psych (version 1.0-51)

fa.parallel: Scree plots of data or correlation matrix compared to random ``parallel" matrices

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, n.obs = 1000, fa="both", main = "Parallel Analysis Scree Plots",ntrials=20)

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

n.obs

n.obs=0 implies a data matrix/data.frame. Otherwise, how many cases were used to find the correlations.

fa

show the eigen values for a principal components (fa="pc") or a principal axis factor analysis (fa="fa") or both principal components and principal factors (fa="both")

main

a title for the analysis

ntrials

Number of simulated analyses to perform

Value

• A plot of the eigen values for the original data, ntrials of resampling of the original data, and of a equivalent size matrix of random normal deviates. If the data are a correlation matrix, specify the number of observations.

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) and Velicer's MAP procedure (included in VSS). fa.parallel plots the eigen values for a principal components and principal factor solution and does the same for random matrices of the same size as the original data matrix. For raw data, the random matrices are 1) a matrix of univariate normal data and 2) random samples (randomized across rows) of the original data.

See Also

VSS,VSS.plot, VSS.parallel

Examples

test.data <- Harman74.cor$cov 
fa.parallel(test.data,n.obs=200)

fa.parallel(attitude) 
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