plot.efa_retention

Plots the result of a factor-retention criterion. Eigenvalue-based criteria
(e.g. <code>efa_ekc()</code>) are shown as an eigenvalue plot, the Hull method (<code>efa_hull()</code>) as
a convex-hull plot. Criteria with more than one sub-variant are faceted.

Provides a complete workflow for exploratory factor analysis
(EFA). It covers data screening and factorability checks, a suite of
factor retention criteria for choosing the number of factors, and factor
extraction by principal axis factoring, maximum likelihood, unweighted
least squares, or diagonally weighted least squares from Pearson,
Spearman, Kendall, polychoric, tetrachoric, or two-stage
full-information maximum likelihood correlations. A built-in rotation
engine offers a range of orthogonal and oblique rotations, and standard
errors for loadings and related quantities can be obtained by analytic,
robust, or bootstrap methods. Further tools support model averaging
across analytic choices, multigroup EFA with factor congruence, EFA on
multiply imputed data, Schmid-Leiman transformation, reliability
coefficients (including McDonald's omegas), factor score estimation,
data simulation, and power analysis. Computationally intensive
procedures are implemented in 'C++' for speed.

Markus Steiner

EFAtools

Fast and Flexible Implementations of Exploratory Factor Analysis
Tools

plot.efa_retention function

<dl><dt>x</dt>
<dd>an object of class efa_retention, returned by a factor-retention
criterion (e.g. <code>efa_ekc()</code> or <code>efa_hull()</code>).</dd>
<dt>...</dt>
<dd>not used.</dd></dl>

Arguments

Plot method for efa_retention objects — plot.efa_retention

<dl>

<dt>x</dt>
<dd>an object of class efa_retention, returned by a factor-retention
criterion (e.g. <code>efa_ekc()</code> or <code>efa_hull()</code>).</dd>


<dt>...</dt>
<dd>not used.</dd>

</dl>

plot.efa_retention: Plot method for efa_retention objects

Description

Usage

Value

Arguments

Examples