bayesMRMApp

Call Shiny to show the results of Bayesian analysis of
multivariate receptor modeling in a web-based application.
This object contains<ul>
<li>plots of the posterior means and 95% posterior intervals of parameters in
an object of class <code>bmrm</code>.</li>
<li>tables of the posterior means of parameters in
an object of class <code>bmrm</code>.</li>
<li>tables of the posterior quantiles of parameters in
an object of class <code>bmrm</code>, for prob=(0.025, 0.05, 0.25, 0.5, 0.75, 0.95, 0.975).</li>
<li>tables of convergence diagnostics of parameters in
an object of class <code>bmrm</code>.</li>
<li>3-dimensional dynamic principal component plots of data (Y) and
source profiles (rows of the estimated source composition matrix P)
in an object of class <code>bmrm</code>. The plot can be rotated by moving the cursor.</li>
<li>trace plots and ACF plots of the first 6 elements of a parameter in an
 object of class <code>bmrm</code>.</li>
</ul>

Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.

Man-Suk Oh

bayesMRM

Bayesian Multivariate Receptor Modeling

Eun-Kyung Lee

Eun Sug Park

bayesMRMApp function

<dl><dt>x</dt>
<dd>an object of class <code>bmrm</code>, the output of the <code>bmrm</code> function</dd></dl>

Arguments

Shiny App for exploring the results of Bayesian multivariate receptor modeling — bayesMRMApp

<dl>

<dt>x</dt>
<dd>an object of class <code>bmrm</code>, the output of the <code>bmrm</code> function</dd>

</dl>

bayesMRMApp: Shiny App for exploring the results of Bayesian multivariate receptor modeling

Description

Usage

Value

Arguments