[R-sig-ME] Documentation for the glm module in jags/rjags?
Martyn Plummer
plummerM at iarc.fr
Mon Apr 4 11:35:38 CEST 2011
I'm not surprised that you are having trouble with Chinese file names.
The rjags package is using "fopen" to open the model file, but this
cannot cope with file names encoded in UCS-2. R uses "RC_fopen" to do
this, but it is not part of the API so I cannot use it.
Martyn
On Mon, 2011-04-04 at 00:17 +0800, Wincent wrote:
> OK, I dig into the problem and found that Chinese character in the
> path should be blamed.
> Once the path rename to English only, it works.
>
>
> Regards,
>
>
> On 3 April 2011 23:43, Douglas Bates <bates at stat.wisc.edu> wrote:
> On Sun, Apr 3, 2011 at 9:53 AM, Wincent
> <ronggui.huang at gmail.com> wrote:
> > 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
>
>
> I didn't have any problem with the seeds examples
> > library(rjags)
> Loading required package: coda
>
> module basemod loaded
> module bugs loaded
>
> > setwd("/var/tmp/classic-bugs/vol1/seeds/")
> > source("../../R/Rcheck.R")
> > load.module("glm")
> module glm loaded
> > 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)
> Compiling model graph
> Resolving undeclared variables
> Allocating nodes
> Graph Size: 167
>
> > update(m, 2500)
> |**************************************************| 100%
> > x <- coda.samples(m, c("alpha0",
> "alpha1","alpha2","alpha12","sigma"),
> + n.iter=10000, thin=10)
> |**************************************************| 100%
> > source("bench-test1.R")
> > check.fun()
> alpha0 alpha1 alpha12 alpha2
> sigma
> 0.027439417 -0.022922026 0.025170384 -0.065288678
> 0.001678632
> OK
> > m <- jags.model("seedszro.bug", d, inits, n.chains=2,
> n.adapt=2500)
> Compiling model graph
> Resolving undeclared variables
> Allocating nodes
> Graph Size: 190
>
> > update(m, 2500)
> |**************************************************| 100%
> > x <- coda.samples(m, c("alpha0",
> "alpha1","alpha2","alpha12","sigma"),
> + n.iter=10000, thin=10)
> |**************************************************| 100%
> > source("bench-test2.R")
> > check.fun()
> alpha0 alpha1 alpha12 alpha2 sigma
> -0.00216258 0.00196343 0.02732117 -0.01017020 -0.01976949
> OK
> > m <- jags.model("seedssig.bug", d, inits, n.chains=2,
> n.adapt=2500)
> Compiling model graph
> Resolving undeclared variables
> Allocating nodes
> Graph Size: 185
>
> |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
> > update(m, 2500)
> |**************************************************| 100%
> > x <- coda.samples(m, c("alpha0",
> "alpha1","alpha2","alpha12","sigma"),
> + n.iter=10000, thin=10)
> |**************************************************| 100%
> > source("bench-test3.R")
> > check.fun()
> alpha0 alpha1 alpha12 alpha2 sigma
> 0.06600343 -0.01107823 0.02933205 -0.04117105 -0.03376439
> OK
> > m <- jags.model("seedsuni.bug", d, inits, n.chains=2,
> n.adapt=2500)
> Compiling model graph
> Resolving undeclared variables
> Allocating nodes
> Graph Size: 186
>
> |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
> > update(m, 2500)
> |**************************************************| 100%
> > x <- coda.samples(m, c("alpha0",
> "alpha1","alpha2","alpha12","sigma"),
> + n.iter=10000, thin=10)
> |**************************************************| 100%
> > source("bench-test4.R")
> > check.fun()
> alpha0 alpha1 alpha12 alpha2 sigma
> -0.04482849 0.05603658 -0.01204552 0.03653643 -0.00798445
> OK
>
>
> > 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?
> >>
> >>
> >>
> -----------------------------------------------------------------------
> >> This message and its attachments are strictly
> confidenti...{{dropped:8}}
> >>
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
> >
> > --
> > Wincent Ronggui HUANG
> > Sociology Department of Fudan University
> > PhD of City University of Hong Kong
> > http://asrr.r-forge.r-project.org/rghuang.html
> >
>
>
>
>
> --
> Wincent Ronggui HUANG
> Sociology Department of Fudan University
> PhD of City University of Hong Kong
> http://asrr.r-forge.r-project.org/rghuang.html
>
>
-----------------------------------------------------------------------
This message and its attachments are strictly confidenti...{{dropped:8}}
More information about the R-sig-mixed-models
mailing list