You are right. In my case it doesn´t make much difference since the mean
of my covariates is about 0.
I made some more plots and again I´m confused. I thought when forcing
all covariates via the newdata argument to be zero I would get the
baseline function since the "linear predictor" is then
Hello Therry,
it´s really great to receive some feedback from a "pro". I´m not sure if
I´ve got the point right:
You suppose that the cox-model isn´t good at forecasting an expected
survival time because of the issues with the prediction of the
survival-function at the right tail and one shoul
Hi,
I just came across another question concerning predict.coxph
Terry Therneau states in "A Package for Survival Analysis in S" that
term <- predict(fit, type="terms")
yields predicted values for the individual components of the linear
predictor X*beta
My coxmodel looks like:
coef
Jeff Xu wrote:
I am confused when trying the function survfit.
my question is: what does the survival curve given by plot.survfit mean?
is it the survival curve with different covariates at different points?
or just the baseline survival curve?
for example, I run the following code and get the
Hi,
if I got it right then the survival-time we expect for a subject is the
integral over the specific survival-function of the subject from 0 to t_max.
If I have a trained cox-model and want to make a prediction of the
survival-time for a new subject I could use
survfit(coxmodel, newdata=new
Hi!
I came across R just a few days ago since I was looking for a toolbox
for cox-regression.
I´ve read
"Cox Proportional-Hazards Regression for Survival Data
Appendix to An R and S-PLUS Companion to Applied Regression" from John Fox.
As described therein plotting survival-functions works wel
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