It is easy to get a cumulative hazard curve. First, decide what values of "age", "a", and "b" you want curves for
tdata<- data.frame(age=55, a=2, b=6) Get the curves, there will be one for each strata in the output sfit<- survfit(coxPhMod, newdata= tdata) Plot them plot(sfit, fun='cumhaz', col=1:4, xlab= etc etc) Hazard functions are something else again, estimating these rigorously is akin to density estimation. A quick and dirty method is to use smooth.spline. temp<- sfit[1] #grab the first curve tfit<- smooth.spline(temp$time, -log(temp$surv), df= 5) #smooth the cum haz plot(predict(tfit, deriv=1)) That value of "df=5" is made up -- you need to decide for yourself how much smoothing to do. I make no claims that this is statistically well grounded, it's just a good way to get a quick idea. PS; There is no such thing as "THE" baseline hazard function; predictions are always for some particular value of the covariates. In a book it is sometimes useful to pick a particular set of x values as a default in order to simplify notation, often x=0, and label that as a baseline. But in actual computation all zeros is usually crazy (age=0, weight=0, blood pressure=0, etc). Terry Therneau Hi, I'm going crazy trying to plot a quite simple graph. i need to plot estimated hazard rate from a cox model. supposing the model i like this: coxPhMod=coxph(Surv(TIME, EV) ~ AGE+A+B+strata(C) data=data) with 4 level for C. how can i obtain a graph with 4 estimated (better smoothed) hazard curve (base-line hazard + 3 proportional) to highlight the effect of C. thanks!! laudan ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
