fields.grid {fields} | R Documentation |

##
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

`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])))
## Not run:
# 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
## End(Not run)
# the nice surface
## Not run:
surface( look)
US( add=TRUE, col="white")
## End(Not run)

[Package

*fields* version 8.4-1

Index]