Since my coding philosophy is "why compute something that is not
needed" I don't have timing data comparing coxph.fit to the stripped
down version. I will try to come up with a test suite.
I do work under Linux (the initial Windows output was because I had both 3.3.1
and 3.4.0 on that machine). So I can get the source code and build the necessary
parts into my function/package, but doesn't it defeat the purpose of
reusable code?
Thanks,
Venkat
-----Original Message-----
From: Therneau, Terry M., Ph.D. [mailto:therneau at mayo.edu]
Sent: Thursday, April 27, 2017 11:41 AM
To: Seshan, Venkatraman E./Epidemiology-Biostatistics; Therneau, Terry M.,
Ph.D.; r-help
Cc: murdoch.duncan at gmail.com
Subject: Re: survival package can't find Ccoxfit6
On 04/27/2017 09:53 AM, SeshanV at mskcc.org wrote:> Thank you Drs. Therneau and Murdoch.
>
> "Why not use coxph.fit?" -- My use case scenario is that I needed
the Cox model coefficients for resampled data. I was trying to reduce the
computational overhead of coxph.fit (since it will repeated a large number of
times) by stripping all the parts that I don't need such as sorting of the
data prior to coxfit6.c call and Martingale residual and concordance
computations after the parameters are estimated.
That is an interesting use case which I had not thought about. The first
question is just how much slower coxph.fit is than the stripped down version
(I'd guess about 1/2 but that is just a guess), and whether that makes a
real difference to your code. If it is spending 10% of its time in the coxph
calculation a change to 5% isn't that much, but 90% is something else. The
next is what is the main impediment (I'd guess concordance, but
again just a guess.) Perhaps I could add concordance= and/or resid= flags to
the fitting
routine.
>
> Under the R v3.4.0 model one cannot create any modified form of
> coxph.fit and expect it to work. Worse yet is the following where I
> copy "coxph.fit" to my workspace as "mycoxph.fit"
(works initially
> because the environment is namespace:survival and fails when
> environment changed to R_GlobalEnv)
>
If you were under linux another solution would be to grab the source from
github, add your routine to the R/ directory, then R CMD build followed by R CMD
INSTALL. Macintosh is essentially as easy, though you need to install Xcode for
the compilers. The compile toolchain for windows is outside my ken.
Let's keep talking.
Terry T.
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