kerndwd (version 2.0.3)

cv.kerndwd: cross-validation

Description

Carry out a cross-validation for kerndwd to find optimal values of the tuning parameter lambda.

Usage

cv.kerndwd(x, y, kern, lambda, nfolds=5, foldid, wt, ...)

Arguments

x

A matrix of predictors, i.e., the matrix x used in kerndwd.

y

A vector of binary class labels, i.e., the y used in kerndwd. y has to be two levels.

kern

A kernel function.

lambda

A user specified lambda candidate sequence for cross-validation.

nfolds

The number of folds. Default value is 5. The allowable range is from 3 to the sample size.

foldid

An optional vector with values between 1 and nfold, representing the fold indices for each observation. If supplied, nfold can be missing.

wt

A vector of length \(n\) for weight factors. When wt is missing or wt=NULL, an unweighted DWD is fitted.

…

Other arguments being passed to kerndwd.

Value

A cv.kerndwd object including the cross-validation results is return..

lambda

The lambda sequence used in kerndwd.

cvm

A vector of length length(lambda): mean cross-validated error.

cvsd

A vector of length length(lambda): estimates of standard error of cvm.

cvupper

The upper curve: cvm + cvsd.

cvlower

The lower curve: cvm - cvsd.

lambda.min

The lambda incurring the minimum cross validation error cvm.

lambda.1se

The largest value of lambda such that error is within one standard error of the minimum.

cvm.min

The cross-validation error corresponding to lambda.min, i.e., the least error.

cvm.1se

The cross-validation error corresponding to lambda.1se.

Details

This function computes the mean cross-validation error and the standard error by fitting kerndwd with every fold excluded alternatively. This function is modified based on the cv function from the glmnet package.

References

Wang, B. and Zou, H. (2018) ``Another Look at Distance Weighted Discrimination," Journal of Royal Statistical Society, Series B, 80(1), 177--198. https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12244 Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33(1), 1--22. https://www.jstatsoft.org/v33/i01/paper

See Also

kerndwd and plot.cv.kerndwd

Examples

# NOT RUN {
set.seed(1)
data(BUPA)
BUPA$X = scale(BUPA$X, center=TRUE, scale=TRUE)
lambda = 10^(seq(3, -3, length.out=10))
kern = rbfdot(sigma=sigest(BUPA$X))
m.cv = cv.kerndwd(BUPA$X, BUPA$y, kern, qval=1, lambda=lambda, eps=1e-5, maxit=1e5)
m.cv$lambda.min
# }