Calculates several measures for the proportion of explained variance in a fitted linear mixed-effects or multilevel model (or a list of fitted models).
multilevelR2(model, print = c("RB1", "RB2", "SB", "MVP"))
Either a fitted linear mixed-effects model as produced by
A character vector denoting which measures should be calculated (see details). Default is to printing all measures.
This function calculates several measures of explained variance (
R^2) for linear-mixed effects models.
It can be used with a single model, as produced by the packages
nlme, or a list of fitted models produced by
In the latter case, the
R^2 measures are calculated separately for each imputed data set and then averaged across data sets.
R^2 measures can be requested using the
RB2 returns the explained variance at level 1 and level 2, respectively, according to Raudenbush and Bryk (2002, pp. 74 and 79).
SB returns the total variance explained according to Snijders and Bosker (2012, p. 112).
MVP returns the total variance explained based on “multilevel variance partitioning” as proposed by LaHuis, Hartman, Hakoyama, and Clark (2014).
A numeric vector containing the
R^2 measures requested in
R^2 measures is currently only supported for two-level models with a single cluster variable.
LaHuis, D. M., Hartman, M. J., Hakoyama, S., & Clark, P. C. (2014). Explained variance measures for multilevel models. Organizational Research Methods, 17, 433-451.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.
require(lme4) data(studentratings) fml <- MathAchiev + ReadAchiev + CognAbility ~ 1 + (1|ID) imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5) implist <- mitmlComplete(imp) fit <- with(implist, lmer(MathAchiev ~ 1 + CognAbility + (1|ID))) multilevelR2(fit)
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