Get an augmented set based on the next-most significant variables

Source: R/get_augmented_set.R

Based on the adjusted p-values from a FWER-controlling procedure and a more general error rate for which control is desired (e.g., generalized FWER, proportion of false positives, or FDR), augment the set based on FWER control with the next-most significant variables.

get_augmented_set(
  p_values = NULL,
  num_rejected = 0,
  alpha = 0.05,
  quantity = "gFWER",
  q = 0.05,
  k = 1
)

Arguments

p_values

the adjusted p-values.

num_rejected

the number of rejected null hypotheses from the base FWER-controlling procedure.

alpha

the significance level.

quantity

the quantity to control (i.e., "gFWER", "PFP", or "FDR").

q

the proportion for FDR or PFP control.

k

the number of false positives for gFWER control.

Value

a list of the variables selected into the augmentation set. Contains the following values:

  • set, a numeric vector where 1 denotes that the variable was selected and 0 otherwise

  • k, the value of k used

  • q_star, the value of q-star used

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$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# estimate SPVIMs (using simple library and V = 2 for illustration only)
set.seed(20231129)
library("SuperLearner")
est <- vimp::sp_vim(Y = y, X = x, V = 2, type = "auc", SL.library = "SL.glm", 
                    cvControl = list(V = 2))
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: One or more original estimates < 0; returning zero for these indices.
# get base set
base_set <- get_base_set(test_statistics = est$test_statistic, p_values = est$p_value, 
                         alpha = 0.2, method = "Holm")
# get augmented set
augmented_set <- get_augmented_set(p_values = base_set$p_values, 
                                   num_rejected = sum(base_set$decision), alpha = 0.2, 
                                   quantity = "gFWER", k = 1)
augmented_set$set
#>  [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
# }