For pseudo R2 by cluster, the squared correlation between observed and predicted values for each cluster unit is returned. For the overall model, the marginal and conditional R2 are calculated as described in the references.

## Usage

```
# S3 method for merMod
R2(object, cluster = FALSE, ...)
```

## Arguments

- object
A model estimated by

`lmer`

.- cluster
A logical whether to calculate individual pseudo R2 values by cluster unit (if

`TRUE`

) or the marginal and conditional R2 for the overall model (if`FALSE`

, the default).- ...
Added for consistency with generic. Not currently used.

## Value

a named vector with the marginal and conditional R2 values,
if `CLUSTER = FALSE`

, otherwise, a data table with the pseudo R2
for each cluster unit.

## References

For estimating the marginal and conditional R-squared values, see: Nakagawa, S. and Schielzeth, H. (2013) <doi:10.1111/j.2041-210x.2012.00261.x> "A general and simple method for obtaining R2 from generalized linear mixed-effects models" and also: Johnson, P. C. (2014) <doi:10.1111/2041-210X.12225> "Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models"

## Examples

```
library(JWileymisc)
data(aces_daily, package = "JWileymisc")
m1 <- lme4::lmer(PosAff ~ STRESS + (1 + STRESS | UserID),
data = aces_daily)
R2(m1)
#> MarginalR2 ConditionalR2
#> 0.1480118 0.6381639
R2(m1, cluster = TRUE)
#> ID IDVariable R2
#> <int> <char> <num>
#> 1: 1 UserID 0.0008365004
#> 2: 2 UserID 0.4615574606
#> 3: 3 UserID 0.0125102870
#> 4: 4 UserID 0.1506410069
#> 5: 5 UserID 0.0133703907
#> ---
#> 187: 187 UserID 0.0017950558
#> 188: 188 UserID 0.3319884249
#> 189: 189 UserID 0.0800763116
#> 190: 190 UserID 0.3956990376
#> 191: 191 UserID 0.0226941915
hist(R2(m1, cluster = TRUE)$R2)
rm(m1)
```