The goal of sparsegl is to fit regularization paths for sparse
group-lasso penalized learning problems. The model is typically fit for
a sequence of regularization parameters
The main focus of this package is for the case where the loglikelihood
corresponds to Gaussian or logistic regression. But we also provide the
ability to fit arbitrary GLMs using stats::family() objects. Details
may be found in Liang, Cohen, Sólon Heinsfeld, Pestilli, and McDonald
(2024).
You can install the released version of sparsegl from CRAN with:
install.packages("sparsegl")You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("dajmcdon/sparsegl")set.seed(1010)
n <- 100
p <- 200
X <- matrix(data = rnorm(n * p, mean = 0, sd = 1), nrow = n, ncol = p)
eps <- rnorm(n, mean = 0, sd = 1)
beta_star <- c(
rep(5, 5), c(5, -5, 2, 0, 0),
rep(-5, 5), c(2, -3, 8, 0, 0), rep(0, (p - 20))
)
y <- X %*% beta_star + eps
groups <- rep(1:(p / 5), each = 5)
fit1 <- sparsegl(X, y, group = groups)
plot(fit1, y_axis = "coef", x_axis = "penalty", add_legend = FALSE)Liang, X., Cohen, A., Sólon Heinsfeld, A., Pestilli, F., and McDonald,
D.J. 2024. “sparsegl: An R Package for Estimating Sparse Group Lasso.”
Journal of Statistical Software 110(6), 1–23.
https://doi.org/10.18637/jss.v110.i06.