capture {repeated} | R Documentation |

`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.

capture(z, n)

`z` |
A Poisson generalized linear model object. |

`n` |
The number of repeated observations. |

`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)

[Package *repeated* version 1.0 Index]