPOD Click Details: ... |
6f773abb74479cf963fe6fe0f209e22c9968b3eeb9d75e4ce7757916db575a12 | R | 13,350 | 358 | # Loglikelihood approach (generative)
# What is done:
# --> loglik <- pbinom(1, prob = probs, q = y, log.p = TRUE) |> sum()
# In this script:
# (1) Per condition (i.e. rep, alt, etc.) in auditory & visual
# NOTE: Data is backtransformed within the loglik-functions with plogis()
# Because for the GLM-Analyses, data ... |
0d6fbecbafe00158e2c4e2b26fc6eaecdbe8998dc64f325f4056f8485d47d3bc | R | 13,392 | 302 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
label=as.array(args[1])
workspace_directory=as.array(args[2])
print(label)
print(workspace_directory)
library(CBDA)
#CBDA_Consolidation.pipeline(top , max_covs , M , misValperc , range_k , range_n ,
# label = label, workspace_dir... |
90165e2cee2c84987642439367dc6872fb205c5de00d56f0b67ae745365d5442 | R | 13,440 | 225 |
#' FR-Match cell-to-cluster matching
#'
#' This is a user-end wrapper function that implements the steps of cell type matching between two single cell RNA-seq experiments
#' (namely, \code{query} and \code{reference}) using the cell-to-cluster FR-Match approach that matches each query cell to a reference cluster.
#'
#... |
7cd85412a5f66723a8c8741aca9eed0e571d0535b448dcd0337eb34add380888 | R | 13,457 | 284 | context("Test Seurat helpers")
## create_SeuratDim is correct
test_that("create_SeuratDim works", {
# load("tests/assets/test_data3.RData")
load("../assets/test_data3.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
n <- nrow(mat_1)
large_clustering_1 <- test_data$clustering_1
large_clustering_... |
4f0d743f4c58a771e80117cde2d85f04b972b20e0d6df7b52cbc0d5f2ecddb90 | R | 13,494 | 298 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
4af8835c1f80f09483ae7af5f94e8c0f74ce765f24bdfde7dc1d6d164c6b2fbd | R | 13,497 | 298 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
ee2351db4ebd5300e5bfcebcf2e5d2c06dce5c035434374e9fd30fa0386dba6d | R | 13,523 | 307 |
#!/usr/bin/env Rscript
#args = commandArgs(trailingOnly=TRUE)
#print(args)
#arg_file = as.array(args[1])
#print(arg_file)
#j_global=as.numeric(args[2])
#dataset_file = as.array(args[3])
#workspace_directory=as.array(args[4])
#i_exp=as.numeric(args[5])
## VERY IMPORTANT -- SCRIPT 2 [FOR THE PIPELINE WORKFLOW]
## Thi... |
29729be21634efdc0958b5dbe965c3a379b190a149eb074d921b6cdc316706a1 | R | 13,598 | 420 | ---
title: "Analysis of Electroporated Cells (E14.5) with Seurat (version 3.1.0) Workflow for scRNA-seq data"
author: "Bismark Appiah"
date: "10 April 2020"
output:
pdf_document: default
word_document: default
html_document: default
---
```{r, setup, include=FALSE}
# Set root directory for markdown ... |
65ebefe0a8db13232223cee43e9f44b56c4bbe3ca2e7a23777320b9a5f55189a | R | 13,607 | 204 | #set working directory
setwd("~/Desktop/FLICproject")
#load FLIC required files
attach("FLICFunctions",pos=2)
require(ggplot2)
require(stats)
#set parameters
p=ParametersClass.SingleWell()
p=SetParameter(p, Feeding.Interval.Minimum = 10)
p=SetParameter(p, Feeding.Threshold.Value = 10)
p=SetParameter(p, Tasting.Thresh... |
91bf74e0e33306307c8467e4724c02028f880ad29cfd05be3d5f30031387f10b | R | 13,755 | 312 | #!/usr/bin/env Rscript
myscale <- function(x,doscale=F) {
if ( doscale ) return( scale(x) )
return( x )
}
dotest<-F
options(digits=3)
Args <- commandArgs()
self<-Args[4]
self<-substring(self,8,nchar(as.character(self)))
getPckg <- function(pckg) install.packages(pckg, repos = "http://cran.r-project.org")
pckg =... |
f26812f3875ffe0746c49205bdeb600e95db509b35080b48892c6cc6b1916af8 | R | 13,774 | 286 | context("Test tiltedCCA")
## tiltedCCA is correct
test_that("(Basic) tiltedCCA works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
