Package: random.polychor.pa
Type: Package
Title: A Parallel Analysis With Polychoric Correlation Matrices
Version: 1.0
Date: 2010-02-20
Author: Fabio Presaghi & Marta Desimoni
Maintainer: Fabio Presaghi <fabio.presaghi@uniroma1.it>
Description: The Function perform a parallel analysis using simulated
polychoric correlation matrices. The nth-percentile of the
eigenvalues distribution obtained from the randomly generated
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.
Depends: psych, nFactors
LazyLoad: yes
License: GPL (>= 2)
Encoding: latin1
Packaged: 2010-03-01 03:21:42 UTC; Administrator
Repository: CRAN
Date/Publication: 2010-03-01 06:57:59

Package: random.polychor.pa
Type: Package
Title: A Parallel Analysis With Polychoric Correlation Matrices
Version: 1.1.1
Date: 2010-07-09
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 a minor bug in showing the estimanted
time needed to conclude the simulation is fixed. In this
version it is also introduced the possibility to manage
datafiles containing factor variables (i.e., variables with
ordered categories) which in past versions may cause the
function to stop computations when the Pearson correlation
matrix is computed (due to the fact that in this instance a
numerical matrix is expected).
Depends: psych, nFactors
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Encoding: latin1
Packaged: 2010-07-09 10:00:06 UTC; Administrator
Repository: CRAN
Date/Publication: 2010-07-09 13:27:58