Documentation for the glm module in jags/rjags?

Does any one run the example without problem?
I download the example and try to run line and seeds from vol1. R crashes.

library(rjags)

Loading required package: coda
Loading required package: lattice
module basemod loaded
module bugs loaded

sessionInfo()

R version 2.12.2 (2011-02-25)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936
[2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936
[3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_People's Republic of China.936
attached base packages:
[1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base
other attached packages:
[1] rjags_2.2.0-4 ? coda_0.14-2 ? ? lattice_0.19-17
loaded via a namespace (and not attached):
[1] grid_2.12.2

library(rjags)

setwd("/var/tmp/classic-bugs/vol1/seeds/")
source("../../R/Rcheck.R")
load.module("glm")

d <- read.jagsdata("seeds-data.R")
inits <- read.jagsdata("seeds-init.R")
m <- jags.model("seeds.bug", d, inits, n.chains=2, n.adapt=2500)

update(m, 2500)

x <- coda.samples(m, c("alpha0", "alpha1","alpha2","alpha12","sigma"),

source("bench-test1.R")
check.fun()

m <- jags.model("seedszro.bug", d, inits, n.chains=2, n.adapt=2500)

update(m, 2500)

x <- coda.samples(m, c("alpha0", "alpha1","alpha2","alpha12","sigma"),

source("bench-test2.R")
check.fun()

m <- jags.model("seedssig.bug", d, inits, n.chains=2, n.adapt=2500)

update(m, 2500)

x <- coda.samples(m, c("alpha0", "alpha1","alpha2","alpha12","sigma"),

source("bench-test3.R")
check.fun()

m <- jags.model("seedsuni.bug", d, inits, n.chains=2, n.adapt=2500)

update(m, 2500)

x <- coda.samples(m, c("alpha0", "alpha1","alpha2","alpha12","sigma"),

source("bench-test4.R")
check.fun()

On 1 April 2011 04:00, Martyn Plummer <plummerM at iarc.fr> wrote:

I'm sorry, the whole project is somewhat under-documented at the moment,
but in addition the glm module is a work in progress.

From a user point of view, it should be fairly transparent. Using rjags,

you type

R> loadModule("glm")

before calling jags.model(). ?If your model contains a GLM then JAGS
should recognize it and provide samplers that do block updating of the
parameters in the linear predictor.

To see if it is working, call list.samplers(m) where m is the JAGS model
object. The return value is a named list: the names correspond to the
sampling method, and the values are the names of the nodes that are
updated by that sampler. Samplers have names prefixed by the module
name, so if you have any entries in the sampler list called "glm::*"
then it is working.

For some examples, you can download the classic bugs examples from
http://sourceforge.net/projects/mcmc-jags/files/Examples/2.x/

The subdirectories "epil", "oxford", and "seeds" (in vol1) contain R
scripts that you can run using rjags, or scripts with the name test*.cmd
that you can run using jags in batch mode.

Under the hood, most of the samplers use data augmentation to reduce the
model from a GLM to an LM, then the block updating relies on Tim Davis's
libraries for sparse matrix algebra (Very much following your lead here
but with a much more basic use of the sparse matrix algebra). ?Variance
parameters for the random effects are still a problem and can show poor
mixing even when everything else is working properly. ?As I said, it is
a work in progress.

I hope this helps.
Martyn

On Wed, 2011-03-30 at 13:11 -0500, Douglas Bates wrote:

In reading about the glm module in JAGS it seems that it is suitable
for sampling from the posterior distribution of the parameters in a
generalized linear mixed model. ?However, I haven't been able to find
documentation on how to use this module in particular. ?Section 5.6 of
the JAGS User Manual for version 2.2.0 hints at abilities but doesn't
really expand on how to use them.

Can anyone point me to further documentation or examples?

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Wincent Ronggui HUANG
Sociology Department of Fudan University
PhD of City University of Hong Kong
http://asrr.r-forge.r-project.org/rghuang.html