acat

Fits an adjacent categories regression model to an ordered
 (preferably) factor response.

models

regression

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

acat function

<dl>
 <dt>link</dt>
<dd>Link function applied to the ratios of the
 adjacent categories probabilities.
 See <code>Links</code> for more choices.
</dd> <dt>parallel</dt>
<dd>A logical, or formula specifying which terms have
 equal/unequal coefficients.
</dd> <dt>reverse</dt>
<dd>Logical.
 By default, the linear/additive predictors used are
 \(\eta_j = \log(P[Y=j+1]/P[Y=j])\)
 for \(j=1,\ldots,M\).
 If <code>reverse</code> is <code>TRUE</code> then
 \(\eta_j = \log(P[Y=j]/P[Y=j+1])\)
 will be used.</dd> <dt>zero</dt>
<dd>An integer-valued vector specifying which
 linear/additive predictors are modelled as intercepts only.
 The values must be from the set {1,2,...,\(M\)}.</dd> <dt>whitespace</dt>
<dd>See <code>CommonVGAMffArguments</code> for information.</dd></dl>

Arguments

Author

No check is made to verify that the response is ordinal if the
 response is a matrix;
 see <code><a href="/link/ordered?package=VGAM&version=1.1-7" data-mini-rdoc="VGAM::ordered">ordered</a></code>.

Warning 

Ordinal Regression with Adjacent Categories Probabilities — acat

<dl>


 <dt>link</dt>
<dd>Link function applied to the ratios of the
 adjacent categories probabilities.
 See <code>Links</code> for more choices.
</dd>

 <dt>parallel</dt>
<dd>A logical, or formula specifying which terms have
 equal/unequal coefficients.
</dd>

 <dt>reverse</dt>
<dd>Logical.
 By default, the linear/additive predictors used are
 \(\eta_j = \log(P[Y=j+1]/P[Y=j])\)
 for \(j=1,\ldots,M\).
 If <code>reverse</code> is <code>TRUE</code> then
 \(\eta_j = \log(P[Y=j]/P[Y=j+1])\)
 will be used.

</dd>

 <dt>zero</dt>
<dd>An integer-valued vector specifying which
 linear/additive predictors are modelled as intercepts only.
 The values must be from the set {1,2,...,\(M\)}.

</dd>

 <dt>whitespace</dt>
<dd>See <code>CommonVGAMffArguments</code> for information.

</dd>

</dl>

No check is made to verify that the response is ordinal if the
 response is a matrix;
 see <code><a href='https://rdrr.io/r/base/factor.html'>ordered</a></code>.

acat: Ordinal Regression with Adjacent Categories Probabilities

Description

Usage

Value

Arguments

Author

Warning

Details

References

See Also

Examples