+
=
The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
+
=
The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(metafor)
dat <- escalc(measure = "RR", ai = tpos, bi = tneg,
ci = cpos, di = cneg, data = dat.bcg)
fit <- rma(yi, vi, data = dat)
pr <- profile(fit) |> futurize()
This vignette demonstrates how to use this approach to parallelize
metafor functions such as profile(), rstudent(),
cooks.distance(), and dfbetas().
The metafor package provides a comprehensive collection of functions for conducting meta-analyses in R. It supports fixed-effects, random-effects, and mixed-effects (meta-regression) models and includes functions for model diagnostics and profiling. Several of these computations involve fitting the model repeatedly, making them excellent candidates for parallelization.
The profile() function computes the likelihood profile for model
parameters such as the variance component in a random-effects
meta-analysis. For example, using the built-in BCG vaccine dataset:
library(metafor)
## Calculate log risk ratios and sampling variances
dat <- escalc(measure = "RR", ai = tpos, bi = tneg,
ci = cpos, di = cneg, data = dat.bcg)
## Fit a random-effects model
fit <- rma(yi, vi, data = dat)
## Compute likelihood profile
pr <- profile(fit)
Here profile() is calculated sequentially. To calculate in
parallel, we can pipe to futurize():
library(futurize)
library(metafor)
dat <- escalc(measure = "RR", ai = tpos, bi = tneg,
ci = cpos, di = cneg, data = dat.bcg)
fit <- rma(yi, vi, data = dat)
pr <- profile(fit) |> futurize()
This will distribute the profile computations across the available parallel workers, given that we have set up parallel workers, e.g.
plan(multisession)
The built-in multisession backend parallelizes on your local
computer and works on all operating systems. There are other
parallel backends to choose from, including alternatives to
parallelize locally as well as distributed across remote machines,
e.g.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following metafor functions are supported by futurize():
profile() for 'rma.uni', 'rma.mv', 'rma.ls', and 'rma.uni.selmodel'rstudent() for 'rma.mv'cooks.distance() for 'rma.mv'dfbetas() for 'rma.mv'For comparison, here is what it takes to parallelize profile()
using the parallel package directly, without futurize:
library(metafor)
library(parallel)
## Calculate log risk ratios and sampling variances
dat <- escalc(measure = "RR", ai = tpos, bi = tneg,
ci = cpos, di = cneg, data = dat.bcg)
## Fit a random-effects model
fit <- rma(yi, vi, data = dat)
## Set up a PSOCK cluster
ncpus <- 4L
cl <- makeCluster(ncpus)
## Compute likelihood profile in parallel
pr <- profile(fit, parallel = "snow", ncpus = ncpus, cl = cl)
## Tear down the cluster
stopCluster(cl)
This requires you to manually create and manage the cluster
lifecycle. If you forget to call stopCluster(), or if your code
errors out before reaching it, you leak background R processes. You
also have to decide upfront how many CPUs to use and what cluster
type to use. Switching to another parallel backend, e.g. a Slurm
cluster, would require a completely different setup. With
futurize, all of this is handled for you - just pipe to
futurize() and control the backend with plan().
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.