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model_performance.merMod

Performance of Mixed Models


Description

Compute indices of model performance for mixed models.

Usage

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

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.

verbose

Toggle off warnings.

...

Arguments passed to or from other methods.

Details

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).

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

Value

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

Examples

if (require("lme4")) {
  model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
  model_performance(model)
}

performance

Assessment of Regression Models Performance

v0.7.1
GPL-3
Authors
Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>), Indrajeet Patil [aut, ctb] (<https://orcid.org/0000-0003-1995-6531>), Philip Waggoner [aut, ctb] (<https://orcid.org/0000-0002-7825-7573>), Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-2042-7063>)
Initial release

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