n <- nrow(mat_1)
large_clustering_1 <- test_data$clustering_1
large_clustering_2 <- test_d... |
797564babcc2d69207117c52b57d92bf48f55270cf08d52bcecb1f4203122830 | R | 13,829 | 384 | # Loglikelihood approach
# What is done:
# --> loglik <- pbinom(1, prob = probs, q = y, log.p = TRUE) |> sum()
# In this script:
# (1) Stimulus-only models are computed too
# (2) Per condition (i.e. rep, alt, etc.) in auditory & visual
# NOTE: Data is backtransformed within the loglik-functions with plogis()
# Beca... |
993155832c5d5e498f6388b9248102539c99bb156b23c376acf91f17cce396a7 | R | 14,092 | 323 | # GLM Analysis (generative values were used as input for learning models)
# Either TP, IF, or AF are tested as predictors for participants' responses
# Stimulus intensity, as an ordered factor, is included in an interaction with
# the respective learning model
library(tidyverse)
library(ggtext)
library(patchwork)
lib... |
ec83c5f161323cb0d8697683f9ba1112e33ae1ad228b2e0e2459172611a46ca8 | R | 14,093 | 329 | ---
title: 'RNAseq Analysis Pipeline: Fractional Factorial Experiment Design - Independent Effects'
author: "Abishek Arora"
date: "23/06/2022"
---
# Differential gene expression analysis using the package DESeq2 in R
```{r}
# Load the required libraries for DE analysis of RNAseq data using DESeq2.
library(DESeq2)
lib... |
9dcc83427c4d84633e74a7857cd8a2fa053c9d8e96786763be2016bce6a29270 | R | 14,099 | 292 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=100, 9000 jobs x 6 experiment - MSE and Accuracy as ranking metrics for the CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opt... |
a581675f8d735054911e519edb511c82645ab2b6c75dbbea564b5da227d18273 | R | 14,166 | 292 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=300, 9000 jobs x 6 experiment - MSE and Accuracy as ranking metrics for the CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opt... |
dc840080ad396d4a1a96558ee81e6adc30f119e7e4f34b83605722e3f4691286 | R | 14,169 | 292 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=900, 9000 jobs x 6 experiment - MSE and Accuracy as ranking metrics for the CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opt... |
3b4aef60e6b355b71112e339cbced8b1f6a7481b311d47dec5fe2a8534f566ae | R | 14,207 | 351 | make.slouch.table<-function(output,model.type,pred.type,num.sp,prev.output=NULL){
#SB Revision 1 Verion
#Code to create or update a table from slouch output
#Format for calling function: make.slouch.table(Slouch output,type of model,type of predictor variable,
#sample size,previous make.slouch output or leave b... |
6ab9a13b5e330ccdd788bee17ea31042c2b6665ac3081634c98508e208771476 | R | 14,207 | 199 | ---
title: "Tilted-CCA Overview"
output:
rmarkdown::html_vignette:
df_print: "kable"
vignette: >
%\VignetteIndexEntry{Tilted-CCA Overview}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
# Purpose
The simulations here are meant to be portable, toy-examples of Tilted-CCA, with the intent ... |
136f61e865f83a1210fead438066543ebe2530884322426aefed1870338ae74c | R | 14,252 | 369 | # analyze Y-maze learning experiment
require(ggplot2)
require(GGally)
require(reshape2)
require(lme4)
require(compiler)
require(parallel)
require(boot)
require(lattice)
library(dplyr)
library(forcats)
library(multcomp)
library(plotrix)
# load data
my_data <- read.table("Allmice_modified.txt", sep="\t", header = TRUE... |
614f46778fa217a5a0a12701ea1d5ad7f9afcaf263781b4c383f33dd533ebfae | R | 14,326 | 259 | ---
title: "CBDA_Knockoff"
output: word_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Knockoff filter
This is a new variable selection procedure, which could controll the FDR in the statistical model when the number of obervations is larger than the number of features.
