There are two ways to view weights. One is to treat them as case weights, i.e., a weight
of 3 means that there were actually three identical observations in the primary data,
which were collapsed to a single observation in the data frame to save space. This is the
assumption of survfit. (Most readers of this list will be too young to remember when
computer memory was so small that we had to use tricks like this.) The second assumption
is that the weights are sampling weights and a Horvitz-Thompsen like estimator should be
used for variance. This is the assumption of the svykm program in the survey package. It
appears you want the second behavior.
Terry Therneau
On 03/26/2013 06:00 AM, r-help-requ...@r-project.org wrote:
As part of a research paper, I would like to draw both weighted and
unweighted Kaplan-Meier estimates, the weight being the ?importance? of the
each project to the mass of projects whose survival I?m trying to estimate.
I know that the function survfit in the package survival accepts weights and
produces confidence intervals. However, I suspect that the confidence
intervals may not be correct. The reason why I suspect this is that
depending on how I define the weights, I get very different confidence
intervals, e.g.
require(survival)
s<- Surv(c(50,100),c(1,1))
sf<- survfit(s~1,weights=c(1,2))
plot(sf)
vs.
require(survival)
s<- Surv(c(50,100),c(1,1))
sf<- survfit(s~1,weights=c(100,200))
plot(sf)
Any suggestions would be more than welcome!
______________________________________________
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.
