rank_DE {adaptest} | R Documentation

## Compute ranking of biomarkers by sorting effect sizes

### 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)

[Package adaptest version 1.6.1 Index]