Revision c9833e40e7af6531f93c92bb4d2ab8a87541faad authored by Gilles Raiche on 09 December 2009, 00:00:00 UTC, committed by Gabor Csardi on 09 December 2009, 00:00:00 UTC
1 parent b6fe861
eigenBootParallel.rd
\name{eigenBootParallel}
\alias{eigenBootParallel}
\title{ Bootstrapping of the Eigenvalues From a Data Frame}
\description{
The \code{eigenBootParallel} function samples observations from a \code{data.frame}
to produces correlation or covariance matrix from which eigenvalues are computed. The
function returns statistics about these bootstrapped eigenvalues. Their means
or their quantile could be used later to replace the eigenvalues inputed to
a parallel analysis. The \code{eigenBootParallel} can also computes random eigenvalues
from empirical data by columns permutation (Buja and Eyuboglu, 1992).
}
\usage{
eigenBootParallel(x, quantile=0.95, nboot=30, option="permutation",
cor=TRUE, model="components", ...)
}
\arguments{
\item{x}{ data.frame: data from which a correlation matrix will be obtained}
\item{quantile}{ numeric: eigenvalues quantile that will be reported }
\item{nboot}{ numeric: number of bootstrap samples }
\item{option}{ character: \code{"permutation"} or \code{"bootstrap"}}
\item{cor}{ logical: if \code{TRUE} computes eigenvalues from a correlation
matrix, else from a covariance matrix (\code{eigenComputes})}
\item{model}{ character: bootstraps from a principal component analysis
(\code{"components"}) or from a factor analysis (\code{"factors"}) }
\item{...}{ variable: additionnal parameters to give to the \code{cor} or
\code{cov} functions}
}
\value{
\item{values}{ data.frame: mean, median, quantile, standard deviation,
minimum and maximum of bootstrapped eigenvalues }
}
\seealso{
\code{\link{principalComponents}},
\code{\link{iterativePrincipalAxis}},
\code{\link{rRecovery}}
}
\references{
Buja, A. and Eyuboglu, N. (1992). Remarks on parallel analysis.
\emph{Multivariate Behavioral Research, 27}(4), 509-540.
Zwick, W. R. and Velicer, W. F. (1986). Comparison of five rules for
determining the number of components to retain.
\emph{Psychological bulletin, 99}, 432-442.
}
\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 the iris data
eigenvalues <- eigenComputes(x=iris[,-5])
# Permutation parallel analysis distribution
aparallel <- eigenBootParallel(x=iris[,-5], quantile=0.95)$quantile
# Number of components to retain
results <- nScree(x = eigenvalues, aparallel = aparallel)
results$Components
plotnScree(results)
# ......................................................
# ......................................................
# Bootstrap distributions stude of the eigenvalues from iris data
# with different correlation methods
eigenBootParallel(x=iris[,-5],quantile=0.05,
option="bootstrap",method="pearson")
eigenBootParallel(x=iris[,-5],quantile=0.05,
option="bootstrap",method="spearman")
eigenBootParallel(x=iris[,-5],quantile=0.05,
option="bootstrap",method="kendall")
}
\keyword{ multivariate }
Computing file changes ...