Greetings,
I have a very, very, simple research question. I want to predict one
dichotomous variable using another dichotomous variable. Straightforward,
right? The issue is that the dataset has two issues causing some
complications for me.
1) The subjects are not independent -- they are sibling pairs. Every person
in the dataset has a sibling in the dataset. This needs to be treated a
nuisance for the purposes of my analysis.
2) The subjects were not sampled randomly. Some of the subjects had a
higher probability of selection, and I want to incorporate
inverse-probability weights into my analysis to account for this. (The
inverse-probability weights are already calculated).
I know that GEE is an appropriate technique to deal with Issue #1, and I've
toyed with the gee pack in R.
R> library("gee")
http://cran.r-project.org/web/packages/gee/gee.pdf
My question is -- how can I incorporate the sampling weights into the GEE
code? I don't see a spot for it based on the documentation here, unless I'm
missing something obvious. Or is there another GEE function I can use that
would allow me to do this?
Thanks!
--
View this message in context:
http://r.789695.n4.nabble.com/GEE-with-Inverse-Probability-Weights-tp4633172.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
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.
