cv.ccsvm: Cross-validation for ccsvm

In zhuwang46/mpath: Regularized Linear Models

Cross-validation for ccsvm

Description

Does k-fold cross-validation for ccsvm

Usage

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

Arguments

formula	symbolic description of the model, see details.
data	argument controlling formula processing via model.frame.
x	x matrix as in ccsvm.
y	response y as in ccsvm.
weights	Observation weights; defaults to 1 per observation
contrasts	the contrasts corresponding to levels from the respective models.
...	Other arguments that can be passed to ccsvm.

Details

Does a K-fold cross-validation to determine optimal tuning parameters in SVM: cost and gamma if kernel is nonlinear. It can also choose s used in cfun.

Value

An object contains a list of ingredients of cross-validation including optimal tuning parameters.

residmat	matrix with row values for kernel="linear" are s, cost, error, k, where k is the number of cross-validation fold. For nonlinear kernels, row values are s, gamma, cost, error, k.
cost	a value of cost that gives minimum cross-validated value in ccsvm.
gamma	a value of gamma that gives minimum cross-validated value in ccsvm
s	value of s for cfun that gives minimum cross-validated value in ccsvm.

Author(s)

Zhu Wang <[e-mail]>

References

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

See Also

ccsvm

Examples

## Not run: 
x <- matrix(rnorm(40*2), ncol=2)
y <- c(rep(-1, 20), rep(1, 20))
x[y==1,] <- x[y==1, ] + 1
ccsvm.opt <- cv.ccsvm(x, y, type="C-classification", s=1, kernel="linear", cfun="acave")
ccsvm.opt$cost
ccsvm.opt$gamma
ccsvm.opt$s

## End(Not run)

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