I will assume that you are talking about uncertainty in the response. Then one simple way
to fit the model is to use case weights that are proprional to 1/variance, along with
+cluster(id) in the model statement to get a correct variance for this case. In linear
models this would be called the "White" or "Horvitz-Thompsen" or "GEE working
independence" variance estimate, depending on which literature you happen to be reading
(economics, survey sampling, or biostat).
Now if you are talking about errors in the predictor variables, that is a much
harder problem.
Terry Therneau
On 06/12/2013 05:00 AM, Kyle Penner wrote:
Hello,
I have some measurements that I am trying to fit a model to. I also
have uncertainties for these measurements. Some of the measurements
are not well detected, so I'd like to use a limit instead of the
actual measurement. (I am always dealing with upper limits, i.e. left
censored data.)
I have successfully run survreg using the combination of well detected
measurements and limits, but I would like to include the measurement
uncertainty (for the well detected measurements) in the fitting. As
far as I can tell, survreg doesn't support this. Does anyone have a
suggestion for how to accomplish this?
Thanks,
Kyle
______________________________________________
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.
