Wrapper for using Super Learner-based extrinsic selection within stability selection

A wrapper function for Super Learner-based extrinsic variable selection within stability selection, using the stabs package.

SL_stabs_fitfun(x, y, q, ...)

Arguments

x

the features.

y

the outcome of interest.

q

the number of features to select on average.

...

other arguments to pass to SuperLearner.

Value

a named list, with elements: selected (a logical vector indicating whether or not each variable was selected); and path ( a logical matrix indicating which variable was selected at each step).

See also

stabsel for general usage of stability selection.

Examples

# \donttest{
data("biomarkers")
# subset to complete cases for illustration
cc <- complete.cases(biomarkers)
dat_cc <- biomarkers[cc, ]
# use only the mucinous outcome, not the high-malignancy outcome
y <- dat_cc$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# use stability selection with SL (using small number of folds for CV, 
# small SL library and small number of bootstrap replicates for illustration only)
set.seed(20231129)
library("SuperLearner")
sl_stabs <- stabs::stabsel(x = x, y = y,
                           fitfun = SL_stabs_fitfun,
                           args.fitfun = list(SL.library = "SL.glm", cvControl = list(V = 2)),
                           q = 2, B = 5, PFER = 5)
sl_stabs
#> 	Stability Selection with unimodality assumption
#> 
#> Selected variables:
#> institution   lab1_actb 
#>           1           2 
#> 
#> Selection probabilities:
#>            lab1_molecules_score           lab1_telomerase_score 
#>                               0                               0 
#>         lab2_fluorescence_score               lab3_muc3ac_score 
#>                               0                               0 
#>               lab3_muc5ac_score                 lab4_areg_score 
#>                               0                               0 
#>              lab4_glucose_score              lab5_mucinous_call 
#>                               0                               0 
#>          lab5_neoplasia_v1_call          lab5_neoplasia_v2_call 
#>                               0                               0 
#>                   lab6_ab_score                             cea 
#>                               0                               0 
#>   lab1_molecules_neoplasia_call  lab1_telomerase_neoplasia_call 
#>                               0                               0 
#> lab2_fluorescence_mucinous_call         lab4_areg_mucinous_call 
#>                               0                               0 
#>      lab4_glucose_mucinous_call     lab4_combined_mucinous_call 
#>                               0                               0 
#>          lab6_ab_neoplasia_call                        cea_call 
#>                               0                               0 
#>                     institution                       lab1_actb 
#>                               1                               1 
#> 
#> ---
#> Cutoff: 0.7; q: 2; PFER (*):  0.303 
#>    (*) or expected number of low selection probability variables
#> PFER (specified upper bound):  5 
#> PFER corresponds to signif. level 0.0138 (without multiplicity adjustment)
#> 
# }