plot.bmrm

plot

Produce plots of the estimated
posterior mean and 95% posterior intervals of A,P, Sigma
based on the MCMC samples in <code>bmrm</code>.

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

plot.bmrm function

<dl><dt>x</dt>
<dd>an object of class <code>bmrm</code>, the output of the function <code>bmrm</code></dd>
<dt>type</dt>
<dd>name of a variable (default="P").
It should be one of "P"(source composition or profile matrix P),
"A"(source contribution matrix A), "both" (both P and A), "Sigma" (error variance).</dd>
<dt>...</dt>
<dd>arguments to be passed to methods</dd></dl>

Arguments

Produce plots of the parameter estimates — plot.bmrm

<dl>

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


<dt>type</dt>
<dd>name of a variable (default="P").
It should be one of "P"(source composition or profile matrix P),
"A"(source contribution matrix A), "both" (both P and A), "Sigma" (error variance).</dd>


<dt>...</dt>
<dd>arguments to be passed to methods</dd>

</dl>

plot.bmrm: Produce plots of the parameter estimates

Description

Usage

Value

Arguments

Details