Fitting a proportion including error

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Arnaud
2013-08-06
2013-08-06
  • Arnaud
    Arnaud
    2013-08-06

    Hi all,

    Using a hierarchical model, I am trying to fit observed data to predicted data obtained through the population dynamic part.

    One of my output is the number of individual dead each year and I want to link this number to the number of dead individual observed each year considering that only a limited proportion can be recovered.

    Thus, I considered that observed individuals are the result of a binomial trial using the following code.

    for (i in 1:Nyears){
        deadObs[i] ~ dbin(pObs, round(deadPred[i]))
    }
    

    I first used a uniform prior for pObs, but now I want to include an error on this parameter.

    • The use of a prior taking the form of a beta distribution seems correct, however, I do not see how to include an error that I can control and make "equivalent" among years.

    • Another thing is that observing a dead animal is like taking a ball from an urn without replacement ... so I wonder if I need to use a hypergeometric distribution in place of the binomial.

    Any suggestion would be greatly appreciated