Generalized Least Squares (GLS) Discrepancy as defined in Cudeck1983cvsem.

The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Anna Wysocki

cvsem

SEM Model Comparison with K-Fold Cross-Validation

Danielle Siegel

Cameron allen

Philippe Rast

gls function

<dl><dt>implied_sigma</dt>
<dd>Model implied covariances matrix from training set</dd>
<dt>test_S</dt>
<dd>Sample covariance matrix from test set</dd></dl>

Arguments

Generalized Least Squares Discrepancy Function — gls

<dl>

<dt>implied_sigma</dt>
<dd>Model implied covariances matrix from training set</dd>


<dt>test_S</dt>
<dd>Sample covariance matrix from test set</dd>

</dl>

gls: Generalized Least Squares Discrepancy Function

Description

Usage

Value

Arguments

References