multilevelR2: Calculate R-squared measures for multilevel models In mitml: Tools for Multiple Imputation in Multilevel Modeling

 multilevelR2 R Documentation

Calculate R-squared measures for multilevel models

Description

Calculates several measures for the proportion of explained variance in a fitted linear mixed-effects or multilevel model (or a list of fitted models).

Usage

``````
multilevelR2(model, print = c("RB1", "RB2", "SB", "MVP"))

``````

Arguments

 `model` Either a fitted linear mixed-effects model as produced by `lme4` or `nlme`, or a list of fitted models as produced by `with.mitml.list`. `print` A character vector denoting which measures should be calculated (see details). Default is to printing all measures.

Details

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 `lme4` or `nlme`, or a list of fitted models produced by `with.mitml.list`. In the latter case, the `R^2` measures are calculated separately for each imputed data set and then averaged across data sets.

Different `R^2` measures can be requested using the `print` argument. Specifying `RB1` and `RB2` returns the explained variance at level 1 and level 2, respectively, according to Raudenbush and Bryk (2002, pp. 74 and 79). Specifying `SB` returns the total variance explained according to Snijders and Bosker (2012, p. 112). Specifying `MVP` returns the total variance explained based on “multilevel variance partitioning” as proposed by LaHuis, Hartman, Hakoyama, and Clark (2014).

Value

A numeric vector containing the `R^2` measures requested in `print`.

Note

Calculating `R^2` measures is currently only supported for two-level models with a single cluster variable.

Simon Grund

References

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.

Examples

``````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)
``````

mitml documentation built on March 31, 2023, 7:01 p.m.