merModList: Apply a multilevel model to a list of data frames

In merTools: Tools for Analyzing Mixed Effect Regression Models

Apply a multilevel model to a list of data frames

Description

Apply a multilevel model to a list of data frames

Apply a Bayesian multilevel model to a list of data frames

Apply a generalized linear multilevel model to a list of data frames

Apply a Bayesian generalized linear multilevel model to a list of data frames

Usage

lmerModList(formula, data, parallel = FALSE, ...)

blmerModList(formula, data, parallel = FALSE, ...)

glmerModList(formula, data, parallel = FALSE, ...)

bglmerModList(formula, data, parallel = FALSE, ...)

Arguments

formula	a formula to pass through compatible with merMod
data	a list object with each element being a data.frame
parallel	logical, should the models be run in parallel? Default FALSE. If so, the 'future_lapply' function from the 'future.apply' package is used. See details.
...	additional arguments to pass to the estimating function

Details

Parallel computing is provided by the 'futures' package, and its extension the 'future.apply' package to provide the 'future_lapply' function for easy parallel computations on lists. To use this package, simply register a parallel backend using the 'plan()' function from 'futures' - an example is to use 'plan(multisession)'

Value

a list of fitted merMod objects of class merModList

a merModList

a merModList

a merModList

Examples

sim_list <- replicate(n = 10,
        expr = sleepstudy[sample(row.names(sleepstudy), 180),],
        simplify=FALSE)
fml <- "Reaction ~ Days + (Days | Subject)"
mod <- lmerModList(fml, data = sim_list)
summary(mod)

merTools documentation built on March 31, 2023, 8:43 p.m.