https://github.com/cran/nFactors
Tip revision: 592b098fc786911733da1c1953e58c9d1c2e9517 authored by Gilles Raiche on 10 April 2010, 00:00:00 UTC
version 2.3.3
version 2.3.3
Tip revision: 592b098
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 produce correlation or covariance matrices 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 inputted to
a parallel analysis. The \code{eigenBootParallel} can also compute random eigenvalues
from empirical data by column 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 to 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 study 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 }