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\name{RFfit}
\alias{RFfit}
%\alias{mleRF}% obsolete
\alias{RFfit.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{
RFfit(model, x, y = NULL, z = NULL, T = NULL, grid=NULL, data,
lower = NULL, upper = NULL, methods,
sub.methods, optim.control = NULL, users.guess = NULL,
distances = NULL, dim, transform = NULL, params=NULL, ...)
}
\arguments{
\item{model,params}{\argModel
All parameters that are set to \code{NA} will be estimated;
see the examples below.
Type \code{\link{RFgetModelNames}(type="variogram")} to get all options
for \code{model}.
}
\item{x}{\argX}
\item{y,z}{\argYz}
\item{T}{\argT}
\item{grid}{\argGrid}
\item{data}{\argData}
\item{lower}{\argLower}
\item{upper}{\argUpper}
\item{methods}{\argFitmethods}
\item{sub.methods}{\argFitsubmethods. See Details.}
\item{users.guess}{\argUsersguess}
\item{distances,dim}{\argDistances}
\item{optim.control}{\argOptimcontrol}
\item{transform}{\argTransform}
\item{...}{\argDots}
}
\details{
For details on the simulation methods see
\itemize{
\item \link{fitgauss} for \link[=RPgauss]{Gaussian random fields}
\item \link{fitgauss} for \link[=RFformula]{linear models}
}
If \code{x}-coordinates are not given, the function will check
\code{data} for \code{NA}s and will perform imputing.
The function has many more options to tune the optimizer,
see \command{\link{RFoptions}} for details.
If the model defines a Gaussian random field, the options
for \code{methods} and \code{submethods} are currently
\code{"ml"} and \code{c("self", "plain", "sqrt.nr", "sd.inv", "internal")},
respectively.
}
\note{
\itemize{
\item
An important optional argument is \code{boxcox} which indicates
a Box-Cox transformation; see \code{boxcox} in \command{\link{RFoptions}}
and \code{RFboxcox} for details.
\item
Instead of \command{\link[stats]{optim}}, other optimisers can be used,
see \link{RFfitOptimiser}.
\item
Several advanced options can be found in sections \sQuote{General
options} and \sQuote{fit} of \command{\link{RFoptions}}.
\item
In particular, \code{boxcox}, \code{boxcox_lb}, \code{boxcox_ub}
allow Box-Cox transformation.
\item
This function does not depend on the value of
\command{\link{RFoptions}}\code{()$PracticalRange}.
The function \code{RFfit} always uses the standard specification
of the covariance model as given in \command{\link{RMmodel}}.
}
}
\value{
The result depends on the logical value of
\code{\link[=RFoptions]{spConform}}.
If \code{TRUE}, an S4 object is created. In case the model indicates
a Gaussian random field, an
\link[=RFfit-class]{RFfit} object is created.
If \code{spConform=FALSE}, a list is returned.
In case the model indicates
a Gaussian random field, the details are given in \link{fitgauss}.
}
\references{
\itemize{
\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
\seealso{
\command{\link{RFfitOptimiser}},
\command{\link{RFlikelihood}},
\command{\link{RFratiotest}},
\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")
% to do bsp mit box cox
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=100) #1000
#########################################################
## estimation; 'NA' means: "to be estimated" ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=2) +
RMtrend(mean=NA)
RFfit(estmodel, data=d)
#########################################################
## coupling alpha and beta ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=NA) +
RMtrend(NA)
RFfit(estmodel, data=d, transform = NA) ## just for information
trafo <- function(a) c(a[1], rep(a[2], 2))
fit <- RFfit(estmodel, data=d,
transform = list(c(TRUE, TRUE, FALSE), trafo))
print(fit)
print(fit, full=TRUE)
\dontshow{\dontrun{
#########################################################
## Estimation with fixed sill (variance + nugget ##
## equals a given constant) ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=NA) +
RMnugget(var=NA) + RMtrend(mean=NA)
RFfit(estmodel, data=d, fit.sill=1, fit.optim_var_elimination="try")
#########################################################
## estimation in a anisotropic framework ##
x <- y <- (1:3)/4
model <- RMexp(Aniso=matrix(nc=2, c(4,2,-2,1)), var=1.5)
d <- RFsimulate(model, x=x, y=y, n=n)
estmodel <- RMexp(Aniso=matrix(nc=2, c(NA,NA,-2,1)), var=NA) +
RMtrend(mean=NA)
RFfit(estmodel, data=d, fit.nphi=20)
#########################################################
## AN EXAMPLE HOW TO USE OF PARAMETER 'transform' ##
## estimation of coupled parameters (first column of ##
## the matrix 'Aniso' has identical entries) ##
# source("RandomFields/tests/source.R")
RFfit(estmodel, data=d, transform=list()) # shows positions of NAs
f <- function(param) param[c(1,2,2)]
RFfit(estmodel, data=d, transform=list(c(TRUE, TRUE, FALSE), f))
}}
\dontshow{RFoptions(modus_operandi="normal")}
\dontshow{FinalizeExample()}}
\keyword{spatial}
\keyword{optimize}
|
