proto

Extracts the prototype from each variable group.

Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
homogeneity structure in high-dimensional data and enjoys the
grouping effect to select groups of important variables
automatically. This feature makes it particularly useful for
high-dimensional datasets with strongly correlated variables,
such as spectroscopic data.

Nan Xiao

OHPL

Ordered Homogeneity Pursuit Lasso for Group Variable Selection

You-Wu Lin

proto function

<dl><dt>X</dt>
<dd>Predictor matrix.</dd>
<dt>y</dt>
<dd>Response matrix with one column.</dd>
<dt>groups</dt>
<dd>An group index vector containing the
group number each variable belongs to. For example:
<code>c(1, 1, 1, 1, 1, 2, 2, 2, ...)</code>.
Variable groups can be generated by the Fisher optimal
partition algorithm implemented in <code>FOP()</code>.</dd>
<dt>type</dt>
<dd>The rule for extracting the prototype.
Possible options are <code>"max"</code> and <code>"median"</code>.</dd>
<dt>mu</dt>
<dd>The mean value of <code>y</code> for standardization.
Default is <code>NULL</code>, which uses the sample mean of <code>y</code>.</dd></dl>

Arguments

Extract the prototype from each variable group — proto

<dl>

<dt>X</dt>
<dd>Predictor matrix.</dd>


<dt>y</dt>
<dd>Response matrix with one column.</dd>


<dt>groups</dt>
<dd>An group index vector containing the
group number each variable belongs to. For example:
<code>c(1, 1, 1, 1, 1, 2, 2, 2, ...)</code>.
Variable groups can be generated by the Fisher optimal
partition algorithm implemented in <code>FOP()</code>.</dd>


<dt>type</dt>
<dd>The rule for extracting the prototype.
Possible options are <code>"max"</code> and <code>"median"</code>.</dd>


<dt>mu</dt>
<dd>The mean value of <code>y</code> for standardization.
Default is <code>NULL</code>, which uses the sample mean of <code>y</code>.</dd>

</dl>

proto: Extract the prototype from each variable group

Description

Usage

Value

Arguments