Variance of prediction in Kriging

Maurizio,

you can use the "index" argument for that. Following your example in
?interpolate:

library(raster)
library(gstat)

r <- raster(system.file("external/test.grd", package="raster"))
data(meuse)
coordinates(meuse) <- ~x+y
projection(meuse) <- projection(r)

gCoK <- gstat(NULL, 'log.zinc', log(zinc)~1, meuse)
gCoK <- gstat(gCoK, 'elev', elev~1, meuse)
gCoK <- gstat(gCoK, 'cadmium', cadmium~1, meuse)
gCoK <- gstat(gCoK, 'copper', copper~1, meuse)
coV <- variogram(gCoK)
plot(coV, type='b', main='Co-variogram')
coV.fit <- fit.lmc(coV, gCoK, vgm(model='Sph', range=1000))
coV.fit
plot(coV, coV.fit, main='Fitted Co-variogram')
coK <- interpolate(r, coV.fit)
coKvar <- interpolate(r, coV.fit, index=2)


unfortunately x <- interpolate(r, coV.fit, index=1:2)  does not (yet) work.

Robert

On Wed, Jun 26, 2013 at 11:34 PM, Maurizio Marchi

Hallo everybody.
Using the krige() function of the gstat package for ordinary and universal
kriging and the predict() function of the same package for ordinary and
universal cokriging I obtain the prediction of the variable and also the
variance of prediction. Is possible to do the same with the raster package,
maybe with an option of the interpolate() function?
Thanks,

Maurizio Marchi
Ph.D student


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Maurizio Marchi
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