CalculatePABinary: Parallel Analysis for Dichotomous 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 dichotomous data.

Usage

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

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 correlation estimation algorithm. Polychoric correlation estimation method: "polycor" for estimates using the polycor package, "polychoric" for estimates using the C++ function Cpolychoric.

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

CalculatePAOrdered, CalculatePAContinuous, CalculatePAMixed, PA, quantile.PA

Examples

# # NOT RUN
# # Run Parallel Analysis for binary data conforming to the Rasch model
# # using the polycor package
# data(simRaschData)
# binaryRaschPA <- PA(simRaschData, percentiles = c(0.95, 0.99),
#                     nReplicates = 200, type = "binary")
# print(binaryRaschPA)

# # Run Parallel Analysis for binary data conforming to the Rasch model
# # using the Cpolychoric C++ function
data(simRaschData)
binaryRaschPA <- PA(simRaschData, percentiles = c(0.95, 0.99), nReplicates = 200,
                    type = "binary", algorithm = "polychoric")
print(binaryRaschPA)

# # NOT RUN
# # Run Parallel Analysis for binary data conforming to the 2PL model
# # using the polycor package
# data(sim2plData)
# binary2plPA <- PA(sim2plData, percentiles = c(0.95, 0.99), nReplicates = 200,
#                   type = "binary")
# print(binary2plPA)

# # Run Parallel Analysis for binary data conforming to the 2PL model
# # using the polychoric C++ function
data(sim2plData)
binary2plPA <- PA(sim2plData, percentiles = c(0.95, 0.99), nReplicates = 200,
                  type = "binary", algorithm = "polychoric")
print(binary2plPA)

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