https://github.com/cran/fields
Tip revision: 8858bc1c5b6cf7e2c206025a6e8a427ebd7cb91b authored by Douglas Nychka on 17 August 2023, 21:02:31 UTC
version 15.2
version 15.2
Tip revision: 8858bc1
fields.grid.Rd
%#
%# fields is a package for analysis of spatial data written for
%# the R software environment.
%# Copyright (C) 2022 Colorado School of Mines
%# 1500 Illinois St., Golden, CO 80401
%# Contact: Douglas Nychka, douglasnychka@gmail.edu,
%#
%# 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
%# the Free Software Foundation; either version 2 of the License, or
%# (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.
%#
%# You should have received a copy of the GNU General Public License
%# along with the R software environment if not, write to the Free Software
%# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
%# or see http://www.r-project.org/Licenses/GPL-2
%##END HEADER
%##END HEADER
\name{fields.grid}
\alias{mKrig.grid}
\title{
Using MKrig for predicting on a grid.
}
\description{
This is an extended example for using the sparse/fast interpolation
methods in mKrig to evaluate a Kriging estimate on a large grid.
}
\details{
\code{mKrig} is a flexible function for surface fitting using
a spatial process model. It can also exploit sparse matrix methods forlarge data sets by using a compactly supported covariance.
The example below shows how ot evaluate a solution on a big grid. (Thanks to Jan Klennin for this example.)
}
\examples{
x<- RMprecip$x
y<- RMprecip$y
Tps( x,y)-> obj
# make up an 80X80 grid that has ranges of observations
# use same coordinate names as the x matrix
glist<- fields.x.to.grid(x, nx=80, ny=80) # this is a cute way to get a default grid that covers x
# convert grid list to actual x and y values ( try plot( Bigx, pch="."))
make.surface.grid(glist)-> Bigx
# include actual x locations along with grid.
Bigx<- rbind( x, Bigx)
# evaluate the surface on this set of points (exactly)
predict(obj, x= Bigx)-> Bigy
# set the range for the compact covariance function
# this will involve less than 20 nearest neighbors that have
# nonzero covariance
#
V<- diag(c( 2.5*(glist$lon[2]-glist$lon[1]),
2.5*(glist$lat[2]-glist$lat[1])))
\dontrun{
# this is an interplotation of the values using a compact
# but thin plate spline like covariance.
mKrig( Bigx,Bigy, cov.function="wendland.cov",k=4, V=V,
lambda=0)->out2
# the big evaluation this takes about 45 seconds on a Mac G4 latop
predictSurface( out2, nx=400, ny=400)-> look
}
# the nice surface
\dontrun{
surface( look)
US( add=TRUE, col="white")
}
}
\keyword{hplot}