cvRegularizedSEM-class

Class for cross-validated regularized SEM

Provides regularized structural equation modeling
(regularized SEM) with non-smooth penalty functions (e.g., lasso) building
on 'lavaan'. The package is heavily inspired by the
['regsem'](<https://github.com/Rjacobucci/regsem>) and
['lslx'](<https://github.com/psyphh/lslx>) packages.

Jannik H. Orzek

lessSEM

Non-Smooth Regularization for Structural Equation Models

cvRegularizedSEM-class function

<dl>
<dt><code>parameters</code></dt>
<dd>data.frame with parameter estimates for the best combination of the
tuning parameters</dd></dl>
<dt><code>transformations</code></dt>
<dd>transformed parameters</dd>
<dt><code>cvfits</code></dt>
<dd>data.frame with all combinations of the
tuning parameters and the sum of the cross-validation fits</dd>
<dt><code>parameterLabels</code></dt>
<dd>character vector with names of all parameters</dd>
<dt><code>regularized</code></dt>
<dd>character vector with names of regularized parameters</dd>
<dt><code>cvfitsDetails</code></dt>
<dd>data.frame with cross-validation fits for each subset</dd>
<dt><code>subsets</code></dt>
<dd>matrix indicating which person is in which subset</dd>
<dt><code>subsetParameters</code></dt>
<dd>optional: data.frame with parameter estimates for all
combinations of the tuning parameters in all subsets</dd>
<dt><code>misc</code></dt>
<dd>list with additional return elements</dd>
<dt><code>notes</code></dt>
<dd>internal notes that have come up when fitting the model</dd>

Slots

Class for cross-validated regularized SEM — cvRegularizedSEM-class

<dl>
<dt><code>parameters</code></dt>
<dd>data.frame with parameter estimates for the best combination of the
tuning parameters</dd>


<dt><code>transformations</code></dt>
<dd>transformed parameters</dd>


<dt><code>cvfits</code></dt>
<dd>data.frame with all combinations of the
tuning parameters and the sum of the cross-validation fits</dd>


<dt><code>parameterLabels</code></dt>
<dd>character vector with names of all parameters</dd>


<dt><code>regularized</code></dt>
<dd>character vector with names of regularized parameters</dd>


<dt><code>cvfitsDetails</code></dt>
<dd>data.frame with cross-validation fits for each subset</dd>


<dt><code>subsets</code></dt>
<dd>matrix indicating which person is in which subset</dd>


<dt><code>subsetParameters</code></dt>
<dd>optional: data.frame with parameter estimates for all
combinations of the tuning parameters in all subsets</dd>


<dt><code>misc</code></dt>
<dd>list with additional return elements</dd>


<dt><code>notes</code></dt>
<dd>internal notes that have come up when fitting the model</dd>


</dl>

cvRegularizedSEM-class: Class for cross-validated regularized SEM

Description

Arguments

Slots