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
studySim.rd
\name{studySim}
\alias{studySim}
\title{ Simulation Study from Given Factor Structure Matrices and Conditions}
\description{
The \code{structureSim} function returns statistical results from simulations
from predefined congeneric factor structures. The main ideas come from the
methodology applied by Zwick and Velicer (1986).
}
\usage{
studySim(var, nFactors, pmjc, loadings, unique, N, repsim, reppar,
stats=1, quantile=0.5, model="components", r2limen=0.75,
all=FALSE, dir=NA, trace=TRUE)
}
\arguments{
\item{var}{ numeric: vector of the number of variables}
\item{nFactors}{ numeric: vector of the number of components/factors}
\item{pmjc}{ numeric: vector of the number of major loadings on each component/factor}
\item{loadings}{ numeric: vector of the major loadings on each component/factor}
\item{unique}{ numeric: vector of the unique loadings on each component/factor}
\item{N}{ numeric: vector of the number of subjects/observations}
\item{repsim}{ numeric: number of replications of the matrix correlation
simulation}
\item{reppar}{ numeric: number of replications for the parallel and permutation analysis}
\item{stats}{ numeric: vector of the statistics to return: mean(1),
median(2), sd(3), quantile(4), min(5), max(6)}
\item{quantile}{ numeric: quantile for the parallel and permutation analysis}
\item{model}{ character: \code{"components"} or \code{"factors"} }
\item{r2limen}{ numeric: R2 limen value for the R2 Nelson index}
\item{all}{ logical: if \code{TRUE} computes the Bentler and Yuan
index (very long computing time to consider)}
\item{dir}{ character: directory where to save output. Default to NA}
\item{trace}{ logical: if \code{TRUE} outputs details of the status of the simulations}
}
\value{
\item{values}{ Returns selected statistics about the number of components/factors
to retain: mean, median, quantile,
standard deviation, minimum and maximum.}
}
\seealso{
\code{\link{generateStructure}},
\code{\link{structureSim}}
}
\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{
\dontrun{
# ....................................................................
# Example inspired from Zwick and Velicer (1986)
# Very long computimg time
# ...................................................................
# 1. Initialisation
# reppar <- 30
# repsim <- 5
# quantile <- 0.50
# 2. Simulations
# X <- studySim(var=36,nFactors=3, pmjc=c(6,12), loadings=c(0.5,0.8),
# unique=c(0,0.2), quantile=quantile,
# N=c(72,180), repsim=repsim, reppar=reppar,
# stats=c(1:6))
# 3. Results (first 10 results)
# print(X[1:10,1:14],2)
# names(X)
# 4. Study of the error done in the determination of the number
# of components/factors. A positive value is associated to over
# determination.
# results <- X[X$stats=="mean",]
# residuals <- results[,c(11:25)] - X$nfactors
# BY <- c("nsubjects","var","loadings")
# round(aggregate(residuals, by=results[BY], mean),0)
}
}
\keyword{ multivariate }