Hi Terry, Thanks for your quick reply. I am talking about uncertainty in the response. I have 2 follow up questions:
1) my understanding from the documentation is that 'id' in cluster(id) should be the same when the predictors are not independent. Is this correct? (To be more concrete: my data are brightnesses at different wavelengths. Each brightness is an independent measurement, so the elements of id should all be different?) 2) I tested survreg with uncertainties on an example where I already know the answer (and where I am not using limits), and it does not converge. Below is the code I used, does anything jump out as incorrect? data = c(144.53, 1687.68, 5397.91) err = c(8.32, 471.22, 796.67) model = c(71.60, 859.23, 1699.19) id = c(1, 2, 3) This works (2.9 is the answer from simple chi_sq fitting): survreg(Surv(time = data, event = c(1,1,1))~model-1, dist='gaussian', init=c(2.9)) This does not converge (2.1 is the answer from chi_sq fitting): survreg(Surv(time = data, event = c(1,1,1))~model-1+cluster(id), weights=1/(err^2), dist='gaussian', init=c(2.1)) And this does, but the answer it returns is wonky: data[2] = 3*err[2] # data[2] is very close to 3*err[2] already survreg(Surv(time = data, event = c(1,2,1))~model-1+cluster(id), weights=1/(err^2), dist='gaussian', init=c(2.1)) Thanks, Kyle On Wed, Jun 12, 2013 at 6:51 AM, Terry Therneau <thern...@mayo.edu> wrote: > 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.
