Dear Frank and Achim,
Thanks for the help. But I must be doing something wrong. I tried to do
as you suggested:
> B11<-lrm(HIGH93~HIEDYRS)
> g<-robcov(B11)
But I got the following message:
Error in residuals.lrm(fit, type = if (method == "huber") "score" else
"hscore") :
you did not specify y=T in the fit
>
By the way, I was wondering if there is a way to use rlm (from MASS)
to estimate robust standard errors for logistic regression? I am more
familiar with rlm than with packages such as sandwich.
rlm has the big advantage of having a very friendly output, similar to
the familiar lm output (for instance, it has a clearly located "standard
errors" column), and (obviously, due to my poor understanding) I find other
outputs a little bit confusing.
Thanks in advance,
Celso
On 7/4/06, Achim Zeileis <[EMAIL PROTECTED]> wrote:
>
> On Tue, 4 Jul 2006 13:14:24 -0300 Celso Barros wrote:
>
> > I am trying to get robust standard errors in a logistic regression.
> > Is there any way to do it, either in car or in MASS?
>
> Package sandwich offers various types of sandwich estimators that can
> also be applied to objects of class "glm", in particular sandwich()
> which computes the standard Eicker-Huber-White estimate.
>
> These robust covariance matrices can be plugged into various inference
> functions such as linear.hypothesis() in car, or coeftest() and
> waldtest() in lmtest.
>
> See the man pages and package vignettes for examples.
> Z
>
> > Thanks for the help,
> >
> > Celso
> >
> > [[alternative HTML version deleted]]
> >
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