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I am trying to compare 2 models (a GLMER with a random effect and a GLM with the random effect removed). However, I was told you can't use an AIC for GLM's but I thought you could!?

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I don't know exactly what models you are working on, however I don't think this is strictly necessary for a general answer. How are you estimating the GLM? Are you using Maximum likelihood? If so, why you should not use Akaike's (same story for LR test)? A couple of links pointing to this may be of some help link 1 link 2

Edited: If your aim is to test the joint significance of the two independent variables (i.e. to test whether your model as a whole is significant or not), then I would use a Wald test link wald or a LR test where you restrict both the variables to be 0 and compare the restricted model to the model with both variablkes left inside. If instead your ambition is to define whether to use 1 or 2 variables for the model and which variables then use a specification crierion or info criteria (different IC may give different answers as they impose a different penalty function on the number of parameters Akaike's link, Bayesian link, Hannan-Quinn link)

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  • $\begingroup$ My model is looking at how 2 categorical variables affected a count variable (e.g. number of days). e.g. x~y*z. However, when I run my AIC and LRT on the model they give contrasting results as to what model is best. My overall aim of the model is to determine if these 2 categorical variables significantly impact the count variable. My models are nested too $\endgroup$
    – user255144
    Commented Aug 2, 2019 at 11:23
  • $\begingroup$ @user255144 Edited my answer in light of your comment $\endgroup$
    – Fr1
    Commented Aug 2, 2019 at 11:40

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