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
generateStructure.rd
\name{generateStructure}
\alias{generateStructure}
\title{ Generate a Factor Structure Matrix.}
\description{
The \code{generateStructure} function returns a \emph{mjc} factor structure matrix.
The number of variables per major factor \emph{pmjc} is equal for each factor.
The argument \emph{pmjc} must be divisible by \emph{nVar}.
The arguments are strongly inspired from Zick and Velicer (1986, p. 435-436) methodology.
}
\usage{
generateStructure(var, mjc, pmjc, loadings, unique)
}
\arguments{
\item{var}{ numeric: number of variables}
\item{mjc}{ numeric: number of major factors (factors with practical significance) }
\item{pmjc}{ numeric: number of variables that load significantly on each major factor }
\item{loadings}{ numeric: loadings on the significant variables on each major factor }
\item{unique}{ numeric: loadings on the non significant variables on each major factor }
}
\value{
\item{values}{ numeric matrix: factor structure }
}
\seealso{
\code{\link{principalComponents}},
\code{\link{iterativePrincipalAxis}},
\code{\link{rRecovery}}
}
\references{
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 inspired from Zwick and Velicer (1986, table 2, p. 437)
## ...................................................................
unique=0.2; loadings=0.5
zwick1 <- generateStructure(var=36, mjc=6, pmjc= 6, loadings=loadings,
unique=unique)
zwick2 <- generateStructure(var=36, mjc=3, pmjc=12, loadings=loadings,
unique=unique)
zwick3 <- generateStructure(var=72, mjc=9, pmjc= 8, loadings=loadings,
unique=unique)
zwick4 <- generateStructure(var=72, mjc=6, pmjc=12, loadings=loadings,
unique=unique)
sat=0.8
## ...................................................................
zwick5 <- generateStructure(var=36, mjc=6, pmjc= 6, loadings=loadings,
unique=unique)
zwick6 <- generateStructure(var=36, mjc=3, pmjc=12, loadings=loadings,
unique=unique)
zwick7 <- generateStructure(var=72, mjc=9, pmjc= 8, loadings=loadings,
unique=unique)
zwick8 <- generateStructure(var=72, mjc=6, pmjc=12, loadings=loadings,
unique=unique)
## ...................................................................
# nsubjects <- c(72, 144, 180, 360)
# require(psych)
# Produce an usual correlation matrix from a congeneric model
nsubjects <- 72
mzwick5 <- sim.structure(fx=as.matrix(zwick5), n=nsubjects)
mzwick5$r
# Factor analysis: recovery of the factor structure
iterativePrincipalAxis(mzwick5$model, nFactors=6,
communalities="ginv")$loadings
iterativePrincipalAxis(mzwick5$r , nFactors=6,
communalities="ginv")$loadings
factanal(covmat=mzwick5$model, factors=6)
factanal(covmat=mzwick5$r , factors=6)
# Number of components to retain
eigenvalues <- eigen(mzwick5$r)$values
aparallel <- parallel(var = length(eigenvalues),
subject = nsubjects,
rep = 30,
quantile = 0.95,
model="components")$eigen$qevpea
results <- nScree(x = eigenvalues,
aparallel = aparallel)
results$Components
plotnScree(results)
# Number of factors to retain
eigenvalues.fa <- eigen(corFA(mzwick5$r))$values
aparallel.fa <- parallel(var = length(eigenvalues.fa),
subject = nsubjects,
rep = 30,
quantile = 0.95,
model="factors")$eigen$qevpea
results.fa <- nScree(x = eigenvalues.fa,
aparallel = aparallel.fa,
model ="factors")
results.fa$Components
plotnScree(results.fa)
# ......................................................
}
\keyword{ multivariate }