# CalculatePAContinuous: Parallel Analysis for continuous data. In pcaPA: Parallel Analysis for Ordinal and Numeric Data using Polychoric and Pearson Correlations with S3 Classes

## Description

Obtains a parallel analysis for continuous data.

## Usage

 ```1 2``` ```CalculatePAContinuous(dataMatrix, percentiles = 0.99, nReplicates = 200, use = "complete.obs", algorithm = "pearson") ```

## Arguments

 `dataMatrix` `matrix` or `data.frame` of binary or dichotomous variables. `percentiles` vector of percentiles to report. `nReplicates` number of simulations to produce for estimating the eigenvalues distribution under independence. `use` Missing value handling method: If `"complete.obs"`, remove observations with any missing data; if `"pairwise.complete.obs"`, compute each correlation using all observations with valid data for that pair of variables. `algorithm` string specifying the estimation algorithm. In the case of continuous variables, only the Pearson correlations are used. Ignored if different to `"pearson"`.

## Value

Returns a `list` object with the following:

 `observed` `data.frame` containing the observed eigenvalues. `percentiles` `data.frame` containing the estimated percentiles of the eigenvalues distribution under independence. `simulatedEigenValues` `data.frame` containing the simulated eigenvalues under independence.

## Note

This is an auxiliary function for the `"PA"` function.

## Author(s)

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

## See Also

`CalculatePABinary`, `CalculatePAOrdered`, `CalculatePAMixed`, `PA`

## Examples

 ```1 2 3 4 5``` ```# # Run Parallel analyis of numeric data (Iris) data(iris) continuousPA <- PA(iris[, -5], percentiles = c(0.90, 0.99), nReplicates = 200, type = "continuous", algorithm = "pearson") print(continuousPA) ```

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