[R-sig-ME] MCMCglmm poisson / not poisson

[Mikhail Matz]

Interesting!
so each mcmc realization of latent variables is perfectly poisson-compatible, but their mean is not. OK! This totally works for me. 
I now wonder why I expected the means to retain Poisson properties in the first place, actually

Thanks a lot!

Misha

On Aug 29, 2012, at 10:39 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:

> Hi,
> 
> library(MCMCglmm)
> l<-rnorm(100,1,2)
> y<-rpois(100, exp(l))
> 
> dat<-data.frame(y=y)
> 
> m1<-MCMCglmm(y~1, data=dat, family="poisson", pl=TRUE)
> 
> pp.poisson(y, colMeans(m1$Liab))
> 
> # looks bad
> 
> pp.poisson(y, colMeans(m1$Liab)+0.5*apply(m1$Liab, 2, var))
> 
> # looks better
> 
> for(i in 1:1000){
> pp.poisson(y, m1$Liab[i,])
> }
> 
> # looks good
> 
> Cheers,
> 
> Jarrod
> 
> 
> 
> 
> 
> Quoting Mikhail Matz <matz at utexas.edu> on Tue, 28 Aug 2012 22:06:45 -0500:
> 
>> 
>> Hello -
>> 
>> I am playing with ways to justify that the MCMCglmm model fits my data well, which is quite important for me since I am hoping to be able to suggest MCMCglmm-based modeling as a general solution for a particular type of analysis.
>> 
>> I am running "poisson" family on counts data, with two random effects. Following Elston, D. A., R. Moss, et al. (2001). Parasitology 122: 563-569., I am checking whether my lognormal residuals (latent variable minus predicted value) are normally distributed (check), if my random effects (saved with pr=T) are normally distributed (more or less check), and then I try to see if the observed counts really look like Poisson samples based on the latent variables. Again, following Elston et al, I am making a p-p plot using this script (expert coders, please don't judge):
>> 
>> pp.poisson=function(counts,latents) {
>> 	sim=c()
>> 	for(i in 1:length(counts)){
>> 		if (is.na(counts[i])) next
>> 		data=counts[i]
>> 		low=ppois(data,exp(latents[i]))-dpois(data,exp(latents[i]))
>> 		up=ppois(data,exp(latents[i]))
>> 		ss=seq(low,up,(up-low)/100)
>> 		sim=append(sim,sample(ss,1))
>> 	}
>> 	sims=sort(sim)
>> 	xx=(rank(sims)-0.5)/length(sims)
>> 	plot(sims~xx)
>> 	abline(0,1)
>> }
>> 
>> … and unfortunately it looks really ugly, like a very strongly bent  ' ~ ' rather than a line.
>> The little script above seems to work; here is a sanity check:
>> 
>> psim=c()
>> nnn=rnorm(500,10,10)
>> for (i in 1:length(nnn)){
>> 	psim=append(psim,rpois(1,exp(nnn[i])))
>> }
>> pp.poisson(psim,nnn)
>> 
>> I will be extremely grateful for any comments on this.
>> 
>> cheers
>> 
>> Misha
>> UT Austin
>> 
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> 
>> 
> 
> 
> 
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