Hi Angelo,
There are two possible options (at least to my knowledge):
1. to use a random-effects model, either using `lme' (packages: nlme, lme4) if you have normal data or `glmmPQL' (package: MASS) or `GLMM' (package: lme4) or `glmmML' (package:glmmML) if you cannot use the normal distribution.
2. to use a gee model with a robust (sandwich) std.error estimation. See at `gee' (package: gee) and `geese' (package: geepack).
I hope this helps.
Best, Dimitris
---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/396887 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: "Angelo Secchi" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Wednesday, October 20, 2004 3:22 PM
Subject: [R] Robust regression with groups
Hi,
I have data on a group of subjects in different years. I should assume
that observations regarding different individuals are independent but
observations for the same individual in different years are not and I
would like to have an estimated standard error (and variance-covariance
matrix) taking into account this problem.
More in general is there a way in R to run a (robust)regression having
different groups in the observations and specifying that the observation
are independent across groups but not necessarily independent within
groups?
Thanks a.
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