# How to subset alternatives in nested multinomial logistic regression?

I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using vanilla logistic regression, where each captain decided to fish or not fish on any given day of the season in a particular fishery. However, this is not a realistic model as many vessels participate in multiple fisheries. Thus, I want to run a nested multinational logistic regression model with the structure at the bottom.

Note that Group 1 and 2 exist to avoid violating the IIA assumption.

Because each species has a different season and within each species captains have variable amounts of fishing quota lbs (when the quota is gone you can no longer fish even if the season is open), not all nodes are available at the same time. I would like to include a dummy variable for whether or not an individual node is an option, but I am not sure if that makes sense or even if this method (multinomial logistic) is still viable.

I could try something like this Coding of semi-numerical variables i.e whuber's response, but I have a lot more subsets and nests within those subsets.

Edit* I have since looked into, but at the moment have not implemented, a varying choice logit model. The description of the model can be found http://web.mit.edu/teppei/www/research/dchoice.pdf.

I am using R and was planning on using lmer4, but I am not sure if I will be able to.

I can't see how you can model this in lmer. There are quite a few specialist choice modeling packages in R. People who estimate a lot of choice models for real-world problem solving (as opposed to academic publication) tend to choose between a latent class logit and a mixed logit (aka random parameters logit). Nested logit is a technique that had its day, 30 years ago. And, just to add to your challenge, this is one of those areas where R is not great, and you will find that some of the models will run for a very long time. Most practitioners will use specialized software that is a lot faster than R for this type of data, such as Stata, SAS, or more specialized packages, such as LIMDEP and Latent Gold.