evaluate_influence_function_multi_factor

Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().

Provides interpretable High-dimensional Mean Comparison methods (HMC). For example, users can use them to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, we focus on the interplay between features and are interested in those that are predictive of the group label. The methods are valid frequentist tests and give sparse estimates indicating which features contribute to the test results.

Tianyu Zhang

High Dimensional Mean Comparison with Projection and
Cross-Fitting

evaluate_influence_function_multi_factor function

<dl><dt>cross_fitting_sample_1</dt>
<dd>Group 1 sample. Each row is a subject and each column corresponds to a feature.</dd>
<dt>cross_fitting_sample_2</dt>
<dd>Group 2 sample. Each row is a subject and each column corresponds to a feature.</dd>
<dt>nuisance_collection</dt>
<dd>A collection of nuisance quantities estimated using "nuisance" samples. It is the output of estimate_nuisance_pc().</dd>
<dt>num_latent_factor</dt>
<dd>Number of principle components to be considered.</dd></dl>

Arguments

Calculate the test statistics on the left-out samples. Called in debiased_pc_testing(). — evaluate_influence_function_multi_factor

<dl>

<dt>cross_fitting_sample_1</dt>
<dd>Group 1 sample. Each row is a subject and each column corresponds to a feature.</dd>


<dt>cross_fitting_sample_2</dt>
<dd>Group 2 sample. Each row is a subject and each column corresponds to a feature.</dd>


<dt>nuisance_collection</dt>
<dd>A collection of nuisance quantities estimated using "nuisance" samples. It is the output of estimate_nuisance_pc().</dd>


<dt>num_latent_factor</dt>
<dd>Number of principle components to be considered.</dd>

</dl>

evaluate_influence_function_multi_factor: Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().

Description

Usage

Value

Arguments