<a href="https://lifecycle.r-lib.org/articles/stages.html#superseded"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img src="figures/lifecycle-superseded.svg?package=EFAtools&version=1.1.0" alt="[Superseded]"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>MAP()</code> has been superseded by <code>efa_map()</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

MAP function

<dl><dt>x</dt>
<dd>A numeric <code>matrix</code> or <code>data.frame</code>. Can be either (a) a correlation matrix, or
(b) raw data (rows = observations, columns = variables) from which correlations are computed.</dd>
<dt>use</dt>
<dd>Character string specifying the treatment of missing values when computing correlations.
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>. Defaults to <code>"pairwise.complete.obs"</code>.</dd>
<dt>cor_method</dt>
<dd>Character string specifying the correlation coefficient to be computed if raw
data are supplied. One of <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). Defaults to <code>"pearson"</code>.</dd></dl>

Arguments

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a href='https://lifecycle.r-lib.org/articles/stages.html#superseded'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img src='figures/lifecycle-superseded.svg' alt='[Superseded]' /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 has been superseded by 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

Minimum average partial — MAP

<dl>

<dt>x</dt>
<dd>A numeric <code>matrix</code> or <code>data.frame</code>. Can be either (a) a correlation matrix, or
(b) raw data (rows = observations, columns = variables) from which correlations are computed.</dd>


<dt>use</dt>
<dd>Character string specifying the treatment of missing values when computing correlations.
Passed to <code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code>. Defaults to <code>"pairwise.complete.obs"</code>.</dd>


<dt>cor_method</dt>
<dd>Character string specifying the correlation coefficient to be computed if raw
data are supplied. One of <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). Defaults to <code>"pearson"</code>.</dd>

</dl>

MAP: Minimum average partial

Description

Usage

Value

Arguments

See Also