Wincent
2011-Apr-04 06:09 UTC
[Rd] [R-sig-ME] Documentation for the glm module in jags/rjags?
It turns out that when I use GUI (file-change dir) to set the working directory, R will crash. If I use setwd() instead, the example runs well. Regards, On 4 April 2011 00:17, Wincent <ronggui.huang@gmail.com> 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@stat.wisc.edu> wrote: > >> On Sun, Apr 3, 2011 at 9:53 AM, Wincent <ronggui.huang@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@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@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 > >-- Wincent Ronggui HUANG Sociology Department of Fudan University PhD of City University of Hong Kong http://asrr.r-forge.r-project.org/rghuang.html [[alternative HTML version deleted]]
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