print.ordinalNetTune

Prints the data frame returned by the <code>summary.ordinalNetTune()</code> method.

Fits ordinal regression models with elastic net penalty.
Supported model families include cumulative probability, stopping ratio,
continuation ratio, and adjacent category. These families are a subset of
vector glm's which belong to a model class we call the elementwise link
multinomial-ordinal (ELMO) class. Each family in this class links a vector
of covariates to a vector of class probabilities. Each of these families
has a parallel form, which is appropriate for ordinal response data, as
well as a nonparallel form that is appropriate for an unordered categorical
response, or as a more flexible model for ordinal data. The parallel model
has a single set of coefficients, whereas the nonparallel model has a set of
coefficients for each response category except the baseline category. It is
also possible to fit a model with both parallel and nonparallel terms, which
we call the semi-parallel model. The semi-parallel model has the flexibility
of the nonparallel model, but the elastic net penalty shrinks it toward the
parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021)
<doi:10.18637/jss.v099.i06>.

Michael Wurm

ordinalNet

Penalized Ordinal Regression

Paul Rathouz

Bret Hanlon

print.ordinalNetTune function

<dl><dt>x</dt>
<dd>An "ordinalNetTune" S3 object.</dd>
<dt>...</dt>
<dd>Not used. Additional print arguments.</dd></dl>

Arguments

Print method for an "ordinalNetTune" object. — print.ordinalNetTune

<dl>

<dt>x</dt>
<dd>An "ordinalNetTune" S3 object.</dd>


<dt>...</dt>
<dd>Not used. Additional print arguments.</dd>

</dl>

print.ordinalNetTune: Print method for an "ordinalNetTune" object.

Description

Usage

Arguments

See Also

Examples