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#' Scree Plot According to a nScree Object Class
#'
#' Plot a scree plot adding information about a non graphical \code{nScree}
#' analysis.
#'
#'
#' @param nScree Results of a previous \code{nScree} analysis
#' @param legend Logical indicator of the presence or not of a legend
#' @param xlab Label of the x axis (default to \code{"Component"})
#' @param ylab Label of the y axis (default to \code{"Eigenvalue"})
#' @param main Main title (default to \code{"Non Graphical Solutions to the
#' Scree Test"})
#' @return Nothing returned.
#' @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}
#' @seealso \code{\link{plotuScree}}, \code{\link{nScree}},
#' \code{\link{plotParallel}}, \code{\link{parallel}}
#' @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.
#' @export
#' @importFrom graphics lines par text plot.default
# #' @importFrom stats lm coef
#' @keywords Graphics
#' @examples
#'
#' ## INITIALISATION
#' data(dFactors) # Load the nFactors dataset
#' attach(dFactors)
#' vect <- Raiche # Use the second example from Buja and Eyuboglu
#' # (1992, p. 519, nsubjects not specified by them)
#' 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(eig = eigenvalues,
#' aparallel = aparallel
#' )
#'
#' results
#'
#' ## PLOT ACCORDING TO THE nScree CLASS
#' plotnScree(results)
#'
"plotnScree" <-
function (nScree,
legend = TRUE,
ylab = "Eigenvalues",
xlab = "Components",
main = "Non Graphical Solutions to Scree Test")
{
if (!inherits(nScree, "nScree")) stop("Method is only for nScree objects")
#if (!exists("legend", mode="logical") ) legend <- TRUE # To develop
#if (!exists("ylab")) ylab <- "Eigenvalues" # To develop
#if (!exists("xlab")) xlab <- "Components" # To develop
#if (!exists("main")) main <- "Non Graphical Solutions to Scree Test" # To develop
if (nScree$Model == "components") nkaiser = "Eigenvalues (>mean = " else nkaiser = "Eigenvalues (>0 = "
if (nScree$Model == "factors") xlab = "Factors"
par(col = 1, pch = 1) # Color and symbol for usual scree
par(mfrow = c(1,1))
eig <- nScree$Analysis$Eigenvalues
k <- 1:length(eig)
#plotuScree(x=eig, ...) # To develop
plotuScree(x=eig, main=main, xlab=xlab, ylab=ylab)
nk <- length(eig)
noc <- nScree$Components$noc
vp.p <- lm(eig[c(noc+1,nk)] ~ k[c(noc+1,nk)])
x <- sum(c(1,1) * coef(vp.p))
y <- sum(c(1,nk)* coef(vp.p))
par(col = 10) # Color for optimal coordinates
lines(k[c(1,nk)],c(x,y))
par(col = 11,pch=2) # Color and symbol for parallel analysis
lines(1:nk, nScree$Analysis$Par.Analysis, type = "b")
if (legend == TRUE) {
leg.txt <- c(paste(nkaiser,nScree$Components$nkaiser,")"),
c(paste("Parallel Analysis (n = ",nScree$Components$nparallel,")")),
c(paste("Optimal Coordinates (n = ",nScree$Components$noc,")")),
c(paste("Acceleration Factor (n = ",nScree$Components$naf,")")) )
legend("topright",
legend = leg.txt,
pch = c(1,2,NA,NA),
text.col = c(1,3,2,4), col = c(1,3,2,4)
)
}
naf <- nScree$Components$naf
text(x = noc , y = eig[noc], label = " (OC)", cex = .70, adj = c(0,0), col = 2)
text(x = naf + 1, y = eig[naf + 1], label = " (AF)", cex = .70, adj = c(0,0), col = 4)
}
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