I could do that. However, the function f that I mentioned below is part of a
bigger program and is nested inside another function, say function A. In
function A I determine the covariates that I want to use and then call my
function f. So even if I use a formula as single argument, I would still need
to construct the formula with the arbitrary number of covariates which then
leads to my original problem.
________________________________________
From: Erik Iverson [er...@ccbr.umn.edu]
Sent: Wednesday, August 11, 2010 12:00 PM
To: Mendolia, Franco
Cc: r-help@r-project.org
Subject: Re: [R] Arbitrary number of covariates in a formula
Are you for some reason against writing your function to accept a single
argument, a formula, that you simply pass on to coxph?
Mendolia, Franco wrote:
> 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
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