Analyzing Principal Components and Reducing Dimensionality
by SAS Institute
JMP 10 Modeling and Multivariate Methods
- Cover Page
- Title Page
- Copyright Page
- Contents
- Learn About JMP
- Launch the Fit Model Platform
- Example of the Fit Model Platform
- Specify Different Model Types
- Construct Model Effects
- Fitting Personalities
- Emphasis Options for Standard Least Squares
- Model Specification Options
- Validity Checks
- Fitting Standard Least Squares Models
- Example Using Standard Least Squares
- The Standard Least Squares Report and Options
- Regression Reports
- Estimates
- Effect Screening
- Factor Profiling
- Row Diagnostics
- Save Columns
- Effect Options
- Restricted Maximum Likelihood (REML) Method
- Method of Moments Results
- Singularity Details
- Examples with Statistical Details
- Fitting Stepwise Regression Models
- Overview of Stepwise Regression
- Example Using Stepwise Regression
- The Stepwise Report
- Models with Crossed, Interaction, or Polynomial Terms
- Models with Nominal and Ordinal Terms
- Using the Make Model Command for Hierarchical Terms
- Performing Logistic Stepwise Regression
- The All Possible Models Option
- The Model Averaging Option
- Using Validation
- Fitting Multiple Response Models
- Fitting Generalized Linear Models
- Performing Logistic Regression on Nominal and Ordinal Responses
- Analyzing Screening Designs
- Performing Nonlinear Regression
- Creating Neural Networks
- Modeling Relationships With Gaussian Processes
- Fitting Dispersion Effects with the Loglinear Variance Model
- Recursively Partitioning Data
- Performing Time Series Analysis
- Performing Categorical Response Analysis
- Performing Choice Modeling
- Introduction to Choice Modeling
- Product Design Engineering
- Data for the Choice Platform
- Example: Pizza Choice
- Launch the Choice Platform and Select Data Sets
- Platform Options
- Example: Valuing Trade-offs
- One-Table Analysis
- Segmentation
- Special Data Rules
- Transforming Data
- Logistic Regression for Matched Case-Control Studies
- Correlations and Multivariate Techniques
- Clustering Data
- Analyzing Principal Components and Reducing Dimensionality
- Performing Discriminant Analysis
- Fitting Partial Least Squares Models
- Scoring Tests Using Item Response Theory
- Plotting Surfaces
- Visualizing, Optimizing, and Simulating Response Surfaces
- Comparing Model Performance
- References
- The Response Models
- The Factor Models
- The Usual Assumptions
- Key Statistical Concepts
- Leverage Plot Details
- Multivariate Details
- Power Calculations
- Inverse Prediction with Confidence Limits
- Details of Random Effects
- Index
Chapter 19
Analyzing Principal Components and Reducing Dimensionality
Using the Principal Components Platform
The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that capture as much of the variability in the original variables as possible.
JMP also offers several types of orthogonal and oblique Factor-Analysis-Style rotations to help interpret the extracted components. The platform also supports factor analysis.
Principal components can be accessed through the Multivariate platform, through the Scatterplot 3D platform, or through the Principal Components command on the Analyze > Multivariate Methods menu. All map to the same routines, documented in this chapter.
Figure 19.1 Example of Principal Components
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