[R-sig-Geo] Example of universal kriging with R/gstat in GRASS needed

[Roger Bivand]

On Fri, 28 Apr 2006, Thomas Adams wrote:

> Roger,
> 
> This got me further along, but I am encountering a problem with:
> 
> z <- predict(UK_fit, newdata=BMcD_SPx)
> 
> The gstat step works for me, where I have:
> 
> UK_fit<-gstat(formula=temps$temp~dem,data=temps,model=efitted)
> 
> temps has class SpatialPointsDataFrame:
> 
>               coordinates cat      x     y    z temp name
> 1     (-341460, -2154.42)   1 -90.05 38.90  166   63  ALN
> 2       (-198769, 301388)   2 -88.47 41.77  215   67  ARR
> 3       (-334899, -40321)   3 -89.95 38.55  140   66  BLV
> 4       (-240028, 163910)   4 -88.92 40.48  268   69  BMI
> 5       (-187957, 114806)   5 -88.27 40.04  229   64  CMI
> 6     (-351730, -37305.9)   6 -90.15 38.57  126   65  CPS
> 7      (-242424, 98244.7)   7 -88.92 39.87  204   66  DEC
> 8       (-179844, 315889)   8 -88.24 41.91  232   69  DPA
> 9     (-136093, -24538.2)   9 -87.61 38.76  131   68  LWV
> 10     (-278964, -126152)  10 -89.25 37.78  125   66  MDH
> 11      (-140792, 302011)  11 -87.75 41.79  187   73  MDW
> 12      (-364737, 274189)  12 -90.51 41.45  180   73  MLI
> 13     (-190503, 54493.9)  13 -88.28 39.48  219   64  MTO
> 
> and dem has class SpatialGridDataFrame and just consists of grid values.

I think 

fullgrid(dem) <- FALSE

should make a SpatialPixelsDataFrame, but you'll have to make sure the 
name of the dem variable is the same as in the formula.

Roger

> 
> I tried to create a SpatialPixelsDataFrame for predict(), but with (for 
> example):
> 
> m = SpatialPixelsDataFrame(points=meuse.grid[c("x","y")],data=meuse.grid)
> 
> I have nothing like meuse.grid, so this does not work. I can use 
> image(dem), which produces a plot of elevation values. My point is that 
> meuse.grid and my dem files have very different structures.
> 
> I'm not sure where to go to from here.
> 
> Regards,
> Tom
> 
> 
> Roger Bivand wrote:
> > On Thu, 27 Apr 2006, Thomas Adams wrote:
> >
> >   
> >> List:
> >>
> >> I can not seem to work out the syntax for using R/gstat within a GRASS 
> >> 6.1 session to do universal kriging. I have a DEM (elevation data on a 
> >> grid) and point data for temperature; theoretically, the temperatures 
> >> should relate to elevation. So, I am trying to spatially interpolate the 
> >> temperature data based on the elevations at the grid points. How do I 
> >> setup the gstat command in R/gstat (and using spgrass6, of course)? I 
> >> have no trouble reading in my elevation data (DEM) from GRASS and I have 
> >> no problem doing ordinary kriging of my temperature data using 
> >> GRASS/R/gstat.
> >>     
> >
> > What do the data look like? Do you have temperature and elevation at the
> > observation points and elevation over the grid? If temperature is the 
> > variable for which you want to interpolate, then the formula argument in 
> > the gstat() function would be temp ~ elev, data=pointsdata (if a 
> > SpatialPointsDataFrame no need for location= ~ x + y). Then the predict() 
> > step would need a SpatialGridDataFrame object as newdata, with elev as 
> > (one of) the columns in the data slot.
> >
> > An example for the Meuse bank data in Burrough and McDonnell:
> >
> > cvgm <- variogram(Zn ~ Fldf, data=BMcD, width=100, cutoff=1000)
> > uefitted <- fit.variogram(cvgm, vgm(psill=1, model="Exp", range=100, 
> >   nugget=1))
> > UK_fit <- gstat(id="UK_fit", formula = Zn ~ Fldf, data = BMcD, 
> >   model=uefitted)
> > z <- predict(UK_fit, newdata=BMcD_SPx)
> >
> > where BMcD_SPx is a SpatialPixelsDataFrame (the grid has ragged edges) 
> > with flood frequencies in Fldf (actually a factor, but works neatly).
> >
> > Hope this helps,
> >
> > Roger
> >
> >   
> >> Regards,
> >> Tom
> >>
> >>
> >>     
> >
> >   
> 
> 
> 

-- 
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no