eigenBootParallel | R Documentation |
The eigenBootParallel
function samples observations from a
data.frame
to produce correlation or covariance matrices from which
eigenvalues are computed. The function returns statistics about these
bootstrapped eigenvalues. Their means or their quantile could be used later
to replace the eigenvalues inputted to a parallel analysis. The
eigenBootParallel
can also compute random eigenvalues from empirical
data by column permutation (Buja and Eyuboglu, 1992).
eigenBootParallel(x, quantile = 0.95, nboot = 30, option = "permutation", cor = TRUE, model = "components", ...)
x |
data.frame: data from which a correlation matrix will be obtained |
quantile |
numeric: eigenvalues quantile to be reported |
nboot |
numeric: number of bootstrap samples |
option |
character: |
cor |
logical: if |
model |
character: bootstraps from a principal component analysis
( |
... |
variable: additionnal parameters to give to the |
values |
data.frame: mean, median, quantile, standard deviation, minimum and maximum of bootstrapped eigenvalues |
Gilles Raiche
Centre sur les Applications des Modeles de
Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca
Buja, A. and Eyuboglu, N. (1992). Remarks on parallel analysis. Multivariate Behavioral Research, 27(4), 509-540.
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.
principalComponents
,
iterativePrincipalAxis
, rRecovery
# ....................................................... # Example from the iris data eigenvalues <- eigenComputes(x=iris[,-5]) # Permutation parallel analysis distribution aparallel <- eigenBootParallel(x=iris[,-5], quantile=0.95)$quantile # Number of components to retain results <- nScree(x = eigenvalues, aparallel = aparallel) results$Components plotnScree(results) # ...................................................... # ...................................................... # Bootstrap distributions study of the eigenvalues from iris data # with different correlation methods eigenBootParallel(x=iris[,-5],quantile=0.05, option="bootstrap",method="pearson") eigenBootParallel(x=iris[,-5],quantile=0.05, option="bootstrap",method="spearman") eigenBootParallel(x=iris[,-5],quantile=0.05, option="bootstrap",method="kendall")
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