capture               package:repeated               R Documentation

_C_a_p_t_u_r_e-_r_e_c_a_p_t_u_r_e _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'capture' fits the Cormack capture-recapture model to 'n' sample
     periods. Set 'n' to the appropriate value and type 'eval(setup)'.

     'n <- periods' # number of periods

     'eval(setup)'

     This produces the following variables -

     'p[i]': logit capture probabilities,

     'pbd': constant capture probability,

     'd[i]': death parameters,

     'b[i]': birth parameters,

     'pw': prior weights.

     Then set up a Poisson model for log linear models:

     'z <- glm(y~model, family=poisson, weights=pw)'

     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 correct 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.

_U_s_a_g_e:

     capture(z, n)

_A_r_g_u_m_e_n_t_s:

       z: A Poisson generalized linear model object.

       n: The number of repeated observations.

_V_a_l_u_e:

     'capture' returns a matrix containing the estimates.

_A_u_t_h_o_r(_s):

     J.K. Lindsey

_E_x_a_m_p_l_e_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)

