[R] Help with Mixed effect modeling in R

From: Kulkarni, Rohit <rohit.kulkarni_at_roche.com>
Date: Tue, 06 May 2008 18:06:23 -0700


Hi everyone,  

I want to fit the following mixed effect model  

  Y_ij = b_0i + b_1i * (t_ij*grp_ij == 1) + b_2i * (t_ij*grp_ij == 2) + v_0i + v_1i*t_ij + e_ij  

with a different covariance matrix of random effects for each group.  

(Y is the response

t is time

grp is the group indicator

b 's are fixed effects

v 's are random effects)  

I know that this is possible in SAS (I am no expert in SAS, I just looked up some notes) as  



proc mixed method=ml data=dent1;

class pt grp;

model y = grp grp*t / noint solution;

random intercept t / type=un group=grp subject=pt g gcorr v vcorr;


The key to specifying different covariance structure for the random effects seems to be the highlighted portion in the code. What would be it's equivalent in R?  

In R, I tried the following  

Model1 <- lme(y ~ g1+Tg1+g2+Tg2-1,random = pdBlocked(list(pdSymm(~g1+Tg1-1),pdSymm(~g2+Tg2-1))),data=X.gr,control=c on)  

where, g1 and g2 are the group indicator variables and Tg1 = t*(grp==1), similarly Tg2 is defined.

I am forced to assume different intercept for each group in this approach but anyway this works and gives me some output.  

Now, I have 5 groups (and there 7 measurements on each subject). I specify the corresponding formula for all 5 groups, it returns a message :  

Warning message:

Fewer observations than random effects in all level 1 groups  

This creates a doubt in me that possibly this is not the correct way to specify the model I am interested in.

I would appreciate if someone can help me sort out things.  


~ Rohit

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