mcmcsamp with Poisson distribution
Nate Fronk <nrf5017 at ...> writes:
I am using a Before-After Control-Impact study design to look at the effect of natural gas activity on bird species.The interaction in my equation tests for the effect of well pads being placed in the forest.
[snip]
I'm aware I could take the Bayesian route but I just wanted to see if there may be some code out there somewhere to get around this. Below is an example of what my data looks like and my code. Any input would be greatly appreciated. Block Pads Time Abundance 34B25 2 1 8
[snip]
35C74 4 1 15
Revi <- read.table("revibaci2.txt", header = TRUE)
library(lme4)
The following should both be unnecessary, although they don't hurt ...
Revi$Block<-as.factor(Revi$Block) Revi$Pads<-as.numeric(Revi$Pads)
mm1<-lmer(Abundance~Pads*Time+(1|Block),family=poisson,data=Revi)
mcmcsamp has never (as far as I know) worked for GLMMs (i.e. family not equal to the default "gaussian")
mms1<- mcmcsamp(mm1,1000) HPDinterval(mcmcsamp(mm1,n=1000))
An alternative to try:
library(glmmADMB)
mm2 <- glmmadmb(Abundance~Pads*Time+(1|Block),
family="poisson",data=Revi,mcmc=TRUE)