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 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))
#> 21 x 2 sparse Matrix of class "dgCMatrix"
#>                      s1          s2
#> (Intercept)  0.25058054  0.32798112
#> V1           4.69685440  4.61179702
#> V2           4.87498687  4.80778825
#> V3           4.79092864  4.70788964
#> V4           4.66068791  4.53530689
#> V5           4.63701728  4.47046037
#> V6           4.83517828  4.67359737
#> V7          -4.32100726 -4.09271636
#> V8           1.78185776  1.67848050
#> V9           0.04970934  0.08302102
#> V10          0.03184873  0.07964946
#> V11         -4.77143301 -4.67240455
#> V12         -4.92212465 -4.82991217
#> V13         -4.76996682 -4.59229699
#> V14         -4.95490098 -4.88966650
#> V15         -4.88915656 -4.79944906
#> V16          .           .         
#> V17          .           .         
#> V18          .           .         
#> V19          .           .         
#> V20          .           .