l0ara fits sparse generalized linear models using an
adaptive ridge approximation to an L0 penalty.
Install the package from CRAN with:
install.packages("l0ara")Fit a sparse Gaussian model:
library(l0ara)
n <- 100
p <- 40
x <- matrix(rnorm(n * p), n, p)
beta <- c(1, 0, 2, 3, rep(0, p - 4))
y <- x %*% beta + rnorm(n)
fit <- l0ara(x, y, family = "gaussian", lam = log(n))
print(fit)
coef(fit)Select the penalty by cross-validation:
lam <- c(0.1, 0.3, 0.5)
cv_fit <- cv.l0ara(x, y, family = "gaussian", lam = lam, measure = "mse")
print(cv_fit)
coef(cv_fit)
plot(cv_fit)
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.