get_augmented_set {flevr} | R Documentation |
Get an augmented set based on the next-most significant variables
Description
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.
Usage
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., |
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
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$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# estimate SPVIMs (using simple library and V = 2 for illustration only)
set.seed(20231129)
library("SuperLearner")
est <- vimp::sp_vim(Y = y, X = x, V = 2, type = "auc", SL.library = "SL.glm",
cvControl = list(V = 2))
# get base set
base_set <- get_base_set(test_statistics = est$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