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

[Edzer J. Pebesma]

Trevor, a few notes in addition to Roger's answer:

Trevor Doerksen wrote:
> I have two specific questions:
> 1) ...
>  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. 
Depends on where you start. Suppose you have a SpatialPolygons or 
SpatialPolygonsDataFrame object, you can generate a regular grid using 
spsample, or start with makegrid, convert to SpatialPixels, and then use 
overlay the result to obtain NA indices for grid points outside any 
polygon. If you don't have a polygon, you may select grid points within 
a certain distance to nearest observations by interpolating them with a 
search radius; again the NA pattern will reveal grid points too far away.
> 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?
>   
IMHO, insignificant spatial correlation in spatial data will usually 
indicate a too small sample. A large relative nugget may indicate that 
statistics under the independence assumption are reasonable, 
insignificant correlation still does not imply zero correlation.

Large nugget effects lead to predictions equal to the grand (or local) 
mean or trend estimate, not the inverse distance weighted interpolated 
value.
--
Edzer