[R-sig-Geo] Cross validation fitted models- krige.cv

[Edzer Pebesma]

On 03/27/2014 06:25 PM, Moshood Agba Bakare wrote:
> Dear All,
> Common variogram models were fitted to empirical variogram with the
> following range of spatial dependency obtained.
> *model *                 *range*
> Spherical                14.2
> Gaussian                   7.5
> Exponential                5.5
> Linear                       10.4
> 
> I intend to cross validate the models to determine the best that capture
> the spatial pattern.I thought of defining *maxdist option *to be the value
> of range so the the prediction can be done by the points within the range
> of spatial dependency. Am I doing the right thing? Using range as maxdist,
> will it give me a reliable RMSE and MSDR diagnostic statistics?
> 
> #### Cross validation of the variogram models by ordinary kriging
> sph.kcv <- krige.cv(yield ~ 1, canmod.sp, model = sph.var,nmax = 100, nfold
> = 5, *maxdist = 14.2*)
> gau.kcv <- krige.cv(yield ~ 1, canmod.sp, model = gau.var,nmax = 100, nfold
> = 5,* maxdist = 7.5*)exp.kcv <- krige.cv(yield ~ 1, canmod.sp, model =
> exp.var,nmax = 100, nfold = 5, *maxdist = 5.5*)
>   lin.kcv <- krige.cv(yield ~ 1, canmod.sp, model =    lin.var,nmax = 100,
> nfold = 5, *maxdist=10.4*)
> 

See also my previous reply earlier today -- if you want to have
distances at which spatial correlation is below 5%, take range * 3 for
Exponential and range * sqrt(3) for Gaussian.

As you use ordinary kriging, also observations further away than the
(effective) range play a role in the prediction, so that is not a reason
why they should be ignored.
-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Heisenbergstraße 2, 48149 Münster, Germany. Phone: +49 251
83 33081 http://ifgi.uni-muenster.de GPG key ID 0xAC227795

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