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by SAS Institute
JMP 13 Multivariate Methods
Contents
Learn about JMP
Documentation and Additional Resources
Formatting Conventions
JMP Documentation
JMP Documentation Library
JMP Help
Additional Resources for Learning JMP
Tutorials
Sample Data Tables
Learn about Statistical and JSL Terms
Learn JMP Tips and Tricks
Tooltips
JMP User Community
JMPer Cable
JMP Books by Users
The JMP Starter Window
Technical Support
Introduction to Multivariate Analysis
Overview of Multivariate Techniques
Correlations and Multivariate Techniques
Explore the Multidimensional Behavior of Variables
Launch the Multivariate Platform
Estimation Methods
Default
REML
ML
Robust
Row-wise
Pairwise
The Multivariate Report
Multivariate Platform Options
Nonparametric Correlations
Scatterplot Matrix
Outlier Analysis
Mahalanobis Distance
Jackknife Distances
T2 Statistic
Saving Distances and Values
Item Reliability
Impute Missing Data
Example of Item Reliability
Computations and Statistical Details
Estimation Methods
Robust
Pearson Product-Moment Correlation
Nonparametric Measures of Association
Spearman’s ρ (rho) Coefficients
Kendall’s τb Coefficients
Hoeffding’s D Statistic
Inverse Correlation Matrix
Distance Measures
Mahalanobis Distance Measures
Jackknife Distance Measures
T2 Distance Measures
Cronbach’s α
Principal Components
Reduce the Dimensionality of Your Data
Overview of Principal Component Analysis
Example of Principal Component Analysis
Launch the Principal Components Platform
Estimation Methods
Default
REML
ML
Robust
Row-wise
Pairwise
Wide
Sparse
Principal Components Report
Principal Components Report Options
Discriminant Analysis
Predict Classifications Based on Continuous Variables
Discriminant Analysis Overview
Example of Discriminant Analysis
Discriminant Launch Window
Stepwise Variable Selection
Updating the F Ratio and Prob>F
Statistics
Buttons
Columns
Stepwise Example
Discriminant Methods
Regularized, Compromise Method
Shrink Covariances
The Discriminant Analysis Report
Principal Components
Canonical Plot and Canonical Structure
Canonical Structure
Canonical Plot
Modifying the Canonical Plot
Classification into Three or More Categories
Classification into Two Categories
Discriminant Scores
Score Summaries
Entropy RSquare
Discriminant Analysis Options
Score Options
Canonical Options
Show Canonical Details
Show Canonical Structure
Example of a Canonical 3D Plot
Specify Priors
Consider New Levels
Save Discrim Matrices
Scatterplot Matrix
Validation in JMP and JMP Pro
Technical Details
Description of the Wide Linear Algorithm
Saved Formulas
Linear Discriminant Method
Quadratic Discriminant Method
Regularized Discriminant Method
Wide Linear Discriminant Method
Multivariate Tests
Approximate F-Tests
Between Groups Covariance Matrix
Partial Least Squares Models
Develop Models Using Correlations between Ys and Xs
Overview of the Partial Least Squares Platform
Example of Partial Least Squares
Launch the Partial Least Squares Platform
Centering and Scaling
Standardize X
Model Launch Control Panel
Partial Least Squares Report
Model Comparison Summary
<Cross Validation Method> and Method = <Method Specification>
Root Mean PRESS Plot
Root Mean PRESS
Calculation of Q2
Calculation of R2X and R2Y When Validation Is Used
Model Fit Report
Partial Least Squares Options
Model Fit Options
Variable Importance Plot
VIP vs Coefficients Plots
Save Columns
Statistical Details
Partial Least Squares
NIPALS
SIMPLS
van der Voet T2
T2 Plot
Confidence Ellipses for X Score Scatterplot Matrix
Standard Error of Prediction and Confidence Limits
Standard Error of Prediction Formula
Mean Confidence Limit Formula
Indiv Confidence Limit Formula
Standardized Scores and Loadings
Standardized Scores
Standardized Loadings
PLS Discriminant Analysis (PLS-DA)
Hierarchical Cluster
Group Observations Using a Tree of Clusters
