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)) # ......................................................