performs Horn's parallel analysis for a principal component.
Usage
an.parallel(x = NA, iterations = 0, centile = 0, seed = 0, mat = NA, n = NA)
Arguments
x
a matrix or a Dataframe that holds the test response data
iterations
a number indicating the amount of iterations that
representing the number of random data sets to be produced in the analysis.
centile
a number between 1 and 99 indicating the centile used in estimating bias.
seed
specifies that the random number is to be seeded with the supplied integer.
mat
specifies that the procedure use the provided correlation matrix rather
than supplying a data matrix through x. The n argument must also be supplied when
mat is used.
n
the number of observations. Required when the correlation matrix is supplied
with the mat option.
Value
Retained Components a scalar integer representing the number of components retained.Adjusted eigenvalues a vector of the estimated eigenvalues adjusted.Unadjusted eigenvalues a vector of the eigenvalues of the observed data from either
an unrotated principal component analysis.Bias a vector of the estimated bias of the unadjusted eigenvalues
Details
Is a implementation of Horn's (1965) tecnique for evaluating the components retained
in a principle component analysis (PCA). This procedure is a adaptation of the
function paran of Package Paran.
References
John L. Horn (1965). A rationale and test for the number of factors
in factor analysis. Psychometrika, Volume 30, Number 2, Page 179.
Dinno A. 2009. Exploring the Sensitivity of Horn's Parallel Analysis to the
Distributional Form of Simulated Data. Multivariate Behavioral Research. 44(3): 362-388