I'm chiming in late since I read the news in digest form, and I won't copy the entire
conversation to date.
The issue raised comes up quite often in Cox models, so often that the Therneau and
Grambsch book has a section on the issue (3.5, p 58). After a few initial iterations the
offending coefficient will increase by a constant at each iteration while the
log-likelihood approaches an asymptote (essentially once the other coefficients "settle
down").
The coxph routine tries to detect this case and print a warning, and this turns out to be
very hard to do accurately. I worked hard at tuning the threshold(s) for the message
several years ago and finally gave up; I am guessing that the warning misses > 5% of the
cases when the issue is true, and that 5% of the warnings that do print are incorrect.
(And these estimates may be too optimistic.) Highly correlated predictors tend to trip
it up, e.g., the truncated power spline basis used by the rcs function in Hmisc.
All in all, I am not completely sure whether the message does more harm than good. I'd be
quite reluctant to go down the same path again with the glm function.
Terry Therneau
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
