I am reviewing the chapter on GLMS and I was wonder what the best approach would be to model something that had 6 outcomes. If you were modelling the decision by a person where the options were fixed to 6 and you had loads of factors that influenced those outcomes. For example, a poker game where a player can decide whether to fold,call,raise(1 big blind), raise(2 big blinds) and raise all in. Thanks
You are looking for multinomial logit or probit models - I think probit is probably a better choice as the logit model assumes that preference for choice is not influenced by addition or removal of other choices, which seems unlikely in the context of a poker game. The problem with datasets with large numbers of parameters is that you are very likely to overfit, as there will be some set of parameters that fit the data very well. An alternative to attempting a regression would be to define a loss model and calculate the loss under each possible choice. However, to look ahead several rounds means the model requires probabilities for choices...
Thanks Calum but is a probit model not restricted to just to 2 dependent variables whereas I am looking to model more than 2 outcomes. Could you advise how the model would be structured? Thanks
Hi Calum I also use R - do you have an idea of the code you'd use for something like this? Wondering if you could state that in the context of how you imagine your data would look, how it's aggregated (or not), what your response variables are etc.
There's a paper demonstrating the MNP package here: http://www.jstatsoft.org/v14/a03/paper Might be more reliable than me as multinomial probits don't feature heavily in my line of work!
