Multinomial Conditional Logit Model

Hello R Help,

I have a question regarding Multinomial (Conditional) LOGIT models in R.

For my masters thesis I like to use Multinomial LOGIT models to analyse 
consumer choice data.

After orientation on the R homepage and internet I found a method for 
multinomial LOGIT. This model is as follows:

P_s(X_i) = exp(B_s * X_i) / som_j(B_j * X_i)

s = Class or Response (bought brand A, B, C or D)
X_i = Characteristics that describe characteristics of purchase incident i.
B_s = Parameter vector for Class s.

This model estimates for each class a B_s vector. (or actually one less 
because B_0 = 1)
I use the function multinom from the nnet package.

Now I am also interested in Conditional Multinomial Logit model with the 
following formulation.

P_s(X_is) = exp(B * X_is) / som_j(B * X_is)
s = Class or Response (bought brand A, B, C or D)
X_is = Characteristics that describe characteristics of purchase incident I 
for Class s.
B = Parameter vector for all classes s (only one..).

This model estimates one B vector.

My question is: “Is it possible to estimate a Conditional Multinomial Logit 
model as described above with R”? And How??

Something that comes close is the implementation by John Hendrix in the 
CATSPEC package. This allows for constraints on the response variable, but 
it estimates a B_s vector for each Class and not one B vector. This is not 
what I like to have and not according to the specification of a Conditional 
LOGIT model as I found in literature.

I’m interested in commands on this. Is the information above correct because 
it could well be that I am wrong since I am new to this kind of models.

Thanks in advance for any responses or suggestions.

Arne Jol
Student Informatics & Economics
Erasmus University Rotterdam
E-mail: arnejol at gmail.com
Tel: +31 618088152