The robust variances are a completely different estimate of standard error. For linear
models the robust variance has been rediscovered many times and so has lots of names: the
White estimate in economics, the Horvitz-Thompson in surveys, working independence
esitmate in GEE models,
I understand that the robust variances may lead to a different
standard error. I want the standard error valid for heteroscedastic
data, ultimately, because I have very good estimates of the
measurement variances (why I'm doing weighted fits in the first
place).
For the simple example here, the
On 02/25/2014 05:00 AM, r-help-requ...@r-project.org wrote:
Hi,
I have some measurements and their uncertainties. I'm using an
uncensored subset of the data for a weighted fit (for now---I'll do a
fit to the full, censored, dataset when I understand the results).
survreg() reports a much
On Tue, Feb 25, 2014 at 10:50 AM, Therneau, Terry M., Ph.D.
thern...@mayo.edu wrote:
On 02/25/2014 05:00 AM, r-help-requ...@r-project.org wrote:
Hi,
I have some measurements and their uncertainties. I'm using an
uncensored subset of the data for a weighted fit (for now---I'll do a
fit
Survreg treats weights as case weights, and lm treats them as sampling
weights.
Here is a simple example. Data set test2 has two copies of every obs in data
set test.
test - data.frame(x=1:6, y=c(1,3,2,4,6,5))
test2 - test[c(1:6, 1:6),]
summary(lm( y ~ x, data=test))$coef
Hi,
I have some measurements and their uncertainties. I'm using an
uncensored subset of the data for a weighted fit (for now---I'll do a
fit to the full, censored, dataset when I understand the results).
survreg() reports a much smaller standard error for the model
parameter than lm(), but only
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