On Nov 19, 2010, at 12:50 PM, David Winsemius wrote:
On Nov 19, 2010, at 12:32 PM, Shi, Tao wrote:
Hi list,
I was trying to use "predict.coxph" to calculate martingale
residuals on a test
data, however, as pointed out before
What about resid(fit) ? It's my reading of Therneau & Gramsch [and
of help(coxph.object) ] that they consider those martingale residuals.
The manner in which I _thought_ this would work was to insert some
dummy cases into the original data and then to get residuals by
weighting the cases appropriately. That doesn't seem to be as
successful as I imagined:
> test1 <- list(time=c(4,3,1,1,2,2,3,3), weights=c(rep(1,7), 0),
+ status=c(1,1,1,0,1,1,0,1),
+ x=c(0,2,1,1,1,0,0,1),
+ sex=c(0,0,0,0,1,1,1,1))
> coxph(Surv(time, status) ~ x , test1, weights=weights)$weights
Error in fitter(X, Y, strats, offset, init, control, weights =
weights, :
Invalid weights, must be >0
# OK then make it a small number
> test1 <- list(time=c(4,3,1,1,2,2,3,3), weights=c(rep(1,7), 0.01),
+ status=c(1,1,1,0,1,1,0,1),
+ x=c(0,2,1,1,1,0,0,1),
+ sex=c(0,0,0,0,1,1,1,1))
> print(resid( coxph(Surv(time, status) ~ x ,
test1,weights=weights) ) ,digits=3)
1 2 3 4 5 6 7 8
-0.6410 -0.5889 0.8456 -0.1544 0.4862 0.6931 -0.6410 0.0509
Now take out constructed case and weights
> test1 <- list(time=c(4,3,1,1,2,2,3),
+ status=c(1,1,1,0,1,1,0),
+ x=c(0,2,1,1,1,0,0),
+ sex=c(0,0,0,0,1,1,1))
> print(resid( coxph(Surv(time, status) ~ x , test1) ) ,digits=3)
1 2 3 4 5 6 7
-0.632 -0.589 0.846 -0.154 0.486 0.676 -0.632
Expecting approximately the same residuals for first 7 cases but not
really getting it. There must be something about weights in coxph that
I don't understand, unless a one-hundreth of a case gets "up indexed"
inside the machinery of coxph?
Still think that inserting a single constructed case into a real
dataset of sufficient size ought to be able to yield some sort of
estimate, and only be a minor perturbation, although I must admit I'm
having trouble figuring out ... why are we attempting such a maneuver?
The notion of "residuals" around constructed cases makes me
statistically suspicious, although I suppose that is just some sort of
cumulative excess/deficit death fraction.
http://tolstoy.newcastle.edu.au/R/e4/help/08/06/13508.html
predict(mycox1, newdata, type="expected") is not implemented yet.
Dieter
suggested to use 'cph' and 'predict.Design', but from my reading so
far, I'm not
sure they can do that.
Do you other ways to calculate martingale residuals on a new data?
Thank you very much!
...Tao
--
David Winsemius, MD
West Hartford, CT
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