studySim {nFactors} | R Documentation |
The 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).
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)
var |
numeric: vector of the number of variables |
nFactors |
numeric: vector of the number of components/factors |
pmjc |
numeric: vector of the number of major loadings on each component/factor |
loadings |
numeric: vector of the major loadings on each component/factor |
unique |
numeric: vector of the unique loadings on each component/factor |
N |
numeric: vector of the number of subjects/observations |
repsim |
numeric: number of replication of the matrix correlation simulation |
reppar |
numeric: number of replication for the parallel and permutation analysis |
stats |
numeric: vector of the statistics to return: mean(1), median(2), sd(3), quantile(4), min(5), max(6) |
quantile |
numeric: quantile for the parallel and permutation analysis |
model |
character: "components" or "factors" |
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) |
dir |
character: Directory where to save output. Default to NA |
trace |
logical: if TRUE output details of the status of the simulations |
values |
Returns selected statistics about the number of components/factors to retain: mean, median, quantile, standard deviation, minimum and maximum. |
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.
generateStructure
,
structureSim
# .................................................................... # 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)