A caterpillar plot is a horizontal plot of 3 quantiles of selected
distributions. This may be used to produce a caterpillar plot of
posterior samples (parameters and monitored variables) from an object
either of class demonoid, demonoid.hpc, iterquad,
laplace, pmc, vb, or a matrix.
caterpillar.plot(x, Parms=NULL, Title=NULL)This required argument is an object of class demonoid,
codedemonoid.hpc, iterquad, laplace, pmc,
vb, or a \(S \times J\) matrix of \(S\) samples
and \(J\) variables. For an object of class demonoid, the
distributions of the stationary posterior summary (Summary2)
will be attempted first, and if missing, then the parameters of all
posterior samples (Summary1) will be plotted. For an object
of class demonoid.hpc, stationarity may differ by chain, so
all posterior samples (Summary1) are used. For an object of
class laplace or vb, the distributions in the
posterior summary, Summary, are plotted according to the
posterior draws, sampled with sampling importance resampling in the
SIR function. When a generic matrix is supplied,
unimodal 95% HPD intervals are estimated with the
p.interval function.
This argument accepts a vector of quoted strings to be matched for
selecting parameters and monitored variables for plotting (though
all parameters are selected when a generic matrix is supplied). This
argument defaults to NULL and selects every parameter for
plotting. Each quoted string is matched to one or more parameter
names with the grep function. For example, if the user specifies
Parms=c("eta", "tau"), and if the parameter names are
beta[1], beta[2], eta[1], eta[2], and tau, then all parameters will
be selected, because the string eta is within beta.
Since grep is used, string matching uses regular
expressions, so beware of meta-characters, though these are
acceptable: ".", "[", and "]".
This argument accepts a title for the plot.
Caterpillar plots are popular plots in Bayesian inference for
summarizing the quantiles of posterior samples. A caterpillar plot is
similar to a horizontal boxplot, though without quartiles, making it
easier for the user to study more distributions in a single plot. The
following quantiles are plotted as a line for each parameter: 0.025 and
0.975, with the exception of a generic matrix, where unimodal 95% HPD
intervals are estimated (for more information, see
p.interval). A vertical, gray line is included at zero.
For all but class demonoid.hpc, the median appears as a black
dot, and the quantile line is black. For class demonoid.hpc, the
color of the median and quantile line differs by chain; the first
chain is black and additional chains appear beneath.
IterativeQuadrature,
LaplaceApproximation,
LaplacesDemon,
LaplacesDemon.hpc,
PMC,
p.interval,
SIR, and
VariationalBayes.
# NOT RUN {
#An example is provided in the LaplacesDemon function.
# }
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