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.
# S3 method for LiblineaR
predict(object, newx, proba = FALSE, decisionValues = FALSE, ...)By default, the returned value is a list with a single entry:
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:
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:
An n x k matrix (k number of classes) of the model decision values. The columns of this matrix are named after class labels.
Object of class "LiblineaR", created by
LiblineaR.
An n x p matrix containing the new input data. A vector will be transformed to a n x 1 matrix. Sparse matrices of class matrix.csr from package SparseM or sparse matrices of class dgCMatrix or dgRMatrix from package Matrix are also accepted. Note that C code at the core of LiblineaR package corresponds to a row-based sparse format. Hence, dgCMatrix inputs are first transformed into dgRMatrix format, which requires small extra computation time.
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.
Logical indicating whether model decision values should
be computed and returned. Only possible for classification models
(type<10). Default is FALSE.
Currently not used
Thibault Helleputte thibault.helleputte@dnalytics.com and
Jerome Paul jerome.paul@dnalytics.com and Pierre Gramme.
Based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin
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.
https://www.csie.ntu.edu.tw/~cjlin/liblinear/
LiblineaR