### ... |
db264c1c6d61e2e9d64fe381f9469a15294b132b056ddfda4a6f141bd1d4f9f0 | R | 14,363 | 326 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retrie... |
1c94415f0f30ef855660a90f333f965a47ab2d7542137e8ac041f2cf3e8f3239 | R | 14,365 | 326 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
667a280ac0d1691c271277b0272379f7d4bf893652eed5c0838e1cc546e35553 | R | 14,427 | 359 | # GLM Analysis (with fixed Omega)
# Either TP, IF, or AF are tested as predictors for participants' responses
# Stimulus intensity, as an ordered factor, is included in an interaction with
# the respective learning model
# Data preprocessing to plot Nagelkerke, when Omega is fixed across learning models
library(tidy... |
f6fd2d44386b09f552ee842f7e506cc88c2a74f81cb2800dcc30f291e99f9565 | R | 14,523 | 451 | ---
title: "Cluster_Annotation"
author: "Bismark Appiah"
date: "12 December 2021"
output:
pdf_document: default
html_document: default
---
```{r, setup, include=FALSE}
# Set root directory for markdown file
knitr::opts_knit$set(root.dir = 'your_directory_here')
knitr::opts_chunk$set(echo = TRUE)
```
... |
cfbbf51af76a12e49c02711df7df0bda7a03e0fb7791ab752a46ce59fd4170bb | R | 14,611 | 306 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=1500, 9000 jobs x 6 experiment - MSE and Accuracy as ranking metrics for the CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::op... |
084d471f778aa37e6ec2ad76fc28e94e4193bfe529b6511cecb7c4eb2fa5bc10 | R | 14,653 | 320 | ---
title: "CBDA-SL and Knockoff Filter Results on the Binomial Dataset - n=300 p=100, 9000 jobs x 12 experiment - MSE and Accuracy as ranking metrics for the CBDA"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::op... |
c348d6757b159c017977b6180121da4b2abc2337f3d888f84dd16674eb69d20a | R | 14,792 | 474 | ---
title: "R2 BDS ms SIMMAP Plots Code 091222"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol.Non.Fol... |
7ddd54e0e072fb42a6bc12bc0c720a28413003bec35405c76e948836ccbcc471 | R | 14,827 | 333 | ---
title: "CBDA-SL and Knockoff Filter Results on the ADNI Dataset - 9000 jobs x 9 experiments - MULTINOMIAL CASE"
author: "Simeone Marino"
date: "January 12, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 9... |
6837b773c87cbfd75f944d0d5acce8ea78d8da59c511fd9c3bcb47a9e52484aa | R | 14,870 | 468 | ---
title: Gene Ontology Analysis on Electroporation Data
author: "Bismark Appiah"
date: "12 April 2021"
output:
pdf_document: default
word_document: default
html_document: default
---
```{r, setup, include=FALSE}
# Set root directory for markdown file
#knitr::opts_knit$set(root.dir = 'your_directo... |
aaa788139e4e64e35c767b0957ab008e1290f79c5ddc9374eefe19d4e9a1183f | R | 14,909 | 588 |
library(dplyr)
library(foreign)
library(kableExtra)
library(pROC) ## ROC
library(caret) ## ROC
library(rpart)
library(rpart.plot)
library(gtsummary)
library("scales")
hex_codes1 <- hue_pal()(6)
# Data Preparation
ADNI = read.csv("../data/ADNI.csv")%>%
mutate(PET = as.factor(ifelse(PET == "1", "P... |
d3680a0c7f8bc190dfc359b6000fe03bf30f69dbe1dd3ea6246fdf3f6b4116c4 | R | 14,913 | 324 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
a95cdad55daa1afb820c69452184c078361d7e44620e461b815d188a2707614a | R | 15,202 | 348 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
2a3e42e8fd8eb1623345181eadbfd5bc70c49816fb2f8340aa1b788a98c5569c | R | 15,262 | 442 | ---
title: "Analysis of Microinjected Cells (E14.5) with Seurat (version 3.1.0) Workflow for scRNA-seq data"
author: "Bismark Appiah"
date: "10 April 2020"
output:
pdf_document: default
word_document: default
html_document: default
---
```{r, setup, include=FALSE}
# Set root directory for markdown fil... |
f793c8bdd349e4de93c98325c02289bdb434e4729bd8c7e0917ca1031454ffcb | R | 15,352 | 435 | ---
title: "Using ggforestplot"
author: "Nightingale Health Ltd."
