# predict.LiblineaR

0th

Percentile

##### Predictions with LiblineaR model

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.

Keywords
multivariate, classes, models, regression, classif, optimize
##### 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.
...
Currently not used
##### Value

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

predictions
A 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:
probabilities
An 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:
decisionValues
An n x k matrix (k number of classes) of the model decision values. The columns of this matrix are named after class labels.

##### Note

If the data on which the model has been fitted have been centered and/or scaled, it is very important to apply the same process on the newx data as well, with the scale and center values of the training data.

##### 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

LiblineaR