Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test
Bentler and Yuan's Computation of the LRT Index and the Linear Trend C...
Principal Component Analysis With Only n First Components Retained
Insert Communalities in the Diagonal of a Correlation or a Covariance ...
Replacing Upper or Lower Diagonal of a Correlation or Covariance Matri...
Bootstrapping of the Eigenvalues From a Data Frame
Computes Eigenvalues According to the Data Type
Identify the Data Type to Obtain the Eigenvalues
Generate a Factor Structure Matrix
Iterative Principal Axis Analysis
Create a Full Correlation/Covariance Matrix from a Matrix With Lower P...
Statistical Summary of a Data Frame
Bartlett, Anderson and Lawley Procedures to Determine the Number of Co...
Bentler and Yuan's Procedure to Determine the Number of Components/Fac...
Cattell-Nelson-Gorsuch CNG Indices
nFactors: Number of factor or components to retain in a factor analysi...
Utility Functions for nFactors Class Objects
Multiple Regression Procedure to Determine the Number of Components/Fa...
Non Graphical Cattel's Scree Test
Utility Functions for nScree Class Objects
Standard Error Scree and Coefficient of Determination Procedures to De...
Parallel Analysis of a Correlation or Covariance Matrix
Scree Plot According to a nScree Object Class
Plot a Parallel Analysis Class Object
Plot of the Usual Cattell's Scree Test
Principal Axis Analysis
Principal Component Analysis
Test of Recovery of a Correlation or a Covariance matrix from a Factor...
Population or Simulated Sample Correlation Matrix from a Given Factor ...
Utility Functions for nScree Class Objects
Simulation Study from Given Factor Structure Matrices and Conditions
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.