Specifying correlation structure
Thanks for the comments! Currently I'm only trying to work on the model specification part with one subject. With multiple subjects, I can easily switch from gls to lme. Also the issue of corARMA or corAR1 or others is not important right now. Instead my focus is, how to correctly specify the correlation structure across the three conditions? You've kindly helped me out on imposing the same correlation structure for the three conditions. The only dangling part is, how to specify a different correlation structure for each condition while modeling the correlation similarity across the three conditions? Gang
On Tue, May 15, 2012 at 12:15 PM, Gavin Simpson <gavin.simpson at ucl.ac.uk> wrote:
On Tue, 2012-05-15 at 11:47 -0400, Highland Statistics Ltd wrote:
Many thanks for the suggestion. It seems that gls does not like that either:
/ ?(fm<- gls(res ~ 1+reg1+reg2+reg3, correlation=corARMA(c(0.02, 0.03), form=~time|condition, p=1,q=1), data=Dat))
/ Error in model.frame.default(formula = ~time + condition + res + reg1 + ?: ? ?variable lengths differ (found for 'condition')
<snip />
That is because 'condition' is not in your Dat object. I guess it should be cond. Why corARMA and not corAR1? I'm actually not sure whether your modelling approach is correct. The unit is the subject....but the time | cond is imposing the correlation inside the observations from the same condition. Is that what you want? It would ignore any correlation between 2 observations from different conditions....but still from the same subject. But perhaps I did not fully understand your original post.
Those are good points Alain. My comment would be that the OP mentioned that there was just a single subject; whether that was just the example or a real property of the data, who knows? ;-) Like you, without further info, whether the nested ARMA(1,1) is sufficient will depend on the OP providing more info. G
Alain /
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