On 06/05/2012 11:32 AM, Prof Brian Ripley wrote:
On 05/06/2012 16:17, Terry Therneau wrote:
I was looking at how the model.frame method for lm works and comparing
it to my own for coxph.
The big difference is that I try to retain xlevels and predvars
information for a new model frame, and lm does not.
I use a call to model.frame in predict.coxph, which is whyI went that
route, but never noted the difference till now (preparing for my course
in Nashville).
Could someone shed light on the rationale for non-preservation?
T'other way round ... it would have needed a conscious decision to
preserve them: these all predate xlevels and predvars.
That's true, but lots of other things have been changed, e.g.
model.frame=T.
I was wondering if there was a positive reason not to do it; this
answers the question.
model.matrix.lm does make use of xlevels, and I think that explains
the difference as most lm() auxiliaries use the model matrix.
But it does not keep predvars, which is interesting. So it fixes one
issue but leaves another undone.
And I don't see predvars used in survival:::model.frame.coxph.
Actually it does, as is proven by the example I showed (ns
reconstruction of the first two lines was the same). Having commented
source code helps to see it of course, I replace formula in the call
with the terms object from the prior fit before invoking model.frame.
This causes inheritance of the both "specials" and predvars.
So, should the survival library change to the old behavior? I prefer my
current one, in that the only time I would do model.frame(prior.fit,
data=new) would be if I want the same treatment of special variables.
If I wanted a new, de novo decision on constrasts or codings I would
have just fit a new model. Is there a plausible situation where the
old behavior is prefereable? Discounting backwards compatability of
course, which is always a thorny issue.
Terry T.
Simple example
> library(survival)
> lfit <- lm(time ~ factor(ph.ecog) + ns(age, 3), data=lung)
> ltemp <- model.frame(lfit, data=lung[1:2,])
> ltemp
time factor(ph.ecog) ns(age, 3).1 ns(age, 3).2 ns(age, 3).3
1 306 1 -0.1428571 0.4285714 0.7142857
2 455 0 0.0000000 0.0000000 0.0000000
> lfit$model[1:2,]
time factor(ph.ecog) ns(age, 3).1 ns(age, 3).2 ns(age, 3).3
1 306 1 0.4443546 0.3286161 0.1900511
2 455 0 0.5697239 0.3618440 -0.1297479
> levels(ltemp[[2]])
[1] "0" "1"
> levels(lfit$model[[2]])
[1] "0" "1" "2" "3"
> cfit <- coxph(Surv(time, status) ~ factor(ph.ecog) + ns(age,3), lung)
> model.frame(cfit, data= lung[1:2,])
Surv(time, status) factor(ph.ecog) ns(age, 3).1 ns(age, 3).2 ns(age,
3).3
1 306 1 0.4443546 0.3286161 0.1900511
2 455 0 0.5697239 0.3618440 -0.1297479
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