print.bama

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

print.bama function

<dl><dt>x</dt>
<dd>An object of class 'bama'.</dd>
<dt>...</dt>
<dd>Additional arguments to pass to print.data.frame or summary.bama</dd></dl>

Arguments

Printing bama objects — print.bama

<dl>

<dt>x</dt>
<dd>An object of class 'bama'.</dd>


<dt>...</dt>
<dd>Additional arguments to pass to print.data.frame or summary.bama</dd>

</dl>

print.bama: Printing bama objects

Description

Usage

Arguments