.consensus_loss

Mean squared discrepancy to a consensus target

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

Silvia Steiner

William Revelle

Max Auerswald

Morten Moshagen

John Ruscio

Brendan Roche

Urbano Lorenzo-Seva

David Navarro-Gonzalez

Johan Braeken

Andreas Soteriades

.consensus_loss function

<dl><dt>aligned_loadings</dt>
<dd>List of aligned loading matrices.</dd>
<dt>target</dt>
<dd>Consensus target matrix.</dd></dl>

Arguments

Mean squared discrepancy to a consensus target — .consensus_loss

<dl>

<dt>aligned_loadings</dt>
<dd>List of aligned loading matrices.</dd>


<dt>target</dt>
<dd>Consensus target matrix.</dd>

</dl>

Mean squared discrepancy to a consensus target

.consensus_loss: Mean squared discrepancy to a consensus target

Description

Usage

Value

Arguments