id stringlengths 40 40 | repo_name stringlengths 5 110 | path stringlengths 2 233 | content stringlengths 0 1.03M ⌀ | size int32 0 60M ⌀ | license stringclasses 15
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|---|---|---|---|---|---|
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | KelaguenDiesel/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | richardmarkhunt/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | chenyan1994/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | juvchan/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | trexdg/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | bartvink/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Jutair/R-programming-Coursera | Swirl/Rsubversion/branches/rprog-correct/Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-2.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | abarretojr/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | NadiiaP/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | rpatiban/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | papas8105/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Maurizio-Mario/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | zbijoux/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | mgiglia/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | lgreski/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | pmPartch/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Data-Camp/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | stuthom/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | biswabandhu21/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | JoanneL/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | AMPrescott/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | PranavShrivastava/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | ndeltortoiii/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | alegrenfell/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | paternogbc/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | samchen/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | gertlae/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | altaf-ali/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | eyidayoadebola/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | bhagu/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | monogavinwong/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | adri894/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
d1e1278d04c3d8786d9c280f30249f3345b136ac | lukejharmon/traitathon | gopherLice/PlotInteractionTreesAndNetworkExample.R | # install.packages(c('devtools', 'igraph'))
devtools::install_github("fmichonneau/rotl", ref="2277f68e0441d83304caf84759c98a1eb66dd358")
devtools::install_github("ropensci/rglobi")
plotTreesAndInteractions <- function(source.taxa, target.taxa) {
interactionTable <- rglobi::get_interaction_table(source.taxon.names ... | 1,625 | mit |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | fbagirov/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
93474a5f0d9cae1613a31b905de6ff765528670d | davidadamsphd/hellbender | src/main/resources/org/broadinstitute/hellbender/tools/recalibration/BQSR.R | library("ggplot2")
library(gplots)
library("reshape")
library("grid")
library("tools") #For compactPDF in R 2.13+
library(gsalib)
if ( interactive() ) {
args <- c("NA12878.6.1.dedup.realign.recal.bqsr.grp.csv", "NA12878.6.1.dedup.realign.recal.bqsr.grp", NA)
} else {
args <- commandArgs(TRUE)
}
data <- read.cs... | 7,075 | bsd-3-clause |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | panteley/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | sebdavila/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | manish211/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | PhilKP/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | jacobmbr/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Evegen55/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | jrgantunes/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | A7medGad/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | bianyin102938/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | josh95/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | vishalshastri/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | mwilson19/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Samson0033/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Deerluluolivia/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | sriramanathan/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | pegasusTH/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | jmacarter/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | JeromeAli/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | hari624/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Kunisalili/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | mcetraro/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | mgahsan/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Romka11/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | vivekkranjan/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | davidhiston/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | MarcoTomasetta/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | deepakkr249/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | abimannans/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | dvbhagavathi/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | yechen1974/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | johnqmurray/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Winbobob/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | amalarrachidi/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | dhduncan/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | magleouf/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | arjitmazumdar/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | smuch/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | mkostovski08/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Sahil-yp/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | xiang-tischhauser/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | swirldev/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | noahchense/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | MeiSenTafsm/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | sagigr/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Mewzician/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | Mousyoung/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
49361ccfee0095ac83cfe4f3d2d7090c829cd9e7 | KrishnakNamburi/swirl_courses | Regression_Models/Variance_Inflation_Factors/vifSims.R | makelms <- function(x1, x2, x3){
# Simulate a dependent variable, y, as x1
# plus a normally distributed error of mean 0 and
# standard deviation .3.
y <- x1 + rnorm(length(x1), sd = .3)
# Find the coefficient of x1 in 3 nested linear
# models, the first including only the predictor x1,
# the second x1 a... | 1,304 | gpl-3.0 |
316bd464a3d252c15ab9776e91fdf46f4a6744c4 | vapniks/ESS | etc/R-ESS-bugs.R | #### File showing off things that go wrong or *went* wrong in the past
#### -- with R-mode (mostly coded in ../lisp/ess-mode.el )
### NOTE: this file is indented with RRR style !!!!!
### but do not change indentations anymore of anything in here:
### expressions are written as we *want* them, not as ESS currently put... | 26,726 | gpl-2.0 |
316bd464a3d252c15ab9776e91fdf46f4a6744c4 | gragusa/ESS | etc/R-ESS-bugs.R | #### File showing off things that go wrong or *went* wrong in the past
