[R-sig-Geo] search,grids,SA significance test

[Trevor Doerksen]

Hi,

I'm new to the mailing list and I see there is a large repository of 
info in the archives. Is there any way to search it, to find answers to 
my potentially redundant questions?

I have some training in IDRISI's GIS and have used R for just over a 
year. I've started using the gstat package in R and worked through some 
of the excellent examples in 1) Pebesma and Bivand. 2005. S classes and 
methods for spatial data: the sp package.  2) Pebesma. 2006. The meuse 
data set: a tutorial for gstat R package. 3) Pebesma. 2004. 
Multivariable geostatistics in S: the gstat package. Computers & 
Geosciences. 30:683-691.

I have two specific questions:
1) I'm having trouble creating a grid of regularly spaced points that 
gstat will accept for use with the krige function. Also, I don't know 
how to mask a specific area (like the nice grid in the meuse data set) 
of interest within that grid. Does anyone have examples/references not 
listed above? Below is one attempt at some code which didn't work. NOTE: 
My samples are irregularly spaced and I don't need predictions over the 
entire rectangular grid.

#FIRST attempt
x=seq(246000,606000,1000)
length(x)    #361
y=seq(4851000,5084000,1000)
length(y)    #234
pts.4.grd=expand.grid(x,y)    # resolution = 1km^2; size = 361*234 = 84474
names(pts.4.grd)=c('x','y')
str(pts.4.grd)    # dataframe
grd.pts = SpatialPixels(SpatialPoints(pts.4.grd))
str(grd.pts)    #SpatialPixels
grd = as(grd.pts, "SpatialGrid")
str(grd)    #X=Var1, Y=Var2

# variogram fit not shown. Trying 'universal kriging', with a N-S trend 
removed.
kr1.f2.v2=krige(bv~UTMN83+UTMN83*UTMN83,locations=~UTME83+UTMN83,data=all,grd,model=fit2.v2)
#doesn't work with the following error. Grid and sample data not same size?
#Error in gstat.formula.predict(d$formula, newdata, na.action = 
na.action) :
#        NROW(locs) != NROW(X): this should not occur
#In addition: Warning messages:
#1: 'newdata' had 84474 rows but variable(s) found have 440 rows
#2: 'newdata' had 84474 rows but variable(s) found have 440 rows

2) Is there a test to compute the significance of spatial 
autocorrelation in a variogram? The biological signal I'm modelling in 
my variograms is very weak, with a huge nugget. How large can the nugget 
be before it negates the spatially specific weights for kriging, thus 
rendering the prediction close to an inverse distance weighting scheme?

Thanks,

Trevor.