# print.cpernet: Print a cpernet object In SALES: Elastic Net and (Adaptive) Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms

## Description

Print a summary of the `cpernet` path at each step along the path.

## Usage

 ```1 2``` ```## S3 method for class 'cpernet' print(x, digits = max(3, getOption("digits") - 3), ...) ```

## Arguments

 `x` fitted `cpernet` object. `digits` significant digits in the output. `...` additional print arguments.

## Details

The call that produced the `cpernet` object is printed, followed by a three-column matrix with columns `Df1`, `Df2` and `Lambda`. The `Df1` and `Df2` columns are the number of nonzero mean and scale coefficients respectively.

## Value

a three-column matrix, the first two columns are the number of nonzero mean and scale coefficients respectively and the third column is `Lambda`.

## Author(s)

Yuwen Gu and Hui Zou
Maintainer: Yuwen Gu <guxxx192@umn.edu>

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```set.seed(1) n <- 100 p <- 400 x <- matrix(rnorm(n*p), n, p) y <- rnorm(n) tau <- 0.30 pf <- abs(rnorm(p)) pf2 <- abs(rnorm(p)) w <- 2.0 lambda2 <- 1 m2 <- cpernet(y = y, x = x, w = w, tau = tau, eps = 1e-8, pf.mean = pf, pf.scale = pf2, standardize = FALSE, lambda2 = lambda2) print(m2) ```

SALES documentation built on May 2, 2019, 5:08 a.m.