efdr_search

Given a vector of probabilities, this function finds the probability 
threshold that matches a target expected false discovery rate as closely
as possible.

Implements the Bayesian FDR control described by
Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
Allows optimisation and visualisation of expected error rates based on
tail posterior probability tests.
Based on code written by Catalina Vallejos for BASiCS, see
Beyond comparisons of means: understanding changes in gene expression at the
single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.

Alan O'Callaghan

bayefdr

Bayesian Estimation and Optimisation of Expected False Discovery
Rate

Catalina Vallejos

efdr_search function

<dl><dt>probs</dt>
<dd>Vector of probabilities.</dd>
<dt>target_efdr</dt>
<dd>Numeric scalar specifying the expected false discovery 
rate to match.</dd>
<dt>min_threshold</dt>
<dd>Minimum probability threshold. If the optimal 
probability threshold is below this number, it is rejected and 
<code>min_threshold</code> is used instead.</dd>
<dt>prob_thresholds</dt>
<dd>Vector for probability thresholds to scan,
with the aim of finding the threshold that matches the target EFDR.</dd></dl>

Arguments

Bayesian EFDR optimisation. — efdr_search

<dl>

<dt>probs</dt>
<dd>Vector of probabilities.</dd>


<dt>target_efdr</dt>
<dd>Numeric scalar specifying the expected false discovery 
rate to match.</dd>


<dt>min_threshold</dt>
<dd>Minimum probability threshold. If the optimal 
probability threshold is below this number, it is rejected and 
<code>min_threshold</code> is used instead.</dd>


<dt>prob_thresholds</dt>
<dd>Vector for probability thresholds to scan,
with the aim of finding the threshold that matches the target EFDR.</dd>

</dl>

efdr_search: Bayesian EFDR optimisation.

Description

Usage

Value

Arguments

Examples