Calculates the Bayesian information criterion (BIC) for
 a fitted model object for which a log-likelihood value
 has been obtained.

BICvlm

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

BICvlm function

Calculates the Bayesian information criterion (BIC) for
 a fitted model object for which a log-likelihood value
 has been obtained.

Bayesian Information Criterion — BICvlm

<dl>

 <dt>object, ...</dt>
<dd>Same as <code>AICvlm</code>.

</dd>

 <dt>k</dt>
<dd>Numeric, the penalty per parameter to be used;
 the default is <code>log(n)</code> where
 <code>n</code> is the number of observations).

</dd>

</dl>

Arguments

Author

Like <code>AICvlm</code>, this code has not been double-checked.
 The general applicability of <code>BIC</code> for the VGLM/VGAM classes
 has not been developed fully.
 In particular, <code>BIC</code> should not be run on some VGAM family
 functions because of violation of certain regularity conditions, etc.

Many VGAM family functions such as
 <code>cumulative</code> can have the number of
 observations absorbed into the prior weights argument
 (e.g., <code>weights</code> in <code>vglm</code>), either
 before or after fitting. Almost all VGAM family
 functions can have the number of observations defined by
 the <code>weights</code> argument, e.g., as an observed frequency.
 <code>BIC</code> simply uses the number of rows of the model matrix, say,
 as defining <code>n</code>, hence the user must be very careful
 of this possible error.
 Use at your own risk!!

BICvlm: Bayesian Information Criterion

Description

Usage

Arguments

Value

Details

See Also

Examples