\name{nFactors-parameters} \alias{nFactors-parameters} \alias{nFactors-parameters} \docType{package} \title{ Argument and Value Parameters Common to the Different Functions Available in Package nFactors} \description{ This help file describes the argument and value parameters used in the different functions available in package \pkg{nFactors}. \cr \emph{Arguments}: \enumerate{ \item{\emph{adequacy}:}{ logical: if \code{TRUE} prints the recovered population matrix from the factor structure (\code{structureSim})} \item{\emph{all}:}{ logical: if \code{TRUE} computes he Bentler and Yuan index (very long computing time to consider) (\code{structureSim, studySim})} \item{\emph{alpha}:}{ numeric: statistical significance level (\code{nBartlett, nBentler})} \item{\emph{aparallel}:}{ numeric: results of a parallel analysis (\code{nScree}) } \item{\emph{cent}:}{ depreciated numeric (use quantile instead): quantile of the distribution (\code{moreStats, parallel})} \item{\emph{communalities}:}{ character: initial values for communalities (\code{"component"}, \code{"ginv"}, \code{"maxr"}, or \code{"multiple"}) (\code{iterativePrincipalAxis, principalAxis})} \item{\emph{cor}:}{ logical: if \code{TRUE} computes eigenvalues from a correlation matrix, else from a covariance matrix (\code{eigenComputes, nBartlett, nBentler, nCng, nMreg, nScree, nSeScree})} \item{\emph{correction}:}{ logical: if \code{TRUE} uses a correction for the degree of freedom after the first eigenvalue (\code{nBartlett})} \item{\emph{criteria}:}{ numeric: by default fixed at \eqn{\hat{\lambda}}. When the \eqn{\lambda}s are computed from a principal components analysis on a correlation matrix, it corresponds to the usual Kaiser \eqn{\lambda >= 1} rule. On a covariance matrix or from a factor analysis, it is simply the mean. To apply the \eqn{\lambda >= 0} sometimes used with factor analysis, fixed the criteria to \eqn{0} (\code{nScree})} \item{\emph{details}:}{ logical: if \code{TRUE} also returns details about the computation for each eigenvalues (\code{nBartlett, nBentler, nCng, nMreg, structureSim})} \item{\emph{diagCommunalities}:}{ logical: if \code{TRUE}, the correlation between the initial solution and the estimated one will use a correlation of one in the diagonal. If \code{FALSE} (default) the diagonal is not used in the computation of this correlation or covariance matrix (\code{rRecovery})} \item{\emph{dir}:}{ character: directory where to save output (\code{studySim})} \item{\emph{eig}:}{ depreciated parameter (use x instead): eigenvalues to analyse (\code{nScree, plotParallel})} \item{\emph{Eigenvalue}:}{ depreciated parameter (use x instead): eigenvalues to analyse (\code{plotuScree})} \item{\emph{fload}:}{ matrix: loadings of the factor structure (\code{structureSim})} \item{\emph{graphic}:}{ logical: specific plot (\code{bentlerParameters, structureSim})} \item{\emph{index}:}{ numeric: vector of the index of the selected indices (\code{plot.structureSim, print.structureSim, summary.structureSim}} \item{\emph{iterations}:}{ numeric: maximum number of iterations to obtain a solution (\code{iterativePrincipalAxis})} \item{\emph{legend}:}{ logical indicator of the presence of a legend (\code{plotnScree, plotParallel}) } \item{\emph{loadings}:}{ numeric: loadings from a factor analysis solution (\code{rRecovery, generateStructure, studySim})} \item{\emph{log}:}{ logical: if \code{TRUE} does the minimization on the log values (\code{bentlerParameters, nBentler})} \item{\emph{main}:}{ character: main title (\code{plotnScree, plotParallel, plotuScree, boxplot.structureSim, plot.structureSim}) } \item{\emph{maxPar}:}{ numeric: maximums for the coefficient of the linear trend (\code{bentlerParameters, nBentler})} \item{\emph{minPar}:}{ numeric: minimums for the coefficient of the linear trend (\code{bentlerParameters, nBentler})} \item{\emph{method}:}{ character: actually only \code{"giv"} is supplied to compute the approximation of the communalities by maximum correlation (\code{corFA, nCng, nMreg, nScree, nSeScree})} \item{\emph{mjc}:}{ numeric: number of major factors (factors with practical significance) (\code{generateStructure}) } \item{\emph{pmjc}:}{ numeric: number of variables that load significantly on each major factor (\code{generateStructure})} \item{\emph{model}:}{ character: \code{"components"} or \code{"factors"} (\code{nScree, parallel, plotParallel, plotuScree, structureSim, eigenBootParallel, eigenBootParallel, studySim})} \item{\emph{N}:}{ numeric: number of subjects (\code{nBartlett, bentlerParameters, nBentler, studySim})} \item{\emph{nboot}:}{ numeric: number of bootstrap samples (\code{eigenBootParallel}) } \item{\emph{nFactors}:}{ numeric: number of components/factors to retained (\code{componentAxis, iterativePrincipalAxis, principalAxis, bentlerParameters, boxplot.structureSim, studySim})} \item{\emph{nScree}:}{ results of a previous nScree analysis (\code{plotnScree})} \item{\emph{option}:}{ character: \code{"permutation"} or \code{"bootstrap"} (\code{eigenBootParallel})} \item{\emph{object}:}{ nScree: an object of the class nScree \code{is.nScree, summary.nScree} } \item{\emph{object}:}{ structureSim: an object of the class structureSim (\code{is.structureSim, summary.structureSim})} \item{\emph{parallel}:}{ numeric: vector of the result of a previous parallel analysis (\code{plotParallel})} \item{\emph{pmjc}:}{ numeric: number of major loadings on each factor factors (\code{generateStructure, studySim}) } \item{\emph{quantile}:}{ numeric: quantile that will be reported (\code{parallel, moreStats, eigenBootParallel, structureSim, studySim}) } \item{\emph{R}:}{ numeric: correlation or covariance matrix (\code{componentAxis, iterativePrincipalAxis, principalAxis, principalComponents, rRecovery, corFA})} \item{\emph{r2limen}:}{ numeric: R2 limen value for the R2 Nelson index (\code{structureSim, nSeScree, studySim})} \item{\emph{rep}:}{ numeric: number of replications of the correlation or the covariance matrix (default is 100) (\code{parallel})} \item{\emph{reppar}:}{ numeric: number of replications for the parallel analysis (\code{structureSim, studySim})} \item{\emph{repsim}:}{ numeric: number of replications of the matrix correlation simulation (\code{structureSim, studySim})} \item{\emph{resParx}:}{ numeric: restriction on the \eqn{\alpha} coefficient (x) to graph the function to minimize (\code{bentlerParameters})} \item{\emph{resolution}:}{ numeric: resolution of the 3D graph (number of points from \eqn{\alpha} and from \eqn{\beta}).} \item{\emph{resPary}:}{ numeric: restriction on the \eqn{\beta} coefficient (y) to graph the function to minimize (\code{bentlerParameters})} \item{\emph{sd}:}{ numeric: vector of standard deviations of the simulated variables (for a parallel analysis on a covariance matrix) \code{parallel})} \item{\emph{show}:}{ logical: if \code{TRUE} prints the quantile chosen (\code{moreStats}) } \item{\emph{stats}:}{ numeric: vector of the statistics to return: mean(1), median(2), sd(3), quantile(4), min(5), max(6) (\code{studySim})} \item{\emph{subject}:}{ numeric: number of subjects (default is 100) (\code{parallel})} \item{\emph{tolerance}:}{ numeric: minimal difference in the estimated communalities after a given iteration (\code{iterativePrincipalAxis})} \item{\emph{trace}:}{ logical: if \code{TRUE} gives details of the status of the simulations (\code{studySim})} \item{\emph{typePlot}:}{ character: plots the minimized function according to a 3D plot: \code{"wireframe"}, \code{"contourplot"} or \code{"levelplot"} (\code{bentlerParameters})} \item{\emph{unique}:}{ numeric: loadings on the non significant variables on each major factor (\code{generateStructure, studySim}) } \item{\emph{upper}:}{ logical: if \code{TRUE} upper diagonal is replaced with lower diagonal. If \code{FALSE}, lower diagonal is replaced with upper diagonal (\code{diagReplace})} \item{\emph{use}:}{ character: how to deal with missing values, same as the parameter from the \code{corr} function (\code{eigenBootParallel}) } \item{\emph{var}:}{ numeric: number of variables (default is 10) (\code{parallel, generateStructure, studySim}) } \item{\emph{vLine}:}{ character: color of the vertical indicator line in the eigen boxplot (\code{boxplot.structureSim})} \item{\emph{x}:}{ numeric: a \code{vector} of eigenvalues, a \code{matrix} of correlations or of