[R] survreg with measurement uncertainties
Kyle Penner
kpenner at as.arizona.edu
Wed Jun 12 21:48:40 CEST 2013
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 <therneau at 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
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