Fc.test

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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

Fc.test function

<dl><dt>Y1</dt>
<dd>vector of the concatenate indipendent latent variables of H1 hypothesis 
of the F-coefficient test.</dd>
<dt>X1</dt>
<dd>matrix of the concatenate predictor latent variables of H1 hypothesis 
of the F-coefficient test.</dd>
<dt>path.name</dt>
<dd>vector of path coefficients labels.</dd>
<dt>info.block</dt>
<dd>list contaning information about the endogenous equations of the pls model.</dd></dl>

Arguments

F-coefficient test — Fc.test

<dl>

<dt>Y1</dt>
<dd>vector of the concatenate indipendent latent variables of H1 hypothesis 
of the F-coefficient test.</dd>


<dt>X1</dt>
<dd>matrix of the concatenate predictor latent variables of H1 hypothesis 
of the F-coefficient test.</dd>


<dt>path.name</dt>
<dd>vector of path coefficients labels.</dd>


<dt>info.block</dt>
<dd>list contaning information about the endogenous equations of the pls model.</dd>

</dl>

Fc.test: F-coefficient test

Description

Usage

Value

Arguments

Details