rank_DE: Compute ranking of biomarkers by sorting effect sizes

Description Usage Arguments Value Examples

View source: R/generate_parameter_wrapper.R

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

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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

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