covariances or a \code{data.frame} of data (\code{eigenFrom, nBartlett, nCng, nMreg})} \item{\emph{xlab}:}{ character: label of the x axis (\code{plotnScree, plotParallel, plotuScree, boxplot.structureSim})} \item{\emph{x}:}{ data.frame: data from which a correlation or covariance matrix will be obtained (\code{eigenBootParallel})} \item{\emph{x}:}{ depreciated: (\code{plotParallel})} \item{\emph{x}:}{ nScree: an object of the class nScree (\code{plot.nScree, print.nScree})} \item{\emph{x}:}{ numeric: matrix (\code{makeCor})} \item{\emph{x}:}{ numeric: matrix or data.frame (\code{moreStats})} \item{\emph{x}:}{ structureSim: an object of the class structureSim (\code{boxplot.structureSim, plot.structureSim, print.structureSim})} \item{\emph{ylab}:}{ character: label of the y axis (\code{plotnScree, plotParallel, plotuScree, boxplot.structureSim}) } } \emph{Values}: \enumerate{ \item{\emph{cor}:}{ numeric: Pearson correlation between initial and recovered estimated correlation or covariance matrix. Compution depend on the logical value of the \code{communalities} argument (\code{rRecovery}) } \item{\emph{details}:}{ numeric: matrix of the details for each index (\code{nBartlett, bentlerParameters, nCng, nMreg})} \item{\emph{difference}:}{ numeric: difference between initial and recovered estimated correlation or covariance matrix (\code{rRecovery})} \item{\emph{iterations}:}{ numeric: maximum number of iterations to obtain a solution (\code{iterativePrincipalAxis})} \item{\emph{loadings}:}{ numeric: loadings of each variable on each component or factor retained (\code{componentAxis, iterativePrincipalAxis, principalAxis, principalComponents}) } \item{\emph{nFactors}:}{ numeric: vector of the number of components or factors retained by the Bartlett, Anderson and Lawley procedures (\code{nBartlett, bentlerParameters, nCng, nMreg}) } \item{\emph{R}: }{ numeric: correlation or covariance matrix (\code{diagReplace, rRecovery})} \item{\emph{recoveredR}:}{ numeric: recovered estimated correlation or covariance matrix (\code{rRecovery}) } \item{\emph{tolerance}:}{ numeric: minimal difference in the estimated communalities after a given iteration (\code{iterativePrincipalAxis})} \item{\emph{values}:}{ numeric: data.frame of information (\code{nScree, parallel, plotnScree, plotParallel, plotuScree, structureSim})} \item{\emph{values}:}{ numeric: data.frame of statistics (\code{moreStats}) } \item{\emph{values}:}{ numeric: full matrix of correlation or covariance (\code{makeCor}) } \item{\emph{values}:}{ numeric: variance of each component or factor (\code{iterativePrincipalAxis, principalComponents}) } \item{\emph{values}:}{ data.frame: mean, median, quantile, standard deviation, minimum and maximum of bootstrapped eigenvalues (\code{eigenBootParallel})} \item{\emph{values}:}{ numeric: matrix of correlation or covariance with communalities in the diagonal (\code{corFA})} \item{\emph{values}:}{ numeric: variance of each component or factor retained (\code{componentAxis, principalAxis}) } \item{\emph{values}:}{ numeric: matrix factor structure (\code{generateStructure})} \item{\emph{varExplained}:}{ numeric: variance explained by each component or factor retained (\code{componentAxis, iterativePrincipalAxis, principalAxis, principalComponents}) } \item{\emph{varExplained}:}{ numeric: cumulative variance explained by each component or factor retained (\code{componentAxis, iterativePrincipalAxis, principalAxis, principalComponents}) } } } \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/} \cr \cr David Magis \cr Departement de mathematiques \cr Universite de Liege \cr \email{David.Magis@ulg.ac.be} } \references{ Raiche, G., Riopel, M. and Blais, J.-G. (2006). \emph{Non graphical solutions for the Cattell's scree test}. Paper presented at the International Annual meeting of the Psychometric Society, Montreal. [\url{http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/}] } \seealso{ Other packages are also very useful for principal component and factor analysis. The \emph{R} psychometric view is instructive at this point. See \url{http://cran.stat.sfu.ca/web/views/Psychometrics.html} for further details. } \keyword{ package }