Likelihood Ratio Test for non-nested mixed-effect-model comparison
The Vuong test <https://en.wikipedia.org/wiki/Vuong's_closeness_test> provides a likelihood-based framework for distinguishing between non-nested models. I'm not immediately aware of an implementation that works with mixed models in R, but you could look around (library("sos"); findFn("vuong") and screen the results for applicability ...) As a crude alternative you could do something parametric bootstrap-y by simulating from each fitted model, fitting each realization with both fitted models, and comparing the goodness of fit distributions (that's intentionally vague, I haven't thought about it very much ...) Ben Bolker
On 15-10-23 11:40 AM, Francesco Sigona wrote:
Hi all, I need to compare two mixed-effects-models that would explain a dependent variable by means of two completely different sets of fixed factors (my random intercept is the same in both models). I understand that the anova() cannot be used to perform a comparison via LRTest in my case, because my models are non-nested. Thus, I would take the model with the lowest AIC (o BIC) value, but I am worried about the statistical significance, so I would prefer a LRTest and the related p-value (as provided by anova() for nested models) to support my model selection. My problem is that I don't know how to compare two non-nested mixed-effect-models via a LRTest. Any suggestion? Thank you in advance. Francesco