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sparseglThe 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 \(\lambda\). Such estimators minimize
\[ -\ell(\beta | y,\ \mathbf{X}) + \lambda(1-\alpha)\sum_{g\in G} ||\beta_g||_2 + \lambda\alpha ||\beta||_1. \]
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.
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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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