rank_DE: Compute ranking of biomarkers by sorting effect sizes

In wilsoncai1992/adaptest: Data-Adaptive Statistics for High-Dimensional Multiple Testing

Description

Computes ranking of biomarkers based effect sizes, which are computed by Targeted Minimum Loss-Based Estimation. This function is designed to be called inside adaptest; it should not be run by itself outside of that context.

Usage

rank_DE(Y, A, W, absolute = FALSE, negative = FALSE,
  learning_library = c("SL.glm", "SL.step", "SL.glm.interaction",
  "SL.gam"))

Arguments

Y	(numeric vector) - continuous or binary biomarkers outcome variables
A	(numeric vector) - binary treatment indicator: 1 = treatment, 0 = control
W	(numeric vector, numeric matrix, or numeric data.frame) - matrix of baseline covariates where each column corrspond to one baseline covariate. Each row correspond to one observation
absolute	(logical) - whether or not to test for absolute effect size. If FALSE, test for directional effect. This overrides argument negative.
negative	(logical) - whether or not to test for negative effect size. If FALSE = test for positive effect size. This is effective only when absolute = FALSE.
learning_library	(character vector) - library of learning algorithms to be used in fitting the "Q" and "g" step of the standard TMLE procedure.

Value

an integer vector containing ranks of biomarkers.

Examples

set.seed(1234)
data(simpleArray)
simulated_array <- simulated_array
simulated_treatment <- simulated_treatment
rank_DE(Y = simulated_array,
        A = simulated_treatment,
        W = rep(1, length(simulated_treatment)),
        absolute = FALSE,
        negative = FALSE)

wilsoncai1992/adaptest documentation built on Jan. 6, 2020, 2:33 p.m.