Hello,
I estimated a mixed-effect logistic regression model (GLMM) and I need to evaluate it. Specifically, I tried a few combinations of the independent variables in the model and I need to compare between them.
I know that for a regular logistic regression model (GLM), the Nagelkerke R-squared fits (a pseudo R-squared measure). But does it fit also for a mixed-effect model? If not, what is the correct way for evaluating a mixed-effect logistic regression model?
Thanks!
Use AIC and BIC for model comparison. The least value is the best model fit.
Cheers!
Thank you Girma Beressa !
Let me be more specific please, I’m looking for a pseudo R-squared measure that can fit a mixed-effect logistic regression model.
If your model outputs the likelihood, there are different pseudo r-square measures you can calculate, including Nagelkerke. See: https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/
But there is a question as to what model you would consider the null model when you have random effects.
However, for logistic regression, you might consider the Efron's pseudo r-square described at that link. Or maybe, a count pseudo r-squared, that evaluates the proportion of observations the model predicts correctly.
Also, as Girma Beressa mentions, measures like AIC, BIC, or AICc may be more appropriate to decide among models.
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