# predict.LiblineaR: Predictions with LiblineaR model

## Description

The function applies a model (classification or regression) produced by the `LiblineaR`

function to every row of a
data matrix and returns the model predictions.

## Usage

# S3 method for LiblineaR
predict(object, newx, proba = FALSE,
decisionValues = FALSE, ...)

## Arguments

object

Object of class `"LiblineaR"`

, created by
`LiblineaR`

.

newx

An n x p matrix containing the new input data. A vector will be
transformed to a n x 1 matrix. A sparse matrix (from SparseM package) will
also work.

proba

Logical indicating whether class probabilities should be
computed and returned. Only possible if the model was fitted with
`type`

=0, `type`

=6 or `type`

=7, i.e. a Logistic Regression.
Default is `FALSE`

.

decisionValues

Logical indicating whether model decision values should
be computed and returned. Only possible for classification models
(`type`

<10). Default is `FALSE`

.

## Value

By default, the returned value is a list with a single entry:

predictionsA vector of predicted labels (or values for regression).

If proba is set to TRUE, and the model is a logistic
regression, an additional entry is returned:
probabilitiesAn n x k matrix (k number of classes) of the class
probabilities. The columns of this matrix are named after class labels.

If decisionValues is set to TRUE, and the model is not a
regression model, an additional entry is returned:
decisionValuesAn n x k matrix (k number of classes) of the model
decision values. The columns of this matrix are named after class labels.

## References

For more information on 'LIBLINEAR' itself, refer to:
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.
*LIBLINEAR: A Library for Large Linear Classification,*
Journal of Machine Learning Research 9(2008), 1871-1874.
http://www.csie.ntu.edu.tw/~cjlin/liblinear