Internal sdwd functions.

1 2 3 4 5 6 7 8 9 10 | ```
cv.sdwdNET(outlist, lambda, x, y, foldid)
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 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@umn.edu

Wang, B. and Zou, H. (2015)
“Sparse Distance Weighted Discrimination", *Journal of Computational and Graphical Statistics*, forthcoming.

http://arxiv.org/abs/1501.06066

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

http://users.stat.umn.edu/~yiyang/resources/papers/JCGS_gcdnet.pdf

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

http://www.jstatsoft.org/v33/i01/paper

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