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\name{RFcrossvalidate}
\alias{RFcrossvalidate}
\alias{print.crossvalidate}
\alias{summary.crossvalidate}
\alias{print.summary.crossvalidate}
%\alias{mleRF}% obsolete
\alias{RFcrossvalidate.default}
\title{Fitting model parameters to spatial data (regionalised variables)
and to linear (mixed) models}
\description{
The function estimates arbitrary parameters of
a random field specification with various methods.
Currently, the models to be fitted can be
\itemize{
\item{\link[=RPgauss]{Gaussian random fields}}
\item{\link[=RFformula]{linear models}}
}
The fitting of max-stable random fields and others
has not been implemented yet.
}
\usage{
RFcrossvalidate(model, x, y=NULL, z=NULL, T=NULL, grid=NULL, data,
params, lower=NULL, upper=NULL, method="ml",
users.guess=NULL, distances=NULL, dim, optim.control=NULL,
transform=NULL, full = FALSE, ...)
}
\arguments{
\item{model,params}{\argModel }
\item{x}{\argX}
\item{y,z}{\argYz}
\item{T}{\argT}
\item{grid}{\argGrid}
\item{data}{\argData}
\item{lower}{\argLower}
\item{upper}{\argUpper}
\item{method}{
Single method to be used for estimating, either
one of the \code{methods} or one of the \code{sub.methods}
see \command{\link{RFfit}}
}
\item{users.guess}{\argUsersguess}
\item{distances,dim}{\argDistances}
\item{optim.control}{\argOptimcontrol}
\item{transform}{\argTransform}
\item{full}{logical.
If \code{TRUE} then cross-validation is also performed
for intermediate models used in
\code{RFfit} (if any).
}
\item{...}{\argDots}
}
\section{Methods}{
\describe{
\item{print}{prints the summary}
\item{summary}{gives a summary}
}
}
\note{
An important option is \code{cross_refit} that determines
whether the model is refitted for each location left out.
Default is \code{FALSE}. See also \command{\link{RFoptions}}.
}
\value{
An object of the \code{\link{class}} \code{"RFcrossvalidate"} which is
a list with the following components, cf. \command{xvalid} in the
package \pkg{geoR} :
\item{data}{the original data. }
\item{predicted}{the values predicted by cross-validation. }
\item{krige.var}{the cross-validation prediction variance. }
\item{error}{the differences \code{data - predicted value}. }
\item{std.error}{the errors divided by the square root of the
prediction variances. }
\item{p}{
In contrast to \pkg{geoR} the p-value is returned,
i.e. the probability
that a difference with absolute value larger than the absolute
value of the actual difference is observed.
A method for \code{summary} returns summary statistics for the errors
and standard errors similar to \pkg{geoR}.
If \code{cross_refit = TRUE} and \code{detailed_output = TRUE}
the returned object also contains a \code{fitted} which is
a list of fitted models.
}
}
\references{
\itemize{
\item Ribeiro, P.J., Jr. and Diggle, P.J (2014) R package \pkg{geoR}.
\item Burnham, K. P. and Anderson, D. R. (2002)
\emph{Model selection and Multi-Model Inference: A Practical
Information-Theoretic Approach.}
2nd edition. New York: Springer.
}
}
\me
\note{This function does not depend on the value of
\command{\link{RFoptions}}\code{()$PracticalRange}.
The function \code{RFcrossvalidate} always uses the standard specification
of the covariance model as given in \command{\link{RMmodel}}.
}
\seealso{
\command{\link{RFratiotest}}
\command{\link{RFfit}}
\command{\link{RMmodel}},
\code{\link[=RandomFields-package]{RandomFields}},
\command{\link{weather}}.
}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
% options(error=recover)
% source("RandomFields/tests/source.R")
## currently disabled!
\dontshow{\dontrun{
## See also papers.jss14.rd !!!!
RFoptions(modus_operandi="sloppy")
#########################################################
## simulate some data first
points <- 100
x <- runif(points, 0, 3)
y <- runif(points, 0, 3) ## random points in square [0, 3]^2
model <- RMgencauchy(alpha=1, beta=2)
d <- RFsimulate(model, x=x, y=y, grid=FALSE, n=n=100) #better n=1000
#########################################################
## estimation; 'NA' means: "to be estimated"
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=2) +
RMtrend(mean=NA)
RFcrossvalidate(estmodel, data=d)
}}
\dontshow{RFoptions(modus_operandi="normal")}
\dontshow{FinalizeExample()}}
\keyword{spatial}
|
