adaptest_old {adaptest} | R Documentation

## OLD Data-Adaptive Algorithm Implementation (for reference only)

### 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 wcai@berkeley.edu, in collaboration with Alan E. Hubbard, with contributions from Nima S. Hejazi.

[Package adaptest version 1.2.0 Index]