Juliann,

For a p-value for the global Moran I think you could do a permutation test,
something like this:

library(raster)
r <- raster(nrows=10, ncols=10)
r[] <- 1:ncell(r)
M <- Moran(r)

s <- r
n <- 100
m <- rep(NA, n)
for (i in 1:n) {
  values(s) <- sample(values(s))
  m[i] <- Moran(s)
}
p <- sum(m > M) / n


This may take a while for a large raster. I do not know how you determine p
values for the local measure. Have a look at spdep.

Robert

On Tue, Feb 26, 2013 at 1:01 PM, Empty Empty <phytophthorasb@yahoo.com>wrote:

> Hi,
>
> I am trying to do some spatial analysis on raster data sets. I am able to
> calculate global and local Moran's I. But I am trying to figure out how to
> get z-values and significance levels. The output of local Moran is a raster
> map of Moran's I values (I believe). I'd like to be able to identify, not
> just where there are clusters, but where those are significant, so another
> raster of p-values would be ideal. Any ideas?
>
> library(raster)
> r<- raster("sample.tif")  #import rasters from geotif files
> Moran(r)     #gives global Moran's I value, but not p-value
> MLr<-MoranLocal(r)     #local Moran's I - output is a raster map
> plot(MLr)
>
> Thanks a lot,
> Juliann
>
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