Extract model coefficients from a sparsegl object.

Computes the coefficients at the requested value(s) for lambda from a sparsegl() object.

Usage

# S3 method for class 'sparsegl'
coef(object, s = NULL, ...)

Arguments

object

Fitted sparsegl() object.

s

Value(s) of the penalty parameter lambda at which coefficients are required. Default is the entire sequence.

...

Not used.

Value

The coefficients at the requested values for lambda.

Details

s is the new vector of lambda values at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices.

See also

sparsegl() and predict.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)
coef(fit1, s = c(0.02, 0.03))
#> 21 x 2 sparse Matrix of class "dgCMatrix"
#>                      s1           s2
#> (Intercept)  0.01260826  0.007013546
#> V1           4.91161061  4.809113387
#> V2           4.70205144  4.640598215
#> V3           4.73672003  4.572029571
#> V4           4.86000086  4.742085450
#> V5           4.76470639  4.691196254
#> V6           4.67390340  4.509768389
#> V7          -4.82460910 -4.674258142
#> V8           1.98354148  1.949724063
#> V9           0.06887575  0.106108315
#> V10         -0.13768353 -0.146588081
#> V11         -4.84355149 -4.748980429
#> V12         -4.70936925 -4.596326983
#> V13         -4.84370729 -4.742405135
#> V14         -4.81357902 -4.689338016
#> V15         -4.93502502 -4.864416765
#> V16          .           .          
#> V17          .           .          
#> V18          .           .          
#> V19          .           .          
#> V20          .           .