From: Mikhail Matz <matz at utexas.edu>
Subject: Re: [R-sig-ME] MCMCglmm poisson / not poisson
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