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 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
>>
>>
>>
>
>
--
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177
EMAIL: [EMAIL PROTECTED]
VOICE: 937-383-0528
FAX: 937-383-0033
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