)
svyDesignObject- svydesign(id=~1,weights=~1,data=lungSubSet)
svyKm - svykm(S~1,design=svyDesignObject,se=T)
##
plot(svyKm,xlim=c(0,1200))
lines(sKm,conf.int=T,mark.time=F,col='green')
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On Thu, Mar 28, 2013 at 5:07 AM, rm r...@wippies.se wrote:
While testing that I get the same results with the package survey as with
the package survival, I encountered the issue of how to draw survival
curves. Apparently the implementations in the two packages differ, as I
show below.
I
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.
(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!
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Of rm
Sent: Montag, 25. März 2013 10:47
To: r-help@r-project.org
Subject: [R] Weighted Kaplan-Meier estimates with R (with confidenceintervals)?
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
pieces
of code give the same survival curve but different confidence intervals.
Why? How should I fix the code to get the “correct” confidence intervals?
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survfitkm() from the survey package.
Regards
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