I have a point pattern data set (SPECIES.ppp) of stem locations for a species, and I want to see if they depend on several Environmental variables such as light and soil properties (working one at a time, for now, generally named Envir, of class im, which was obtained using IDW of sample locations evenly distributed throughout the study region). I realize that since I'm working on the assumption that my variables are continuously varying, I should use KS-tests rather than X-squared quadrat-based tests.
I would like to use the KS test as outlined in the spatstat notes, but I am confused about the difference in null models for the below tests: 1) KS=kstest(X=SPECIES.ppp,covariate=Envir) #Using ppp and covariate 2) fit=ppm(SPECIES.ppp,~E , covariates=list(E=Envir)) KSfit=kstest(model=fit,covariate=Envir) # Using ppm and covariate I get slightly different results (though both p-values are very low). Thank you! Erika -- ------------------------------------------- Erika Mudrak Graduate Student Department of Botany University of Wisconsin-Madison 430 Lincoln Dr Madison WI, 53706 608-265-2191 mud...@wisc.edu http://botany.wisc.edu/waller/lab/Mudrak/ _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
