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
nScreeObjectMethods.rd
\name{nScreeObjectMethods}
\alias{is.nScree}
\alias{plot.nScree}
\alias{print.nScree}
\alias{summary.nScree}
\title{ Utility Functions for nScree Class Objects}
\description{
Utility functions for \code{nScree} class objects. Some of these functions are
already implemented in the \code{nFactors} package, but are easier to use with
generic functions like these.
}
\usage{
\method{is}{nScree} (object)
\method{plot}{nScree} (x, ...)
\method{print}{nScree} (x, ...)
\method{summary}{nScree}(object, ...)
}
\arguments{
\item{x}{ nScree: an object of the class \code{nScree}}
\item{object}{ nScree: an object of the class \code{nScree}}
\item{...}{ variable: additionnal parameters to give to the \code{print}
function with \code{print.nScree}, the \code{plotnScree} with
\code{plot.nScree} or to the \code{summary} function with
\code{summary.nScree}}
}
\value{
Generic functions for the nScree class:
\item{is.nScree}{ logical: is the object of the class \code{nScree}? }
\item{plot.nScree }{ graphic: plots a figure according to the \code{plotnScree}
function}
\item{print.nScree }{ numeric: vector of the number of components/factors to
retain: same as the \code{Components} vector from the \code{nScree}
object}
\item{summary.nScree }{ data.frame: details of the results from a nScree analysis:
same as the \code{Analysis} data.frame from the \code{nScree}
object, but with easier control of the
number of decimals with the \code{digits} parameter}
}
\seealso{
\code{\link{plotuScree}},
\code{\link{plotnScree}},
\code{\link{parallel}},
\code{\link{plotParallel}},
}
\references{
Raiche, G., Riopel, M. and Blais, J.-G. (2006). \emph{Non graphical solutions for the Cattell's scree test}.
Paper presented at the International Annual meeting of the Psychometric Society, Montreal.
[\url{http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/}]
}
\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{
## INITIALISATION
data(dFactors) # Load the nFactors dataset
attach(dFactors)
vect <- Raiche # Use the example from Raiche
eigenvalues <- vect$eigenvalues # Extract the observed eigenvalues
nsubjects <- vect$nsubjects # Extract the number of subjects
variables <- length(eigenvalues) # Compute the number of variables
rep <- 100 # Number of replications for the parallel analysis
cent <- 0.95 # Centile value of the parallel analysis
## PARALLEL ANALYSIS (qevpea for the centile criterion, mevpea for the mean criterion)
aparallel <- parallel(var = variables,
subject = nsubjects,
rep = rep,
cent = cent
)$eigen$qevpea # The 95 centile
## NOMBER OF FACTORS RETAINED ACCORDING TO DIFFERENT RULES
results <- nScree(x=eigenvalues, aparallel=aparallel)
is.nScree(results)
results
summary(results)
## PLOT ACCORDING TO THE nScree CLASS
plot(results)
}
\keyword{ multivariate }