% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/studySim.r
\name{studySim}
\alias{studySim}
\title{Simulation Study from Given Factor Structure Matrices and Conditions}
\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.}
}
\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).
}
\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)
 }

}
\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.

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.
}
\seealso{
\code{\link{generateStructure}}, \code{\link{structureSim}}
}
\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}