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I'm about to use the glm() function in R, and I know that I have to specify which family of variance/link functions I want to use (either gaussian, binomial, poisson, Gamma, inverse.gaussian, or quasi-which I take to mean user-defined).

I understand that binomial is to be used for things like logistic regression, but it's unclear to me under what scenarios the others should be used. Does anybody have useful advice?

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It depends on the nature of your dependent variable:

Gaussian is for continuous DV (this is ordinary least squares)

Binomial, as you note, is for logistic regression .

Poisson is for count data (non-negative integers). See also quasipoisson.

Gamma is for continuous DV that is always positive (although often you can use Gaussian here, if the mean is $>> 0$ and the sd isn't huge - that is, if all the values are quite far from 0).

Inverse Gaussian is, I believe, used for survival data (time to event).

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  • $\begingroup$ You mean independent variable, right? And also, are you saying that a binomial link can be used for both count data and binary traits (say, presence or absence of a viral infection)? $\endgroup$
    – Atticus29
    Commented Oct 18, 2012 at 1:37
  • $\begingroup$ No, I mean dependent variable. Not sure what you are referring to with you second question. $\endgroup$
    – Peter Flom
    Commented Oct 18, 2012 at 10:23
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    $\begingroup$ Does anyone know of (or would anyone be willing to provide) a concise guide to choosing an appropriate family & link function based on the shape of the data/residuals? $\endgroup$
    – Freya Harrison
    Commented Oct 23, 2014 at 16:21

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