Predicted values based on a constrained quadratic ordination (CQO)
 object.

predictqrrvglm

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

predictqrrvglm function

Predict Method for a CQO fit — predictqrrvglm

<dl>

 <dt>object</dt>
<dd>Object of class inheriting from <code>"qrrvglm"</code>.</dd>

 <dt>newdata</dt>
<dd>An optional data frame in which to look for variables with which
 to predict. If omitted, the fitted linear predictors are used.

</dd>

 <dt>type, se.fit, dispersion, extra</dt>
<dd>See <code>predictvglm</code>.

</dd>

 <dt>deriv</dt>
<dd>Derivative. Currently only 0 is handled.</dd>

 <dt>varI.latvar, refResponse</dt>
<dd>Arguments passed into <code>Coef.qrrvglm</code>.

</dd>

 <dt>...</dt>
<dd>Currently undocumented.</dd>

</dl>

predictqrrvglm: Predict Method for a CQO fit

Description

Usage

Arguments

Value

Details

References

See Also

Examples