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])))

    # 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
        predict.surface( out2, nx=400, ny=400)-> look

    # the nice surface
    ## Not run:

        surface( look)
        US( add=TRUE, col="white")
    ## End(Not run)

[Package fields version 6.01 Index]