R/componentAxis.r

In nFactors: Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test

#' Principal Component Analysis With Only n First Components Retained
#'
#' The \code{componentAxis} function returns a principal component analysis
#' with the first \emph{n} components retained.
#'
#'
#' @param R numeric: correlation or covariance matrix
#' @param nFactors numeric: number of components/factors to retain
#' @return \item{values}{ numeric: variance of each component/factor retained }
#' \item{varExplained}{ numeric: variance explained by each component/factor
#' retained } \item{varExplained}{ numeric: cumulative variance explained by
#' each component/factor retained } \item{loadings}{ numeric: loadings of each
#' variable on each component/factor retained }
#' @author Gilles Raiche \cr Centre sur les Applications des Modeles de
#' Reponses aux Items (CAMRI) \cr Universite du Quebec a Montreal\cr
#' \email{raiche.gilles@@uqam.ca}
#' @seealso \code{\link{principalComponents}},
#' \code{\link{iterativePrincipalAxis}}, \code{\link{rRecovery}}
#' @references Kim, J.-O. and Mueller, C. W. (1978). \emph{Introduction to
#' factor analysis. What it is and how to do it}. Beverly Hills, CA: Sage.
#'
#' Kim, J.-O. and Mueller, C. W. (1987). \emph{Factor analysis. Statistical
#' methods and practical issues}. Beverly Hills, CA: Sage.
#' @keywords multivariate
#' @export
#' @examples
#'
#' # .......................................................
#' # Example from Kim and Mueller (1978, p. 10)
#' # Simulated sample: lower diagnonal
#'  R <- matrix(c( 1.000, 0.560, 0.480, 0.224, 0.192, 0.16,
#'                 0.560, 1.000, 0.420, 0.196, 0.168, 0.14,
#'                 0.480, 0.420, 1.000, 0.168, 0.144, 0.12,
#'                 0.224, 0.196, 0.168, 1.000, 0.420, 0.35,
#'                 0.192, 0.168, 0.144, 0.420, 1.000, 0.30,
#'                 0.160, 0.140, 0.120, 0.350, 0.300, 1.00),
#'                 nrow=6, byrow=TRUE)
#'
#' # Factor analysis: Selected principal components - Kim and Mueller
#' # (1978, p. 20)
#'  componentAxis(R, nFactors=2)
#'
#' # .......................................................
"componentAxis" <-
function(R, nFactors=2) {
  nVar            <- dim(R)[2]
  acp             <- principalComponents(R)
  values          <- acp$values[(1:nFactors)]
  varExplained    <- round((values/nVar)*100,    2)
  cumVarExplained <- round(cumsum(varExplained), 2)
  loadings        <- acp$vectors[,(1:nFactors)]  %*% diag(values^0.5)  # F1 * diag(E)
  communalities   <- apply(loadings*loadings,1,sum)
  apa             <- list(values          = values,
                          varExplained    = varExplained,
                          cumVarExplained = cumVarExplained,
                          loadings        = loadings,
                          communalities   = communalities)
  return(apa)
  }