[R] Robust regression with groups
gunter.berton at gene.com
Thu Oct 21 18:28:10 CEST 2004
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
-- 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: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Angelo Secchi
> Sent: Thursday, October 21, 2004 7:58 AM
> To: r-help at stat.math.ethz.ch
> Subject: Re: [R] Robust regression with groups
> 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
> 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?
> 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.
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