Capture-recapture Models
capture fits the Cormack capture-recapture model to n sample
periods. Set n to the appropriate value and type eval(setup).
capture(z, n)
z |
A Poisson generalized linear model object. |
n |
The number of repeated observations. |
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.
capture returns a matrix containing the estimates.
J.K. Lindsey
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)
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