On Thu, 7 Feb 2008, Edzer Pebesma wrote:
> Ilona Naujokaitis-Lewis wrote:
>> NO
>>
>> Dear list-serve,
>> Thanks in advance to all those who help out with the inquiries, it is has
>> helped me numerous times. Here go my questions...
>>
>> I am trying to vary existing landscapes which are composed
Marta Rufino wrote:
> Great!
Hi Marta,
I now have in the example section of gstat.cv help:
# multivariable; thanks to M. Rufino:
meuse.g <- gstat(id = "zn", formula = log(zinc) ~ 1, data = meuse)
meuse.g <- gstat(meuse.g, "cu", log(copper) ~ 1, meuse)
meuse.g <- gstat(meuse.g, model = vgm(1, "Sph
Ilona Naujokaitis-Lewis wrote:
> NO
>
> Dear list-serve,
> Thanks in advance to all those who help out with the inquiries, it is has
> helped me numerous times. Here go my questions...
>
> I am trying to vary existing landscapes which are composed of habitat
> (patches) and non-habitat. My goals ar
Great!
This works wonderfully...maybe would be nice if you add it to the
example in the help page :-)
Further comments in /CV/... from the gstat.cv output, which
cross-validation measures should be considered when establishing the
performance of kriging, in relation to other methods, for examp
That was not the problem, the problem was that you used meuse.g instead
of meuse.fit to pass on to gstat.cv. For meuse.g, you have perfect
correlation between Cu and Zn, so that collocated observations (meaning
a Zn and a Cu observation at each obs location) act as a duplicate in
univarite krig
