.parallel_sim

Function called from within efa_parallel() so usually no call to this is needed by the user.
Provides a C++ implementation of the efa_parallel() simulation procedure

internal

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

.parallel_sim function

<dl><dt>n_datasets</dt>
<dd>numeric. Number of datasets with dimensions (N, n_vars) to simulate.</dd>
<dt>n_vars</dt>
<dd>numeric. Number of variables / indicators in dataset.</dd>
<dt>N</dt>
<dd>numeric. Number of cases / observations in dataset.</dd>
<dt>eigen_type</dt>
<dd>numeric. Whether PCA (eigen_type = 1; i.e., leaving diagonal of correlation matrix at 1), SMC (eigen_type = 2; i.e., setting diagonal of correlation matrix to SMCs), or both from the same simulated datasets (eigen_type = 3), in which case the returned matrix holds the PCA eigenvalues in the first n_vars columns and the SMC eigenvalues in the next n_vars.</dd>
<dt>maxit</dt>
<dd>numeric. Maximum iterations to perform after which to abort.</dd></dl>

Arguments

Parallel analysis on simulated data. — .parallel_sim

<dl>

<dt>n_datasets</dt>
<dd>numeric. Number of datasets with dimensions (N, n_vars) to simulate.</dd>


<dt>n_vars</dt>
<dd>numeric. Number of variables / indicators in dataset.</dd>


<dt>N</dt>
<dd>numeric. Number of cases / observations in dataset.</dd>


<dt>eigen_type</dt>
<dd>numeric. Whether PCA (eigen_type = 1; i.e., leaving diagonal of correlation matrix at 1), SMC (eigen_type = 2; i.e., setting diagonal of correlation matrix to SMCs), or both from the same simulated datasets (eigen_type = 3), in which case the returned matrix holds the PCA eigenvalues in the first n_vars columns and the SMC eigenvalues in the next n_vars.</dd>


<dt>maxit</dt>
<dd>numeric. Maximum iterations to perform after which to abort.</dd>

</dl>

.parallel_sim: Parallel analysis on simulated data.

Description

Usage

Arguments