actually, maybe I haven't quite solved this - the plot doesn't appear to include the 23 that are still alive at the end.
I have tried using a status variable (1 or 0 dependant upon survival as in page 800, roughly, in the R book) ... Call: survfit(formula = Surv(ssl$long, ssl$status) ~ ssl$sex) 23 observations deleted due to missingness (these 23 will be the 23 long - longevity - scores of NA) and with the etype (http://stat.ethz.ch/R-manual/R-devel/library/survival/html/survfit.formula.html) and censor=T (http://stat.ethz.ch/R-manual/R-devel/library/survival/html/survfit.coxph.html) arguments but I can't seem to get them to work, maybe it's because its a monday night, but I seem to have hit a dead end... bad times. robgriffin247 wrote > > Hi David thanks for the help, i was just looking back to say I have found > my solution (i think) and as you suggested survfit was the way to go. > > my solution was this: > model<-survfit(Surv(a$long)~a$sex) > plot(model,ylab="proportion alive",xlab="time (days)",col=c("red","blue")) > -- View this message in context: http://r.789695.n4.nabble.com/Survival-Curves-tp4561685p4562155.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
