Gad Abraham <g.abraham <at> ms.unimelb.edu.au> writes: > > Hi, > > I'm using natural cubic splines from splines::ns() in survival > regression (regressing inter-arrival times of patients to a queue on > queue size). The queue size fluctuates between 3600 and 3900. > > I would like to be able to run predict.survreg() for sizes <3600 and > >3900 by assuming that the rate for <3600 is the same as for 3600 and > that for >4000 it's the same as for 4000 (i.e., keep the splines cubic > within the boundaries but make them constant outside the boundaries). >
[snip] > Any suggestions? Here is one. > range(ovarian$age) [1] 38.8932 74.5041 > trim <- function(x) pmin(74.5041 ,pmax(38.8932 , x)) > s <- survreg(Surv(futime, fustat) ~ ns(age, knots=c(50, 60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian) > s2 <- survreg(Surv(futime, fustat) ~ ns(trim(age), knots=c(50, 60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian) > matplot(newage, cbind(predict(s,newdata=newage),predict(s2,newdata=newage))) > HTH, Chuck > > Thanks, > Gad > ______________________________________________ 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.