fa.parallel: Scree plots of data or correlation matrix compared to random ``parallel" matrix
Description
One way to determine the number of factors or components in a data matrix or a correlation matrix is to examine the ``scree" plot of the successive eigenvalues. Sharp breaks in the plot suggest the appropriate number of components or factors to extract. ``Parallel" analyis is an alternative technique that compares the scree of the observed data with that of a random data matrix of the same size as the original.
Usage
fa.parallel(x, ncases = 0, main = "Parallel Analysis Scree Plots")
Arguments
x
A data.frame or data matrix of scores. If the matrix is square, it is assumed to be a correlation matrix. Otherwise, correlations (with pairwise deletion) will be found
ncases
ncases=0 implies a data matrix/data.frame. Otherwise, how many cases were used to find the correlations.
main
a title for the analysis
Value
A plot of the eigen values for the original data, a resampling of the original data, and of a equivalent size matrix of random normal deviates.
Details
Cattell's ``scree" test is one of most simple tests for the number of factors problem. Humphreys and Montanelli's ``parallel" analysis is an equally compelling procedure. Other procedures for determining the most optimal number of factors include finding the Very Simple Structure (VSS) criterion (VSS).