Problems:
1) some creels use boat counts as index of effort and boats/angler in interviews
2) some creels assume boat (or car/trailer) are "perfect" counts of angler "crafts"...(no extra car, trailer, boat, no undercount of car, trailer, boat)...in this case bias param = 1.0
Solutions:
1) modify data wrangle to add interview counts (by angler group) of boats, anglers (anglers already exist), like trailers, cars
2) modify data wrangle to create now user input for analysis--> dummy variable for car, trailer, boat counts of whether you want to treat as a census or not (fixing bias for each individually to 1.0)
3) modify BSS model to include likelihoods for boats/angler
4) modify BSS model to include time/space variation in bias
5) modify BSS model to enable bias to either be stochastic or be 1 for each bias param.
Problems: 1) some creels use boat counts as index of effort and boats/angler in interviews 2) some creels assume boat (or car/trailer) are "perfect" counts of angler "crafts"...(no extra car, trailer, boat, no undercount of car, trailer, boat)...in this case bias param = 1.0
Solutions: 1) modify data wrangle to add interview counts (by angler group) of boats, anglers (anglers already exist), like trailers, cars 2) modify data wrangle to create now user input for analysis--> dummy variable for car, trailer, boat counts of whether you want to treat as a census or not (fixing bias for each individually to 1.0) 3) modify BSS model to include likelihoods for boats/angler 4) modify BSS model to include time/space variation in bias 5) modify BSS model to enable bias to either be stochastic or be 1 for each bias param.