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 why I 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?
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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