A set of functions to perform parallel analysis for principal components analysis intended mainly for large data sets. It performs a parallel analysis of continuous, ordered (including dichotomous/binary as a special case) or mixed type of data associated with a principal components analysis. Polychoric correlations among ordered variables, Pearson correlations among continuous variables and polyserial correlation between mixed type variables (one ordered and one continuous) are used. Whenever the use of polyserial or polychoric correlations yields a non positive definite correlation matrix, the resulting matrix is transformed into the nearest positive definite matrix. This is a continued work based on a previous version developed at the Colombian Institute for the evaluation of education - ICFES.
|Author||Carlos A. Arias <email@example.com> and Victor H. Cervantes <Herulor@gmail.com>.|
|Date of publication||2016-09-16 18:15:52|
|Maintainer||Carlos A. Arias <firstname.lastname@example.org>|
|License||GPL (>= 2)|
CalculatePABinary: Parallel Analysis for Dichotomous Data.
CalculatePAContinuous: Parallel Analysis for continuous data.
CalculatePAMixed: Parallel Analysis for numeric and ordered mixed data.
CalculatePAOrdered: Parallel Analysis for Ordered Data.
Check.PA: Verifies that an object belongs to the '"PA"' class.
coef.PA: Eigenvalue and percentile extraction of a '"PA"' object.
CountEigen.PA: Number of observed eigenvalues that exceed a given set of...
mixedScience: Simulated data from a normal distribution added to the...
PA: General function to perform parallel analysis of continuous,...
plot.PA: Plot method for PA objects.
print.PA: Print method for PA objects.
quantile.PA: Generate new quantiles based on given percentiles for a PA...
sim2plData: Simulated data conforming to the 2pl model.
simRaschData: Simulated data conforming to the Rasch Model.