beggs

datasets

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, 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

beggs function

Data frame of a two way table.
<dl> <dt>b0, b1, b2, b3, b4</dt>
<dd>The <code>b</code> refers to bacon.
 The number of times bacon was purchased was 0, 1, 2, 3, or 4.</dd> <dt>e0, e1, e2, e3, e4</dt>
<dd>The <code>e</code> refers to eggs.
 The number of times eggs was purchased was 0, 1, 2, 3, or 4.</dd>
 
</dl>

Format

Bacon and Eggs Data — beggs

Data frame of a two way table.
<dl>

 <dt>b0, b1, b2, b3, b4</dt>
<dd>The <code>b</code> refers to bacon.
 The number of times bacon was purchased was 0, 1, 2, 3, or 4.

</dd>

 <dt>e0, e1, e2, e3, e4</dt>
<dd>The <code>e</code> refers to eggs.
 The number of times eggs was purchased was 0, 1, 2, 3, or 4.

</dd>


 
</dl>

beggs: Bacon and Eggs Data

Description

Usage

Arguments

Format

Details

References

See Also

Examples