Dear R-sig-eco,
 
I have been running linear mixed models with lmer with a gaussian error distribution and log link such as:
 
m1<-lmer(formula = growth ~ dbh + ba + (1 | Plot), method = "Laplace", family=gaussian(link="log"))

 
The model above does not converge when I add an interaction term so I tried an exponential model of the following type with nlme: 
 
m2 <- nlme(growth ~ exp(j) * exp(a*dbh) * exp(b*ba) ,
            fixed = j + a + b ~ 1,
            random = j ~ 1,
            groups = ~Plot,
            start = c(a=0.003, b= -0.1, j=0.1))
 
Models m2 and m1 are supposed to be equivalent, and I am using m2 (nlme) because it does converge when I add interaction terms. Although the answers I get for m1 and m2 (no interaction term added) are pretty similar (eg similar R2 between observed and predicted values), it puzzles me that the AICs are completely different , even if trying different methods (eg REML or ML). Can I still consider these models as equivalent or am I missing something?
 
Also, I am wondering if it makes sense to test any interaction effects with this type of model. With a log link = exponential model the effects of different descriptor variables are multiplicative. Does it make any sense to test an interaction effect like the one below if the effects of the variables are already multiplicative?
 
m3<- nlme(growth ~ exp(j) * exp(a*dbh) * exp(b*ba) * exp(c*dbh*ba) ,
            fixed = j + a + b + c ~ 1,
            random = j ~ 1,
            groups = ~Plot,
            start = c(a=0.003, b=-0.1, c=0.1, j= 0.1))
 
Any comments appreciated. Thanks in advance,
 
Tomas Easdale
Landcare Research, NZ
  
 




	[[alternative HTML version deleted]]

