\name{eigenComputes} \alias{eigenComputes} \title{ Computes Eigenvalues According to the Data Type } \description{ The \code{eigenComputes} function computes eigenvalues from the identified data type. It is used internally in many fonctions of the \pkg{nFactors} package in order to apply these to a vector of eigenvalues, a matrix of correlations or covariance or a data frame. } \usage{ eigenComputes(x, cor=TRUE, model="components", ...) } \arguments{ \item{x}{ numeric: a \code{vector} of eigenvalues, a \code{matrix} of correlations or of covariances or a \code{data.frame} of data} \item{cor}{ logical: if \code{TRUE} computes eigenvalues from a correlation matrix, else from a covariance matrix} \item{model}{ character: \code{"components"} or \code{"factors"} } \item{...}{ variable: additionnal parameters to give to the \code{cor} or \code{cov} functions} } \value{ \item{value}{ numeric: return a vector of eigenvalues } } \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{ # ....................................................... # Different data types # Vector of eigenvalues data(dFactors) x1 <- dFactors$Cliff1$eigenvalues eigenComputes(x1) # Data from a data.frame x2 <- data.frame(matrix(20*rnorm(100), ncol=5)) eigenComputes(x2, cor=TRUE, use="everything") eigenComputes(x2, cor=FALSE, use="everything") eigenComputes(x2, cor=TRUE, use="everything", method="spearman") eigenComputes(x2, cor=TRUE, use="everything", method="kendall") # From a covariance matrix x3 <- cov(x2) eigenComputes(x3, cor=TRUE, use="everything") eigenComputes(x3, cor=FALSE, use="everything") # From a correlation matrix x4 <- cor(x2) eigenComputes(x4, use="everything") # ....................................................... } \keyword{ multivariate }