Using "snow" parallel operations in bootstrap_parameters/model on merMod object (R)

Question

I've been using bootstrap_parameters (parameters package in R) on generalised linear mixed models produced using glmmTMB. These work fine without parallel processing (parallel = "no") and also works fine on my old and slow mac using parallel = "multicore". I'm working on a new PC (Windows OS) so need to use parallel = "snow" however I get the following error:

system.time(b <- bootstrap_parameters(m1, iterations = 10, parallel = "snow", n_cpus = 6)) Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 0, 1 In addition: Warning message: In lme4::bootMer(model, boot_function, nsim = iterations, verbose = FALSE, : some bootstrap runs failed (10/10) Timing stopped at: 0.89 0.3 7.11

If I select n_cpus = 1, the function works or if I feed bootstrap_parameters or bootstrap_model an lm object (where the underlying code uses boot::boot) it also works fine. I have narrowed the problem down to bootMer (lme4). I suspect the dataset exported using clusterExport is landing in an environment that is different from where clustered bootMer function is looking. The following is a reproduceable example

library(glmmTMB)
library(parameters)
library(parallel)
library(lme4)

m1 <- glmmTMB(count ~ mined + (1|site), zi=~mined,
              family=poisson, data=Salamanders)
summary(m1)

cl <- makeCluster(6)
clusterEvalQ(cl, library("lme4"))
clusterExport(cl, varlist = c("Salamanders"))

system.time(b <- bootstrap_parameters(m1, iterations = 10, parallel = "snow", n_cpus = 6))

stopCluster(cl)

Any ideas on solving this problem?

Answer 1

You need to clusterEvalQ(cl, library("glmmTMB")) . From https://github.com/glmmTMB/glmmTMB/issues/843 :

This issue is more or less resolved by a documentation patch (we need to explicitly clusterEvalQ(cl, library("glmmTMB"))). The only question is whether we can make this any easier for users. There are two problems here: (1) when the user sets up their own cluster rather than leaving it to be done in bootMer, more explicit clusterEvalQ/clusterExport stuff is necessary in any case; (2) bootMer internally does parallel::clusterExport(cl, varlist=getNamespaceExports("lme4")) if it is setting up the cluster (not if the cluster is set up and passed to bootMer by the user), but we wouldn't expect it to extend the same courtesy to glmmTMB...