parallel | R Documentation |
This function gives the distribution of the eigenvalues of correlation or a covariance matrices of random uncorrelated standardized normal variables. The mean and a selected quantile of this distribution are returned.
parallel(subject = 100, var = 10, rep = 100, cent = 0.05, quantile = cent, model = "components", sd = diag(1, var), ...)
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 |
depreciated numeric (use quantile instead): quantile of the distribution on which the decision is made (default is 0.05) |
quantile |
numeric: quantile of the distribution on which the decision is made (default is 0.05) |
model |
character: |
sd |
numeric: vector of standard deviations of the simulated variables (for a parallel analysis on a covariance matrix) |
... |
variable: other parameters for the |
Note that if the decision is based on a quantile value rather than on the
mean, care must be taken with the number of replications (rep
). In
fact, the smaller the quantile (cent
), the bigger the number of
necessary replications.
eigen |
Data frame consisting of the mean and the quantile of the eigenvalues distribution |
eigen$mevpea |
Mean of the eigenvalues distribution |
eigen$sevpea |
Standard deviation of the eigenvalues distribution |
eigen$qevpea |
quantile of the eigenvalues distribution |
eigen$sqevpea |
Standard error of the quantile of the eigenvalues distribution |
subject |
Number of subjects |
variables |
Number of variables |
centile |
Selected quantile |
Otherwise, returns a summary of the parallel analysis.
Gilles Raiche
Centre sur les Applications des Modeles de
Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca
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.
plotuScree
, nScree
,
plotnScree
, plotParallel
## 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 quantile <- 0.95 results <- parallel(subject, var, rep, quantile) results ## IF THE DECISION IS BASED ON THE CENTILE USE qevpea INSTEAD ## OF mevpea ON THE FIRST LINE OF THE FOLLOWING CALL plotuScree(x = eig, main = "Parallel Analysis" ) lines(1:var, results$eigen$qevpea, type="b", col="green" ) ## ANOTHER SOLUTION IS SIMPLY TO plotParallel(results)
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