lme should do the job (r1,r2,r3 are your random factors):

   library(nlme)
   y.lme <- lme(y ~ 1,random = ~ 1 | r1/r2/r3)
   summary(y.lme)

This is equivalent to a call to varcomp in S-Plus

Pascal

--

Dr. Pascal A. Niklaus
Institute of Botany
University of Basel
Sch�nbeinstrasse 6
CH-4056 Basel / Switzerland


Russell Senior wrote:


Given a set of data:



names(data)


[1] "city" "house" "visit" "value"

I am looking for a way to compute the variance components of the
nested model (ie, visit 1 at house 2 at city 3 isn't related to visit
1 and house 2 at city 4), but different houses in the same city may be
related, and different visits to the same house are probably related.
I want to be able to compute how much of the total variance of "value"
is explained by each of these.  How can I do that in R?

Thanks!





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