[R] deviance in GLM vs. summary.glm

Berwin A Turlach berwin.turlach at gmail.com
Wed May 31 09:54:44 CEST 2017

Dear Yi,

On Wed, 31 May 2017 04:53:25 +0000 (UTC)
array chip via R-help <r-help at r-project.org> wrote:

> Hi, I am running a logistic regression on a simple dataset (attached)
> using glm: [...] As you can see, the interaction term is very
> insignificant (p = 0.996)! 

Well, all terms are not significant (actually, AFAIK, the phrase "very
insignificant" does not exist; and if it does, than it ought not).  

But look at the estimates and the standard errors too (might be easier
if you had formatted your e-mail in a way that was readable)!  What
does an estimate for the intercept of 19.57 on the linear predictor
scale mean?  What it the estimated probability if you transform back?

Perhaps the following command

R> xtabs(~x1+x2+y,dat)

will shed some more light on what is going on in your data set, and why
the interaction term is highly significant.

> But if I use a anova() to compare a full
> vs reduced model to evaluate the interaction term:
> > anova(glm(y~x1+x2,dat,family='binomial'),
> > glm(y~x1*x2,dat,family='binomial'))

To you know about the (optional) argument 'test="ChiSq"' for the
anova() command if you use it to compare models fitted by glm()? (see

> So I get very different p value on the interaction term, can someone
> share what's going wrong here?

Data separation, aka Hauck-Donner phenomenon, discussed in any good
book on logistic regression.

Best wishes,


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