BwUniCluster2.0/Software/R/Glmnet: Difference between revisions
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== Install <code>glmnet</code> == |
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# Prepare .R directory (if it does not already exists) |
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mkdir -p ~/.R |
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== Test the installation == |
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# Write the following environment variables to Makevars |
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As a quick test of the installation of <code>glmnet</code> the following lines of code can be run: |
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# Skip the 2nd and 3rd commands below if Makevars already consist these variables (1st command shows content of Makevars) |
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cat ~/.R/Makevars |
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echo "CXX17=icpc" >> ~/.R/Makevars |
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echo "CXX17FLAGS=-O3 -fPIC -std=c++17 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars |
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<pre> |
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# Run a quick test |
# Run a quick test |
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> library(glmnet) |
> library(glmnet) |
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Latest revision as of 10:48, 30 October 2024
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)