\name{eigenvals}
\Rdversion{1.1}
\alias{eigenvals}
\alias{eigenvals.default}
\alias{eigenvals.prcomp}
\alias{eigenvals.princomp}
\alias{eigenvals.cca}
\alias{eigenvals.wcmdscale}
\alias{eigenvals.pcnm}
\alias{eigenvals.dudi}
\alias{eigenvals.pca}
\alias{eigenvals.pco}
\alias{summary.eigenvals}

\title{
  Extract Eigenvalues from an Ordination Object
}
\description{
  Function extracts eigenvalues from an object that has them. Many
  multivariate methods return such objects. 
}
\usage{
eigenvals(x, ...)
\method{eigenvals}{cca}(x, constrained = FALSE, ...)
\method{summary}{eigenvals}(object, ...)
}

\arguments{
  \item{x}{
    An object from which to extract eigenvalues.
}
  \item{object}{
    An \code{eigenvals} result object.
}
   \item{constrained}{
    Return only constrained eigenvalues.
}
   \item{\dots}{
    Other arguments to the functions (usually ignored)
}
}

\details{
  This is a generic function that has methods for \code{\link{cca}},
  \code{\link{wcmdscale}}, \code{\link{pcnm}}, \code{\link{prcomp}},
  \code{\link{princomp}}, \code{\link[ade4]{dudi}} (of \pkg{ade4}), and 
  \code{\link[labdsv]{pca}} and \code{\link[labdsv]{pco}} (of 
  \pkg{labdsv}) result objects. The default method also
  extracts eigenvalues if the result looks like being from
  \code{\link{eigen}} or \code{\link{svd}}.  Functions
  \code{\link{prcomp}} and \code{\link{princomp}} contain square roots
  of eigenvalues that all called standard deviations, but
  \code{eigenvals} function returns their squares.  Function
  \code{\link{svd}} contains singular values, but function
  \code{eigenvals} returns their squares. For constrained ordination
  methods \code{\link{cca}}, \code{\link{rda}} and
  \code{\link{capscale}} the function returns the both constrained and
  unconstrained eigenvalues concatenated in one vector, but the partial
  component will be ignored. However, with argument 
  \code{constrained = TRUE} only constrained eigenvalues are returned. 

  The \code{summary} of \code{eigenvals} result returns eigenvalues,
  proportion explained and cumulative proportion explained. The result
  object can have some negative eigenvalues (\code{\link{wcmdscale}},
  \code{\link{capscale}}, \code{\link{pcnm}}) which correspond to
  imaginary axes of Euclidean mapping of non-Euclidean distances
  (Gower 1985). In these cases, the sum of absolute values of
  eigenvalues is used in calculating the proportions explained, and
  real axes (corresponding to positive eigenvalues) will only explain
  a part of total variation (Mardia et al. 1979, Gower 1985).  
}

\value{
  An object of class \code{"eigenvals"} which is a vector of eigenvalues.
}

\author{
 Jari Oksanen.
}
\references{
  Gower, J. C. (1985). Properties of Euclidean and non-Euclidean
  distance matrices. \emph{Linear Algebra and its Applications} 67,
  81--97.

  Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979).  Chapter 14 of
  \emph{Multivariate Analysis}, London: Academic Press.
}

\seealso{
 \code{\link{eigen}}, \code{\link{svd}}, \code{\link{prcomp}},
 \code{\link{princomp}}, \code{\link{cca}}, \code{\link{rda}},
 \code{\link{capscale}}, \code{\link{wcmdscale}},
 \code{\link{cca.object}}. 
}
\examples{
data(varespec)
data(varechem)
mod <- cca(varespec ~ Al + P + K, varechem)
ev <- eigenvals(mod)
ev
summary(ev)
}
\keyword{ multivariate }

