Mixed Models

First, we calculate the r-squared value and intra-class correlation coefficient (ICC) for a mixed model, using r2() and icc(). r2() internally calls the appropriate function for the given model. In case of mixed models this will be r2_nakagawa(). r2_nakagawa() computes the marginal and conditional r-squared values, while icc() calculates an adjusted and conditional ICC, both based on the proposals from Nakagawa et al. 2017. For more details on the computation of the variances, see get_variance().

# Load the lme4 package
library(lme4)

# Fit a mixed model
model <- lmer(Sepal.Width ~ Petal.Length + (1|Species), data = iris)

# compute R2, based on Nakagawa et al. 2017
r2(model)
> # R2 for mixed models
> 
>   Conditional R2: 0.913
>      Marginal R2: 0.216
# compute intra-class correlation coefficient (ICC)
icc(model)
> # Intraclass Correlation Coefficient
> 
>      Adjusted ICC: 0.889
>   Conditional ICC: 0.697

Now let’s compute all available indices of performance appropriate for a given model. This can be done via the model_performance().

# Compute all performance indices
model_performance(model)
>   AIC BIC R2_conditional R2_marginal  ICC RMSE
> 1 107 119           0.91        0.22 0.89 0.31

Bayesian Mixed Models

For Bayesian mixed models, we have the same features available (r-squared, ICC, …). In this example, we focus on the output from model_performance() only.

# Load the rstanarm package
library(rstanarm)

# Fit a Bayesian mixed model
model <- stan_glmer(Sepal.Width ~ Petal.Length + (1|Species), data = iris)

# Compute performance indices
model_performance(model)
>   ELPD ELPD_SE LOOIC LOOIC_SE WAIC   R2 R2_SE R2_marginal R2_marginal_SE
> 1  -43      10    87       20   87 0.47 0.045        0.26          0.048
>   R2_LOO_adjusted RMSE
> 1            0.45 0.31

• Don’t forget to check out the documentation here for more!

More details about performance’s features are comming soon, stay tuned 😉