genData.IG

Generates random data conforming to a population variance-covariance matrix using
the independent generator approach (IG, Foldnes &amp; Olsson, 2016) approach
specifying third and fourth moments of the marginals.

Provides a-priori, post-hoc, and compromise power-analyses
for structural equation models (SEM).

Morten Moshagen

semPower

Power Analyses for SEM

Martina Bader

genData.IG function

<dl><dt>N</dt>
<dd>sample size.</dd>
<dt>Sigma</dt>
<dd>population covariance matrix.</dd>
<dt>nSets</dt>
<dd>number of data sets to generate</dd>
<dt>skewness</dt>
<dd>vector specifying skewness for each variable</dd>
<dt>kurtosis</dt>
<dd>vector specifying excess kurtosis for each variable</dd></dl>

Arguments

genData.IG — genData.IG

<dl>

<dt>N</dt>
<dd>sample size.</dd>


<dt>Sigma</dt>
<dd>population covariance matrix.</dd>


<dt>nSets</dt>
<dd>number of data sets to generate</dd>


<dt>skewness</dt>
<dd>vector specifying skewness for each variable</dd>


<dt>kurtosis</dt>
<dd>vector specifying excess kurtosis for each variable</dd>

</dl>

genData.IG: genData.IG

Description

Usage

Value

Arguments

Details