data_adapt: S3-Style Constructor for Data Adaptive Parameter Class

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

Description

S3-Style Constructor for Data Adaptive Parameter Class

Usage

data_adapt(Y, A, W = NULL, n_top, n_fold, absolute, negative,
  parameter_wrapper, learning_library)

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 correspond to one baseline covariate. Each row correspond to one observation
n_top	(integer vector) - value for the number of candidate covariates to generate using the data-adaptive estimation algorithm.
n_fold	(integer vector) - number of cross-validation folds.
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.
parameter_wrapper	(function) - user-defined function that takes input (Y, A, W, absolute, negative) and outputs a (integer vector) containing ranks of biomarkers (outcome variables). For detail, please refer to the documentation for rank_DE.
learning_library	(character vector) - library of learning algorithms to be used in fitting the "Q" and "g" step of the standard TMLE procedure.

Value

S3 object of class "data_adapt" for data-adaptive multiple testing.

adaptest documentation built on April 28, 2020, 7:24 p.m.