Parallel Analysis for Ordinal and Numeric Data using Polychoric and Pearson Correlations with S3 Classes

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.


The R package pcaPA is 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

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install.packages("pcaPA")

2.0.2 by Carlos A. Arias, a year ago


Browse source code at https://github.com/cran/pcaPA


Authors: Carlos A. Arias <caariasr22@gmail.com> and Victor H. Cervantes <Herulor@gmail.com>.


Documentation:   PDF Manual  


GPL (>= 2) license


Depends on polycor, ltm, stats, ggplot2, mc2d, sfsmisc


See at CRAN