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Can AIC be used for non nested models?
The AIC does not require nested models. One of the neat things about the AIC is that you can compare very different models. However, make sure the likelihoods are computed on the same data. Beware of AIC values computed using conditional likelihoods because the conditioning may be different for different models.
Can you compare AIC across models?
To compare models using AIC, you need to calculate the AIC of each model. If a model is more than 2 AIC units lower than another, then it is considered significantly better than that model. You can easily calculate AIC by hand if you have the log-likelihood of your model, but calculating log-likelihood is complicated!
What are non-nested models?
Broadly speaking, two models (or hypotheses) are said to be ‘non-nested’ if neither can be obtained from the other by the imposition of appropriate parametric restrictions or as a limit of a suitable approximation; otherwise they are said to be ‘nested’.
Can a linear model be fitted with nested designs?
Nested designs can be fitted in a classical linear model with nested fixed effects, but this task is not entirely trivial, because the degrees of freedom need to be adapted to the experimental design in order to get the correct mean sum of squares (Underwood 1997; Quinn & Keough 2002; Gelman 2005 ).
What is the difference between nested and non-nested tests?
Nested means here that all terms of a smaller model occur in a larger model. This is a necessary condition for using most model comparison tests like likelihood ratio tests. In the context of multilevel models I think it’s better to speak of nested and non-nested factors.
Do you need a nested likelihood ratio test?
That’s a lot of models. If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require that models be nested (specifically, the likelihood ratio test, based on model deviance). This is particularly important while you’re trying to do model building.
What’s the difference between nested and crossed effects?
The key difference between nested and crossed effects in mixed models is the estimation and interpretation of the interaction variance. With nested data structures, the interaction variance is pooled with the main effect variance of the nested factor. Crossed designs are required to separate the two components.