 

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

 

* Fit a model with a same diagonal Ri matrix for;

* each group. 

* D = (2x2) unstructured matrix differs across groups;

* Specified in the RANDOM statement by the GROUP=grp option;

title 'RANDOM COEFFICIENT MODEL WITH DIAGONAL WITHIN-PATIENT';

title1 'DIFFERENT D MATRIX FOR BOTH GENDERS';

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;

run;

 

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.

 

Thanks.

 

~ Rohit


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