Dear R users,
 I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model.  I am trying to extract the within group correlation matrix or covariance matrix. 
 
here is my code:
f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj, data=dat,corr =corAR1())
> f$sigma
[1] 2.330854

b=summary(f)$modelStruct$corStruct
> b
Correlation structure of class corAR1 representing
      Phi 
0.8518711 

I think  0.8518711 and f$sigma is what I need to reconstruct the variance-cavariance matrix. So, How can I extract 0.8518711 so that I can assign it to a variable? Also, I don't understand what the following parameters estimates mean?  
 
 >summary(f)$modelStruct
reStruct  parameters:
     subj 
-20.15833 
corStruct  parameters:
[1] 2.525869
 
I appreciate your time on this. 
 
Best wishes,
Liqiu 
 
 
 
 
 
 

		
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