Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.

P.-O. Caron

Rnest

Next Eigenvalue Sufficiency Test

EKC function

<dl><dt>.data</dt>
<dd>a data frame, a numeric matrix, covariance matrix or correlation matrix from which to determine the number of factors.</dd>
<dt>n</dt>
<dd>the number of cases (subjects, participants, or units) if a covariance matrix is supplied in <code>.data</code>.</dd>
<dt>nv</dt>
<dd>the number of variables if the critical values are required.</dd>
<dt>lowest.eig</dt>
<dd>minimal eigenvalues to retain. Default is Kaiser's suggestion of 1.</dd>
<dt>...</dt>
<dd>further argument for <code>cor_nest()</code>.</dd></dl>

Arguments

Empirical Kaiser Criterion (EKC) — EKC

<dl>

<dt>.data</dt>
<dd>a data frame, a numeric matrix, covariance matrix or correlation matrix from which to determine the number of factors.</dd>


<dt>n</dt>
<dd>the number of cases (subjects, participants, or units) if a covariance matrix is supplied in <code>.data</code>.</dd>


<dt>nv</dt>
<dd>the number of variables if the critical values are required.</dd>


<dt>lowest.eig</dt>
<dd>minimal eigenvalues to retain. Default is Kaiser's suggestion of 1.</dd>


<dt>...</dt>
<dd>further argument for <code>cor_nest()</code>.</dd>

</dl>

EKC: Empirical Kaiser Criterion (EKC)

Description

Usage

Value

Arguments

References

Examples