Learn R Programming

pcaPA

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

Copy Link

Version

Install

install.packages('pcaPA')

Monthly Downloads

10

Version

2.0.2

License

GPL (>= 2)

Maintainer

Carlos Arias

Last Published

September 14th, 2016

Functions in pcaPA (2.0.2)

CalculatePABinary

Parallel Analysis for Dichotomous Data.
coef.PA

Eigenvalue and percentile extraction of a "PA" object.
simRaschData

Simulated data conforming to the Rasch Model.
sim2plData

Simulated data conforming to the 2pl model.
CalculatePAOrdered

Parallel Analysis for Ordered Data.
CountEigen.PA

Number of observed eigenvalues that exceed a given set of percentiles.
CalculatePAContinuous

Parallel Analysis for continuous data.
PA

General function to perform parallel analysis of continuous, ordered or mixed type data.
CalculatePAMixed

Parallel Analysis for numeric and ordered mixed data.
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 object.
mixedScience

Simulated data from a normal distribution added to the Science data set from package "ltm".
Check.PA

Verifies that an object belongs to the "PA" class.