cv_param_est: Compute data-adaptive parameter estimate for a single...

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

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

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