Hello,
  I have two gamm question (I am using gamm in mgcv).
   
   
  1. In have, say 5 time series. Monthly data, 20 year. The 5 time series are from 5 stations. The data are in vectors, so I have fitted something along the lines of:
   
  tmp<-gamm(Y ~ s(Year,by=station1)+s(Year,by=station2)+
                          s(Year,by=station3)+s(Year,by=station4)+ 
                          s(Year,by=station5)+
                           factor(station)*factor(month),
                           correlation=corAR1(form=~MyTime|station),
                           famliy=gaussian)
   
   
  station is just a long vector with ones, twos, threes, fours and fives. MyTime defines the order of time (and has values 1 to 240)
   
  This model fits a Year smoother on each station and, and has one auto-regressive parameter (whihc is about 0.3). How woul I allow for 5 different AR1 parameters (one per station)?
   
  So far so good. It runs..and  get the output. The problem is that the errors from station 1 are correlated with those of station 2 (as they are close in space). Same holds for other stations. The cross-correlation is about 0.5. How do I build in this between-station correlation? 
   
  So..I have within station auto-correlation (dealt by the AR1 parameter), and between station correlation.
   
   
  Question 2: Why is Simon Wood using in his 2006 book only ML estimation and not REML? I thought that REML was used to compare models with different random components and ML estimation models with different fixed components?
   
  Kind regards,
  Piet Bell
   
   
   

 		
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