Implementation of Sparse-group SLOPE (SGS), a sparse-group penalisation regression approach. SGS performs adaptive bi-level selection, controlling the FDR under orthogonal designs. Linear (Gaussian) and logistic (Binomial) regression are supported, both with dense and sparse matrix implementations. Cross-validation functionality is also supported. SGS is implemented using adaptive three operator splitting (ATOS) and the package also contains a general implementation of ATOS.
A detailed description of SGS can be found in F. Feser, M. Evangelou (2023) “Sparse-group SLOPE: adaptive bi-level selection with FDR-control”.
You can install the current stable release from CRAN with
install.packages("sgs")
Your R configuration must allow for a working Rcpp. To install a develop the development version from GitHub run
library(devtools)
install_github("ff1201/sgs")
The code for fitting a basic SGS model is:
library(sgs)
= fit_sgs(X = X, y = y, groups = groups, vFDR=0.1, gFDR=0.1) model
where X
is the input matrix, y
the response
vector, groups
a vector containing indices for the groups
of the predictors, and vFDR
and gFDR
are the
the target variable/group false discovery rates.