# CountEigen.PA: Number of observed eigenvalues that exceed a given set of... In pcaPA: Parallel Analysis for Ordinal and Numeric Data using Polychoric and Pearson Correlations with S3 Classes

## Description

Counts the number of observed eigenvalues that exceed the given percentiles.

## Usage

 `1` ```CountEigen.PA(PA, percentiles = NULL) ```

## Arguments

 `PA` an object of class `"PA"`. `percentiles` the percentiles that ought to be plotted, defaults to those in the object.

## Value

A named numeric vector indicating the number of eigenvalues that are greater than the eigenvalues distribution percentiles under independence.

## Author(s)

Carlos A. Arias [email protected] and Victor H. Cervantes [email protected]

## See Also

`PA`, `print.PA`, `coef.PA`, `Check.PA`, `plot.PA`, `quantile.PA`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```# # Run Parallel Analysis for binary data conforming to the Rasch model data(simRaschData) binaryRaschPA <- PA(simRaschData, percentiles = c(0.95, 0.99), nReplicates = 200, type = "binary", algorithm = "polychoric") print(binaryRaschPA) # # Number of retained factors nComponents <- CountEigen.PA(binaryRaschPA, percentiles = .99) nComponents["p99"] # # Run Parallel Analysis for binary data conforming to the 2PL model data(sim2plData) binary2plPA <- PA(sim2plData, percentiles = c(0.95, 0.99), nReplicates = 200, type = "binary", algorithm = "polychoric") print(binary2plPA) # # Number of retained factors nComponents <- CountEigen.PA(binary2plPA, percentiles = .99) nComponents["p99"] ```

pcaPA documentation built on May 29, 2017, 6:53 p.m.