# A quetion

[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I posted a question to the R-Mixed-Model mailing list but have not had any responses yet:**Shige's Research Blog**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

—————————————————————————————

Dear All, I wonder if anybody has tried to make glmmBUGS work with JAGS. My attempt was not successful. Here is the simple example I copied from the glmmBUGS Vignettes: ------------------------ library(MASS) data(bacteria) bacterianew <- bacteria bacterianew$yInt = as.integer(bacterianew$y == "y") levels(bacterianew$trt) <- c("placebo", "drug", "drugplus") library(glmmBUGS) bacrag <- glmmBUGS(formula = yInt ~ trt + week, data=bacterianew, effects = "ID", modelFile="model.bug", family="bernoulli") names(bacrag$ragged) source("getInits.R") startingValues = bacrag$startingValues ------------------------ With WinBUGS, it runs well: ------------------------ library(R2WinBUGS) bacResult1 = bugs(bacrag$ragged, getInits, model.file="model.bug", n.chain=3, n.iter=2000, n.burnin=100, parameters=names(getInits()), n.thin=10) ------------------------ But with JAGS, I got error message "Error in FUN(50L[[1L]], ...) : invalid first argument" ------------------------ library(R2jags) jags.parms=names(getInits()) bacResult = jags(data=bacrag$ragged, n.chain=3, n.iter=2000, model.file=model.bug) ------------------------ I hope somebody can help me figuring out how to make this work. Many thanks. Best, Shige---------------------------------------------------------------------------------------

Here is the answer from Jens Åströ

---------------------------------------------------------------------------------------

Hi!

Not an expert by any means, but I got it to run by doing this:

1) change inprod2 to inprod in the model file (JAGS does not have

inprod2 function)

2) change ~dflat() to other uninformative prior, e.g.

~dnorm(0.0,1.0E-6) (Jags does not have dflat distribution)

3) specify/compile the model with e.g.

bac.jags<-jags.model("model.bug",data=bacrag$ragged,n.chains=4)

4) update it, update(bac.jags,1000)

5) Collect coda samples,

bacResult<-coda.samples(temp,names(getInits()),n.iter=10000,thin=10)

This seemed to converge well, (gelman.diag(bacResult))

Good luck

----------------------------------------------------------------------------------------

It works.

To

**leave a comment**for the author, please follow the link and comment on their blog:**Shige's Research Blog**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.