From: Peter Holzer <holzer at stat.math.ethz.ch> Date: Thu, 13 Jan 2000 17:56:27 +0100 (MET) I don't understand, what happens in glm in the following example (note that in S-Plus this example finishes with an almost perfect fit, but also 49 warnings):
Yes, this is known as linear (complete) separation, and means your model is not at all appropriate (and IWLS does not fit it well). There is a linear combination of the predictors which can give all positive observations positive sign and all negative observations negative sign. Then no MLE exists, but the likelihood has a supremum corresponding to giving all observations fitted probabilities zero or one. Most books do not cover this, but Santer & Duffy (1989) (for one) does.
fit.small <- glm(SKR.ein.aus ~ ., family = binomial, data = daten,
maxit=100)
Error in (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, :
inner loop 2; can't correct step size That's not what I get, but I guess the problem is the same: computations on very large numbers are generating NaNs. S-PLUS is much more careful that R, and I will try to add some care to R.
cbind(daten$SKR.ein.aus, round(fit.small$fitted,2), fit.small$residuals,
fit.small$weights) [...]
It is somehow strange to me that e.g. the second observation fits almost perfectly (actually it is 0.99999773), but that it has such a high residual. Obviously this is somehow compensated by the extremely low weight, but I don't understand actually what happens. Are the problems due to the possible perfect fit as it results in S-Plus?
Yes. Actually, that is not a high residual, that is a large _working_ residual, and it is large precisely because it has been divided by the weight. Please do not assume that the $residual and $fitted components are the residuals and fitted values, but use the extractor functions provided. The help page for glm does not describe (in R) what the components mean. Look up what the IWLS algorithm does in the binomial(logit) case: it is quite simple to describe.
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._