Subject: [Instr Note] fitLMER.fnc() and alternatives...
From: 36617-A on Piazza <[email-masked]>
To: [email-masked]
Date: Thu, 18 Nov 2021 19:33:31 +0000 (UTC)

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Instructor Brian Junker posted a new Note. Your instructor selected to notify everyone in real time of this post, bypassing user email preferences.

fitLMER.fnc() and alternatives...

As several people have reported to me in email and private piazza posts, the fitLMER.fnc() function is not the most polished or stable piece of software that we have used.

There seem to be cases, especially when all fixed effects are eliminated, when the fitLMER.fnc() function cannot terminate and produce the final model.

One way to get an idea of what it is doing is to modify your function call from 

    fitLMER.fnc([whatever you have now]) 

to 

    fitLMER.fnc([whatever you have now], alpha=0.05, log.file.name="mylogfile.txt")

Then you can look at the contents of "mylogfile.txt" to see what sorts of decisions fitLMER.fnc() was making, and from that you can deduce what the final model should have been.

As alternatives, you can always look at 

    summary(my.big.lmer.model)

or 

    display(my.big.lmer.model)

to see what fixed effects seem to be needed, or compare models with

    anova(one.of.my.models, another.of.my.models, a.third.model, etc)

The AIC and BIC values printed by the anova() function can be used to compare any two models, and the likelihood ratio (deviance) tests can be used to compare models if their fixed effects are nested and their random effects are the same.

Don't forget that `library(RLRsim)` can be helpful for testing specific random effects, as well.

hope this helps,

-BJ

ps - Also, remember the general principle that if you get the same result using several different methods (e.g. BIC vs RLRsim, or two different maxinizers in lmer()) or several different criteria (e.g. BIC vs AIC or LRT), then that increases the evidence that the result is trustworthy.

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