| structureSim {nFactors} | R Documentation |
The structureSim function return a population and a sample correlation
matrices from a predefined congeneric factor structure.
structureSim(fload, reppar=30, repsim=100, N, quantile=0.95,
model="components", adequacy=FALSE, details=TRUE,
r2limen=0.75, all=FALSE)
fload |
matrix: loadings of the factor structure |
reppar |
numeric: number of replication for the parallel analysis |
repsim |
numeric: number of replication of the matrix correlation simulation |
N |
numeric: number of subjects |
quantile |
numeric: quantile for the parallel analysis |
model |
character: "components" or "factors" |
adequacy |
logical: if TRUE print the recovered population
matrix from the factor structure |
details |
logical: if TRUE output details of the
repsim simulations |
r2limen |
numeric: R2 limen value for the R2 index of Nelson |
all |
logical: if TRUE computes athe Bentler and Yuan
index (very long computating time to consider) |
values |
the output depends of the logical value of details. If FALSE,
returns only statistics about the eigenvalues: mean, median, quantile,
standard deviation, minimum and maximum. If TRUE,
returns also details about the repsim simulations.
If adequacy = TRUE return the recovered factor structure |
Gilles Raiche
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
Zwick, W. R. and Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological bulletin, 99, 432-442.
principalComponents,
iterativePrincipalAxis,
rRecovery
# .......................................................
# Example inspired from Zwick and Velicer (1986, table 2, p. 437)
## ...................................................................
nFactors <- 3
unique <- 0.2
loadings <- 0.5
nsubjects <- 180
repsim <- 30
zwick <- generateStructure(var=36, mjc=nFactors, pmjc=12,
loadings=loadings,
unique=unique)
## ...................................................................
# Produce statistics about a replication of a parallel analysis on
# 30 sampled correlation matrices
mzwick.fa <- structureSim(fload=as.matrix(zwick), reppar=30,
repsim=repsim, N=nsubjects, quantile=0.5,
model="factors")
mzwick <- structureSim(fload=as.matrix(zwick), reppar=30,
repsim=repsim, N=nsubjects, quantile=0.5, all=TRUE)
# Very long execution time that could be used only with model="components"
# mzwick <- structureSim(fload=as.matrix(zwick), reppar=30,
# repsim=repsim, N=nsubjects, quantile=0.5, all=TRUE)
par(mfrow=c(2,1))
plot(x=mzwick, nFactors=nFactors, index=c(1:14), cex.axis=0.7, col="red")
plot(x=mzwick.fa, nFactors=nFactors, index=c(1:11), cex.axis=0.7, col="red")
par(mfrow=c(1,1))
par(mfrow=c(2,1))
boxplot(x=mzwick, nFactors=3, cex.axis=0.8, vLine="blue", col="red")
boxplot(x=mzwick.fa, nFactors=3, cex.axis=0.8, vLine="blue", col="red",
xlab="Components")
par(mfrow=c(1,1))
# ......................................................