get_communality

internal

Implements the Quantile Composite-based Path Modeling approach
(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>;
Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.

Giuseppe Lamberti

qcpm

Quantile Composite Path Modeling

Cristina Davino

Pasquale Dolce

Domenico Vistocco

get_communality function

<dl><dt>mv_name</dt>
<dd>manifest variable label (string)</dd>
<dt>lv_name</dt>
<dd>latent variable label (string)</dd>
<dt>tau</dt>
<dd>the quantile(s) to be estimated</dd>
<dt>data</dt>
<dd>dataset. It includes manifest variable and latent score 
for a specific quantile</dd></dl>

Arguments

get_communality — get_communality

<dl>

<dt>mv_name</dt>
<dd>manifest variable label (string)</dd>


<dt>lv_name</dt>
<dd>latent variable label (string)</dd>


<dt>tau</dt>
<dd>the quantile(s) to be estimated</dd>


<dt>data</dt>
<dd>dataset. It includes manifest variable and latent score 
for a specific quantile</dd>

</dl>

get_communality: get_communality

Description

Usage

Value

Arguments

Details