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

In zhuwang46/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.

Usage

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

Details

The model is fit by the IRCO algorithm.

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

print, predict, coef.

Examples

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

zhuwang46/mpath documentation built on March 21, 2022, 4:27 a.m.