<a href="https://lifecycle.r-lib.org/articles/stages.html#superseded"><img src="figures/lifecycle-superseded.svg?package=EFAtools&version=1.1.0" alt="[Superseded]"></a>
<code>KMO()</code> has been superseded by <code>efa_kmo()</code>, which is the recommended interface
going forward. It remains available and unchanged so existing code keeps working.

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

KMO function

<dl><dt>x</dt>
<dd>data.frame or matrix. Dataframe or matrix of raw data or matrix with
correlations.</dd>
<dt>use</dt>
<dd>character. The missing-data policy for raw data. Passed to
<code><a href="/link/stats%3A%3Acor()?package=EFAtools&version=1.1.0" data-mini-rdoc="EFAtools::stats::cor()">stats::cor()</a></code> for <code>"pearson"</code>, <code>"spearman"</code>, and <code>"kendall"</code>; for <code>"poly"</code> /
<code>"tetra"</code> the same policies are applied to the raw data before the polychoric
estimation, where <code>"all.obs"</code> and <code>"everything"</code> abort on a missing value instead
of returning <code>NA</code> correlations. Default is "pairwise.complete.obs".</dd>
<dt>cor_method</dt>
<dd>character. Correlation computed from raw data: <code>"pearson"</code>,
<code>"spearman"</code>, or <code>"kendall"</code> (passed to <code><a href="/link/stats%3A%3Acor()?package=EFAtools&version=1.1.0" data-mini-rdoc="EFAtools::stats::cor()">stats::cor()</a></code>), or <code>"poly"</code> /
<code>"tetra"</code> for polychoric / tetrachoric correlations of ordinal / binary data
(a two-step estimator).
Default is "pearson".</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#superseded'><img src='figures/lifecycle-superseded.svg' alt='[Superseded]' /></a>
<code>KMO()</code> has been superseded by <code>efa_kmo()</code>, which is the recommended interface
going forward. It remains available and unchanged so existing code keeps working.

Kaiser-Meyer-Olkin criterion — KMO

<dl>

<dt>x</dt>
<dd>data.frame or matrix. Dataframe or matrix of raw data or matrix with
correlations.</dd>


<dt>use</dt>
<dd>character. The missing-data policy for raw data. Passed to
<code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code> for <code>"pearson"</code>, <code>"spearman"</code>, and <code>"kendall"</code>; for <code>"poly"</code> /
<code>"tetra"</code> the same policies are applied to the raw data before the polychoric
estimation, where <code>"all.obs"</code> and <code>"everything"</code> abort on a missing value instead
of returning <code>NA</code> correlations. Default is "pairwise.complete.obs".</dd>


<dt>cor_method</dt>
<dd>character. Correlation computed from raw data: <code>"pearson"</code>,
<code>"spearman"</code>, or <code>"kendall"</code> (passed to <code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code>), or <code>"poly"</code> /
<code>"tetra"</code> for polychoric / tetrachoric correlations of ordinal / binary data
(a two-step estimator).
Default is "pearson".</dd>

</dl>

KMO: Kaiser-Meyer-Olkin criterion

Description

Usage

Value

Arguments

See Also