Extract coefficients from a cv.sparsegl object.

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 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))
#> 21 x 2 sparse Matrix of class "dgCMatrix"
#>                      s1          s2
#> (Intercept)  0.23832573  0.31515438
#> V1           4.70065228  4.61231894
#> V2           4.89000933  4.82578854
#> V3           4.78764378  4.70398207
#> V4           4.64572033  4.51399572
#> V5           4.64049673  4.47952627
#> V6           4.82997559  4.66365908
#> V7          -4.33582168 -4.11167381
#> V8           1.76136889  1.65380262
#> V9           0.02243789  0.05316397
#> V10          0.04610199  0.10186359
#> V11         -4.76159866 -4.65991534
#> V12         -4.90272249 -4.80827862
#> V13         -4.74953262 -4.56960092
#> V14         -4.94328922 -4.87745351
#> V15         -4.87918420 -4.78884130
#> V16          .           .         
#> V17          .           .         
#> V18          .           .         
#> V19          .           .         
#> V20          .           .