Factor analysis

• works on datasets or correlation matrices
• principal-components factor
• principal factor
• iterated principal factor
• ML factors
• rotations
• anti-image correlation matrices
• Kaiser–Meyer–Olkin measure of sampling adequacy
• squared multiple correlations
• Bartlett scoring
• regression scoring

Principal components

• works with datasets or correlation or covariance matrices
• standard errors of eigenvalues and vectors
• rotations
• anti-image correlation matrices
• Kaiser–Meyer–Olkin measure of sampling adequacy
• loading plots, score plots, scree plots
• squared multiple correlations
• Orthogonal and oblique rotations
  
• Kaiser normalization
  
• Varimax, quartimax, oblimax, parsimax, equamax, and promax rotation
  
• Minimum entropy rotation
  
• Comrey’s tandem
  
• Rotate toward a target matrix
  

• Linear
• Quadratic
• Logistic
• kth nearest neighbor
  
• Classification tables
  
• Error rates
  

Multidimensional scaling

• Modern metric and nonmetric multidimensional scaling
  
• classic metric multidimensional scaling
• works on datasets or matrices of distances
• 33 similarity/dissimilarity measures
• coordinates of approximating configuration
• correlations between dissimilarities and distances
• Kruskal stress measure
• Shepard diagram
• Plots of approximation Euclidian configuration

• Complete implementation

• One- and multisample
  
• Means, covariances, and correlations
• Tests of normality
• Doornik–Hansen
  
• Henze–Zirkler
  
• Two by Mardia
  

 Multidimensional scaling

• Modern metric and nonmetric multidimensional scaling
  
• Classic metric multidimensional scaling
  
• Works on datasets or matrices of distances
  
• 33 similarity/dissimilarity measures
  
• Coordinates of approximating configuration
  
• Correlations between dissimilarities and distances
  
• Kruskal stress measure
  
• Shepard diagram
  
• Plots of approximating Euclidean configuration
  

• Two-way correspondence analysis
  
• Multiple correspondence analysis (MCA)
  
• Joint correspondence analysis (JCA)
  
• Work with cross-tabulations of categorical variables or matrices of counts
  
• Coordinates in column space
  
• Coordinates in row space (with two-way CA)
  
• Stacked (crossed) variables
  
• Chi-squared distances
  
• Inertia contributions
  
• Row and column profiles (conditional distributions)
  
• Fitted, observed, and expected correspondence tables
  
• Matrix of inertias (after JCA)
  
• Biplots
• Projection plots
  
• Correlations of profiles and loadings
  

Procrustes analysis

• orthogonal, oblique, and unrestricted transformations
• overlayed graphs comparing target variables and fitted values of source variables

Biplots

• Display your choice of any two biplot dimensions
  
• Distinguish groups of data within the biplot
  
• Display table of biplot coordinates
  
• Generate new variables containing biplot coordinates
  

Canonical correlations

• Correlation matrices
  
• Loading matrices
  
• Rotate raw coefficients, standard coefficients, or loading matrices
• Compare rotated and unrotated coefficients or loadings
  
• Plot canonical correlations
  

Tetrachoric correlations

• Maximum likelihood or noniterative Edwards and Edwards estimator
• Tetrachoric correlation coefficient and standard error
  
• Exact two-sided significance level
  

Zellner's seemingly unrelated least squares

• Two-step or maximum likelihood estimates
  
• Linear constraints
  
• Breusch-Pagan test for independent equations
  

Multivariate linear regression

• Breusch-Pagan test for independent equations
• Use with multiple imputation for missing data 
  

Hotelling's T-squared

Test for identifying multivariate outliers

Cluster analysis

• Complete implementation

MANOVA

• Complete implementation