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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