[R] Robust standard errors in logistic regression
tlumley at u.washington.edu
Wed Jul 5 17:56:40 CEST 2006
On Wed, 5 Jul 2006, Martin Maechler wrote:
>>>>>> "Celso" == Celso Barros <celso.barros at gmail.com>
>>>>>> on Wed, 5 Jul 2006 04:50:29 -0300 writes:
> Celso> By the way, I was wondering if there is a way to use rlm (from MASS)
> Celso> to estimate robust standard errors for logistic regression?
> rlm stands for 'robust lm'. What you need here is 'robust glm'.
> I've already replied to a similar message by you,
> mentioning the (relatively) new package "robustbase".
> After installing it, you can
We have a clash of terminology here. The "robust standard errors" that
"sandwich" and "robcov" give are almost completely unrelated to glmrob().
My guess is that Celso wants glmrob(), but I don't know for sure.
The Huber/White sandwich variance estimator for parameters in an ordinary
generalized linear model gives an estimate of the variance that is
consistent if the systematic part of the model is correctly specified and
conservative otherwise. It is a computationally cheap linear
approximation to the bootstrap. These variance estimators seem to usually
be called "model-robust", though I prefer Nils Hjort's suggestion of
"model-agnostic", which avoids confusion with "robust statistics". This is
what sandwich and robcov() do.
glmrob() and rlm() give robust estimation of regression parameters. That
is, if the data come from a model that is close to the exponential family
model underlying glm, the estimates will be close to the parameters from
that exponential family model. This is a more common statistical sense of
the term "robust".
I think the confusion has been increased by the fact that earlier S
implementations of robust regression didn't provide standard errors,
whereas rlm() and glmrob() do. This was partly a quality-of-implementation
issue and partly because of theoretical difficulties with, eg, lms().
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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