Dear R-users,
I would like to use the package flexmix to fit latent classes to a
regression model. My data are repeated measurements of bernouilli
variables so I can use the binomial family link to the glm function. The
design is not balanced, meaning that for some individuals in my data set
I have 10 measurements or more, for others I only have 5 or even less. 
My question is the following. Can flexmix handle this unbalancedness in
the dataset (I looked in the jstatsoft.org paper "Flexmix: A General
Framework for Finite Mixture Models and Latent Class Regression in R"
and it only mentioned a dataset with balanced repeated measurements - 50
persons with 4 measurements each). When I look at the formula for the
loglikelihood of the data, it seems to overweigh individuals with more
measurements in an unbalanced design, am I correct in this and are there
ways to correct for this?
 
Thanks for the help,
Jan
 
Jan Wijffels
University Center for Statistics 
W. de Croylaan 54
3001 Heverlee
Belgium
tel: +32 (0)16 322784
fax: +32 (0)16 322831
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