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Using ggforestplot}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE... |
d9a1d6037e516810e860d880230a519fe2317d8d5f4a33abaaabf7972827b447 | R | 15,500 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
06fa1b9e57ef7ed1b9ecee5ac69e7c726c828864d9b2b90ff10ebc8db431d7cc | R | 15,503 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
64d020e48772d06a37a8ad1588bcec3a6b02ae87916293b3165d9ea0d2a11c4c | R | 15,503 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
8eebf4131b09d0ab04fffcbb471abd8eb44e82fee5a4be253aca66cb2edb3d39 | R | 15,503 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
ea2991dc0846d9a326fac34a3e5bb7331815b412646aedd23c5e9da45caa9248 | R | 15,503 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
eea3b1a6ef14f6aa22789a72126d79b0b1a22cd3c6368249087abb343e8b06e1 | R | 15,503 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
05288c7915425c10f36d56bbbade389f579e5ac0356e343af5367aa30edef129 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
124158d6ec2672f7eebc508c420207c3b0222668f9f4a3a88ec7ee4e6e2b9cb9 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
1ecb31e6d264fc496b4443f58201e5b428d1444e76722f5b5cb7e8a4416b154a | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
32e553a3e7c01300bd907e2a41a55f352df55f70e519b7822b64c2e4ef474889 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
4eae479a5f2b5ff43000b13c7087417d94452c579afa032d26cc07d3d3f00069 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
63c5ee395eb8a1f36b036213d0ea0489a03ed7c3b69868edf471aa1e23d3f6a8 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
69181fb5c04c0f141195eec0aed32f7752dad4b5c55cfbbdd3088a8d910533f6 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
738db074109894916a1e057b094d141b0614c93bca390487d99492bb00a5f8c6 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
843fe1bac5cfddcc6028d821859bd67dd1c3a8a90014ec07a6163cf8de445c27 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
f5e3e4bb5d39a9393cbe75843f0348f5b71558b3322d530e4290fc2a4bddb1b8 | R | 15,504 | 359 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
21b953bd63e0eabad556ec4af952f0bc365270a5ac068d9536f0bbaaeb0d2c84 | R | 15,570 | 385 | ## This generates a "light" Binomial dataset 10kx1k
cat("GENERATNG THE BINOMIAL 10K - 1K\n\n")
start_time_total=Sys.time()
for (i in 1:10){
start_time=Sys.time()
print(i)
X2 <- NULL
n = 1000 # number of observations
p = 1000 # number of variables
X1 <- matrix(round(rnorm(n*p),3),nrow = n, ... |
6a008fd3f944e9e5e0bfe8d16863092163aac541697cc90b0423b78b306ded1d | R | 15,620 | 342 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
26c41a0adc1cf7c92e3d2222163315c22fd5b3c57958653e41f2ae93819f069b | R | 15,643 | 387 | ---
title: "Averaged correlations and correlated averages"
author: "Ulrike Horn"
date: "11 4 2023"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
In this document we explore the difference between averaging correlations of resting-state ... |
5613ab3aa7c52f0788c094406a3d256c920eb173960fa102edb6440cb2e8d0b9 | R | 15,670 | 532 | if (!require("devtools")) install.packages("devtools")
devtools::install_github("ck37/varImpact")
