Thank you vary much Edzer,
 actually I experimented the same problem (before RK) trying to apply external 
drift kriging.
Your comment makes me feel better (after months spent thinking how to solve the 
problem).
The only pseudo-solution I've found has been to apply a 'idw' on the covariate 
variable over the grid, setting parameter to have less smooth as possible and 
then i tried to apply the KDE

casa.grid_res <- idw(res~1, casa032009.cal, casa.grid, nmax=24, maxdist=20, 
idp=2.5)
 k.grid.KED <- krige(sm ~ res, loc=casa032009.cal, newdata= casa.grid_res,   
model=vrmf)

However this was not satisfactory,because target variable variances were still 
too smoothed.
---------
Giuseppe

>I would take a very good look at the regression kriging literature (or
>that of universal kriging, or external drift kriging -- terms that were
>there before regression kriging came up). It looks like the method
>cannot be applied in your case.
--
Edzer

On 02/23/2011 08:51 PM, giuseppe calamita wrote:
> Hi, 
> I want to perform spatial interpolation of soil moisture (here named 'sm')  
> using regression kriging (RK) following the example given by Tom Hengl 
>http://spatial-analyst.net/wiki/index.php?title=Spatial_prediction_of_soil_moisture)
>)
>  and compare results with OK. 
> 
> The covariate is the soil electrical resistivity (her named 'res') and is 
>stored 
>
> in the same data.frame of the target variable.
> sm and res data were collected on a regular grid 200x60 m with a 5m sampling 
> step and the coordinates are given in ED50, UTM zone33. CRS.
> Searching on the sig-Geo archive i discovered  that other peoples had more or 
> less the same problem as mine, but there are some differences.
> All the examples i read used the meuse.grid data set which  already stores 
> covariates (dist, soils, ffreq...) and coordinates at an  higher spatial 
> resolution than the target variable sample. This is not  my case. because res 
>is 
>
> sampled at the points of sm.
> I had to define the grid by myself. 
> 
> #read data 
>> casa032009  <- read.csv("C:/data/morfeo/morfeo_data/casa_032009.csv")
>> str(casa032009)
> 'data.frame':   533 obs. of  9 variables:
>  $ objectid: int  489 490 491 492 493 494 495 496 1 2 ...
>  $ row     : int  185 180 175 170 165 160 155 150 195 190 ...
>  $ col     : Factor w/ 13 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 2 2 ...
>  $ key     : Factor w/ 533 levels "A0","A10","A100",..: 21 20 19 18 17 16 15 14 
>
> 64 63 ...
>  $ ka      : num  23.4 19 14.4 14.5 16.6 16.8 14.2 14.8 16.7 21.5 ...
>  $ sm      : num  37.3 32 26.7  26.7 29 29.3 26.5 27.1 29.2 35.2 ...
>  $ x       : num  288097 288102 288107 288112 288117 ...
>  $ y       : num  4754051 4754051 4754052 4754052 4754053 ...
>  $ res     : num  55.1 56.3 63 62.9 57.2 ...
> #explicit spatial object
>> coordinates(casa032009) <- c("x", "y")
> # divide the data set in calibration and  validation groups
>   > dim(casa032009@data)[1]/3
>    >valid.id <- sample(1:dim(casa032009@data)[1], 
> 180)                            
> 
>    >casa032009.val <- casa032009[valid.id,]  #valiadtion data set
>    >str(casa032009.val)                                                      
>     >casa032009.cal <-  casa032009[!is.element(1:dim(casa032009@data)[1], 
> valid.id),]   #calibration data set, almost 350 points
>    >str(casa032009.cal)
> #regression model
>   >lm.casa <-  lm(sm ~ res, data= as.data.frame(casa032009.cal))
>    >summary(lm.casa) 
> #variogram analysis
>> v.rk <- variogram(residuals(lm.casa)~1, casa032009.cal)
>   > plot(v.rk, pl=T)
>    >vm.rk <- vgm(psill=16, "Exp", range=9, nugget=2) #eye-ball fit
>    >plot(v.rk, pl=T, model=vm.rk)
>    >vmf.rk <- fit.variogram(v.rk, vm.rk) #statistical fit
>    >vmf.rk
>   >plot(v.rk, pl=T, model=vmf.rk)
>> bbox(casa032009)
>> (min.x <- bbox(casa032009)["x","min"]-2)
>> (min.y <- bbox(casa032009)["y","min"]-1.75)
>> (max.x  <- bbox(casa032009)["x","max"]+2)
>> (max.y <- bbox(casa032009)["y","max"]+1.75)
>> (cells.x <- (max.x - min.x)/4)
>> (cells.y <- (max.y - min.y)/4)
>> casa.grid <- (GridTopology(c(min.x+2.5, min.y+2.5), c(5, 5), c(cells.x, 
>> cells.y)))
>> casa.grid <- SpatialGrid(casa.grid)
>> str(casa.grid)
> 
> sm.rk <- krige(sm ~ res, location=casa032009.cal, newdata=casa.grid, 
> model=vmf.rk)
> 
> The problem is : I understand the 'newdata' parameter of the krige function 
> needs a SpatialGrifDataFrame containig res data at each grid node.  I don't 
>know 
>
> how create a SpatialGridDataFrames storing res data on points that are 
>different 
>
> from the ones that were sampled.
> 
> Thanks for any help.
> 
> Giuseppe





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