VGAM: posbernUC – R documentation

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posbernUC

Positive Bernoulli Sequence Model

Description

Density, and random generation for multiple Bernoulli responses where each row in the response matrix has at least one success.

Usage

rposbern(n, nTimePts = 5, pvars = length(xcoeff), xcoeff = c(-2, 1, 2),
         Xmatrix = NULL, cap.effect = 1, is.popn = FALSE,
         link = "logitlink", earg.link = FALSE)
dposbern(x, prob, prob0 = prob, log = FALSE)

Arguments

`x`	response vector or matrix. Should only have 0 and 1 values, at least two columns, and each row should have at least one 1.
`nTimePts`	Number of sampling occasions. Called τ in `posbernoulli.b` and `posbernoulli.t`.
`n`	number of observations. Usually a single positive integer, else the length of the vector is used. See argument `is.popn`.
`is.popn`	Logical. If `TRUE` then argument `n` is the population size and what is returned may have substantially less rows than `n`. That is, if an animal has at least one one in its sequence then it is returned, else that animal is not returned because it never was captured.
`Xmatrix`	Optional X matrix. If given, the X matrix is not generated internally.
`cap.effect`	Numeric, the capture effect. Added to the linear predictor if captured previously. A positive or negative value corresponds to a trap-happy and trap-shy effect respectively.

`pvars`	Number of other numeric covariates that make up the linear predictor. Labelled `x1`, `x2`, ..., where the first is an intercept, and the others are independent standard `runif` random variates. The first `pvars` elements of `xcoeff` are used.
`xcoeff`	The regression coefficients of the linear predictor. These correspond to `x1`, `x2`, ..., and the first is for the intercept. The length of `xcoeff` must be at least `pvars`.
`link, earg.link`	The former is used to generate the probabilities for capture at each occasion. Other details at `CommonVGAMffArguments`.
`prob, prob0`	Matrix of probabilities for the numerator and denominators respectively. The default does not correspond to the M_b model since the M_b model has a denominator which involves the capture history.
`log`	Logical. Return the logarithm of the answer?

Details

The form of the conditional likelihood is described in posbernoulli.b and/or posbernoulli.t and/or posbernoulli.tb. The denominator is equally shared among the elements of the matrix x.

Value

rposbern returns a data frame with some attributes. The function generates random deviates (τ columns labelled y1, y2, ...) for the response. Some indicator columns are also included (those starting with ch are for previous capture history). The default setting corresponds to a M_{bh} model that has a single trap-happy effect. Covariates x1, x2, ... have the same affect on capture/recapture at every sampling occasion (see the argument parallel.t in, e.g., posbernoulli.tb).

The function dposbern gives the density,

Note

The r-type function is experimental only and does not follow the usual conventions of r-type R functions. It may change a lot in the future. The d-type function is more conventional and is less likely to change.

Author(s)

Thomas W. Yee.

Examples

rposbern(n = 10)
attributes(pdata <- rposbern(n = 100))
M.bh <- vglm(cbind(y1, y2, y3, y4, y5) ~ x2 + x3, posbernoulli.b(I2 = FALSE),
             data = pdata, trace = TRUE)
constraints(M.bh)
summary(M.bh)

VGAM

Vector Generalized Linear and Additive Models

v1.1-5

GPL-3

Authors

Thomas Yee [aut, cre], Cleve Moler [ctb] (author of several LINPACK routines)

Initial release

2021-01-13