library("varImpact")
curl http://bioconductor.org/packages/3.1/bioc/src/contrib/PACKAGES
download.file(url = "http://bioconductor.org/packages/3.1/bioc/src/contrib/PACKAGES", tempfile())
library("")
source("https://bioco... |
f25f99989ce2b7e00fb4de22a32a5a61cff889417aca2bc631bee9d795853847 | R | 15,700 | 371 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
#setwd("~/Documents/NIH-grant/SOCR/GITHUB/")
setwd("~... |
0f55279f699c96b37ab623e87322c6c86ba53879bd512e4e227cdd264d2ef850 | R | 15,747 | 365 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
87abeff14c6496774828150b9afb4738cd284b8bbfd15a40a8c54a400dfb319e | R | 15,747 | 365 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
d8b92eaacce340e073bbc177a28efe988f73c8be3108b952d936ab7b89e6f694 | R | 15,747 | 365 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
# setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retr... |
74e8fdbf3c2a236500762c6cdba755f65bfe87d9ecc6dce7f09803b8a2ab8d1d | R | 15,806 | 356 | # Load a session
#load("~/Documents/NIH-grant/SOCR/CBDA_v0.RData")
#load("~/Documents/NIH-grant/SOCR/CBDA_v2_temp.RData")
## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
setwd("/Documents/NIH-grant/SOCR/GITHUB/")
## Retrie... |
1604b62f2b997c2a6895123f6c5fb159c5526abc78a4389f398d5817fa81259b | R | 15,842 | 399 | ## This generates a "light" Binomial dataset 10kx1k
cat("GENERATNG THE BINOMIAL 10K - 1K\n\n")
start_time_total=Sys.time()
for (i in 1:10){
start_time=Sys.time()
print(i)
X2 <- NULL
n = 1000 # number of observations
p = 1000 # number of variables
#X1 <- matrix(rnorm(n*p, mean=0,sd=2.5),nro... |
e73335b62c2fa154940e69a8776c69820726e163ddf6daaa3042dedec93d4768 | R | 15,898 | 322 | library(data.table)
library(dplyr)
pheno <- fread("/edgehpc/dept/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKBB_maxHGS_WBLM.pheno")
cov <- fread("/edgehpc/dept/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKBB_maxHGS_WBLM.cov")
dat <- merge(pheno,cov,by=c("FID","IID"))
loco <- fread("... |
ab53730a33360c4de65e13d5c4c0618052a2274aaedf018a9fe0818640423b44 | R | 15,913 | 431 | ---
title: "BWAS: Effect of categorical perturbation variables on c-Fos"
author: "Will Townes"
output: html_document
---
```{r}
#library(glmGamPoi)
library(cowplot)
library(ggplot2)
#library(ggridges)
library(MASS)
fp<-file.path
mkdir_p<-function(x){
if(!dir.exists(x)){dir.create(x,recursive=TRUE)}
}
rp<-fp("resul... |
509f26b802634adadd6d7b94cc07b25c273f170a103ab9076fc637b420552ec9 | R | 16,000 | 260 | ---
title: "FR-Match: A cell type matching algorithm for single cell RNA sequencing (scRNAseq) data"
author: "Yun (Renee) Zhang, zhangy@jcvi.org"
date: "`r format(Sys.time(), '%B %d, %Y')` - update raw data link"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{FR-Match: A cell type... |
9885898bebf35ba0f7a15cd31a5ec05d29239da385b40f175bdc17466fa395e2 | R | 16,008 | 386 | #Adaptation to ccn_hmClass to prevent re-ordering the groups by name
acn_queryClassHm <- function (classMat, grps = NULL, isBig = FALSE, cRow = FALSE,
cCol = FALSE, fontsize_row = 4, fontsize_col = 4, main = NA,
scale = FALSE, customAnnoColor = NULL, ...)
