Dear R users,

I am fitting a  hierarchical logistic model with some `unbalanced'
covariates and also some `unbalanced random' intercepts.
The model is as follows:

Let Y_ijk be the probability of success for the ith individual in class k
 of school j. Assume that each individual can pass through a maximum of 4
classes  and that class 1 is of type I, .., class 4 is of type IV. Assume
also that we are not going to consider random effects for class 1 and class
4. So our model is

logit(Y_ijk)= alpha1+beta1*X1_ij
, for class 1
                  alpha2+beta1*X1_ij+beta2*X2_ij + b2_kj + b_j      , for
class 2
                  alpha3+beta1*X1_ij+beta3*X2_ij + b3_kj + b_j      , for
calss 3
                  alpha4+beta1*X1_ij
, for class 4

Note that alpha1-alpha4 are fixed effects intercepts for being in the first
to the 4th class. X1 has the same effect in all classes (beta1). X2 can only
be observed for class 2 and class 3 and its effects depend on the
class(beta2 + beta3).  The random intercepts of class 1 and class 4, that is
though we have up to 4 classes they are not necesarrily the same individuals
in the same school.

So my question is how can one fit such a model in R.

Regards,

-- 
Luwis Diya, Phd student (Biostatistics),
Biostatistical Center,
School Of Public Health,
Catholic University of Leuven,
U.Z. St Raphael,
Kapucijnenvoer 35,
B-3000 Leuven,
Belgium,

Cell: +32(0)497 22 94 83
Phone:+32(0)16 32 68 86 [Office]
Phone:+32(0)16 32 98 76 [Home]
Fax: +32(0)16 33 70 15

Email: luwis.diya@med.kuleuven.be
http://med.kuleuven.be/biostat/staff/LD.htm

	[[alternative HTML version deleted]]


