Construct stglmnet backend options — stglmnet

Convenience constructor for the stglmnet= list accepted by lss(method = "stglmnet"). Unknown fields are allowed via ... for forward compatibility.

Usage

stglmnet_options(
  mode = c("cv", "fixed"),
  alpha = 0.2,
  lambda = NULL,
  overlap_strategy = c("none", "multiplicative", "additive", "hybrid", "threshold"),
  pool_to_mean = FALSE,
  pool_strength = 1,
  pool_mean_penalty = 0,
  whiten = c("inherit", "auto", "never", "always"),
  cv_folds = 5L,
  cv_type.measure = c("auto", "mse", "correlation", "reliability", "composite"),
  cv_select = c("optimal", "1se"),
  return_fit = FALSE,
  ...
)

Arguments

mode: "cv" (default) selects lambda by internal cross-validation, while "fixed" uses the supplied lambda sequence or the smallest fitted lambda when no scalar is provided.
alpha: Elastic-net mixing parameter passed to glmnet.
lambda: Optional lambda sequence (or scalar in fixed mode).
overlap_strategy: Trial-overlap penalty mapping. One of "none", "multiplicative", "additive", "hybrid", or "threshold".
pool_to_mean: Logical; reparameterize trial effects into a pooled mean plus orthogonal contrasts.
pool_strength: Penalty multiplier applied to pooled contrasts.
pool_mean_penalty: Penalty applied to the pooled mean coefficient.
whiten: One of "inherit" (default), "auto", "never", or "always". "inherit" uses the top-level prewhiten= argument only.
cv_folds: Number of folds used when mode = "cv".
cv_type.measure: Cross-validation objective.
cv_select: Lambda selection rule in CV mode. "optimal" uses the best-scoring lambda, "1se" applies the one-standard-error rule.
return_fit: Logical; when TRUE, lss(method="stglmnet") returns a list containing beta, fit metadata, and the selected lambda.
...: Additional backend options.

Value

A list with class "fmrilss_stglmnet_options".

Examples

stglmnet_options(mode = "fixed", lambda = 0.05, alpha = 0.5)
#> $mode
#> [1] "fixed"
#> 
#> $alpha
#> [1] 0.5
#> 
#> $lambda
#> [1] 0.05
#> 
#> $overlap_strategy
#> [1] "none"
#> 
#> $pool_to_mean
#> [1] FALSE
#> 
#> $pool_strength
#> [1] 1
#> 
#> $pool_mean_penalty
#> [1] 0
#> 
#> $whiten
#> [1] "inherit"
#> 
#> $cv_folds
#> [1] 5
#> 
#> $cv_type.measure
#> [1] "auto"
#> 
#> $cv_select
#> [1] "optimal"
#> 
#> $return_fit
#> [1] FALSE
#> 
#> attr(,"class")
#> [1] "fmrilss_stglmnet_options" "list"