cv_param_est {adaptest} | R Documentation

## Compute data-adaptive parameter estimate for a single cross-validation fold

### Description

Compute data-adaptive parameter estimate for a single cross-validation fold

### Usage

    cv_param_est(fold, data, parameter_wrapper, absolute, negative, n_top,
      learning_library, Y_name, A_name, W_name)

### Arguments

`fold` | fold output from `origami`
`data` | entire training data
`parameter_wrapper` | user-defined function
`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
`n_top` | integer value for the number of candidate covariates to generate using the data-adaptive estimation algorithm
`learning_library` | character of `SuperLearner` library
`Y_name` | (character) `colnames` of all biomarkers
`A_name` | (character) `colnames` of treatment
`W_name` | (character) `colnames` of all baseline covariates

### Value

`data_adaptive_index` (integer vector) rank for each gene

`index_grid` (integer matrix) gene index from rank 1 to rank K

`psi_est` estimand of DE for rank 1 to rank K genes

`EIC_est` estimand of EIC for rank 1 to rank K genes

[Package adaptest version 1.6.1 Index]