Hello,
in adddtition to what Alex points, I would also recomend function K1K2
in the package ecespa: it implements differences of uivariate and
bivariate Ripley's K-functions (based on spatstat) to test for
aggregation and segregation of point patterns in the way suggested by
Dixon (2002,
Could also take a look at the dbmss package and related publications. I
recently used their M(r) function for something similar to what you
describe.
Marcon, E., Puech, F. Traissac, S. (2012b). Characterizing the relative
spatial structure of point
patterns. International Journal of Ecology,
The code below shows a sample of point locations of Brent Goose on mudflats.
The black points are the subspecies Dark-bellied Brent Goose, and white
points the subspecies Light-bellied Brent Goose:
geese - data.frame(lightLat=c(55.66735, 55.66735, 55.67341, 55.66735,
55.66735),
Hello,
This looks like a job for point pattern statistics. Have a look at the
spatstat package and the related docs. There are functions (pcfcross
e.g.) to compare the spatial distribution of marked point patterns (in
your case the marks will be species name).
Make sure that point pattern
