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sdwd (version 1.0.5)

plot.cv.sdwd: plot the cross-validation curve of the sparse DWD

Description

Plots the cross-validation curve against a function of lambda values. The function also provides the upper and lower standard deviation curves.

Usage

# S3 method for cv.sdwd
plot(x, sign.lambda, ...)

Arguments

x

A fitted cv.sdwd object.

sign.lambda

Whether to plot against log(lambda) (default) or its negative if sign.lambda=-1.

Other graphical parameters to plot.

Details

This function depicts the cross-validation curves. This function is modified based on the plot.cv function from the glmnet and the gcdnet packages.

References

Wang, B. and Zou, H. (2016) ``Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, 25(3), 826--838. https://www.tandfonline.com/doi/full/10.1080/10618600.2015.1049700

Yang, Y. and Zou, H. (2013) ``An Efficient Algorithm for Computing the HHSVM and Its Generalizations", Journal of Computational and Graphical Statistics, 22(2), 396--415. https://www.tandfonline.com/doi/full/10.1080/10618600.2012.680324

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

cv.sdwd.

Examples

Run this code
# NOT RUN {
data(colon)
colon$x = colon$x[ , 1:100] # this example only uses the first 100 columns 
set.seed(1)
cv = cv.sdwd(colon$x, colon$y, lambda2=1, nfolds=5)
plot(cv)
# }

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