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nFactors (version 2.4.1.2)

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

Description

Indices, heuristics, simulations and strategies to help determine the number of factors/components to retain in exploratory factor analysis and principal component analysis.

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Version

Install

install.packages('nFactors')

Monthly Downloads

6,043

Version

2.4.1.2

License

GPL-3

Maintainer

Gilles Raiche

Last Published

June 19th, 2025

Functions in nFactors (2.4.1.2)

nBartlett

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

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

Statistical Summary of a Data Frame
nCng

Cattell-Nelson-Gorsuch CNG Indices
nScree

Non Graphical Cattel's Scree Test
nBentler

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

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

Population or Simulated Sample Correlation Matrix from a Given Factor Structure Matrix
summary.structureSim

Utility Functions for nScree Class Objects
principalAxis

Principal Axis Analysis
parallel

Parallel Analysis of a Correlation or Covariance Matrix
plotParallel

Plot a Parallel Analysis Class Object
plotuScree

Plot of the Usual Cattell's Scree Test
plotnScree

Scree Plot According to a nScree Object Class
principalComponents

Principal Component Analysis
nSeScree

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

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

Simulation Study from Given Factor Structure Matrices and Conditions
diagReplace

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

Iterative Principal Axis Analysis
dFactors

Eigenvalues from classical studies
corFA

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

Generate a Factor Structure Matrix
eigenComputes

Computes Eigenvalues According to the Data Type
eigenBootParallel

Bootstrapping of the Eigenvalues From a Data Frame
componentAxis

Principal Component Analysis With Only n First Components Retained
bentlerParameters

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

Identify the Data Type to Obtain the Eigenvalues