Thanks for your replies! It seems, that I can fit my model now, when I can provide the "right" starting values; however there remain warnings, such as:

1: In log(ifelse(y == 1, 1, (1 - y)/(1 - mu))) : NaNs wurden erzeugt
2: step size truncated due to divergence
3: step size truncated: out of bounds
...

That makes me feel uncomfortable and I wonder whether I can "trust" the fitted model. Why is this kind of regression so picky about starting values compared to logistic regression? And is there a way to explain the difference between binomial - quasibinomial to a simple mind like mine?

Best,
Felix

Von: Ravi Varadhan [mailto:ravi.varadhan@jhu.edu]
Gesendet: Mittwoch, 30. Januar 2013 17:02
An: Fischer, Felix
Cc: r-help@r-project.org
Betreff: [R] starting values in glm(..., family = binomial(link =log))

Try this:

Age_log_model = glm(Arthrose ~ Alter, data=x, start=c(-1, 0), family=quasibinomial(link = log))

Ravi

Ravi Varadhan, Ph.D.
Assistant Professor
The Center on Aging and Health
Division of Geriatric Medicine & Gerontology
Johns Hopkins University
rvaradhan@jhmi.edu<mailto:rvaradhan@jhmi.edu>
410-502-2619


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