Compute indices of model performance for mixed models.
# S3 method for merMod
model_performance(
model,
metrics = "all",
estimator = "REML",
verbose = TRUE,
...
)A data frame (with one row) and one column per "index" (see
metrics).
A mixed effects model.
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.
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)).
Toggle warnings and messages.
Arguments passed to or from other methods.
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()).
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.
Furthermore, see 'Details' in model_performance.lm() for more details
on returned indices.
model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
model_performance(model)
Run the code above in your browser using DataLab