Adds a Bayesian estimate of the variogram model to a plot typically with an empirical variogram.
The estimate is a chosen summary (mean, mode or mean) of the
posterior distribution returned by the function krige.bayes.
# S3 method for krige.bayes
lines.variomodel(x, summary.posterior, max.dist, uvec,
posterior = c("variogram", "parameters"), ...)A line with the estimated variogram plot is added to the plot in the current graphics device. No values are returned.
an object of the class krige.bayes, typically an output
of the function krige.bayes.
specify which summary of the posterior
distribution should be used as the parameter estimate.
Options are "mean", "median" or
"mode". See DETAILS below.
numerical, the maximum distance for the x-axis.
a numerical vector with support points to compute the
variogram values. Only used if posterior = "variogram".
Defaults to seq(0, max.dist, length = 51).
indicates whether the the variogram line is based on the posterior of the variogram function (default) or the posterior of the model parameters.
Paulo J. Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
The function krige.bayes returns samples from the
posterior distribution of the parameters \((\sigma^2, \phi,
\tau^{2}_{rel})\).
This function allows for two basic options to draw a line with a summary of the variogram function.
for each sample of the parameters the variogram
function is computed at the support points defined in the
argument uvec. Then a function provided by the user in the
argument summary.posterior is used to compute a summary of
the values obtained at each support point.
in this case summaries of the posterior
distribution of the model parameters as “plugged-in” in the
variogram function.
One of the options "mode" (default) ,"median" or "mean"
can be provided in the argument summary.posterior.
The option mode, uses the mode of \((\phi,
\tau^{2}_{rel})\) and the mode of
of \(\sigma^2\) conditional on the modes of the former parameters.
For the options mean and median these summaries are
computed from the samples of the posterior.
Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR/.
lines.variomodel, krige.bayes and lines.
#See examples in the documentation of the function krige.bayes().
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