This package provides regularization paths for the lasso, (fitted) group lasso, and (fitted) 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 (fitted) sparse-group lasso contains two penalty terms, which are
combined via a mixing parameter
0 <= alpha <= 1. Thus, if the
parameter is set to either
0, the resulting regularization
operator is the lasso or the (fitted) group lasso, respectively.
The lasso, (fitted) group lasso, and sparse-group lasso are implemented via proximal gradient descent.
The fitted sparse-group lasso (fitSGL) is implemented via proximal-averaged gradient descent.
By default, a grid search for the penalty parameter
performed. Warm starts are implemented to effectively accelerate
The step size between consecutive iterations is automatically determined via backtracking line search - except for the fitSGL, which needs a fixed step size to be provided upon its call.
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)
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