I have fitted a mixed Poisson Point Process Model to Several Point Patterns using the
mppm function from the R package
I am able to fit the model to a full model with several predictors which are
im objects in which pixels contain either distance to or density of the predictor.
It seems possible to do some model selection if the model does not include a random part. On the contrary, the introduction on the Random component seems to complicate things and that available methods stop working.
How model selection should be performed when analysing Poisson processes on replicated point pattern? In particular how should it be performed on mixed models? Is it possible
While this post arise from technical issues in using
spatstat functions, it doesn't want to be only a programming question, rather I'm interested in finding solution for do model selection in this context.
Following, a reproducible example. Data can be found here:
library(spatstat) load('data4stack-model_sel-20200516.RData') urc # without Random factor # ------------------------ m50a<-mppm(urc_ppp~ pref_dist+kelp50+bushy50+ota50+star_dens+dsh_dens + are_dens+ dol_dens, data=urc) m150a<-mppm(urc_ppp~ pref_dist+kelp150+bushy150+ota150+dsh_dens + are_dens+ dol_dens, data=urc) # Warning messages: # 2: Values of the covariates ‘dsh_dens’, ‘are_dens’, ‘dol_dens’ were NA or undefined at 0.64% (34 out of 5344) of the quadrature points. Occurred while executing: # 3: data contain duplicated points
Regarding the warning on duplicated points: I personally digitized the points, double checked them, and verified their coordinates. There are no duplicated points, however for some reason (rounding?) it give this warning.
Regarding the warning on NA in covariates: I believe it is due to the fact that some covariates are not defined in some areas where point of the response variable may be. I could do the analysis without those points, so if the model fitting does not include those points I have no problems. I guess that other NAs, my originate in some discrepancy introduced when discretizing into pixels to produce the
In anycase these 2 warnings do not seem to affect model selection for models with no random component. See the rest:
anova(m50a, test='LRT') m50b<-update(m50a, .~.-ota50) anova(m50a,m50b, test='LRT') # This works step(m50a) # This seems to work AIC(m50a) extractAIC(m150a) AIC(m50a,m50b,m150a) # Just give AIC for the first model # Work around for AIC-based model comparison ? AIC(m50a); AIC(m50b); AIC(m150a) # with Random factor # -------------------- m50r_a<-mppm(urc_ppp~ pref_dist+kelp50+bushy50+ota50+dsh_dens+are_dens+dol_dens, random=~1|ID,data=urc) AIC(m50r_a) ; extractAIC(m50r_a) # both give NA drop1(m50r_a) # it runs but it doesn't do any selection since AIC are not provided step(m50r_a) # it doesn't even run # hypothesys testing: m50r_b<-update(m50r_a, .~.-are_dens) anova(m50r_a,m50r_b, test='Chisq') # Error in anova.lme(structure(list(modelStruct = structure(list(reStruct = structure(list( : # objects must inherit from classes "gls", "gnls", "lm", "lmList", "lme", "nlme", "nlsList", or "nls" # Error in anova.mppm(m50r_a, m50r_b, test = "Chisq") : anova failed
Any suggestion? Thanks