plot,SimInf_pmcmc-method

Display the (approximate) posterior distributions obtained from
 fitting a particle Markov chain Monte Carlo algorithm, or the
 corresponding trace plots.

Provides an efficient and very flexible framework to
conduct data-driven epidemiological modeling in realistic large
scale disease spread simulations. The framework integrates
infection dynamics in subpopulations as continuous-time Markov
chains using the Gillespie stochastic simulation algorithm and
incorporates available data such as births, deaths and movements
as scheduled events at predefined time-points. Using C code for
the numerical solvers and 'OpenMP' (if available) to divide work
over multiple processors ensures high performance when simulating
a sample outcome. One of our design goals was to make the package
extendable and enable usage of the numerical solvers from other R
extension packages in order to facilitate complex epidemiological
research. The package contains template models and can be extended
with user-defined models. For more details see the paper by
Widgren, Bauer, Eriksson and Engblom (2019)
<doi:10.18637/jss.v091.i12>. The package also provides
functionality to fit models to time series data using the
Approximate Bayesian Computation Sequential Monte Carlo
('ABC-SMC') algorithm of Toni and others (2009)
<doi:10.1098/rsif.2008.0172> or the Particle Markov Chain Monte
Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010)
<doi:10.1111/j.1467-9868.2009.00736.x>.

Stefan Widgren

SimInf

A Framework for Data-Driven Stochastic Disease Spread
Simulations

Robin Eriksson

Stefan Engblom

Pavol Bauer

Thomas Rosendal

Ivana Rodriguez Ewerlöf

Attractive Chaos 

plot,SimInf_pmcmc-method function

<dl><dt>x</dt>
<dd>The <code>SimInf_pmcmc</code> object to plot.</dd>
<dt>y</dt>
<dd>The trace of all variables and logPost are plotted when
<code>y = "trace"</code> or <code>y = ~trace</code>, else the posterior
distributions are plotted. Default is to plot the posterier
distributions.</dd>
<dt>start</dt>
<dd>The start iteration to remove some burn-in
iterations. Default is <code>start = 1</code>.</dd>
<dt>end</dt>
<dd>the last iteration to include. Default is <code>NULL</code>
which set <code>end</code> to the last iteration in the chain.</dd>
<dt>thin</dt>
<dd>keep every <code>thin</code> iteration after the
<code>start</code> iteration. Default is <code>thin = 1</code>, i.e., keep
every iteration.</dd>
<dt>...</dt>
<dd>Additional arguments affecting the plot.</dd></dl>

Arguments

Display the PMCMC posterior distribution — plot,SimInf_pmcmc-method

<dl>

<dt>x</dt>
<dd>The <code>SimInf_pmcmc</code> object to plot.</dd>


<dt>y</dt>
<dd>The trace of all variables and logPost are plotted when
<code>y = "trace"</code> or <code>y = ~trace</code>, else the posterior
distributions are plotted. Default is to plot the posterier
distributions.</dd>


<dt>start</dt>
<dd>The start iteration to remove some burn-in
iterations. Default is <code>start = 1</code>.</dd>


<dt>end</dt>
<dd>the last iteration to include. Default is <code>NULL</code>
which set <code>end</code> to the last iteration in the chain.</dd>


<dt>thin</dt>
<dd>keep every <code>thin</code> iteration after the
<code>start</code> iteration. Default is <code>thin = 1</code>, i.e., keep
every iteration.</dd>


<dt>...</dt>
<dd>Additional arguments affecting the plot.</dd>

</dl>

plot,SimInf_pmcmc-method: Display the PMCMC posterior distribution

Description

Usage

Arguments