posbernUC: Positive Bernoulli Sequence Model

In VGAM: Vector Generalized Linear and Additive Models

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 \tau 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 (\tau 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.

See Also

posbernoulli.tb, posbernoulli.b, posbernoulli.t.

Examples

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

VGAM documentation built on Sept. 18, 2024, 9:09 a.m.