regression kriging, newdata parameter

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 at data)[1]/3
   >valid.id <- sample(1:dim(casa032009 at data)[1], 

180)                            

   >casa032009.val <- casa032009[valid.id,]  #valiadtion data set
   >str(casa032009.val)                                                      
    >casa032009.cal <-  casa032009[!is.element(1:dim(casa032009 at 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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