print.gcdnet: Print a gcdnet object

In gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

Print a gcdnet object

Description

Print a summary of the gcdnet path at each step along the path.

Usage

## S3 method for class 'gcdnet'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	fitted gcdnet object
digits	significant digits in printout
...	additional print arguments

Details

The call that produced the gcdnet object is printed, followed by a two-column matrix with columns Df and Lambda. The Df column is the number of nonzero coefficients.

Value

a two-column matrix, the first columns is the number of nonzero coefficients and the second column is Lambda.

Author(s)

Yi Yang, Yuwen Gu and Hui Zou

Maintainer: Yi Yang <[e-mail]>

References

Yang, Y. and Zou, H. (2012). "An Efficient Algorithm for Computing The HHSVM and Its Generalizations." Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: https://github.com/emeryyi/gcdnet

Gu, Y., and Zou, H. (2016). "High-dimensional generalizations of asymmetric least squares regression and their applications." The Annals of Statistics, 44(6), 2661–2694.

Friedman, J., Hastie, T., and Tibshirani, R. (2010). "Regularization paths for generalized linear models via coordinate descent." Journal of Statistical Software, 33, 1.
https://www.jstatsoft.org/v33/i01/

Examples

data(FHT)
m1 <- gcdnet(x = FHT$x, y = FHT$y, delta = 1, lambda2 = 0.1)
print(m1)

gcdnet documentation built on Aug. 14, 2022, 9:05 a.m.