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nFactors (version 2.4.1.1)

Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test

Description

Indices, heuristics and strategies to help determine the number of factors/components to retain: 1. Acceleration factor (af with or without Parallel Analysis); 2. Optimal Coordinates (noc with or without Parallel Analysis); 3. Parallel analysis (components, factors and bootstrap); 4. lambda > mean(lambda) (Kaiser, CFA and related); 5. Cattell-Nelson-Gorsuch (CNG); 6. Zoski and Jurs multiple regression (b, t and p); 7. Zoski and Jurs standard error of the regression coeffcient (sescree); 8. Nelson R2; 9. Bartlett khi-2; 10. Anderson khi-2; 11. Lawley khi-2 and 12. Bentler-Yuan khi-2.

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Version

Install

install.packages('nFactors')

Monthly Downloads

5,981

Version

2.4.1.1

License

GPL (>= 3.5.0)

Maintainer

Gilles Raiche

Last Published

October 10th, 2022

Functions in nFactors (2.4.1.1)

eigenBootParallel

Bootstrapping of the Eigenvalues From a Data Frame
componentAxis

Principal Component Analysis With Only n First Components Retained
diagReplace

Replacing Upper or Lower Diagonal of a Correlation or Covariance Matrix
dFactors

Eigenvalues from classical studies
generateStructure

Generate a Factor Structure Matrix
corFA

Insert Communalities in the Diagonal of a Correlation or a Covariance Matrix
eigenFrom

Identify the Data Type to Obtain the Eigenvalues
iterativePrincipalAxis

Iterative Principal Axis Analysis
eigenComputes

Computes Eigenvalues According to the Data Type
bentlerParameters

Bentler and Yuan's Computation of the LRT Index and the Linear Trend Coefficients
moreStats

Statistical Summary of a Data Frame
nBentler

Bentler and Yuan's Procedure to Determine the Number of Components/Factors
nScree

Non Graphical Cattel's Scree Test
summary.nScree

Utility Functions for nScree Class Objects
makeCor

Create a Full Correlation/Covariance Matrix from a Matrix With Lower Part Filled and Upper Part With Zeros
is.nFactors

Utility Functions for nFactors Class Objects
nBartlett

Bartlett, Anderson and Lawley Procedures to Determine the Number of Components/Factors
nMreg

Multiple Regression Procedure to Determine the Number of Components/Factors
nFactors

nFactors: Number of factor or components to retain in a factor analysis
nCng

Cattell-Nelson-Gorsuch CNG Indices
principalComponents

Principal Component Analysis
structureSim

Population or Simulated Sample Correlation Matrix from a Given Factor Structure Matrix
studySim

Simulation Study from Given Factor Structure Matrices and Conditions
plotParallel

Plot a Parallel Analysis Class Object
plotnScree

Scree Plot According to a nScree Object Class
summary.structureSim

Utility Functions for nScree Class Objects
rRecovery

Test of Recovery of a Correlation or a Covariance matrix from a Factor Analysis Solution
plotuScree

Plot of the Usual Cattell's Scree Test
principalAxis

Principal Axis Analysis
parallel

Parallel Analysis of a Correlation or Covariance Matrix
nSeScree

Standard Error Scree and Coefficient of Determination Procedures to Determine the Number of Components/Factors