getF.Sigma

Computes the minimum of the chosen fitting-function given the model-implied and the observed (or population) covariance matrix.
The ML fitting function is:
<code>F_min = tr(S %*% SigmaHat^-1) - p + ln(det(SigmaHat)) - ln(det(S))</code>. When a meanstructure is included,
<code>(mu - muHat)' SigmaHat^-1 (mu - muHat)</code> is added.
The WLS fitting function is:
<code>F_min = (Sij - SijHat)' V (Sij - SijHat)</code>
where V is the inverse of N times the asymptotic covariance matrix of the sample statistics (Gamma; N x ACOV(mu, vech(S))).
For DWLS, V is the diagonal of the inverse of diag(NACOV), i.e. diag(solve(diag(Gamma))).
For ULS, V = I. ULS has an unknown asymptotic distribution, so it is actually irrelevant, but provided for the sake of completeness.

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

Morten Moshagen

semPower

Power Analyses for SEM

Martina Bader

getF.Sigma function

<dl><dt>SigmaHat</dt>
<dd>model implied covariance matrix</dd>
<dt>S</dt>
<dd>observed (or population) covariance matrix</dd>
<dt>muHat</dt>
<dd>model implied mean vector</dd>
<dt>mu</dt>
<dd>observed (or population) mean vector</dd>
<dt>fittingFunction</dt>
<dd>one of <code>ML</code> (default), <code>WLS</code>, <code>DWLS</code>, <code>ULS</code></dd></dl>

Arguments

getF.Sigma — getF.Sigma

<dl>

<dt>SigmaHat</dt>
<dd>model implied covariance matrix</dd>


<dt>S</dt>
<dd>observed (or population) covariance matrix</dd>


<dt>muHat</dt>
<dd>model implied mean vector</dd>


<dt>mu</dt>
<dd>observed (or population) mean vector</dd>


<dt>fittingFunction</dt>
<dd>one of <code>ML</code> (default), <code>WLS</code>, <code>DWLS</code>, <code>ULS</code></dd>

</dl>

getF.Sigma: getF.Sigma

Description

Usage

Value

Arguments