I'll re-enter the fray.
The data set is an example where coxph is incorrect; due to round off error it
is treating
a 5 column rank 3 matrix as if it were rank 4. This of course results in 0 digits of
precision.
Immediate fix, for the user, is to add "toler.chol= 1e-10" to their coxph
call. It is
very likely that they will never ever have to change this value.
I will look into changing the default from its current value of
.Machine$double.eps^.75.
However, I first have to check that this does not break anything else. All
"epsilon"
constants are a delicate dance between mutiple data sets, and anyone with long
experience
in numerical anlysis will tell you that it is impossible to find constants that
will work
for every data set. This is true for linear models, logistic, Cox, ... you
name it.
In summary:
I appreciate the example.
I'll add to my list of "nasty" problems.
I may be able to fix long term, and maybe not. Changing the constant may break something
else.
I've given a good short term fix.
Terry T.
On 12/18/2013 05:00 AM, r-help-requ...@r-project.org wrote:
Your comprehension of the issue seem to be entirely wrong. Between r11513 and r11516,
some tuning of internal parmeters were done, so the process of finding the rank of a
singular matrix no longer converges (within the time/tolerance implicitly specified).
There are warnings issued, but then there are misc warnings before and after (and one
gets "desensitised" about them). Also the nature of the problem, which is to
test for possibility of interactions - or lacking thereof -
outcome ~ factor A + factor B + factor A x factor B
or just extra terms in "outcome ~ factor A + factor B + ..." as an exploration
of auxiliary effects, more often than not extra terms won't make
any difference and the matrix involved just isn't the nicest to manipulate; it
is in the nature of that kind of exploratory work.
Professor Therneau replied that it is possible to get the older convergent
behaviour by manual tuning of some of the convergence criteria parameters; I
have responded that while that is possible, often one is simultaneously
exploring many models with many possible auxiliary effects (and lacking
thereof), manual tuning for each is neither feasible nor appropriate; and we
sort of left it at that.
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