eigenComputes.rd
\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 }