Hello ,

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. To assess the magnitude and uncertainty in the interaction
parameter I want to generate a MCMC sample from the posterior distribution of
each parameter using the mcmcsamp function and then compute  the
HPD 95% confidence limits of the MCMC samples (HPDinterval). However,
using a Poisson distribution I get "Update not written", I was wondering if
anyone has a suggestion of how to go about this a different way? 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. 

Thanks,
Nate

Block    Pads    Time   
Abundance    
34B25    2   
1    8    
34C43   
6    1    7   

34D33    0    1   
9    
35B54    2   
1    4    
35C44   
5    1    8   

35C74    4    1   
15    


Revi
<- read.table("revibaci2.txt", header =
T)
library(lme4)
summary(Revi)

Revi$Block<-as.factor(Revi$Block)
Revi$Pads<-as.numeric(Revi$Pads)

mm1<-lmer(Abundance~Pads*Time+(1|Block),family=poisson,data=Revi)
summary(mm1)
mms1<- mcmcsamp(mm1,1000)
HPDinterval(mcmcsamp(mm1,n=1000))

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