BwUniCluster2.0/Software/R/Glmnet

Note that the instructions provided below refer to R 4.4.1 (but not R 4.2.1)!

General information

glmnet is an R package that fits generalized linear and similar models via penalized maximum likelihood, particularly, lasso and elastic-net.

Installation instructions

Preparations

Installing glmnet involves compiling source code. Therefore, ensure that the following flags are set in $HOME/.R/Makevars:

cat ~/.R/Makevars

CXX14=g++
CXX17=g++
CXX14FLAGS += -std=c++14
CXX17FLAGS += -std=c++17
CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option

If necessary, the appropriate compiler flags can be set by running the following lines of code:

mkdir -p ~/.R
echo "CXX14=g++" > ~/.R/Makevars
echo "CXX17=g++" >> ~/.R/Makevars
echo "CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option" >> ~/.R/Makevars
echo "CXX14FLAGS += -std=c++14" >> ~/.R/Makevars
echo "CXX17FLAGS += -std=c++17" >> ~/.R/Makevars

Since installing glmnet involves compiling, we start an interactive session on one of the compute nodes:

salloc -n 1 -t 30  -p dev_single

Install glmnet

# Load the R software module:
module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3

# Install the glmnet package from within R session
R -q

R> install.packages("glmnet")

Test the installation

As a quick test of the installation of glmnet the following lines of code can be run:

# Run a quick test
> library(glmnet)
> data(QuickStartExample)
> x <- QuickStartExample$x
> y <- QuickStartExample$y
> fit <- glmnet(x, y)
> print(fit)