CalculatePAContinuous: Parallel Analysis for continuous data.

Description Usage Arguments Value Note Author(s) See Also Examples

View source: R/CalculatePAContinuous.R

Description

Obtains a parallel analysis for continuous data.

Usage

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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 vcerva[email protected]

See Also

CalculatePABinary, CalculatePAOrdered, CalculatePAMixed, PA

Examples

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# # 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.