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

[Philippi, Tom]

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
> >
> >
> >
>
>         [[alternative HTML version deleted]]
>
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