https://github.com/cran/fields
Tip revision: 9ddd6d6d22827db57d1983021d5f85563d1a8112 authored by Douglas Nychka on 29 January 2018, 21:53:33 UTC
version 9.6
version 9.6
Tip revision: 9ddd6d6
predictSE.Krig.Rd
%# fields is a package for analysis of spatial data written for
%# the R software environment .
%# Copyright (C) 2018
%# University Corporation for Atmospheric Research (UCAR)
%# Contact: Douglas Nychka, nychka@ucar.edu,
%# National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000
%#
%# This program is free software; you can redistribute it and/or modify
%# it under the terms of the GNU General Public License as published by
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%# (at your option) any later version.
%# This program is distributed in the hope that it will be useful,
%# but WITHOUT ANY WARRANTY; without even the implied warranty of
%# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
%# GNU General Public License for more details.
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%# along with the R software environment if not, write to the Free Software
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\name{predictSE}
\alias{predictSE}
\alias{predictSE.Krig}
\alias{predictSE.mKrig}
\alias{predictSEUsingKrigA}
\title{
Standard errors of predictions for Krig spatial process estimate
}
\description{
Finds the standard error ( or covariance) of prediction based on a linear
combination of
the observed data. The linear combination is usually the "Best Linear
Unbiased Estimate" (BLUE) found from the Kriging equations.
This statistical computation is done under the assumption that the
covariance function is known.
}
\usage{
predictSE(object, ...)
\method{predictSE}{Krig}(object, x = NULL, cov = FALSE, verbose = FALSE,...)
\method{predictSE}{mKrig}(object, xnew = NULL, Z = NULL, verbose = FALSE, drop.Z
= FALSE, ...)
}
\arguments{
\item{drop.Z}{If FALSE find standard error without including the additional spatial covariates
described by \code{Z}. If TRUE find full standard error with spatial covariates if they are part of the model.}
\item{object}{ A fitted object that can be used to find prediction standard errors. This is usually from fitting a spatial model to data. e.g. a Krig or mKrig object.
}
\item{xnew}{
Points to compute the predict standard error or the prediction
cross covariance matrix.
}
\item{x}{
Same as \code{xnew} -- points to compute the predict standard error or the prediction
cross covariance matrix.
}
\item{cov}{
If TRUE the full covariance matrix for the predicted values is returned.
Make sure this will not be big if this option is used. ( e.g. 50X50 grid
will return a matrix that is 2500X2500!) If FALSE just the marginal
standard deviations of the predicted values are returned. Default is
FALSE -- of course.
}
\item{verbose}{If TRUE will print out various information for debugging.}
\item{\dots}{
These additional arguments passed to the predictSE function.
}
\item{Z}{Additional matrix of spatial covariates used for prediction. These are used to
determine the additional covariance contributed in teh fixed part of the model.}
}
\value{
A vector of standard errors for the predicted values of the Kriging fit.
}
\details{
The predictions are represented as a linear combination of the dependent
variable, Y. Call this LY. Based on this representation the conditional
variance is the same as the expected value of (P(x) + Z(X) - LY)**2.
where
P(x)+Z(x) is the value of the surface at x and LY is the linear
combination that estimates this point. Finding this expected value is
straight forward given the unbiasedness of LY for P(x) and the covariance
for Z and Y.
In these calculations it is assumed that the covariance parameters are fixed.
This is an approximation since in most cases they have been estimated from the
data. It should also be noted that if one assumes a Gaussian field and known
parameters in the covariance, the usual Kriging estimate is the
conditional mean of the field given the data. This function finds the
conditional standard deviations (or full covariance matrix) of the
fields given the data.
There are two useful extensions supported by this function. Adding the
variance to the estimate of the spatial mean if this is a correlation
model. (See help file for Krig) and calculating the variances under
covariance misspecification. The function \code{predictSE.KrigA} uses
the smoother matrix ( A(lambda) ) to find the standard errors or
covariances directly from the linear combination of the spatial
predictor. Currently this is also the calculation in
\code{predictSE.Krig} although a shortcut is used
\code{predictSE.mKrig} for mKrig objects.
}
\seealso{
Krig, predict.Krig, predictSurfaceSE
}
\examples{
#
# Note: in these examples predictSE will default to predictSE.Krig using
# a Krig object
fit<- Krig(ChicagoO3$x,ChicagoO3$y,cov.function="Exp.cov", theta=10) # Krig fit
predictSE.Krig(fit) # std errors of predictions at obs.
# make a grid of X's
xg<-make.surface.grid(
list(East.West=seq(-27,34,,20),North.South=seq(-20,35,,20)))
out<- predictSE(fit,xg) # std errors of predictions
#at the grid points out is a vector of length 400
#reshape the grid points into a 20X20 matrix etc.
out.p<-as.surface( xg, out)
surface( out.p, type="C")
# this is equivalent to the single step function
# (but default is not to extrapolation beyond data
# out<- predictSurfaceSE( fit)
# image.plot( out)
}
\keyword{spatial}
% docclass is function
% Converted by Sd2Rd version 1.21.