R: Calculate R-squared measures for multilevel models

multilevelR2 {mitml} | 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 (i.e.,) 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` will return the explained variance at level 1 and level 2, respectively, according to Raudenbush and Bryk (2002, pp. 74 and 79). Specifying `SB` will return the total variance explained according to Snijders and Bosker (2012, p. 112). Specifying `MVP` will return the total variance explained based on “multilevel variance partitioning” as proposed by LaHuis, Hartman, Hakoyama, and Clark (2014).

### Value

Returns 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.

### Author(s)

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, print=1:5)

    fit <- with(implist, lmer(MathAchiev ~ 1 + CognAbility + (1|ID)))
    multilevelR2(fit)

[Package mitml version 0.3-5 Index]