summary.ordinalNetTune

Provides a data frame which summarizes the cross validation results and may
be useful for selecting an appropriate value for the tuning parameter lambda.

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

summary.ordinalNetTune function

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

Arguments

Summary method for an "ordinalNetTune" object. — summary.ordinalNetTune

<dl>

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


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

</dl>

summary.ordinalNetTune: Summary method for an "ordinalNetTune" object.

Description

Usage

Value

Arguments

See Also

Examples