hdbm.data

datasets

Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. High dimensional
Bayesian mediation (HDBM), developed by Song et al (2018)
<doi:10.1101/467399>, 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.

Alexander Rix

hdbm

High Dimensional Bayesian Mediation Analysis

Yanyi Song

hdbm.data function

A data.frame with 1000 observations on 102 variables:<dl class="dl-horizontal">
<dt>y</dt><dd>Numeric response variable.</dd>
<dt>a</dt><dd>Numeric exposure variable.</dd>
<dt>m[1-100]</dt><dd>Numeric mediator variables</dd>
</dl>

Format

Synthetic example data for hdbm — hdbm.data

A data.frame with 1000 observations on 102 variables:<dl class='dl-horizontal'>
<dt>y</dt><dd>Numeric response variable.</dd>
<dt>a</dt><dd>Numeric exposure variable.</dd>
<dt>m[1-100]</dt><dd>Numeric mediator variables</dd>
</dl>

hdbm.data: Synthetic example data for hdbm

Description

Usage

Arguments

Format