predict.ordinalNet

object

Optional covariate matrix. If NULL, fitted values will be obtained
for the training data, as long as the model was fit with the argument
<code>keepTrainingData=TRUE</code>.

newx

Optional index number of the desired lambda value within
the solution path sequence.

whichLambda

Selects the best lambda value by AIC or BIC. Only used
if <code>whichLambda=NULL</code>.

criteria

The type of prediction required. Type "response" returns a
matrix of fitted probabilities. Type "class" returns a vector containing the
class number with the highest fitted probability. Type "link" returns a
matrix of linear predictors.

type

Not used. Additional predict arguments.

Obtains predicted probabilities, predicted class, or linear predictors.

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>.

predict.ordinalNet: Predict method for an "ordinalNet" object

Description

Usage

Arguments

Value

See Also

Examples