flevr (version 0.0.4)

get_base_set: Get an initial selected set based on intrinsic importance and a base method

Description

Using the estimated intrinsic importance and a base method designed to control the family-wise error rate (e.g., Holm), obtain an initial selected set.

Usage

get_base_set(
  test_statistics = NULL,
  p_values = NULL,
  alpha = 0.05,
  method = "maxT",
  B = 10000,
  Sigma = diag(1, nrow = length(test_statistics)),
  q = NULL
)

Value

the initial selected set, a list of the following:

• decision, a numeric vector with 1 indicating that the variable was selected and 0 otherwise

• p_values, the p-values used to make the decision

Arguments

test_statistics

the test statistics (used with "maxT")

p_values

(used with "minP" or "Holm")

alpha

the alpha level

method

the method (one of "maxT", "minP", or "Holm")

B

the number of resamples (for minP or maxT)

Sigma

the estimated covariance matrix for the test statistics

q

the false discovery rate (for method = "BY")

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$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# estimate SPVIMs (using simple library and V = 2 for illustration only)
set.seed(20231129)
library("SuperLearner")
est <- vimp::sp_vim(Y = y, X = x, V = 2, type = "auc", SL.library = "SL.glm", 
                    cvControl = list(V = 2))
# get base set
base_set <- get_base_set(test_statistics = est$test_statistic, p_values = est$p_value, 
                         alpha = 0.2, method = "Holm")
base_set$decision
# }