On Aug 25, 2011, at 5:11 PM, array chip wrote:
Hi, in Design package, a plot of survival probability vs. a
covariate can be generated by survplot() on a cph object using the
folliowing code:
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('male','female'), n, TRUE))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)
library(Design)
f <- cph(S ~ age, surv=TRUE,x=T,y=T)
plot(f,age=NA,time=5)
But the same code won't work if I used rms package:
detach(package:Design)
library(rms)
f <- cph(S ~ age, surv=TRUE,x=T,y=T)
plot(f,age=NA,time=5)
Error in xy.coords(x, y, xlabel, ylabel, log) :
'x' and 'y' lengths differ
Is there a way to plot the same graph using rms package.
I don't remember what that would have done in Design and you have not
explained what you expected. You should read the rms help page for cph
and walk through the examples where ht euse of the Predict function is
illustrated. The plotting support for Predict objects is excellent.
I like to use Frank Harrell's new package rms and try to avoid using
old Design package.
Thanks
John
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David Winsemius, MD
West Hartford, CT
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and provide commented, minimal, self-contained, reproducible code.
