eigenBootParallel {nFactors} | R Documentation |
The eigenBootParallel
function samples observations from a data.frame
to produces correlation or covariance matrix 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 inputed to
a parallel analysis. The eigenBootParallel
can also computes random eigenvalues
from empirical data by columns 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 that will be reported |
nboot |
numeric: number of bootstrap samples |
option |
character: "permutation" or "bootstrap" |
cor |
logical: if TRUE computes eigenvalues from a correlation
matrix, else from a covariance matrix (eigenComputes ) |
model |
character: bootstraps from a principal component analysis
("components" ) or from a factor analysis ("factors" ) |
... |
variable: additionnal parameters to give to the cor or
cov functions |
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, http://www.er.uqam.ca/nobel/r17165/
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 stude 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")