Tomislav,

Thank you for the advice and the scripts. It seems that your suggestion will
work well for what I want to do. I'll try that.

Thanks again,

Marcelo

Tomislav Hengl wrote:

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.sp@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.sp@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.sp@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/

- Show quoted text -
-----Original Message-----
From: r-sig-geo-bounces@stat.math.ethz.ch
[mailto:r-sig-geo-bounces@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

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