studySim | 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 replications of the matrix correlation simulation |
reppar |
numeric: number of replications 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: |
r2limen |
numeric: R2 limen value for the R2 Nelson index |
all |
logical: if |
dir |
character: directory where to save output. Default to NA |
trace |
logical: if |
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
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. Psychological Bulletin, 99, 432-442.
generateStructure
, structureSim
## Not run: # .................................................................... # 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) ## End(Not run)
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