ttScreening (version 1.8)

Rare_Screening: Rare_Screening: resampling-based screening with limma

Description

Rare_Screening: a resampling-based method that repeatedly samples cases and controls with replacement and applies bayes moderated linear modeling (limma) to identify predictors that are consistently significant. The function runs a rare-event resampling procedure on user provided data and returns the selected predictors along with iteration-wise selection counts.

Dr. Abrar maintains and is the author of this functions, for questions contact him at [e-mail].

Usage

Rare_Screening(predictor_list, Outcome, iteration, cut)

Value

A list with:

• final_selection: character vector of selected predictor IDs (selection count >= cut)

• counts: integer vector of selection counts for all predictors (names = predictors)

• sel_mat: p x iteration matrix of 0/1 selections per iteration

• selected: integer indices of the predictors that met cut

Arguments

predictor_list

Numeric matrix of size n x p (rows = subjects, columns = predictors). Column names are treated as predictor IDs; if missing, they will be generated.

Outcome

Integer or logical vector of length n with values in {0,1}.

iteration

Integer, number of bootstrap iterations (e.g., 100).

cut

Integer, selection count threshold (e.g., 70) used to define final selection.

Examples

if (FALSE) {
# predictor_list: n x p matrix; Outcome: 0/1 vector of length n
res <- Rare_Screening(predictor_list, Outcome, iteration = 100, cut = 70)
head(res$final_selection)
}