[Limdep Nlogit List] MNL Dummy Variable issues

[contactemt]

Hi,
 
I have a discrete choice model with no universal choice set and a variable number of choices.

I code it thus:

NLOGIT ; Lhs = CHOICE, SETSIZE
    ; Rhs = v
    ; Prob = probs $

Where v is the set of attributes common across all choices ( may be several dozen).

This seems to work fine, with each attribute returned as a variable parameter.

Now I add a further "category type" variable coded as a dummy (1,0) or (1,-1)
The variable is constant across an individual set  of alternatives (it describes a category)
but does vary across choices as one would expect. 

I thought the above model could handle this type of attribute without adjustment.

However, when running the enhanced model I get:

"
Hessian is not positive definite at start values.
  Error   803: Hessian is not positive definite at start values.
B0 is too far from solution for Newton method.
Switching to BFGS as a better solution method.
"

And several of the variables are now returned as fixed parameters.
Also, if I change the value of just one element in a choice set from 1 to -1 (or vice versa) the model runs 
as before, so clearly the fact that the dummy variable does not change within a choice set causes the 
issue.

Is this to be expected and what substantive effects does this have on my model?

I may have, in the full dataset, several dummy variables which describe categories for the choice set and 
be constant across a choice set as above. I wanted to produce a "global model" and combine these 
categories into one set of data. By doing so, using a larger dataset, I hoped to increase the accuracy of 
the parameter esitimates and so the model when classifying new data. 

So my questions are:

Can I just plough ahead and accept that some/many of the variables will be fixed parameters in my general 
model or should I run each category as a separate model? - the danger here would be the datasets, given 
the number of attributes may not be large enough.
Is there a transformation I could/should apply to the category(s) which will allow the "global model" to 
run "better".

Thanks