] survreg with measurement uncertainties
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
, weights = 1/err^2)
Call:
lm(formula = data ~ model - 1, weights = 1/err^2)
Coefficients:
model
2.606
-Original Message-
From: Kyle Penner [mailto:kpen...@as.arizona.edu]
Sent: Wednesday, June 12, 2013 3:49 PM
To: Terry Therneau
Cc: r-help@r-project.org
Subject: Re: [R] survreg
survreg allows interval censored data, if that is how you want to represent
measurement uncertainty. See
?Surv
-Original Message-
From: Kyle Penner [mailto:kpen...@as.arizona.edu]
Sent: Tuesday, June 11, 2013 8:02 PM
To: r-help@r-project.org
Subject: [R] survreg with measurement
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
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
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.)
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