Model-implied simulation-based power estimation (MSPE) for
nonlinear (and linear) SEM, path analysis and regression analysis. A
theoretical framework is used to approximate the relation between
power and sample size for given type I error rates and effect sizes.
The package offers an adaptive search algorithm to find the optimal N for
given effect sizes and type I error rates. Plots can be used to visualize
the power relation to N for different parameters of interest (POI).
Theoretical justifications are given in Irmer et al.
(2024a) <doi:10.31219/osf.io/pe5bj> and detailed description
are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.

Julien Patrick Irmer

powerNLSEM

Simulation-Based Power Estimation (MSPE) for Nonlinear SEM

FSR function

<dl><dt>lavModel_Analysis</dt>
<dd>the lavModel_Analysis object</dd>
<dt>data</dt>
<dd>set to fit</dd>
<dt>FSmethod</dt>
<dd>Method to be used to extract factor scores. Default to <code>"SL"</code> for the Skrondal and Laake approach that uses regression (<code>"regression"</code>) factor scores for the independendent variables and <code>"Bartlett"</code> factor scores for the dependent variables.</dd>
<dt>data_transformations</dt>
<dd>Data transformations</dd></dl>

Arguments

Factor Score Regression approach — FSR

<dl>

<dt>lavModel_Analysis</dt>
<dd>the lavModel_Analysis object</dd>


<dt>data</dt>
<dd>set to fit</dd>


<dt>FSmethod</dt>
<dd>Method to be used to extract factor scores. Default to <code>"SL"</code> for the Skrondal and Laake approach that uses regression (<code>"regression"</code>) factor scores for the independendent variables and <code>"Bartlett"</code> factor scores for the dependent variables.</dd>


<dt>data_transformations</dt>
<dd>Data transformations</dd>

</dl>

FSR: Factor Score Regression approach

Description

Usage

Value

Arguments

References