Dear list members,
I constructed this model:
bao1<-lme(sla~mg, random=~pop/nr.tree, weights=varPower(form=~sla|pop)
variables:
- sla = continuus
- mg = factor
- pop = factor
- nr.tree = factor
So, the variance of sla increases with sla, dependent of the pop.
However, I fitted another (homoscedastic) model, where I transformed sla to
log(sla):
bao2<-lme(log(sla)~mg, random=~pop/nr.tree)
This second model predicts in a better way the observations than the first
model (observed with a sla against fitted(.) plot).
Which model is the most convenient? Are there advantages/disadvantages in the
use of these models?
Thanks in advance,
Sebastiaan De Smedt
Department of Bioscience Engineering
University of Antwerp
Belgium
Tel.: +32 (0)3 265 35 17
Fax.: +32 (0)3 265 32 25
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