sdwd-internal: internal sdwd functions
In sdwd: Sparse Distance Weighted Discrimination

Description Usage Details Author(s) References

Internal sdwd functions.

cv.sdwdNET(outlist, lambda, x, y, foldid, pred.loss)
cvcompute(mat, foldid, nlams)
err(n, maxit, pmax)
error.bars(x, upper, lower, width=0.02, ...)
getmin(lambda, cvm, cvsd)
getoutput(fit, maxit, pmax, nvars, vnames)
lambda.interp(lambda, s)
lamfix(lam)
nonzero(beta, bystep=FALSE)
zeromat(nvars, nalam, vnames, stepnames)

These internal functions are not intended for use by users. coef.sdwdNET computes the coefficient of the sdwd object. cv.sdwdNET does cross-validation for the sdwd object. cvcompute computes the mean and the standard deviation of the cross-validation error. err obtains the error message from fortran code. error.bars helps to plot the cross-validation error curve. getmin addresses the best lambda through the cross-validation either using or not using the one-standard-deviation rule. getoutput organizes the output of the sdwd object. lambda.interp conducts the linear interpolation of the lambdas values to obtain the coefficients at new lambda values. Note the obtained coefficients are not the exact values. lamfix fixes the largest lambda value in the lambda sequence. nonzero and zeromat organize the nonzero coefficients. Most of the aforementioned functions are modified or directly copied from the gcdnet and the glmnet packages.

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu

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