I have a dataset of individual farms and I want to model the probability for a farm to select a certain type of irrigation (options: none, surface, sprinkler, drip, other).
As independent variables I have individual farm characteristics, such as soil quality and farmer experience. I read that it is also possible to include alternative-specific variables in the model (i.e. characteristics of the irrigation types). Does it make sense to include these if they do not vary over individuals? For example, the variable 'water efficiency' contributes to the irrigation decision, but is not different per individual.
In case it is useful to include these variables, which R package is suitable for this? From my understanding, the
nnet package does not distinguish between different types of variables. Also, the
mlogit makes the distinction but gives the error
system is computationally singular because I have quadratic variables inside the model (at least, I think that this is the reason).