Method to find simulation envelopes for association (pair correlation) between adults and juveniles using homogeneous Thomas process in R

Question

I have marked ppp object (shp) that consist of ; x co-ordinate, y co-ordinate, growth ("adult", "juvenile","sapling").

I found simulation envelopes for association (using pair correlation function) between adults and juveniles using homogeneous Poisson process.

aj1<-envelope(shp,pcfcross,nsim = 199,i = "adult", j = "juvenile",savefuns=TRUE) 

Now I want to find the same thing using homogeneous Thomas process. So I fit a homogeneous Thomas process to a point pattern object using

fit1 <- kppm(shp ~1, "Thomas",method ="palm").

Then I tried to find simulation envelopes using

aj2<-envelope(fit1,pcfcross,nsim = 199,i = "adult", j = "juvenile",savefuns=TRUE) 

But this did not work. If anyone has any suggestion or any alternative method to find simulation envelopes for association (using pair correlation function) between adults and juveniles using homogeneous Thomas process, I would be really grateful.

Answer 1

Currently kppm does not handle marked point patterns. You would have got an error message about "cannot handle marked point patterns" when you tried to fit the model kppm(shp ~ 1, "Thomas", method="palm").

This functionality will be added to spatstat sometime in 2016.

Answer 2

Unfortunately I don't think any generic software to estimate multitype Poisson cluster models exists. We hope to add such functionality to spatstat in the not too distant future (but realistically many months from now). I know of some people that have worked on this and you might ask them for code. I think the paper

Jalilian, A., Guan, Y., Mateu, J. and Waagepetersen, R. (2014) Multivariate product-shot-noise Cox models, Biometrics, 71, 1022-1033.

may be very relevant for you and you can ask the authors whether they have any code you can use.

Answer 3

From Spatial Lecture Note, is the following example helpful

require('spatstat')
data("redwood")
X <- redwood
plot(X)
plot(envelope(X))
fit <- kppm(X, ~1, "Thomas")
plot(fit)
plot(envelope(fit))