adaptest_old: OLD Data-Adaptive Algorithm Implementation (for reference...

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

Description

Performs targeted minimum loss-based estimation (TMLE )of a marginal additive treatment effect of a binary point treatment on an outcome. The data-adaptive algorithm is used to perform variable reduction to avoid the disadvantages associated with multiple testing. INTERNAL USE ONLY.

Usage

adaptest_old(Y, A, W = NULL, n_top, n_fold, folds_vec = NULL,
  parameter_wrapper = adaptest::rank_DE, learning_library = c("SL.glm",
  "SL.step", "SL.glm.interaction", "SL.gam", "SL.earth"),
  absolute = FALSE, negative = FALSE)

Arguments

Y	continuous or binary outcome variable
A	binary treatment indicator: 1 = treatment, 0 = control
W	matrix containing baseline covariates
n_top	integer value for the number of candidate covariates to generate using the data-adaptive estimation algorithm
n_fold	integer number of folds to be used for cross-validation
folds_vec	Vector of numeric indicating the validation fold in which a given observation falls for a standard V-fold cross-validation procedure. Default is NULL, in which case a custom cross-validation procedure is used.
parameter_wrapper	function
learning_library	character
absolute	boolean: TRUE = test for absolute effect size. This FALSE = test for directional effect. This overrides argument negative.
negative	boolean: TRUE = test for negative effect size, FALSE = test for positive effect size

Value

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

Author(s)

Wilson Cai [e-mail], in collaboration with Alan E. Hubbard, with contributions from Nima S. Hejazi.

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