Package: random.polychor.pa
Type: Package
Title: A Parallel Analysis With Polychoric Correlation Matrices
Version: 1.1.2
Date: 2010-10-06
Author: Fabio Presaghi & Marta Desimoni
Maintainer: Fabio Presaghi <fabio.presaghi@uniroma1.it>
Description: The Function performs a parallel analysis using simulated
polychoric correlation matrices. The nth-percentile of the
eigenvalues distribution obtained from both the randomly
generated and the real data polychoric correlation matrices is
returned. A plot comparing the two types of eigenvalues (real
and simulated) will help determine the number of real
eigenvalues that outperform random data. The function is based
on the idea that if real data are non-normal and the polychoric
correlation matrix is needed to perform a Factor Analysis, then
the Parallel Analysis method used to choose a non-random number
of factors should also be based on randomly generated
polychoric correlation matrices and not on Pearson correlation
matrices. Version 1.1.1, fixed a minor bug in the regarding the
estimated time needed to complete the simulation. Also in this
version, the function is now able to manage supplied
data.matrix in which variables representing factors (i.e.,
variables with ordered categories) are present and may cause an
error when the Pearson correlation matrix is calculated.
Version 1.1.2 simply has updated the function that calculates
the polycoric correlation matrix due to changes in the psych()
package.
Depends: psych, nFactors
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Encoding: latin1
Packaged: 2010-11-03 09:00:12 UTC; presaghi
Repository: CRAN
Date/Publication: 2010-11-03 10:10:08

Description: The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.