\name{componentAxis} \alias{componentAxis} \title{ Principal Component Analysis With Only n First Components Retained} \description{ The \code{componentAxis} function returns a principal component analysis with the first \emph{n} components retained. } \usage{ componentAxis(R, nFactors=2) } \arguments{ \item{R}{ numeric: correlation or covariance matrix} \item{nFactors}{ numeric: number of components/factors to retain} } \value{ \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 } } \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. } \seealso{ \code{\link{principalComponents}}, \code{\link{iterativePrincipalAxis}}, \code{\link{rRecovery}} } \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}, \url{http://www.er.uqam.ca/nobel/r17165/} } \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) # ....................................................... } \keyword{ multivariate }