Parallel computation of multiple imputations by using mitml

[sophieschneemelcher]

Dear Simon,

I was wondering if there is any possibility to run multiple imputation using jomoImpute with parallel processing like futuremice in the mice package does. This would speed up my imputation process.

Thank you very much in advance
Sophie

[simongrund1]

Currently, mitml does not have out-of-the-box parallelization, but you can use packages like parallel or future for this. This can save computation time, but it also comes with the disadvantage of having to (a) re-run the burn-in phase of the imputation, and (b) inspect multiple output objects for the results.

Here's an example with parallel and some timings:

library(parallel)
library(mitml)

dat <- studentratings
fml <- ReadDis + SES ~ ReadAchiev + (1|ID)

# parallelized
.cl <- parallel::makeCluster(2, type = "PSOCK")
parallel::clusterEvalQ(.cl, library(mitml))
parallel::clusterExport(.cl, varlist = c("dat", "fml"))
t1 <- system.time({
par.seed <- c(5947, 7135)
imp1 <- parallel::parLapply(.cl, par.seed, function(s) jomoImpute(dat, formula = fml, n.burn = 5000, n.iter = 100, m = 50, seed = s))
})
parallel::stopCluster(.cl)

# non-parallelized
t2 <- system.time({
imp2 <- jomoImpute(dat, formula = fml, n.burn = 5000, n.iter = 100, m = 100, seed = 2452)
})

t1   # ~6 sec
t2   # ~10 sec

# summaries
lapply(imp1, summary)
summary(imp2)

# create lists of imputed data sets
implist1 <- as.mitml.list(do.call(c, lapply(imp1, mitmlComplete)))
implist2 <- mitmlComplete(imp2)

[simongrund1]

No further response, so I'm marking this as solved (but can open again if needed).