Hi,
 I have a bivariate longitudinal dataset.  As an example say,
i have the data frame with column names

var1  var2  Unit  time  trt

(trt represents the treatment)

 Now suppose I want to fit a joint model of the form for the *i* th unit
 var1jk = alpha1 + beta1*timejk  + gamma1* trtjk + delta1* timejk:trtjk +
error1jk

 var2 = alpha2 + beta2*timejk  + gamma2* trtjk + delta2* timejk:trtjk +
error2jk

where j index time and k index the treatment received

Using indicator variables I have been able to fit and run the code for
a bivariate model using unstructured covariance matrix. However,
I want to fit a model for a structured variance covariance matrix.
The error structure for the grouping unit is as follows

sigma = ( sigma1      sigma12 )
             ( sigma12       sigma2)

sigma1, sigma2 and sigma12 are matrices with
where
sigma1 = sig1 * AR1(rho1)
sigma2 = sig2* AR1(rho2)
sigma12 = sig12 * AR1(rho12)

My question is whether there is any method to fit such data using
packages like gee or geepack (or may be any other package ) in R.  The
function  genZcor() of geepack can be used to construct correlation but
I have been unable to use it in the present context.
Any help is greatly appreciated.

Regards
Souvik Banerjee
Lecturer
Department of statistics
Memari College
Burdwan
India

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