{
cools ... |
052a0af60b073cd7980319266ea5e24908ceff73f372cad044f20382cb731b2c | R | 16,336 | 292 | library(devtools)
library(Seurat)
library(Matrix)
library(xlsx)
library(umap)
library(reshape2)
library(dplyr)
library(ggplot2)
library(cowplot)
## Setup the Seurat Object- intact female
ifemale.data <- Read10X(data.dir = "../P29_intact_F/outs/filtered_feature_bc_matrix/")
ifemale.atleastone <- apply(ifema... |
ed0335ff6cc639aef59eb94f7afa95b6fb6d35abcb5bcd2fa6bf7efb90f53cf4 | R | 16,423 | 369 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(args)
print(label)... |
94c6c7e5311a7819de205199bb8e9b646d6aa152a7d1b056993e0d5907c806cd | R | 16,535 | 468 | #heatmap.3
# From https://gist.github.com/amcdavid/5439787
#Like heatmap.2, but handles multiple layers of color labels for rows and columns
# 1/7/2015 Added ability to color row/column labels. Based on method suggested
# in http://stackoverflow.com/questions/13206335/color-labels-text-in-r-heatmap
heatmap.3 <- func... |
c97ec69b34005c5215d5bb859c67b45c1b9e9a0c2db01bf9ab18a0ce1456079b | R | 16,537 | 465 | ##########################
##### READ LIBRARIES #####
##########################
library(crayon, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(backports, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(withr, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES... |
37a065ea69fc5d1cc467b8fbe26440e0810885899125a6aae29e806bd8a77874 | R | 16,563 | 535 | ---
title: "Statistical analysis ADC Study - Supplementary"
author: "Vitus Gosch"
date: '2022-11-17'
output: html_document
---
Df "Xrepro_annonymized" was created by reading in text file output out of shell scripts written to analyze image data. The Df is available in annonymized form, meaning all possible identifiers... |
42615de99e328987afbb73abfbb6f4a4d326eb70ab2872c09c2fe1b560f6be49 | R | 16,640 | 447 | ---
title: "Gene List Enrichment Analysis"
author: "Abishek Arora"
date: "23/06/2022"
---
# Gene List Enrichment - phyper Function
The phyper function of the base package in R makes it possible to check for enrichment of a list of differentially expressed genes or differential exon usage, in a gene list of interest. ... |
d16c836f3ae79db82dbfda6c77ef8b3f4d1fc18f6e1d8f22d7d4003b396638ce | R | 16,979 | 382 | # working directory where the files are retrieved from and where they are saved
workspace_directory <- setwd("/Users/zhao_fengyu/CBDA/Pipeline/DISTRIBUTED/BINOMIALdataset")
# specs for the experiments to run
arg_file <- c("arg_Binomial_new_Test.txt")
# dataset to analyze
dataset_file <- c("Binomial_dataset_5_new.txt")
... |
79050c94e734b119585a8a25df6c23f9850a03dbf192be509569270b6ec2e2d2 | R | 17,039 | 417 | library(tidyverse)
library(GGally)
library(car) #Anova
Sys.setenv(tz="GMT")
# 1.Add dolphin encounter number to click details files: ----
dat3=data.table::fread(here::here("AI-ARS_ToDryad","OFB_VarForARS_ClickDetails-Buzzes-2018SutChaCPODS.txt"),sep="\t",header = T ) %>%
as_tibble()
encdat10 = ... |
0c264f7e3c9e6c94a3b2866a4905c04fa3f7b2f689f53240d08a3086acf78f25 | R | 17,193 | 362 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
label=c("Gaussian")
# /ifs/loni/ccb/temp/CBDA_SL
#print(c(j_global,i_exp))
... |
5990081fcdd6ea4f2f7fabc28aa23139de9bcd5a87c5c45bb2cdf012e8b223e8 | R | 17,398 | 392 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(args)
print(label)... |
580dbdeea637536cfdbd37443bb760d0cbba7e949bcf2af3f10a24c5280f3631 | R | 17,554 | 383 | ---
title: "SB R2 BDS ms Intercept Plots - All Primates"
output: html_notebook
editor_options:
chunk_output_type: console
---
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol.Non.Fol.Strict
Fol.N... |
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