Hi, I am running a model with count data and one categorical predictor (simple model for me to understand it fully), I did in R a glm like this: glm(Recruitment~Depth, family=poisson). I get the coefficientes and confidence intervals and all is ok. But then I want to do the same model with Bayesian stats, here is my code:
model { for (i in 1:232) { Recruitment[i]~dpois(lambda[i]) log(lambda[i])<-a+b[Depth[i]]*Depth[i] } a~dnorm(0,0.000001) b[1]~dnorm(0,0.000001) b[2]~dnorm(0,0.000001) b[3]~dnorm(0,0.0000001) } list(a=0, b=c(0,0,0)) I have two problems: 1) the resulting credible intervals for the coefficients (a, b1, b2 and b3) are HUGE don t make any reasonable sense; 2) Using OpenBugs and Winbugs I get different results, if anyone can help me I appreciate a lot your time, thanks Guillermo [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.