KL_divergence

Compute KL-Divergence on two covariance matrices. KL-Divergence corresponds to the
Maximum Wishart Likelihood (MWL) discrepancy described 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

KL_divergence 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

Compute KL-Divergence on two covariance matrices. KL-Divergence corresponds to the
Maximum Wishart Likelihood (MWL) discrepancy described in Cudeck1983cvsem. — KL_divergence

<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>

Compute KL-Divergence on two covariance matrices. KL-Divergence corresponds to the
Maximum Wishart Likelihood (MWL) discrepancy described in Cudeck1983cvsem.

KL_divergence: Compute KL-Divergence on two covariance matrices. KL-Divergence corresponds to the Maximum Wishart Likelihood (MWL) discrepancy described in Cudeck1983cvsem.

Description

Usage

Value

Arguments

References