mpath (version 0.4-2.26)

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

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"

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

Author

Zhu Wang <[e-mail]>

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.

References

Zhu Wang (2024) Unified Robust Estimation, Australian & New Zealand Journal of Statistics. 66(1):77-102.

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")