Hello! I have something like this:
test1 <- data.frame(intx=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x1=c(0,2,1,1,1,0,0), x2=c(1,1,0,0,2,2,0), sex=c(0,0,0,0,1,1,1)) and I can easily fit a cox model: library(survival) coxph(Surv(intx,status) ~ x1 + x2 + strata(sex),test1) However, I want to write my own function, fit the model inside this function and then do some further computations. f <- function(time, event, stratum, covar ) { fit <- coxph(Surv(time,event) ~ covar[[1]] + covar[[2]] + strata(stratum)) fit #... do some other stuff } attach(test1) f(intx, status, sex, list(x1,x2)) This works fine when I have exactly two covariates. However, I would like to have something that I can use with an arbitrary number of covariates. More precisely, I need something more general than covar[[1]] + covar[[2]]. Any ideas? Thanks, Franco ______________________________________________ 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.