I am not sure that ties are the only reason. If I create a few ties in the
ovarian dataset that Therneau and Lumley provide, all I get are some
warnings:
ovarian[4:5, 1] <- mean(ovarian[4:5, 1])
ovarian[6:8, 1] <- mean(ovarian[6:8, 1])
fit <- coxph( Surv(futime, fustat) ~ age + rx, ovarian)
temp<- cox.zph(fit)
Warning messages:
1: In approx(xx, xtime, seq(min(xx), max(xx), length.out = 17)[2 * ?:
?collapsing to unique 'x' values
2: In approx(xtime, xx, temp) : collapsing to unique 'x' values
The error message you get is requesting a finite ylim. Have you considered
acceding with that request?
Alternative: Assuming the number of tied survival times is modest, have you
tried jitter-ing the rem.Remtime variable a few times to see it the results
are stable?
If the number of ties is large, then you need to review Thernaeu & Gramsch
section 3.3
--
David Winsemius
On Apr 3, 2009, at 7:57 AM, Laura Bonnett wrote:
Dear All,
Sorry to bother you again.
I have a model:
coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma)
and I'm trying to do a plot of Schoenfeld residuals using the code:
plot(cox.zph(coxfita))
abline(h=0,lty=3)
The error message I get is:
Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In sqrt(x$var[i, i] * seval) : NaNs produced
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
My data (nearma) has a lot of rem.Remtime entries which are equal i.e
large amounts of tied data. ?If I remove the entries where this is the
case from the dataset I get the results I want!
Please can someone explain why removing paients with tied remission
time has such an effect on the code and also how to remedy the problem
without removing patients?
Thank you very much,
Laura.