% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{dFactors}
\alias{dFactors}
\title{Eigenvalues from classical studies}
\format{A list of examples. For each example, a list is also used to give the eigenvalues
vector and the number of subjects.
\describe{
  \item{Bentler}{$eigenvalues and $nsubjects}
  \item{Buja}{$eigenvalues and $nsubjects}
  \item{Cliff1}{$eigenvalues and $nsubjects}
  \item{Cliff2}{$eigenvalues and $nsubjects}
  \item{Cliff3}{$eigenvalues and $nsubjects}
  \item{Hand}{$eigenvalues and $nsubjects}
  \item{Harman}{$eigenvalues and $nsubjects}
  \item{Lawley}{$eigenvalues and $nsubjects}
  \item{Raiche}{$eigenvalues and $nsubjects}
  \item{Tucker1}{$eigenvalues and $nsubjects}
  \item{Tucker2}{$eigenvalues and $nsubjects}
}}
\source{
Lawley and Hand dataset:  Bartholomew \emph{et al}. (2002, p. 123, 126)

Bentler dataset:          Bentler and Yuan (1998, p. 139-140)

Buja datasets:            Buja and Eyuboglu (1992, p. 516, 519) < Number of subjects not specified by Buja and Eyuboglu >

Cliff datasets:           Cliff (1970, p. 165)

Raiche dataset:           Raiche, Langevin, Riopel and Mauffette (2006)

Raiche dataset:           Raiche, Riopel and Blais (2006, p. 9)

Tucker datasets:          Tucker \emph{et al}. (1969, p. 442)
}
\usage{
dFactors
}
\description{
Classical examples of eigenvalues vectors used to study the number of factors
 to retain in the litterature. These examples generally give the number of
 subjects use to obtain these eigenvalues.
 The number of subjects is used with the parallel analysis.
}
\details{
Other datasets will be added in future versions of the package.
}
\examples{

# EXAMPLES FROM DATASET
 data(dFactors)

# COMMAND TO VISUALIZE THE CONTENT AND ATTRIBUTES OF THE DATASETS
 names(dFactors)
 attributes(dFactors)
 dFactors$Cliff1$eigenvalues
 dFactors$Cliff1$nsubjects

# SCREE PLOT OF THE Cliff1 DATASET
 plotuScree(dFactors$Cliff1$eigenvalues)

}
\references{
Bartholomew, D. J., Steele, F., Moustaki, I. and Galbraith, J.
I. (2002).  \emph{The analysis and interpretation of multivariate data for
social scientists}. Boca Raton, FL: Chapman and Hall.

Bentler, P. M. and Yuan, K.-H. (1998). Tests for linear trend in the
smallest eigenvalues of the correlation matrix. \emph{Psychometrika, 63}(2),
131-144.

Buja, A. and Eyuboglu, N. (1992). Remarks on parallel analysis.
\emph{Multivariate Behavioral Research, 27}(4), 509-540.

Cliff, N. (1970). The relation between sample and population characteristic
vectors.  \emph{Psychometrika, 35}(2), 163-178.

Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J. and Ostrowski, E.
(1994).  \emph{A handbook of small data sets}. Boca Raton, FL: Chapman and
Hall.

Lawley, D. N. and Maxwell, A. E. (1971). \emph{Factor analysis as a
statistical method} (2nd edition). London: Butterworth.

Raiche, G., Langevin, L., Riopel, M. and Mauffette, Y. (2006). Etude
exploratoire de la dimensionnalite et des facteurs expliques par une
traduction francaise de l'Inventaire des approches d'enseignement de
Trigwell et Prosser dans trois universite quebecoises. \emph{Mesure et
Evaluation en Education, 29}(2), 41-61.

Raiche, G., Walls, T. A., Magis, D., Riopel, M. and Blais, J.-G. (2013).
Non-graphical solutions for Cattell's scree test. Methodology, 9(1), 23-29.

Tucker, L. D., Koopman, R. F. and Linn, R. L. (1969). Evaluation of factor
analytic research procedures by mean of simulated correlation matrices.
\emph{Psychometrika, 34}(4), 421-459.

Zoski, K. and Jurs, S. (1993). Using multiple regression to determine the
number of factors to retain in factor analysis. \emph{Multiple Linear
Regression Viewpoint, 20}(1), 5-9.
}
\keyword{datasets}