test_models

Correlation matrices created from simulated data from four of the
<code>population_models</code> cases, each with strong factor intercorrelations.
These are used in Grieder &amp; Steiner (2020) to compare the psych and SPSS
implementations in this package with the actual implementations of the programs.
For details on the cases, see <code>population_models</code>.

datasets

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

test_models function

A list of 4 lists "baseline", "case_1a", "case_6b", and"case_11b", each with the following elements.<dl>
 <dt>cormat</dt>
<dd>(matrix) - The correlation matrix of the simulated data.</dd> <dt>n_factors</dt>
<dd>(numeric) - The true number of factors.</dd> <dt>N</dt>
<dd>(numeric) - The sample size of the generated data.</dd> 
</dl>

Format

Four test models used in Grieder and Steiner (2020) — test_models

A list of 4 lists "baseline", "case_1a", "case_6b", and"case_11b", each with the following elements.<dl>
 <dt>cormat</dt>
<dd>(matrix) - The correlation matrix of the simulated data.</dd>

 <dt>n_factors</dt>
<dd>(numeric) - The true number of factors.</dd>

 <dt>N</dt>
<dd>(numeric) - The sample size of the generated data.</dd>

 
</dl>

test_models: Four test models used in Grieder and Steiner (2020)

Description

Usage

Arguments

Format