pairs.debinfer_result

Plots pairwise correlations of posterior marginals

A Bayesian framework for parameter inference in differential equations.
This approach offers a rigorous methodology for parameter inference as well as
modeling the link between unobservable model states and parameters, and
observable quantities. Provides templates for the DE model, the
observation model and data likelihood, and the model parameters and their prior
distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
to estimate the posterior distributions of the parameters and any derived
quantities, including the model trajectories. Further functionality is provided
to facilitate MCMC diagnostics and the visualisation of the posterior distributions
of model parameters and trajectories.

Philipp H Boersch-Supan

deBInfer

Bayesian Inference for Differential Equations

Leah R Johnson

Sadie J Ryan

pairs.debinfer_result function

<dl><dt>x</dt>
<dd>a deBInfer_result object</dd>
<dt>trend</dt>
<dd>logical, add loess smooth</dd>
<dt>scatter</dt>
<dd>logical, add scatterplot of posterior samples</dd>
<dt>burnin</dt>
<dd>integer, number of samples to discard from start of chain before plotting</dd>
<dt>medians</dt>
<dd>logical, plot marginal medians on contour plot</dd>
<dt>...</dt>
<dd>further arguments to plot.default (the call that draws the scatter/contour plot)</dd></dl>

Arguments

Pairwise posterior marginals — pairs.debinfer_result

<dl>

<dt>x</dt>
<dd>a deBInfer_result object</dd>


<dt>trend</dt>
<dd>logical, add loess smooth</dd>


<dt>scatter</dt>
<dd>logical, add scatterplot of posterior samples</dd>


<dt>burnin</dt>
<dd>integer, number of samples to discard from start of chain before plotting</dd>


<dt>medians</dt>
<dd>logical, plot marginal medians on contour plot</dd>


<dt>...</dt>
<dd>further arguments to plot.default (the call that draws the scatter/contour plot)</dd>

</dl>

pairs.debinfer_result: Pairwise posterior marginals

Description

Usage

Arguments