.paf_iter

Function called from within PAF so usually no call to this is needed by the user.
Provides a C++ implementation of the PAF procedure

Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.

Markus Steiner

EFAtools

Fast and Flexible Implementations of Exploratory Factor Analysis
Tools

.paf_iter function

<dl><dt>h2</dt>
<dd>numeric. The initial communality estimates.</dd>
<dt>criterion</dt>
<dd>double. The convergence criterion to use.</dd>
<dt>R</dt>
<dd>matrix. The correlation matrix with the initial communality estimates in the diagonal.</dd>
<dt>n_fac</dt>
<dd>numeric. The number of factors to extract.</dd>
<dt>abs_eig</dt>
<dd>logical. Whether absolute eigenvalues should be used to compute the loadings.</dd>
<dt>crit_type</dt>
<dd>numeric. Whether maximum absolute differences (crit_type = 1), or sum of differences (crit_type = 2) should be used</dd>
<dt>max_iter</dt>
<dd>numeric. The number of iterations after which to end the procedure if no convergence has been reached by then.</dd></dl>

Arguments

Perform the iterative PAF procedure — .paf_iter

<dl>

<dt>h2</dt>
<dd>numeric. The initial communality estimates.</dd>


<dt>criterion</dt>
<dd>double. The convergence criterion to use.</dd>


<dt>R</dt>
<dd>matrix. The correlation matrix with the initial communality estimates in the diagonal.</dd>


<dt>n_fac</dt>
<dd>numeric. The number of factors to extract.</dd>


<dt>abs_eig</dt>
<dd>logical. Whether absolute eigenvalues should be used to compute the loadings.</dd>


<dt>crit_type</dt>
<dd>numeric. Whether maximum absolute differences (crit_type = 1), or sum of differences (crit_type = 2) should be used</dd>


<dt>max_iter</dt>
<dd>numeric. The number of iterations after which to end the procedure if no convergence has been reached by then.</dd>

</dl>

.paf_iter: Perform the iterative PAF procedure

Description

Usage

Arguments