% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nScreeObjectMethods.r
\name{summary.nScree}
\alias{summary.nScree}
\alias{print.nScree}
\alias{plot.nScree}
\alias{is.nScree}
\title{Utility Functions for nScree Class Objects}
\usage{
\method{summary}{nScree}(object, ...)

\method{print}{nScree}(x, ...)

\method{plot}{nScree}(x, ...)

is.nScree(object)
}
\arguments{
\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}}

\item{x}{Results of a previous \code{nScree} analysis}
}
\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}
}
\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.
}
\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)


}
\references{
Raiche, G., Walls, T. A., Magis, D., Riopel, M. and Blais, J.-G. (2013). Non-graphical solutions
for Cattell's scree test. Methodology, 9(1), 23-29.
}
\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}
}
\keyword{multivariate}