Angelo: If I understand you correctly, what you want is exactly the mixed effects model that Dmitris has already suggested. As you appear to be confused about the underlying statistical concepts, I suggest that you read at least the first and fourth chapters of MIXED-EFFECTS MODELS IN S AND S-PLUS by Bates and Pinheiro. Chapter 10 of MASS (4th Edition) by Venables and Ripley (which I would unequivocally say should be on every S language user's shelf) contains a much terser overview, but consequently requires a stronger statistical background to understand.
My apologies if I have misunderstood, but the references are good ones anyway. Cheers, Bert -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA "The business of the statistician is to catalyze the scientific learning process." - George E. P. Box > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Angelo Secchi > Sent: Thursday, October 21, 2004 7:58 AM > To: [EMAIL PROTECTED] > Subject: Re: [R] Robust regression with groups > > > Hi, > Bert you are definitely right I've been confuse > and unclear on the nature of my problem (sorry about that). > > In my message "robust regression" was referred to techniques able to > deal (when you estimate the variance of your coefficients) with > departures from the set of assumptions in a standard linear > regression, > like for example the presence of heteroskedaciticy. In this case the > robust estimator of the variance of \beta (i.e. the coefficients) is > obtained considering a correction that take into account the > contribution from each observation to the score(d(ln L)/d\beta). Now I > would like to consider also the possibility that observations are not > independent as they are but they can be divided into groups that are > independent. In this case to obtain an estimator for the variance > that take into account this departure from the standard assumptions I > need a correction that take into account the contribution of > each group > (and not of each observation) to the score(d(ln L)/d\beta). > In summary, > I do not need more sophisticated way to estimate my coefficients but > only a routine to obtain a meaningful estimate for the > variance of them. > Does this routine already exist in R? > > Thanks, > a. > > PS Thanks Dimitris but it seems that I cannot use a random > effects model > since the Hausmann specification test casts doubt on the assumptions > justifying the use of a GLS estimator. > > > > ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
