Question

I want to do a cross-validation for the ca20-Dataset from the geoR
package. With for example the meuse-dataset, this works fine, but for
this dataset, I encounter a strange problem with the dimensions of the
SpatialPointsDataFrame. Maybe you can try this for yourself and explain
why the autoKrige.cv function does not work (I tried several
nfold-values but this only changes the locations-value of the error
message…):

library(geoR)
library(gstat)
library(automap)
data(ca20)
east=ca20$coords[,1]
north=ca20$coords[,2]
concentration=ca20$data
frame=data.frame(east,north)
data=data.frame(concentration)
points<-SpatialPoints(data.frame(east,north),proj4string=CRS(as.character(NA)))
pointsframe<-SpatialPointsDataFrame(points,data, coords.nrs = numeric(0),proj4string = CRS(as.character(NA)), match.ID = TRUE)
krig=autoKrige(pointsframe$concentration~1,pointsframe)
plot(krig)
cv=autoKrige.cv(pointsframe$concentration~1,pointsframe)

I hope someone can reproduce the problem, my R version is 2.15, all packages are up to date (at least not older than a month or so…).

Thanks for your help!!

Answer 1

First, the way you build your SpatialPointsDataFrame can be done more easily:

library(geoR)
library(gstat)
library(automap)

…and build the SPDF:

pointsframe = data.frame(ca20$coords)
pointsframe$concentration = ca20$data
coordinates(pointsframe) = c("east", "north")

The problem you have is in how you use the formula argument. You add the spatial object pointsframe to the formula, in essence putting a vector directly into the formula. You should just use the column name in the formula, like this:

cv=autoKrige.cv(concentration~1,pointsframe)

and it works:

> summary(cv)
            [,1]      
mean_error  -0.01134  
me_mean     -0.0002237
MAE         6.02      
MSE         60.87     
MSNE        1.076     
cor_obspred 0.7081    
cor_predres 0.01343   
RMSE        7.802     
RMSE_sd     0.7041    
URMSE       7.802     
iqr         9.519