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

posbernUC

An implementation of about 6 major classes of
statistical regression models. The central algorithm is
Fisher scoring and iterative reweighted least squares.
At the heart of this package are the vector generalized linear
and additive model (VGLM/VGAM) classes. VGLMs can be loosely
thought of as multivariate GLMs. VGAMs are data-driven
VGLMs that use smoothing. The book "Vector Generalized
Linear and Additive Models: With an Implementation in R"
(Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
the statistical framework and the package. Currently only
fixed-effects models are implemented. Many (100+) models and
distributions are estimated by maximum likelihood estimation
(MLE) or penalized MLE. The other classes are RR-VGLMs
(reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained
RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs,
RCIMs (row-column interaction models)---these classes perform
constrained and unconstrained quadratic ordination (CQO/UQO)
models in ecology, as well as constrained additive ordination
(CAO). Hauck-Donner effect detection is implemented.
Note that these functions are subject to change;
see the NEWS and ChangeLog files for latest changes.

Thomas Yee

VGAM

Vector Generalized Linear and Additive Models

Cleve Moler

posbernUC function

Positive Bernoulli Sequence Model — posbernUC

<dl>

 <dt>x</dt>
<dd>response vector or matrix.
 Should only have 0 and 1 values, at least two columns, and each
 row should have at least one 1.

</dd>


 <dt>nTimePts</dt>
<dd>Number of sampling occasions.
 Called \(\tau\) in <code>posbernoulli.b</code>
 and <code>posbernoulli.t</code>.

</dd>

 <dt>n</dt>
<dd>number of observations.
 Usually a single positive integer, else the length of the vector
 is used.
 See argument <code>is.popn</code>.

</dd>


 <dt>is.popn</dt>
<dd>Logical.
 If <code>TRUE</code> then argument <code>n</code> is the population size and
 what is returned may have substantially less rows than <code>n</code>.
 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.



</dd>

 <dt>Xmatrix</dt>
<dd>Optional X matrix.
 If given, the X matrix is not generated internally.

</dd>

 <dt>cap.effect</dt>
<dd>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.

</dd>


</dl>

























<dl>


 <dt>pvars</dt>
<dd>Number of other numeric covariates that make up
 the linear predictor.
 Labelled <code>x1</code>, <code>x2</code>, ...,
 where the first is an intercept, and the others are
 independent standard <code><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></code> random
 variates. The first <code>pvars</code> elements of <code>xcoeff</code>
 are used.

</dd>


 <dt>xcoeff</dt>
<dd>The regression coefficients of the linear predictor.
 These correspond to <code>x1</code>, <code>x2</code>, ...,
 and the first is for the intercept.
 The length of <code>xcoeff</code> must be at least <code>pvars</code>.

</dd>


 <dt>link, earg.link</dt>
<dd>The former is used to generate the probabilities for capture
 at each occasion.
 Other details at <code>CommonVGAMffArguments</code>.

</dd>

 <dt>prob, prob0</dt>
<dd>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.

</dd>

 <dt>log</dt>
<dd>Logical. Return the logarithm of the answer?

</dd>


</dl>

posbernUC: Positive Bernoulli Sequence Model

Description

Usage

Arguments

Value

Details

See Also

Examples