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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