# Parallelize 'lme4' functions

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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()`:

- `allFit()`
- `bootMer()`