Density, and random generation for multiple Bernoulli responses where each row in the response matrix has at least one success.
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)
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,
response vector or matrix. Should only have 0 and 1 values, at least two columns, and each row should have at least one 1.
Number of sampling occasions.
Called \(\tau\) in posbernoulli.b
and posbernoulli.t
.
number of observations.
Usually a single positive integer, else the length of the vector
is used.
See argument 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.
Optional X matrix. If given, the X matrix is not generated internally.
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.
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.
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
.
The former is used to generate the probabilities for capture
at each occasion.
Other details at CommonVGAMffArguments
.
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.
Logical. Return the logarithm of the answer?
Thomas W. Yee.
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
.
posbernoulli.tb
,
posbernoulli.b
,
posbernoulli.t
.
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)
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