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Parallelize 'lme4' functions

The 'lme4' image + The 'futurize' hexlogo = The 'future' logo

The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)
library(lme4)

gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
             data = cbpp, family = binomial)
gm_all <- allFit(gm) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize lme4 functions such as allFit() and bootMer().

The lme4 package fits linear and generalized linear mixed-effects models. Its allFit() function fits models using all available optimizers to check for convergence issues, and bootMer() performs parametric bootstrap inference. Both are excellent candidates for parallelization.

Example: Fitting with multiple optimizers

The allFit() function fits a model with each available optimizer, which can be done in parallel:

library(lme4)

## Fit a generalized linear mixed model
gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
            data = cbpp, family = binomial)

## Try all available optimizers
gm_all <- allFit(gm)

Here allFit() evaluates sequentially, but we can easily make it evaluate in parallel by piping to futurize():

library(futurize)
library(lme4)

gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
            data = cbpp, family = binomial)
gm_all <- allFit(gm) |> futurize()

This will distribute the optimizer fits 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)

Example: Parametric bootstrap

The bootMer() function performs parametric bootstrap inference on fitted models:

library(futurize)
plan(multisession)
library(lme4)

## Fit a linear mixed model
fm <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)

## Bootstrap the fixed-effect coefficients
boot_coef <- function(model) fixef(model)
b <- bootMer(fm, boot_coef, nsim = 100) |> futurize()

Supported Functions

The following lme4 functions are supported by futurize():

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.
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