Marcelo, Surprisingly, I could not find any function in the spatstat package (or splancs package) that specifically derives cross-correlations between multiple point processes:
> data(lansing) > plot(split(lansing)) # distribution of occurrence records for five+1 species; > plot(density(split(lansing)), ribbon = FALSE) # fit stationary marked Poisson process with different intensity for each species: > lansing.ppm <- ppm(lansing, ~marks, Poisson()) > summary(lansing.ppm) ...but this does not say anything about which species are most correlated (and which are negatively correlated). See also "Mark correlation function" in PART V. MARKED POINT PATTERNS: Baddeley, A., 2008. Analysing spatial point patterns in R. CSIRO, Canberra, Australia. http://www.csiro.au/files/files/pn0y.pdf I guess that there is no reason NOT to do what you suggest: > dens.lansing <- density(split(lansing)) > dens.lansing.sp <- as(dens.lansing[[1]], "SpatialGridDataFrame") > names(dens.lansing.sp)[1] <- names(dens.lansing)[1] > for(i in 2:length(dens.lansing)) { dens.lansing...@data[names(dens.lansing)[i]] <- as(dens.lansing[[i]], "SpatialGridDataFrame")$v } > spplot(dens.lansing.sp, col.regions=grey(rev(seq(0,1,0.025)))) > round(cor(log1p(dens.lansing...@data[names(dens.lansing)]), > use="complete.obs"), 2) blackoak hickory maple misc redoak whiteoak blackoak 1.00 0.55 -0.73 -0.64 -0.51 0.23 hickory 0.55 1.00 -0.84 -0.63 -0.52 -0.27 maple -0.73 -0.84 1.00 0.75 0.50 -0.09 misc -0.64 -0.63 0.75 1.00 0.70 0.09 redoak -0.51 -0.52 0.50 0.70 1.00 0.25 whiteoak 0.23 -0.27 -0.09 0.09 0.25 1.00 # PCA: > sp.formula <- as.formula(paste("~", paste("log1p(", names(dens.lansing), ")", > collapse="+"), sep="")) > PCA.sp <- prcomp(sp.formula, scale=TRUE, dens.lansing...@data) > biplot(PCA.sp, arrow.len=0.1, xlabs=rep(".", length(PCA.sp$x[,1])), main="PCA > biplot", ylabs=names(dens.lansing)) which clearly shows that the most positively correlated species are "hickory" and "blackoak", while the most 'competing' species are "maple"/"redoak" and "hickory". HTH T. Hengl http://home.medewerker.uva.nl/t.hengl/ > -----Original Message----- > From: r-sig-geo-boun...@stat.math.ethz.ch > [mailto:r-sig-geo-boun...@stat.math.ethz.ch] On Behalf > Of Marcelo Tognelli > Sent: Friday, October 30, 2009 7:50 PM > To: r-sig-geo@stat.math.ethz.ch > Subject: [R-sig-Geo] Spatial analysis question > > Dear List, > > I have probability maps of the distribution of 4 species of venomous snakes > (raster files output from species distribution modeling software) and point > locality data with information on snake bite events (most of them without > the id of the species involved in the accident). I would like to run an > analysis to see what species correlates best with snake bite events. My idea > is to generate a kernel density raster from the point event data and then do > some kind of spatial correlation against the species distribution maps. > I would greatly appreciate any suggestions on the type of analysis that I > can perform with these data and on the software and/or R package to run it. > > Thanks in advance, > > Marcelo > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo