Skip to content

marklhc/bootmlm

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

58 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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))

About

R package under development

Resources

Stars

Watchers

Forks

Packages

No packages published

Languages