TopDown_C_binary_subset

Computes a confidence set for the number of false hypotheses among a subset of
using a binary search

Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <arXiv:2108.04731>.

Phillip Mogensen

TMTI

'Too Many, Too Improbable' (TMTI) Test Procedures

TopDown_C_binary_subset function

<dl><dt>LocalTest</dt>
<dd>A function that returns a double in (0, 1).</dd>
<dt>pSub</dt>
<dd>A vector of p-values from the subset of interest.</dd>
<dt>pRest</dt>
<dd>A vector of the remaining p-values.</dd>
<dt>alpha</dt>
<dd>A double indicating the significance level</dd>
<dt>low</dt>
<dd>integer denoting the starting point for the search. Should start at zero.</dd>
<dt>high</dt>
<dd>integer denoting the end point of the search. Should end at pvals.size() - 1.</dd>
<dt>verbose</dt>
<dd>boolean, indicating whether to print progress.</dd></dl>

Arguments

Leading NA — TopDown_C_binary_subset

<dl>

<dt>LocalTest</dt>
<dd>A function that returns a double in (0, 1).</dd>


<dt>pSub</dt>
<dd>A vector of p-values from the subset of interest.</dd>


<dt>pRest</dt>
<dd>A vector of the remaining p-values.</dd>


<dt>alpha</dt>
<dd>A double indicating the significance level</dd>


<dt>low</dt>
<dd>integer denoting the starting point for the search. Should start at zero.</dd>


<dt>high</dt>
<dd>integer denoting the end point of the search. Should end at pvals.size() - 1.</dd>


<dt>verbose</dt>
<dd>boolean, indicating whether to print progress.</dd>

</dl>

TopDown_C_binary_subset: Leading NA

Description

Usage

Arguments