Hi everyone, I have been constructing simple linear models of seabird colony size as a function of habitat availability. Given that my data obviously contain a spatial component, I would like to check whether both my response and residuals exhibit any spatial auto-correlation. I understand that I can test for this using Moran's I, calculated for pairs of colonies separated into different distance classes. However, this presents a number of problems:
1. All of the distance based methods I have come across for defining nearest neighbours require a matrix of point coordinates. The species I am working with do not fly over land, so the Euclidean or greater circle distance between colonies is not really appropriate. Hence, I have computed a matrix of the 'at sea' distance between all colonies. Is there any way to pass this to a function that defines nearest neighbours? 2. I only have data for 48 colonies and they are clustered in space. As such, I suspect I will end up with either many distance classes with none or very few pairs of colonies in them or just one or two distance classes with a larger number of pairs of colonies. Is there any rule of thumb for how many data are required for a reasonable estimate of Moran's I? Indeed, is Moran's I even appropriate in this case? Sorry not to include any code, but that doesn't seem appropriate to my question. Kind regards, Ewan Wakefield (PhD Student) British Antarctic Survey High Cross Madingley Road Cambridge CB3 0ET UK tel. +44(0)1223 221215 website www.antarctica.ac.uk _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
