[R] Coxph convergence
htl10 at users.sourceforge.net
Thu Feb 6 00:20:13 CET 2014
Dear Prof Therneau,
I have explored a bit more on this issue - I found that for this specific problem,
one can get the older convergent behavior with just setting toler.chol to 4 e-12.
This is only a little larger than 2x the current default = .Machine$double.eps^.75
Since you have mentioned 1e-10 a few times, and the change happened between
a few specific svn commits (r11513 and r11516), I had assumed that the parameter
must have been re-tuned - but am rather surprised that this is *not* the case.
It appears that toler.chol have always been .Machine$double.eps^.75 since at least
June 2000. This means the meaning of it, and how it is used, has changed between
those commits? This is worrying.
Also on the general issues of warnings - it warns on the 5 column matrix
having rank less than 5, so it doesn't help before/after the changes (rank 3 or rank 4).
But convergence and non-convergence probably should be treated quite
differently - the answer from non-convergence is "definitely" wrong, but the
rank 3 convergence result was supposed to be a valid answer to an scientific question.
This rather suggests that those two should be treated/shown differently.
I'd also be interested to hear what went wrong with r11515 (memory consumption just
climbs...). Am quite tempted to set this as a "character-building" exercise to some
of my more capable students. :-).
On Wed, 18/12/13, Terry Therneau <therneau at mayo.edu> wrote:
Subject: Re: Coxph convergence
To: r-help at R-project.org, "Hin-Tak Leung" <htl10 at users.sourceforge.net>, "David Winsemius" <dwinsemius at comcast.net>
Date: Wednesday, 18 December, 2013, 19:12
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
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
I will look into changing the default from its current value
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.
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
I've given a good short term fix.
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