CalculatePAContinuous: Parallel Analysis for continuous data.

In caariasr/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

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 covariances or the Pearson correlations are used. Ignored if different to either "cov" or "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 [e-mail] and Victor H. Cervantes [e-mail]

See Also

CalculatePABinary, CalculatePAOrdered, CalculatePAMixed, PA

Examples

# # 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)

caariasr/pcaPA documentation built on May 13, 2019, 10:37 a.m.