Re: [R] Robust standard errors in logistic regression

From: Celso Barros <>
Date: Wed 05 Jul 2006 - 17:50:29 EST

     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,


On 7/4/06, Achim Zeileis <> 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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        [[alternative HTML version deleted]] mailing list PLEASE do read the posting guide! Received on Wed Jul 05 17:54:28 2006

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