Hi,

I apologize in advance if these questions are too basic, but I’m a new  
comer to the mixed-effects models world:

I’m analyzing several time-series of different length of fish catches  
in several rivers spread along a latitudinal gradient and modelling  
them with a random intercept and slope model as follows:

Model1<- 
lme 
(Y 
~ 
X1 
+ 
X2 
+ 
X1 
*X2 
,random=list(river=pdDiag(~X1)),data=mydata,correlation=corAR1(form=~1| 
river))

Where Y is the dependent variable (catches), X1 is a continuous  
variable (temperature) and X2 is a factor variable (presence/absence  
of dams). My question is related with the assumption that there is no  
correlation between residuals of different time series, but that could  
be violated if fishes on one river are affecting those on other  
rivers. Calculating the correlation coefficients between the residual  
time-series show some degree of correlation between proximate rivers.  
Whatever the biological/environmental reason for this could be, is  
there any way of allowing both temporal and spatial correlation in the  
same model? Is it my reasoning correct?

My second question is more general: Does the stratification variable  
used in variance functions like varIdent, or optionally in varPower,  
varExp…, need to appear as well in the fixed and/or the random part of  
the model? Or, one could use a completely different stratification  
variable?

Many thanks for your expert input.



Jaime.

  
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