Performance of Mixed Models

Compute indices of model performance for mixed models.

Usage

# S3 method for class 'merMod'
model_performance(
  model,
  metrics = "all",
  estimator = "REML",
  verbose = TRUE,
  null_model = NULL,
  ...
)

Arguments

model

A mixed effects model.

metrics

Can be "all", "common" or a character vector of metrics to be computed (some of c("AIC", "AICc", "BIC", "R2", "ICC", "RMSE", "SIGMA", "LOGLOSS", "SCORE")). "common" will compute AIC, BIC, R2, ICC and RMSE.

estimator

Only for linear models. Corresponds to the different estimators for the standard deviation of the errors. If estimator = "ML" (default, except for performance_aic() when the model object is of class lmerMod), the scaling is done by n (the biased ML estimator), which is then equivalent to using AIC(logLik()). Setting it to "REML" will give the same results as AIC(logLik(..., REML = TRUE)).

verbose

Toggle warnings and messages.

null_model

Optional, a null model to compute the random effect variances, which is passed to insight::get_variance(). Usually only required if calculation of r-squared or ICC fails when null_model is not specified. If calculating the null model takes longer and you already have fit the null model, you can pass it here, too, to speed up the process.

...

Arguments passed to or from other methods.

Value

A data frame (with one row) and one column per "index" (see metrics).

Details

Intraclass Correlation Coefficient (ICC)

This method returns the adjusted ICC only, as this is typically of interest when judging the variance attributed to the random effects part of the model (see also icc()).

Null model

For non-Gaussian mixed models, both R2 and ICC need the null model. When both are requested, the null model is fitted only once and used for both indices. A null model that was already fitted can be passed via null_model, so that it is not fitted at all.

REML versus ML estimator

The default behaviour of model_performance() when computing AIC or BIC of linear mixed model from package lme4 is the same as for AIC() or BIC() (i.e. estimator = "REML"). However, for model comparison using compare_performance() sets estimator = "ML" by default, because comparing information criteria based on REML fits is usually not valid (unless all models have the same fixed effects). Thus, make sure to set the correct estimator-value when looking at fit-indices or comparing model fits.

Other performance indices

Furthermore, see 'Details' in model_performance.lm() for more details on returned indices.

Examples

model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
model_performance(model)
#> # Indices of model performance
#> 
#> AIC  | AICc |  BIC | R2 (cond.) | R2 (marg.) |   ICC |  RMSE | Sigma
#> --------------------------------------------------------------------
#> 77.3 | 77.6 | 89.4 |      0.972 |      0.096 | 0.969 | 0.279 | 0.283