Hierarchical Cluster Overview
Overview of Platforms for Clustering Observations
Example of Clustering
Launch the Hierarchical Cluster Platform
Clustering Method
Method for Distance Calculation
Data Structure
Not Enough Nonmissing Data Alert
Transformations to Y, Columns Variables
Hierarchical Cluster Report
Dendrogram Report
Distance Graph
Illustration of Dendrogram and Distance Graph
Clustering History Report
Hierarchical Cluster Options
Additional Examples of the Hierarchical Clustering Platform
Example of a Distance Matrix
Example of Wafer Defect Classification Using Spatial Measures
Statistical Details
Spatial Measures
Choose Spatial Components Window
Spatial Measures Reports
Distance Method Formulas
K Means Cluster
Group Observations Using Distances
K Means Cluster Platform Overview
Overview of Platforms for Clustering Observations
Example of K Means Cluster
Launch the K Means Cluster Platform
Iterative Clustering Report
Iterative Clustering Options
Iterative Clustering Control Panel
K Means NCluster=<k> Report
Cluster Comparison Report
K Means NCluster=<k> Report
K Means NCluster=<k> Report Options
Self Organizing Map
Self Organizing Map Control Panel
Self Organizing Map Report
Description of SOM Algorithm
Normal Mixtures
Group Observations Using Probabilities
Normal Mixtures Clustering Platform Overview
Overview of Platforms for Clustering Observations
Example of Normal Mixtures Clustering
Launch the Normal Mixtures Clustering Platform
Options
Iterative Clustering Report
Iterative Clustering Options
Iterative Clustering Control Panel
Normal Mixtures NCluster=<k> Report
Cluster Comparison Report
Normal Mixtures NCluster=<k> Report
Normal Mixtures NCluster=<k> Report Options
Robust Normal Mixtures
Robust Normal Mixtures Control Panel
Robust Normal Mixture Reports
Statistical Details for the Normal Mixtures Method
Additional Details for Robust Normal Mixtures
Latent Class Analysis
Group Observations of Categorical Variables
Latent Class Analysis Platform Overview
Example of Latent Class Analysis
Launch the Latent Class Analysis Platform
The Latent Class Analysis Report
Latent Class Analysis Platform Options
Fit Group Options
Latent Class Analysis Options
Additional Example: Plot Probabilities of Cluster Membership
Statistical Details for the Latent Class Analysis Platform
Cluster Variables
Group Similar Variables into Representative Groups
Cluster Variables Platform Overview
Example of the Cluster Variables Platform
Launch the Cluster Variables Platform
The Cluster Variables Report
Color Map on Correlations
Cluster Summary
Cluster Members
Standardized Components
Cluster Variables Platform Options
Additional Examples of the Cluster Variables Platform
Example of Color Map on Correlations
Example of Cluster Variables Platform for Dimension Reduction
Cluster Variables
Fit Models
Statistical Details for the Cluster Variables Platform
Variable Clustering Algorithm
Statistical Details
Multivariate Methods
Wide Linear Methods and the Singular Value Decomposition
The Singular Value Decomposition
The SVD and the Covariance Matrix
The SVD and the Inverse Covariance Matrix
Calculating the SVD
References
Index
Multivariate Methods
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Correlations and Multivariate Techniques
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Launch the Multivariate Platform
Contents
Launch the Multivariate Platform
Estimation Methods
The Multivariate Report
Multivariate Platform Options
Nonparametric Correlations
Scatterplot Matrix
Outlier Analysis
Item Reliability
Impute Missing Data
Example of Item Reliability
Computations and Statistical Details
Estimation Methods
Pearson Product-Moment Correlation
Nonparametric Measures of Association
Inverse Correlation Matrix
Distance Measures
Cronbach’s
α
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