[R-sig-ME] glmmTMB and foreach parallel adaptor

[Mollie Brooks]

Hi Sophia,

I think we fixed this bug https://github.com/glmmTMB/glmmTMB/issues/209 <https://github.com/glmmTMB/glmmTMB/issues/209>

Have you installed from GitHub  in the past 2 weeks? I think this should also be fixed in the CRAN version.

If updating from GitHub doesn’t fix the problem, please file an issue or let me know and I will.

cheers,
Mollie

———————————
Mollie E. Brooks, Ph.D.
Postdoctoral Researcher
National Institute of Aquatic Resources
Technical University of Denmark

> On 17Mar 2017, at 1:10, Philippi, Tom <tom_philippi at nps.gov> wrote:
> 
> I try to avoid variable scoping (environment) confusion by wrapping my
> inner stuff as a simple function:
> 
> cl <- makeCluster(3)
> registerDoParallel(cl)
> 
> fn <- function(i) {
>  dat <- data.frame(subject=rep(1:15,each=2),x=-14:15,m=rep(6,30),
> yprop=simSamples[,i]/6)
>  fitLA <- glmmTMB(yprop ~ x +(1|subject),family=binomial,
> weights=m,data=dat)
>  return(fitLA$fit$par)
> }
> 
> TMBfit <- foreach(i=1:10, .combine= cbind, .inorder=FALSE,
> .packages=c("glmmTMB")) %dopar% fn(i)
> stopCluster(cl)
> 
> With .inorder=FALSE you may or may not need to return a list with i as well:
> return(list(i=i,par=fitLA$fit$par))
> 
> Tom 2
> 
> 
> 
> 
> 
> On Thu, Mar 16, 2017 at 4:51 PM, Sophia Kyriakou <
> sophia.kyriakou17 at gmail.com> wrote:
> 
>> Thanks João, indeed the scoping assignment works.
>> All the best,
>> Sophia
>> 
>> On Fri, Mar 17, 2017 at 1:09 AM, João Veríssimo <jl.verissimo at gmail.com>
>> wrote:
>> 
>>> Hi Sophia,
>>> 
>>> Not sure why this happens, but using <<- assignment seems to work.
>>> (i.e., dat <<- ..., rather than dat <- ...)
>>> 
>>> João
>>> 
>>> On Fri, 2017-03-17 at 00:34 +0200, Sophia Kyriakou wrote:
>>>> Dear members,
>>>> 
>>>> I am trying to set some code running on Windows, which calculates and
>>>> returns the Laplace-based maximum likelihood estimates of a generalized
>>>> linear mixed model.
>>>> Both glmer and glmmTMB functions work on R 3.3.3 when the code is
>>> executed
>>>> sequentially.
>>>> However, when I use the parallel algorithm, then glmer still works, but
>>>> glmmTMB does not.
>>>> The error I get is Error in { : task 1 failed - "object 'dat' not
>> found"
>>> ,
>>>> where dat is the simulated dataset in each replication.
>>>> 
>>>> Any ideas on how to tackle this error? I am interested in using glmmTMB
>>>> instead of lme4, because I need some of glmmTMB output components.
>>>> 
>>>> Below is a sample code that illustrates the above:
>>>> 
>>>> 
>>>> library(doParallel)
>>>> library(glmmTMB)
>>>> library(lme4)
>>>> 
>>>> simSamples <- matrix(sample(0:6,300,replace=TRUE),30,10)
>>>> 
>>>> ## Sequential version
>>>> LA <- LA2 <- matrix(NA,3,10)
>>>> for (i in 1:10){
>>>> dat <-
>>>> data.frame(subject=rep(1:15,each=2),x=-14:15,m=rep(6,30),
>>> y=simSamples[,i],yprop=simSamples[,i]/6)
>>>> fitLA <- glmmTMB(yprop ~ x +(1|subject),family=binomial,
>>> weights=m,data=dat)
>>>> LA[,i] <- fitLA$fit$par
>>>> fitLA2 <- glmer(cbind(y,6-y) ~ x +(1|subject),family=binomial,
>> data=dat)
>>>> LA2[,i] <- c(fixef(fitLA2),sqrt(unlist(VarCorr(fitLA2))))
>>>> }
>>>> 
>>>> 
>>>> ## Parallel version
>>>> cl <- makeCluster(3)
>>>> registerDoParallel(cl)
>>>> TMBfit <- foreach(i=1:10, .combine= cbind, .packages=c("glmmTMB"))
>>> %dopar% {
>>>> dat <-
>>>> data.frame(subject=rep(1:15,each=2),x=-14:15,m=rep(6,30),
>>> yprop=simSamples[,i]/6)
>>>> fitLA <- glmmTMB(yprop ~ x +(1|subject),family=binomial,
>>> weights=m,data=dat)
>>>> fitLA$fit$par
>>>> }
>>>> stopCluster(cl)
>>>> 
>>>> 
>>>> cl <- makeCluster(3)
>>>> registerDoParallel(cl)
>>>> LME4fit <- foreach(i=1:10, .combine= cbind, .packages=c("lme4"))
>> %dopar%
>>> {
>>>> dat <-
>>>> data.frame(subject=rep(1:15,each=2),x=-14:15,m=rep(6,30),
>>> y=simSamples[,i])
>>>> fitLA <- glmer(cbind(y,6-y) ~ x +(1|subject),family=binomial,data=dat)
>>>> c(fixef(fitLA),sqrt(unlist(VarCorr(fitLA))))
>>>> }
>>>> stopCluster(cl)
>>>> 
>>>> Any help is much appreciated.
>>>> Thanks in advance!
>>>> Sophia
>>>> 
>>>>      [[alternative HTML version deleted]]
>>>> 
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>> 
>>> 
>>> 
>> 
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>> 
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