calculateCorVCV

Large-sample variance-covariance matrix of the unique sample correlations of
the columns of <code>.X</code>, using the estimator of Steiger1982;textualcSEM.

internal

Estimate, assess, test, and study linear, nonlinear, hierarchical
and multigroup structural equation models using composite-based approaches
and procedures, including estimation techniques such as partial least squares
path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc),
generalized structured component analysis (GSCA), generalized structured
component analysis with uniqueness terms (GSCAm), generalized canonical
correlation analysis (GCCA), principal component analysis (PCA),
factor score regression (FSR) using sum score, regression or
Bartlett scores (including bias correction using Croon’s approach),
as well as several tests and typical postestimation procedures
(e.g., verify admissibility of the estimates, assess the model fit,
test the model fit etc.).

Florian Schuberth

cSEM

Composite-Based Structural Equation Modeling

calculateCorVCV function

<dl><dt>.X</dt>
<dd>A matrix of processed data (scaled, cleaned and ordered).</dd></dl>

Arguments

Internal: Distribution-free asymptotic variance-covariance matrix of correlations — calculateCorVCV

<dl>

<dt>.X</dt>
<dd>A matrix of processed data (scaled, cleaned and ordered).</dd>

</dl>

calculateCorVCV: Internal: Distribution-free asymptotic variance-covariance matrix of correlations

Description

Usage

Value

Arguments

Details

References