README.md

In marklhc/bootmlm: Bootstrap Resampling for Multilevel Models

bootmlm

The bootmlm package does bootstrap resampling for multilevel models. Currently only models fitted with lme4::lmer() is supported. It's still in developmental stage and is not yet on CRAN. However, you can install the package on GitHub:

if (!require("remotes")) {
  install.packages("remotes")
}
remotes::install_github("marklhc/bootmlm")

See this paper for a performance comparison of different bootstrapped confidence intervals for multilevel effect size estimations:

Lai, M. H. C. (2020). Bootstrap confidence interval for multilevel standardized effect size. Multivariate Behavioral Research, 56(4), 558--578. Advance online publication. https://doi.org/10.1080/00273171.2020.1746902

Example

Here is an example to get the bootstrap distributions of the fixed effects and the level-1 error SD:

library(lme4)
fm01ML <- lmer(Yield ~ (1 | Batch), Dyestuff, REML = FALSE)
mySumm <- function(x) {
  c(getME(x, "beta"), sigma(x))
}
# Covariance preserving residual bootstrap
library(bootmlm)
boo01 <- bootstrap_mer(fm01ML, mySumm, type = "residual", nsim = 100)
# Plot bootstrap distribution of fixed effect
library(boot)
plot(boo01, index = 1)
# Get confidence interval
boot.ci(boo01, index = 2, type = c("norm", "basic", "perc"))
# BCa using influence values computed from `empinf_mer`
boot.ci(boo01, index = 2, type = "bca", L = empinf_mer(fm01ML, mySumm, 2))

marklhc/bootmlm documentation built on May 24, 2023, 9:59 a.m.