Hello!

I wonder why two models (gls and lme) provide different
estimates of autocorrelation. A mixed-effect model (s11 below) shows a low
autocorrelation (0.13), while a generalized least square model (ss below) shows
a high autocorrelation (0.68). I am surprised why fitting a random intercept to
each subject can dramatically decrease autocorrelation?
 
Which model is correct then (with or without
autocorrelation)? The likelihood-ratio test showed no significant differences
between s1 and s11 proving that the autocorrelation is not significant. However, the
same test showed that the model with autocorrelation (ss) is significantly better
fit compared to a model without autocorrelation (s).
 
s1<-lme(CenA~Infection*IntervalNum, random=~1|Mouse.ID,
data=a) 
s11<-update(s1, correlation=corAR1(form=~1|Mouse.ID))
 
s<-gls(CenA~Infection*IntervalNum, data=a)
ss<-update(s, correlation=corAR1(form=~1|Mouse.ID))
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