studySim: Simulation Study from Given Factor Structure Matrices and...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/studySim.r

Description

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).

Usage

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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)

Arguments

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: "components" or "factors"

r2limen

numeric: R2 limen value for the R2 Nelson index

all

logical: if TRUE computes the Bentler and Yuan index (very long computing time to consider)

dir

character: directory where to save output. Default to NA

trace

logical: if TRUE outputs details of the status of the simulations

Value

values

Returns selected statistics about the number of components/factors to retain: mean, median, quantile, standard deviation, minimum and maximum.

Author(s)

Gilles Raiche
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca

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.

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.

See Also

generateStructure, structureSim

Examples

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## 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)

nFactors documentation built on April 14, 2020, 6:55 p.m.

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