begin included message ---
I have a coxph model like
coxph(Surv(start, stop, censor) ~ x + y, mydata)
I would like to calculate the Schoenfeld residuals for the null, i.e the
same model where the beta hat vector (in practical terms, the coeff
vector spat out by summary()) is constrained to
Hi,
I have a coxph model like
coxph(Surv(start, stop, censor) ~ x + y, mydata)
I would like to calculate the Schoenfeld residuals for the null, i.e the same
model where the beta hat vector (in practical terms, the coeff vector spat out
by summary()) is constrained to be all 0s --all lese
Thank you for the reply. I really appreciate it.
I calculated the Scoenfeld residual per event and my results are the
following:
finage race
17 -0.33942334 -2.0722187270.29024804
20 0.394600944 5.303968774 0.517689472
25
-- begin included message
Thank you for the reply. I really appreciate it.
I calculated the Scoenfeld residual per event and my results are the
following:
finage race
17 -0.33942334 -2.0722187270.29024804
20 0.394600944
Dear all,
I am struggling with calculation of Schoenfeld residuals of my Cox Ph
models.
Based on the formula as attached, I calculated the Schoenfeld residuals for
both non tied and tied data, respectively.
And then I validated my results with R using the same data sets. However, I
found that
Formally, the Schoenfeld residuals are defined as one residual per event time.
I found that when there are tied events, however, that plots of the residuals
could be hard to visually interpret: sometimes a residual was large because of
a
lack of fit at that point, sometimes because several
Hi,
Thank you for your comments and apologies for the delay in replying.
rem.Rcens =1 for the censored variables. The problem arises because I
am not strictly looking at time to death. Instead I am looking at
time to 12-month remission in epilepsy. Therefore a lot of people
have the same event
Laura Bonnett was kind enough to send me a copy of the data that caused the
plotting error, since it was an error I had not seen before.
1. The latest version of survival gives a nicer error message:
fit - coxph(Surv(rem.Remtime, rem.Rcens) ~ all.sex, nearma)
cfit - cox.zph(fit)
plot(cfit)
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
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)
Thank you for your comments. I have about 200 out of 2000 tied data
points which makes the situation more complicated! I'll have at look
at the book section you referred to. With regards to making the ylim
finite, I'm not sure how I can go about that given that I don't
understand why it isn't
11 matches
Mail list logo