I assume I'm missing something obvious here...
The short form of my main question is: how do I extract variance
components from an lme object?
The longer form (plus optional supplementary question!): I'm looking at
some quantitative genetics, and want to estimate two variance components
so that I can then calculate a statistic called Qst from them. So I
have this:
reg1 <- lme(y ~ temp*food, random =~1|POP/SIRE/DAM,
na.action=na.exclude))
summary(reg1)
which shows me the random effects as standard deviations. I then want
the SIRE and POP variance components (as Vsire and Vpop respectively) to
put into the calculation of Qst as
Qst <- Vpop/(Vpop + 8*Vsire)
And obviously writing down the components and then plugging them in is
not elegant. Especially as I then want to calculate the standard error
for Qst, which leads me to my supplementary question...
I could calculate the standard errors by bootstrapping, but it would
probably be quicker to sample the relevant components from the
likelihood, and calculate Qst from that. But I would need to know the
distribution of the components. Can anyone point me in teh right
direction to a reference on this? For balanced data it's easy, but what
about un-balanced data? That wasn't an R query, but you can probably
see whats' coming... If you know the answer to the last question, then
how do I sample from the distribution? Again, if it's a gamma/chi^2
then I already know, but if not...
Thanks in advance!
Bob
--
Bob O'Hara
Rolf Nevanlinna Institute
P.O. Box 4 (Yliopistonkatu 5)
FIN-00014 University of Helsinki
Finland
Telephone: +358-9-191 23743
Mobile: +358 50 599 0540
Fax: +358-9-191 22 779
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