summary.fdr.bama

<code>summary.fdr.bama</code> summarizes the <code>beta.m</code> estimates from
<code>fdr.bama</code> and for each mediator generates an overall estimate,
credible interval, posterior inclusion probability (PIP), and PIP threshold
for significance controlling for the specified false discovery rate (FDR).

Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. Bayesian Mediation
Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
Song et al (2020) <arXiv:2009.11409>,
relies on two Bayesian sparse linear mixed models to simultaneously analyze
a relatively large number of mediators for a continuous exposure and outcome
assuming a small number of mediators are truly active. This sparsity
assumption also allows the extension of univariate mediator analysis by
casting the identification of active mediators as a variable selection
problem and applying Bayesian methods with continuous shrinkage priors on
the effects.

Michael Kleinsasser

bama

High Dimensional Bayesian Mediation Analysis

Alexander Rix

Mike Kleinsasser

Yanyi Song

summary.fdr.bama function

<dl><dt>object</dt>
<dd>An object of class "bama".</dd>
<dt>rank</dt>
<dd>Whether or not to rank the output by posterior inclusion
probability. Default is TRUE.</dd>
<dt>ci</dt>
<dd>The credible interval to calculate. <code>ci</code> should be a length 2
numeric vector specifying the upper and lower bounds of the CI. By
default, <code>ci = c(0.025, .975)</code>.</dd>
<dt>fdr</dt>
<dd>False discovery rate. By default, it is set to whatever the
<code>fdr</code> of <code>object</code> is. However, it can be changed to recalculate
the PIP cutoff threshold.</dd>
<dt>filter</dt>
<dd>Whether or not to filter out mediators with PIP less than the
PIP threshold.</dd>
<dt>...</dt>
<dd>Additional optional arguments to <code>summary</code></dd></dl>

Arguments

Summarize objects of type "fdr.bama" — summary.fdr.bama

<dl>

<dt>object</dt>
<dd>An object of class "bama".</dd>


<dt>rank</dt>
<dd>Whether or not to rank the output by posterior inclusion
probability. Default is TRUE.</dd>


<dt>ci</dt>
<dd>The credible interval to calculate. <code>ci</code> should be a length 2
numeric vector specifying the upper and lower bounds of the CI. By
default, <code>ci = c(0.025, .975)</code>.</dd>


<dt>fdr</dt>
<dd>False discovery rate. By default, it is set to whatever the
<code>fdr</code> of <code>object</code> is. However, it can be changed to recalculate
the PIP cutoff threshold.</dd>


<dt>filter</dt>
<dd>Whether or not to filter out mediators with PIP less than the
PIP threshold.</dd>


<dt>...</dt>
<dd>Additional optional arguments to <code>summary</code></dd>

</dl>

summary.fdr.bama: Summarize objects of type "fdr.bama"

Description

Usage

Value

Arguments