R/data.R In nFactors: Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test

#' Eigenvalues from classical studies
#'
#'  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.
#'
#' Other datasets will be added in future versions of the package.
#'
#' @name dFactors
#' @docType data
#'
#' @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)
#'
#' @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.
#'
#' @keywords datasets
#'
#' @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)
#'
"dFactors"


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nFactors documentation built on Oct. 10, 2022, 5:07 p.m.