The goal of glmerGOF is to provide a goodness of fit test of the
presumed Gaussian distribution of the random effect in logistic mixed
models fit with lme4::glmer(family = "binomial")
. The method
implemented is introduced in Tchetgen Tchetgen and Coull (2006):
Tchetgen Tchetgen, E. J., & Coull, B. A. (2006) A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model. Biometrika, 93(4), 1003-1010. DOI: 10.1093/biomet/93.4.1003
glmerGOF is available through github:
remotes::install_github("BarkleyBG/glmerGOF")
An introductory tutorial is provided that describes how to use this
package. The function that implements the test is glmerGOF::testGOF()
,
which takes as mandatory input:
- a fitted
lme4::glmer()
model - a fitted
survival::clogit()
model - the original dataset
- a list providing two variable names
Once the two models are fitted, then test statistics can be found:
test_results <- testGOF(
data = my_data,
fitted_model_clogit = fit_clogit,
fitted_model_glmm = fit_glmm,
var_names = list(DV = "y", grouping = "id"),
gradient_derivative_method = "simple"
)
The test results can be shown as:
test_results$results
# $D
# [1] 1.230103
#
# $p_value
# [1] 0.267387
Please see the Introduction vignette for a working example.
Please refer to the lifecycle badge for its current status. This package was created in early 2019 and may undergo future developments. Please email the maintainer if any of these changes are of interest, or if you would like to work on them:
- Ability to manually align glmer and clogit coefficients
- Parallelization backend
- Updated model options
Please note that the ‘glmerGOF’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.