Yip a écrit :
> Hello,
>
> I was trying a glm fitting (as shown below) and I got a warning and a fitted
> residual deviance larger than the null deviance. Is this the expected
> behavor of glm? I would expect that even though the warning might be
> warranted I should not get worse fitting with an additional covariate in the
> model. Could anyone tell me what I'm missing?
[ Big snip ... ]
>> table(f,g)
> g
> f 0 1 2
> 0 0 0 44
> 1 2 38 3
Ah, ah : please note that (f==1)==(g<2)
>> glm(f~x1+x2+g, family=binomial(link="logit"), na.action=na.omit)
>
> Call: glm(formula = f ~ x1 + x2 + g, family = binomial(link = "logit"),
> na.action = na.omit)
>
> Coefficients:
> (Intercept) x1 x2 g
> 9.184e+15 -4.359e+15 7.889e+14 -6.190e+15
>
> Degrees of Freedom: 86 Total (i.e. Null); 83 Residual
> Null Deviance: 120.6
> Residual Deviance: 216.3 AIC: 224.3
> Warning message:
> fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y,
> weights = weights, start = start, etastart = etastart,
This is discussed in MASS, chap. 7, "Generalized" Linear models" in a
section named "Problems with binomial GLMs", pp 197-9 an in the
literature cited herein. I won't insult Venables and Ripley's excellent
writing by trying to paraphrase them.
However, I note that glm tells you that he fitted probabilities to 0 or
1, i. e. infinite odd-ratios. Is that representable in a computer ?
Furthermore, the extremely large absolute values of the coefficients
should also ring an alarm bell...
Did you try this ? :
glm(f~x1+x2, subset=(g==2), family=binomial(link=logit))
Emmanuel Charpentier
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