coef.cv.sparsegl {sparsegl}

Extract coefficients from a cv.sparsegl object.

Description

This function etracts coefficients from a cross-validated sparsegl() model, using the stored "sparsegl.fit" object, and the optimal value chosen for lambda.

Usage

## S3 method for class 'cv.sparsegl'
coef(object, s = c("lambda.1se", "lambda.min"), ...)

Arguments

object

Fitted cv.sparsegl() object.

s

Value(s) of the penalty parameter lambda at which coefficients are desired. Default is the single value s = "lambda.1se" stored in the CV object (corresponding to the largest value of lambda such that CV error estimate is within 1 standard error of the minimum). Alternatively s = "lambda.min" can be used (corresponding to the minimum of cross validation error estimate). If s is numeric, it is taken as the value(s) of lambda to be used.

...

Not used.

Value

The coefficients at the requested value(s) for lambda.

See Also

cv.sparsegl() and predict.cv.sparsegl().

Examples

n <- 100
p <- 20
X <- matrix(rnorm(n * p), nrow = n)
eps <- rnorm(n)
beta_star <- c(rep(5, 5), c(5, -5, 2, 0, 0), rep(-5, 5), rep(0, (p - 15)))
y <- X %*% beta_star + eps
groups <- rep(1:(p / 5), each = 5)
fit1 <- sparsegl(X, y, group = groups)
cv_fit <- cv.sparsegl(X, y, groups)
coef(cv_fit, s = c(0.02, 0.03))