plot_coefficient_function

Internal function to plot coefficient functions using ggplot2.

internal

Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying
coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded
in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter
selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by
HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies.
(Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).

Wen-Ting Wang

QuantRegGLasso

Adaptively Weighted Group Lasso for Semiparametric Quantile
Regression Models

Wei-Ying Wu

Toshio Honda

Ching-Kang Ing

plot_coefficient_function function

<dl><dt>data</dt>
<dd>Dataframe. A dataframe containing columns <code>z</code>, <code>coefficient</code>.</dd>
<dt>variate</dt>
<dd>Character. A character representing the title.</dd></dl>

Arguments

Internal Function: Plot Coefficient Function — plot_coefficient_function

<dl>

<dt>data</dt>
<dd>Dataframe. A dataframe containing columns <code>z</code>, <code>coefficient</code>.</dd>


<dt>variate</dt>
<dd>Character. A character representing the title.</dd>

</dl>

plot_coefficient_function: Internal Function: Plot Coefficient Function

Description

Usage

Value

Arguments