Elastic Net regression models combine both the L1 and L2 penalties of lasso and ridge regression. There are two penalty terms, lambda and alpha. Lambda is a complexity parameter and alpha is a balance between lasso and ridge.
The cv.glmnet
function in glmnet
will perform cross validation to find the value of lambda for a given value of
alpha. cv.glmnet
does not search over values of alpha. The ensr package
builds a grid of alpha and lambda values and, using cross-validation, suggests
preferable values for both lambda and alpha.
After installing this package we encourage you to read the vignette to see examples.
vignette("ensr-examples", package = "ensr")
ensr is on CRAN.
install.packages('ensr', repos = 'https://cran.rstudio.com')
Install the development version from github:
if ('remotes' %in% rownames(installed.packages())) {
install.packages('remotes', repos = "https://cran.rstudio.com")
}
remotes::install_github("dewittpe/ensr", build_opts = c("--no-resave-data"))
There are several ways you can install ensr. If you are working on a Windows machine you will need to have Rtools installed.
There are detailed instructions for cloning the repo in the CONTRIBUTING.md
file. After cloning use the makefile to build, check, and install the ensr
package, e.g.,
make install
Please read the CONTRIBUTING.md
file. There are details on the how to clone
the repo and the structure of this package.