mpath (version 0.4-2.21)

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

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 COCO algorithm

cfun, s

original input arguments

delta

delta value used for cfun="gcave"

Details

The model is fit by the COCO algorithm.

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

References

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

See Also

print, predict, coef.

Examples

# NOT RUN {
#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")
# }