parallel {nFactors}R Documentation

Parallel Analysis of a Correlation Matrix

Description

This function gives the distribution of the eigenvalues of correlation matrices of random uncorrelated standardized normal variables. The mean and a selected centile of this distribution are returned.

Usage

 parallel(subject = 100,
          var     = 10,
          rep     = 100,
          cent    = 0.05)
 

Arguments

subject numeric: Nmber of subjects (default is 100)
var numeric: Number of variables (default is 10)
rep numeric: Number of replications of the correlation matrix (default is 100)
cent numeric: Centile of the distribution on which the decision is made (default is 0.05)

Details

Note that if the decision is based on a centile value rather than on the mean, care must be taken with the number of replications (rep). In fact, the smaller the centile (cent), the bigger the number of replications.

Value

eigen Data frame consisting of mean and the centile of the eigenvalues distribution
eigen$mevpea Mean of the eigenvalues distribution
eigen$sevpea Standard deviation of the eigenvalues distribution
eigen$qevpea Centile of the eigenvalues distribution
eigen$sqevpea Standard error of the centile of the eigenvalues distribution
subject Number of subjects
variables Number of variables
centile Selected centile

Otherwise, returns a summary of the parallel analysis.

Author(s)

Gilles Raiche, Universite du Quebec a Montreal raiche.gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/

References

Drasgow, F. and Lissak, R. (1983) Modified parallel analysis: a procedure for examining the latent dimensionality of dichotomously scored item responses. Journal of Applied Psychology, 68(3), 363-373.

Hoyle, R. H. and Duvall, J. L. (2004). Determining the number of factors in exploratory and confirmatory factor analysis. In D. Kaplan (Ed.): The Sage handbook of quantitative methodology for the social sciences. Thousand Oaks, CA: Sage.

Horn, J. L. (1965). A rationale and test of the number of factors in factor analysis. Psychometrika, 30, 179-185.

See Also

plotuScree, nScree, plotnScree, plotParallel

Examples

## SIMPLE EXAMPLE OF A PARALLEL ANALYSIS
## OF A CORRELATION MATRIX WITH ITS PLOT
 data(dFactors)
 eig      <- dFactors$Raiche$eigenvalues
 subject  <- dFactors$Raiche$nsubjects
 var      <- length(eig)
 rep      <- 100
 cent     <- 0.95
 results  <- parallel(subject,var,rep,cent)

 results

## IF THE DECISION IS BASED ON THE CENTILE USE qevpea INSTEAD
## OF mevpea ON THE FIRST LINE OF THE FOLLOWING CALL
 plotuScree(eig,
             main = "Parallel Analysis"
             )
 lines(1:var,
       results$eigen$qevpea,
       type="b",
       col="green"
       )

## ANOTHER SOLUTION IS SIMPLY TO
 plotParallel(results)

## RESULTS
# $eigen
#        mevpea     sevpea    qevpea    sevpea.1
# V1  1.5421626 0.09781869 1.4037201 0.020670924
# V2  1.3604323 0.05728471 1.2768656 0.012105332
# V3  1.2249034 0.04704870 1.1482431 0.009942272
# V4  1.1189148 0.03662555 1.0605407 0.007739666
# V5  1.0221635 0.04048780 0.9599296 0.008555832
# V6  0.9318382 0.04053704 0.8647949 0.008566237
# V7  0.8381154 0.04026090 0.7758708 0.008507883
# V8  0.7493151 0.04729122 0.6727706 0.009993521
# V9  0.6568985 0.04664676 0.5756055 0.009857334
# V10 0.5552561 0.04942935 0.4800394 0.010445348

# $subject
# [1] 100

# $variables
# [1] 10

# $centile
# [1] 0.05

# attr(,"class")
# [1] "parallel"

 

[Package nFactors version 2.2 Index]