CalculatePAMixed: Parallel Analysis for numeric and ordered mixed data.

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

Description

Obtains a parallel analysis for numeric and ordered mixed data.

Usage

CalculatePAMixed(dataMatrix, percentiles = 0.99, nReplicates = 200,
                 use = "complete.obs", algorithm = "polycor")

Arguments

dataMatrix	matrix or data.frame of ordered variables.
percentiles	vector of percentiles to report.
nReplicates	number of simulations to produce for estimating the eigenvalues distribution under independence.
use	Missing values 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 correlation estimation method. In the case of mixed variables, only the polycor package is currently used, so this value must always be "polycor".

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, CalculatePAContinuous, CalculatePAOrdered, PA

Examples

# # NOT RUN
# # Run Parallel analysis of mixed ordered and continuous data
# data(mixedScience)
# mixedPA <- PA(mixedScience, percentiles = c(0.90, 0.99),
#               nReplicates = 200, type = "mixed")
# print(mixedPA)

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