get_loadings

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_loadings function

<dl><dt>data</dt>
<dd>the matrix of data (manifest variables)</dd>
<dt>sets</dt>
<dd>outer model</dd>
<dt>mvs</dt>
<dd>number of manifest variables</dd>
<dt>lvs</dt>
<dd>number of latent variables</dd>
<dt>IDM</dt>
<dd>the path matrix</dd>
<dt>tau</dt>
<dd>the quantile(s) to be estimated</dd>
<dt>LV</dt>
<dd>the estimated latent variables</dd>
<dt>qcorr</dt>
<dd>boolean. If it si equal to <code>TRUE</code>,
loadings are standardized.</dd></dl>

Arguments

get_loadings — get_loadings

<dl>

<dt>data</dt>
<dd>the matrix of data (manifest variables)</dd>


<dt>sets</dt>
<dd>outer model</dd>


<dt>mvs</dt>
<dd>number of manifest variables</dd>


<dt>lvs</dt>
<dd>number of latent variables</dd>


<dt>IDM</dt>
<dd>the path matrix</dd>


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


<dt>LV</dt>
<dd>the estimated latent variables</dd>


<dt>qcorr</dt>
<dd>boolean. If it si equal to <code>TRUE</code>,
loadings are standardized.</dd>

</dl>

get_loadings: get_loadings

Description

Usage

Value

Arguments

Details