

   CCaappttuurree--rreeccaappttuurree MMooddeell

        n <- periods # number of periods
        eval(setup)
        z <- glm(y~...,family=poisson,weights=pw)
        capture(z,n)

   AArrgguummeennttss::

          z: A Poisson generalized linear model

          n: The number of repeated observations.

   DDeessccrriippttiioonn::

        `capture' fits the Cormack capture-recapture model to n
        sample periods. Set n to the appropriate value and type
        eval(setup). This produces the following variables -
        p[i]: logit capture probabilities, pbd: constant cap-
        ture probability, d[i]: death parameters, b[i]: birth
        parameters, pw: prior weights.  Then set up a Poisson
        model for log linear models and call the function,
        `capture'.

        If there is constant effort, then all estimates are
        correct.  Otherwise, n[1], p[1], b[1], are correct only
        if there is no birth in period 1.  n[s], p[s], are cor-
        rect only if there is no death in the last period.
        phi[s-1] is correct only if effort is constant in (s-1,
        s).  b[s-1] is correct only if n[s] and phi[s-1] both
        are.

   VVaalluuee::

        `capture' returns a matrix containing the estimates.

   AAuutthhoorr((ss))::

        J.K. Lindsey

   EExxaammpplleess::

        y <- c(0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,14,1,1,0,2,1,2,1,16,0,2,0,11,
             2,13,10,0)
        n <- 5
        eval(setup)
        # closed population
        print(z0 <- glm(y~p1+p2+p3+p4+p5, family=poisson, weights=pw))
        # deaths and emigration only
        print(z1 <- update(z0, .~.+d1+d2+d3))
        # immigration only
        print(z2 <- update(z1, .~.-d1-d2-d3+b2+b3+b4))
        # deaths, emigration, and immigration
        print(z3 <- update(z2, .~.+d1+d2+d3))
        # add trap dependence
        print(z4 <- update(z3, .~.+i2+i3))
        # constant capture probability over the three middle periods
        print(z5 <- glm(y~p1+pbd+p5+d1+d2+d3+b2+b3+b4, family=poisson, weights=pw))
        # print out estimates
        capture(z5, n)