#### -- with R-mode (mostly coded in ../lisp/ess-mode.el )
### NOTE: this file is indented with RRR style !!!!!
### but do not change indentations anymore of anything in here:
### expressions are written as we *want* them, not as ESS currently put... | 26,726 | gpl-2.0 |
316bd464a3d252c15ab9776e91fdf46f4a6744c4 | lengstrom/dotfiles | .emacs.d/elpa/ess-20150729.819/etc/R-ESS-bugs.R | #### File showing off things that go wrong or *went* wrong in the past
#### -- with R-mode (mostly coded in ../lisp/ess-mode.el )
### NOTE: this file is indented with RRR style !!!!!
### but do not change indentations anymore of anything in here:
### expressions are written as we *want* them, not as ESS currently put... | 26,726 | gpl-3.0 |
7d1ae5992ffa4421206ffd2ac1cffee6716540ae | jonathan-taylor/R | forLater/josh/sim.carve.R | library(intervals)
source("selectiveInference/R/cv.R")
source("../../selectiveInference/R/funs.groupfs.R")
source("../../selectiveInference/R/funs.quadratic.R")
source("../../selectiveInference/R/funs.common.R")
set.seed(1)
niters <- 400
known <- FALSE
n <- 100
p <- 50
maxsteps <- 20
sparsity <- 10
snr <- 1
rho <- 0.1... | 2,244 | gpl-2.0 |
9ba0e5d27130c1ccee6a6315285619e0a460dbe2 | distributions-io/erlang-pdf | test/fixtures/test.matrix.R | options( digits = 16 )
library( jsonlite )
k = 1
lambda = 1.7
x = 0:24
y = dgamma( x, k, lambda )
cat( y, sep = ",\n" )
data = list(
k = k,
lambda = lambda,
data = x,
expected = y
)
write( toJSON( data, digits = 16, auto_unbox = TRUE ), "./test/fixtures/matrix.json" )
| 278 | mit |
7d1ae5992ffa4421206ffd2ac1cffee6716540ae | jonathan-taylor/R-selective | forLater/josh/sim.carve.R | library(intervals)
source("selectiveInference/R/cv.R")
source("../../selectiveInference/R/funs.groupfs.R")
source("../../selectiveInference/R/funs.quadratic.R")
source("../../selectiveInference/R/funs.common.R")
set.seed(1)
niters <- 400
known <- FALSE
n <- 100
p <- 50
maxsteps <- 20
sparsity <- 10
snr <- 1
rho <- 0.1... | 2,244 | gpl-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | h2oai/h2o | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | elkingtonmcb/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | vbelakov/h2o | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | 111t8e/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | h2oai/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | eg-zhang/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | rowhit/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
7d1ae5992ffa4421206ffd2ac1cffee6716540ae | selective-inference/R | forLater/josh/sim.carve.R | library(intervals)
source("selectiveInference/R/cv.R")
source("../../selectiveInference/R/funs.groupfs.R")
source("../../selectiveInference/R/funs.quadratic.R")
source("../../selectiveInference/R/funs.common.R")
set.seed(1)
niters <- 400
known <- FALSE
n <- 100
p <- 50
maxsteps <- 20
sparsity <- 10
snr <- 1
rho <- 0.1... | 2,244 | gpl-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | 100star/h2o | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | calvingit21/h2o-2 | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
7d1ae5992ffa4421206ffd2ac1cffee6716540ae | selective-inference/R-software | forLater/josh/sim.carve.R | library(intervals)
source("selectiveInference/R/cv.R")
source("../../selectiveInference/R/funs.groupfs.R")
source("../../selectiveInference/R/funs.quadratic.R")
source("../../selectiveInference/R/funs.common.R")
set.seed(1)
niters <- 400
known <- FALSE
n <- 100
p <- 50
maxsteps <- 20
sparsity <- 10
snr <- 1
rho <- 0.1... | 2,244 | gpl-2.0 |
d8c08e51130dce20492ef9694a85c0932b7ce8c9 | woobe/h2o | R/tests/testdir_jira/runit_pub_213_nacomparisons.R | #
# na comparisons
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../findNSourceUtils.R')
na_comparisons <- function(conn){
Log.info('uploading testing dataset')
df.h <- h2o.uploadFile(conn, locate('smalldata/jira/pub_213.csv'))
Log.info('printing from h2o')
Log.info( head... | 953 | apache-2.0 |
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