index_spliter

Split the sample index into n_folds many groups so that we can perform cross-fitting

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

index_spliter function

<dl><dt>array</dt>
<dd>Sample index. Usually just an array from 1 to the number of samples in one group.</dd>
<dt>n_folds</dt>
<dd>Number of splits</dd></dl>

Arguments

Split the sample index into n_folds many groups so that we can perform cross-fitting — index_spliter

<dl>

<dt>array</dt>
<dd>Sample index. Usually just an array from 1 to the number of samples in one group.</dd>


<dt>n_folds</dt>
<dd>Number of splits</dd>

</dl>

index_spliter: Split the sample index into n_folds many groups so that we can perform cross-fitting

Description

Usage

Value

Arguments