splitopt

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internal

It provides an interesting solution for handling a high number
of segmentation variables in partial least squares structural equation
modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez,
and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient
test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>)
to detect the path coefficients responsible for the identified differences).
The package also allows running the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>).

Giuseppe Lamberti

genpathmox

Pathmox Approach Segmentation Tree Analysis

splitopt function

<dl><dt>x</dt>
<dd>matrix or data frame containing the manifest variables</dd>
<dt>inner</dt>
<dd>a square (lower triangular) boolean matrix representing 
the inner model (i.e. the path relationships between latent variables)</dd>
<dt>outer</dt>
<dd>list of vectors with column indices or column names
from <code>Data</code> indicating the sets of manifest variables forming 
each block (i.e. which manifest variables correspond to each block)</dd>
<dt>mode</dt>
<dd>character vector indicating the type of measurement for each
block. Possible values are: <code>"A", "B"</code> 
The length of <code>modes</code> must be equal to the length of <code>blocks</code></dd>
<dt>scheme</dt>
<dd>string indicating the type of inner weighting
scheme. Possible values are <code>"centroid"</code>, <code>"factorial"</code>, or
<code>"path"</code></dd>
<dt>splits</dt>
<dd>vector indicating the binary partition</dd>
<dt>fact</dt>
<dd>vector indicating the categorical variable</dd>
<dt>size_candidate</dt>
<dd>number indicating the minimum threshold for a node</dd></dl>

Arguments

Best partition for a specific segmentation variable — splitopt

<dl>

<dt>x</dt>
<dd>matrix or data frame containing the manifest variables</dd>


<dt>inner</dt>
<dd>a square (lower triangular) boolean matrix representing 
the inner model (i.e. the path relationships between latent variables)</dd>


<dt>outer</dt>
<dd>list of vectors with column indices or column names
from <code>Data</code> indicating the sets of manifest variables forming 
each block (i.e. which manifest variables correspond to each block)</dd>


<dt>mode</dt>
<dd>character vector indicating the type of measurement for each
block. Possible values are: <code>"A", "B"</code> 
The length of <code>modes</code> must be equal to the length of <code>blocks</code></dd>


<dt>scheme</dt>
<dd>string indicating the type of inner weighting
scheme. Possible values are <code>"centroid"</code>, <code>"factorial"</code>, or
<code>"path"</code></dd>


<dt>splits</dt>
<dd>vector indicating the binary partition</dd>


<dt>fact</dt>
<dd>vector indicating the categorical variable</dd>


<dt>size_candidate</dt>
<dd>number indicating the minimum threshold for a node</dd>

</dl>

splitopt: Best partition for a specific segmentation variable

Description

Usage

Value

Arguments

Details