Description Usage Arguments Details Value Note Author(s) References Examples

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

1 | ```
multilevelR2(model, print = c("RB1", "RB2", "SB", "MVP"))
``` |

`model` |
Either a fitted linear mixed-effects model as produced by |

`print` |
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 `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).

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

.

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

Simon Grund

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.

1 2 3 4 5 6 7 8 9 10 | ```
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)
``` |

```
*** This is beta software. Please report any bugs!
*** See the NEWS file for recent changes.
Loading required package: lme4
Loading required package: Matrix
Running burn-in phase ...
Creating imputed data set ( 1 / 5 ) ...
Creating imputed data set ( 2 / 5 ) ...
Creating imputed data set ( 3 / 5 ) ...
Creating imputed data set ( 4 / 5 ) ...
Creating imputed data set ( 5 / 5 ) ...
Done!
RB1 RB2 SB MVP
0.34132988 -0.05908676 0.33037596 0.33343776
```

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