Parallel Analysis for Dichotomous Data.

Share:

Description

Obtains a parallel analysis for dichotomous data.

Usage

1
2
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 carias@icfes.gov.co and Victor H. Cervantes vcervantes@icfes.gov.co

See Also

CalculatePAOrdered, CalculatePAContinuous, CalculatePAMixed, PA, quantile.PA

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
# # 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)