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nFactors (version 2.3.3)

Parallel Analysis and 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

7,265

Version

2.3.3

License

GPL (>= 2)

Maintainer

Gilles Raiche

Last Published

December 20th, 2011

Functions in nFactors (2.3.3)

plotnScree

Scree Plot According to a nScree Object Class
dFactors

Eigenvalues Vectors From the Litterature
parallel

Parallel Analysis of a Correlation or Covariance Matrix
plotuScree

Plot of the Usual Cattell's Scree Test
plotParallel

Plot a Parallel Analysis Class Object
nScree

Non Graphical Cattel's Scree Test
diagReplace

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

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

Bentler and Yuan's Computation of the LRT Index and the Linear Trend Coefficients
nFactors-parameters

Argument and Value Parameters Common to the Different Functions Available in Package nFactors
moreStats

Statistical Summary of a Data Frame
iterativePrincipalAxis

Iterative Principal Axis Analysis
makeCor

Create a Full Correlation/Covariance Matrix from a Matrix With Lower Part Filled and Upper Part With Zeros
generateStructure

Generate a Factor Structure Matrix.
nBartlett

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

Utility Functions for nScree Class Objects
componentAxis

Principal Component Analysis With Only n First Components Retained
nBentler

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

Bootstrapping of the Eigenvalues From a Data Frame
rRecovery

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

Computes Eigenvalues According to the Data Type
eigenFrom

Identify the Data Type to Obtain the Eigenvalues
nMreg

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

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

Utility Functions for nScree Class Objects
studySim

Simulation Study from Given Factor Structure Matrices and Conditions
nFactorsObjectMethods

Utility Functions for nFactors Class Objects
structureSim

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

Principal Component Analysis
principalAxis

Principal Axis Analysis
nFactors-package

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

Cattell-Nelson-Gorsuch CNG Indices