This package provides regularization paths for the lasso, group lasso, and sparse-group lasso. The underlying mathematical model is a mixed model, i.e., a model with fixed and random effects. (Whereas it is actually optional to include any fixed effect.)
The sparse-group lasso contains two penalty terms, which are combined
via a mixing parameter
0 <= alpha <= 1. Thus, if the parameter is set
0, the resulting regularization operator is the lasso
or the the group lasso, respectively.
The lasso, group lasso, and sparse-group lasso are implemented via proximal gradient descent
By default, a grid search for the penalty parameter
lambdais performed. Warm starts are implemented to effectively accelerate this procedure.
The step size between consecutive iterations is automatically determined via backtracking line search.
To get the current release version from CRAN, please type:
To get the current development version from github, please type:
# install.packages("devtools") devtools::install_github("jklosa/seagull")
A data set is included and can be loaded:
Furthermore, the following functions are available to the user:
Please load the data as shown in the section above and get started:
## Call the lasso: fit_l <- seagull(y = phenotypes[, 1], Z = genotypes, alpha = 1) ## Call the group lasso: fit_gl <- seagull(y = phenotypes[, 1], Z = genotypes, groups = groups, alpha = 0) ## Call the sparse-group lasso: fit_sgl <- seagull(y = phenotypes[, 1], Z = genotypes, groups = groups)