flevr (version 0.0.4)

pool_selected_sets: Pool selected sets from multiply-imputed data

Description

Pool the selected sets from multiply-imputed or bootstrap + imputed data. Uses the "stability" of the variables over the multiple selected sets to select variables that are stable across the sets, where stability is determined by presence in a certain fraction of the selected sets (and the fraction must be above the specified threshold to be "stable").

Usage

pool_selected_sets(sets = list(), threshold = 0.8)

Value

a vector denoting the final set of selected variables (1 denotes selected, 0 denotes not selected)

Arguments

sets

a list of sets of selected variables from the multiply-imputed datasets. Expects each set of selected variables to be a binary vector, where 1 denotes that the variable was selected.

threshold

a numeric threshold between 0 and 1 detemining the "stability" of a feature; only features with stability above the threshold after pooling will be in the final selected set of variables.

Examples

# \donttest{
data("biomarkers")
x <- biomarkers[, !(names(biomarkers) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
library("dplyr")
library("SuperLearner")
# do multiple imputation (with a small number for illustration only)
library("mice")
n_imp <- 2
set.seed(20231129)
mi_biomarkers <- mice::mice(data = biomarkers, m = n_imp, printFlag = FALSE)
imputed_biomarkers <- mice::complete(mi_biomarkers, action = "long") %>%
  rename(imp = .imp, id = .id)
# set up a list to collect selected sets
all_selected_vars <- vector("list", length = 5)
for (i in 1:n_imp) {
  # fit a Super Learner using simple library for illustration only
  these_data <- imputed_biomarkers %>%
    filter(imp == i)
  this_y <- these_data$mucinous
  this_x <- these_data %>%
    select(starts_with("lab"), starts_with("cea"))
  this_x_df <- as.data.frame(this_x)
  fit <- SuperLearner::SuperLearner(Y = this_y, X = this_x_df,
                                  SL.library = "SL.glm",
                                  cvControl = list(V = 2),
                                  family = "binomial")
  # do extrinsic selection
  all_selected_vars[[i]] <- extrinsic_selection(
    fit = fit, feature_names = feature_nms, threshold = 5, import_type = "all"
  )$selected
}
# perform extrinsic variable selection
selected_vars <- pool_selected_sets(sets = all_selected_vars, threshold = 1 / n_imp)
feature_nms[selected_vars]
# }