If you fit the model as suggested below and store it in an object, say "model.lme", then you can update the model as follows to include the continuous AR1 structure
 
model2.lme<-update(model.lme, correlation=corCAR1(form=~time|cow) )
 
To compare the two models, you might use the LRT as:
 
anova(model.lme,model2.lme)
 
Harold

	-----Original Message----- 
	From: r-help-bounces@stat.math.ethz.ch on behalf of Spencer Graves 
	Sent: Sun 6/6/2004 12:21 PM 
	To: Alex Bach 
	Cc: r-help@stat.math.ethz.ch 
	Subject: Re: [R] Repeated measures
	
	

	      1.  You didn't say which "manual" you were reading on "lme", if
	you have not consulted Pinheiro and Bates (2000) Mixed-Effects Models in
	S and S-Plus (Springer), I suggest you do so.  The issues are discussed
	in greater depth with many examples.  I found this book well worth the
	time and money I invested in it.
	
	      2.  Have you considered the following:
	
	      lme(milk yield = cow + treatment + time + treatment*time, random =
	~time|cow ... )
	
	      This will NOT have the ARCH(1) R RCORR error structure;  "lme" can
	handle certain types of autocorrelated error structures, but I don't
	remember the details at the moment.  Pinheiro and Bates discuss some
	capabilities of this nature.
	
	      hope this helps.
	      spencer graves
	
	Alex Bach wrote:
	
	> Dear R-gurus,
	>
	> I am pretty much new on R.
	> I am trying to to do a repeated analysis of a linear mixed model with
	> R, and I consistently fail...
	>
	> The problem is: Cow is the random factor, treatment is the fixed
	> factor. The dependent variable is milk yield, which is measured
	> several times (repeatedly over time), thus there is another variable
	> which is time (i.e. week).
	>
	> The model would be something like this: milk yield = cow + treatment +
	> time + treatment*time
	>
	> With time as a repeated measure.
	>
	> Would some one be kind enough to guide on how could I set up the
	> statement in R?
	> I imagine I have to use LME but I have not been smart enough to figure
	> out how to do it by just reading its manual. Also, with SAS there is a
	> nice contrast, called SLICE that allows you to test when in time there
	> is difference between treatments. I do not know if there is something
	> like this is R.
	>
	> Thank you very much!
	>
	> PD. For SAS users, what I am using in SAS to perform this analysis
	> (with an Autoregressive covariance structure) the program would read
	> like this:
	>
	> proc MIXED covtest;
	> CLASS cow treat time;
	> MODEL yield=  treat time treat*time;
	> REPEATED time/SUB=cow(treat) TYPE=ARH(1) R RCORR;
	> RANDOM cow;
	> LSMEANS treat time treat*time/SLICE = time;
	> RUN;
	>
	>
	> Thank you very much,
	>
	> Sincerely,
	>
	> Alex
	>
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