msaenet.mae

Compute mean absolute error (MAE).

Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in
Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
with support for multi-step adaptive MCP-net (MSAMNet) and
multi-step adaptive SCAD-net (MSASNet) methods.

Nan Xiao

msaenet

Multi-Step Adaptive Estimation Methods for Sparse Regressions

Qing-Song Xu

msaenet.mae function

<dl><dt>yreal</dt>
<dd>Vector. True response.</dd>
<dt>ypred</dt>
<dd>Vector. Predicted response.</dd></dl>

Arguments

Nan Xiao &lt;<a href="https://nanx.me">https://nanx.me</a>&gt;

Author

Mean Absolute Error (MAE) — msaenet.mae

<dl>

<dt>yreal</dt>
<dd>Vector. True response.</dd>


<dt>ypred</dt>
<dd>Vector. Predicted response.</dd>

</dl>

Nan Xiao &lt;<a href='https://nanx.me'>https://nanx.me</a>&gt;

msaenet.mae: Mean Absolute Error (MAE)

Description

Usage

Value

Arguments

Author