summary.bama

summary.bama summarizes the 'beta.m' estimates from <code>bama</code> and generates
an overall estimate, credible interval, and posterior inclusion probability.

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

Arguments

Summarize objects of type "bama" — summary.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>...</dt>
<dd>Additional optional arguments to <code>summary</code></dd>

</dl>

summary.bama: Summarize objects of type "bama"

Description

Usage

Value

Arguments