irsvm: fit case weighted support vector machines with robust loss...

In mpath: Regularized Linear Models

fit case weighted support vector machines with robust loss functions

Description

Fit case weighted support vector machines with robust loss functions. This is the wrapper function of irsvm_fit, which does the computing.

Usage

## S3 method for class 'formula'
irsvm(formula, data, weights, contrasts=NULL, ...)
## S3 method for class 'matrix'
irsvm(x, y, weights, ...)
## Default S3 method:
irsvm(x,  ...)

Arguments

formula	symbolic description of the model, see details.
data	argument controlling formula processing via model.frame.
weights	optional numeric vector of weights
x	input matrix, of dimension nobs x nvars; each row is an observation vector
y	response variable. Quantitative for type="eps-regression", "nu-regression" and -1/1 for type="C-classification", "nu-Classification".
contrasts	the contrasts corresponding to levels from the respective models
...	Other arguments passing to irsvm_fit

Details

Fit a robust SVM where the loss function is a composite function cfunotype + penalty. The model is fit by the iteratively reweighted SVM, an application of the iteratively reweighted convex optimization (IRCO). Here convex is the loss function induced by type.

For linear kernel, the coefficients of the regression/decision hyperplane can be extracted using the coef method.

Value

An object with S3 class "wsvm" for various types of models.

call	the call that produced this object
weights_update	weights in the final iteration of the IRCO algorithm
cfun, s	original input arguments
delta	delta value used for cfun="gcave"

Author(s)

Zhu Wang <[e-mail]>

References

Zhu Wang (2020) Unified Robust Estimation, arXiv e-prints, https://arxiv.org/abs/2010.02848

See Also

irsvm_fit, print, predict, coef.

Examples

#binomial
x=matrix(rnorm(100*20),100,20)
g2=sample(c(-1,1),100,replace=TRUE)
fit=irsvm(x,g2,s=1,cfun="ccave",type="C-classification")

mpath documentation built on Jan. 7, 2023, 1:17 a.m.