ggplot.evmSim

ggtraceplots

ggdensplots

ggacfplots

Diagnostic plots for the Markov chains in an evmSim object

hplot

Statistical extreme value modelling of threshold excesses, maxima
and multivariate extremes. Univariate models for threshold excesses and maxima
are the Generalised Pareto, and Generalised Extreme Value model respectively.
These models may be fitted by using maximum (optionally penalised-)likelihood,
or Bayesian estimation, and both classes of models may be fitted with covariates
in any/all model parameters. Model diagnostics support the fitting process.
Graphical output for visualising fitted models and return level estimates is
provided. For serially dependent sequences, the intervals declustering algorithm
of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
diagnostic support to aid selection of threshold and declustering horizon.
Multivariate modelling is performed via the conditional approach of Heffernan
and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
threshold selection and to diagnose estimation convergence.

Harry Southworth

texmex

Statistical Modelling of Extreme Values

Yiannis Papastathopoulos

Alec Stephenson

Stuart Coles

ggplot.evmSim function

<dl><dt>data</dt>
<dd>An object of class 'evmSim'.</dd>
<dt>mapping, environment</dt>
<dd>ignored</dd>
<dt>which.plots</dt>
<dd>Which plots to produce. Density plots correspond
to 1, trace plots of the Markov chains to 2 and autocorrelation
function plots to 3.</dd>
<dt>chain</dt>
<dd>An integer indicating which chain to plot (only relevant if there
is more than 1 chain). Defaults to 1. If you ran multiple chains, you
should look at diagnostics for all of them.</dd>
<dt>denscol</dt>
<dd>Colour for the density plots. Defaults to 'dark blue'.</dd>
<dt>acfcol</dt>
<dd>Colour for the ACF plots. Defaults to 'light blue'.</dd>
<dt>plot.it</dt>
<dd>Whether or not to actually print the plots. Defaults
to <code>plot.it=TRUE</code>. If <code>plot.it=FALSE</code>, you might
want to control the layout. Do this with
<code>do.call("grid.arrange", c(plots, ncol=2))</code>, for example,
where <code>plots</code> is the objected returned by
<code>ggplot.evmSim</code>.</dd>
<dt>...</dt>
<dd>Additional arguments to <code>ggplot</code>, currently unused.</dd></dl>

Arguments

Diagnostic plots for the Markov chains in an evmSim object — ggplot.evmSim

<dl>

<dt>data</dt>
<dd>An object of class 'evmSim'.</dd>


<dt>mapping, environment</dt>
<dd>ignored</dd>


<dt>which.plots</dt>
<dd>Which plots to produce. Density plots correspond
to 1, trace plots of the Markov chains to 2 and autocorrelation
function plots to 3.</dd>


<dt>chain</dt>
<dd>An integer indicating which chain to plot (only relevant if there
is more than 1 chain). Defaults to 1. If you ran multiple chains, you
should look at diagnostics for all of them.</dd>


<dt>denscol</dt>
<dd>Colour for the density plots. Defaults to 'dark blue'.</dd>


<dt>acfcol</dt>
<dd>Colour for the ACF plots. Defaults to 'light blue'.</dd>


<dt>plot.it</dt>
<dd>Whether or not to actually print the plots. Defaults
to <code>plot.it=TRUE</code>. If <code>plot.it=FALSE</code>, you might
want to control the layout. Do this with
<code>do.call("grid.arrange", c(plots, ncol=2))</code>, for example,
where <code>plots</code> is the objected returned by
<code>ggplot.evmSim</code>.</dd>


<dt>...</dt>
<dd>Additional arguments to <code>ggplot</code>, currently unused.</dd>

</dl>

ggplot.evmSim: Diagnostic plots for the Markov chains in an evmSim object

Description

Usage

Arguments