[R-sig-ME] Generalized Linerar Model vs Logistic regression

Ben Bolker bbolker at gmail.com
Tue Nov 22 21:24:37 CET 2016

On 16-11-22 12:44 PM, Cleber Iack wrote:
> Dear,
> Good night.
> I am Phd student, but I have a model with reply Binaria, and I own 23
> predictive variables, among them school (4) and Posto (5)

  I'm guessing that what you mean by this is that school is a
categorical predictor with 4 levels and Posto is categorical with 5 levels?

  According to Frank Harrell's (_Regression Modeling Strategies_) rules
of thumb, you need about 20 times as many effective observations as the
number of parameters in the model you're trying to fit; in the case of
binary data, 'effective' observations means the minimum of (number of
zeros, number of ones) in your response, i.e. the number of the
less-common response. So I hope you have a large data set (at least
hundreds and preferably thousands of observations ...)

> a) My ICC on the school is showing 0.23, can I use this information to
> corroborate the use of Generalized Linerar Model instead of a Logistic
> regression?
> b) If the letter a) is not true, I can verify through the AIC and BIC?

  I don't know about ICC. You can in principle use AIC or BIC, or a
likelihood ratio test (LRT), to justify the use of a mixed model. My
personal preference would be to use the mixed model if it makes sense in
the context of your observational/experimental design (e.g. you have a
number of discrete groups in your population that can be thought of as
exchangeable, i.e. changing the identity of the groups wouldn't change
your conclusions), and not to try to use quantitative tests for this
purpose.  You can read more about the use and caveats of AIC, BIC, LRT,
etc. at http://tinyurl.com/glmmFAQ

> I appreciate any feedback.
> Thank you
> Cleber
> 	[[alternative HTML version deleted]]
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