• Contents
  • About
  • Credits and Acknowledgments
    • Credits
      • Documentation
      • Software
      • Testing
      • Internationalization Testing
      • Technical Support
    • Acknowledgments
  • What’s New in SAS/STAT 14.1 High-Performance Procedures
    • Overview
    • SAS/STAT 14.1 High-Performance Procedure Enhancements
      • HPGENSELECT Procedure
      • HPLOGISTIC Procedure
      • HPPRINCOMP Procedure
      • HPSPLIT Procedure
    • Highlights of Enhancements in SAS/STAT 13.2 High-Performance Procedures
  • Introduction
    • Overview of SAS/STAT High-Performance Procedures
    • About This Book
      • Chapter Organization
      • Typographical Conventions
      • Options Used in Examples
    • Online Documentation
    • SAS Technical Support Services
  • Shared Concepts and Topics
    • Overview
    • Processing Modes
      • Single-Machine Mode
      • Distributed Mode
      • Controlling the Execution Mode with Environment Variables and Performance Statement Options
      • Determining Single-Machine Mode or Distributed Mode
    • Data Access Modes
      • Single-Machine Data Access Mode
      • Distributed Data Access Mode
      • Determining the Data Access Mode
    • Alongside-the-Database Execution
    • Alongside-LASR Distributed Execution
    • Running High-Performance Analytical Procedures Alongside a SAS LASR Analytic Server in Distributed Mode
      • Starting a SAS LASR Analytic Server Instance
      • Associating a SAS Libref with the SAS LASR Analytic Server Instance
      • Running a High-Performance Analytical Procedure Alongside the SAS LASR Analytic Server Instance
      • Terminating a SAS LASR Analytic Server Instance
    • Alongside-LASR Distributed Execution on a Subset of the Appliance Nodes
    • Running High-Performance Analytical Procedures in Asymmetric Mode
      • Running in Asymmetric Mode on Distinct Appliances
    • Alongside-HDFS Execution
      • Alongside-HDFS Execution by Using the SASHDAT Engine
      • Alongside-HDFS Execution by Using the Hadoop Engine
    • Output Data Sets
    • Working with Formats
    • PERFORMANCE Statement
  • Shared Statistical Concepts
    • Common Features of SAS High-Performance Statistical Procedures
    • Syntax Common to SAS High-Performance Statistical Procedures
      • CLASS Statement
      • FREQ Statement
      • ID Statement
      • SELECTION Statement
      • VAR Statement
      • WEIGHT Statement
    • Levelization of Classification Variables
    • Specification and Parameterization of Model Effects
      • Effect Operators
        • Bar and At Sign Operators
        • Colon, Dash, and Double Dash Operators
      • GLM Parameterization of Classification Variables and Effects
        • Intercept
        • Regression Effects
        • Main Effects
        • Interaction Effects
        • Nested Effects
        • Continuous-Nesting-Class Effects
        • Continuous-by-Class Effects
        • General Effects
      • Reference Parameterization
    • Model Selection
      • Methods
        • Full Model Fitted
        • Forward Selection
        • Backward Elimination
        • Stepwise Selection
        • Forward-Swap Selection
        • Least Angle Regression
        • Lasso Selection
        • Adaptive Lasso Selection
    • References
  • The HPCANDISC Procedure
    • Overview: HPCANDISC Procedure
      • PROC HPCANDISC Features
      • PROC HPCANDISC Compared with PROC CANDISC
    • Getting Started: HPCANDISC Procedure
    • Syntax: HPCANDISC Procedure
      • PROC HPCANDISC Statement
      • BY Statement
      • CLASS Statement
      • FREQ Statement
      • ID Statement
      • PERFORMANCE Statement
      • VAR Statement
      • WEIGHT Statement
    • Details: HPCANDISC Procedure
      • Missing Values
      • Computational Method
        • General Formulas
        • Multithreading
      • Output Data Sets
        • OUT= Data Set
        • OUTSTAT= Data Set
      • Displayed Output
      • ODS Table Names
    • Examples: HPCANDISC Procedure
      • Analyzing Iris Data with PROC HPCANDISC
      • Performing Canonical Discriminant Analysis in Single-Machine and Distributed Modes
    • References
  • The HPFMM Procedure
    • Overview: HPFMM Procedure
      • Basic Features
      • PROC HPFMM Contrasted with PROC FMM
      • Assumptions
      • Notation for the Finite Mixture Model
        • Homogeneous Mixtures
        • Special Mixtures
    • Getting Started: HPFMM Procedure
      • Mixture Modeling for Binomial Overdispersion: "Student," Pearson, Beer, and Yeast
      • Modeling Zero-Inflation: Is it Better to Fish Poorly or Not to Have Fished At All?
      • Looking for Multiple Modes: Are Galaxies Clustered?
        • Comparison with Roeder’s Method
    • Syntax: HPFMM Procedure
      • PROC HPFMM Statement
      • BAYES Statement
      • BY Statement
      • CLASS Statement
      • FREQ Statement
      • ID Statement
      • MODEL Statement
        • Response Variable Options
        • Model Options
      • OUTPUT Statement
      • PERFORMANCE Statement
      • PROBMODEL Statement
      • RESTRICT Statement
      • WEIGHT Statement
    • Details: HPFMM Procedure
      • A Gentle Introduction to Finite Mixture Models
        • The Form of the Finite Mixture Model
        • Mixture Models Contrasted with Mixing and Mixed Models: Untangling the Terminology Web
        • Overdispersion
      • Log-Likelihood Functions for Response Distributions
      • Bayesian Analysis
        • Conjugate Sampling
        • Metropolis-Hastings Algorithm
        • Latent Variables via Data Augmentation
        • Prior Distributions
      • Parameterization of Model Effects
      • Computational Method
        • Multithreading
      • Choosing an Optimization Algorithm
        • First- or Second-Order Algorithms
        • Algorithm Descriptions
      • Output Data Set
      • Default Output
        • Performance Information
        • Model Information
        • Class Level Information
        • Number of Observations
        • Response Profile
        • Default Output for Maximum Likelihood
        • Default Output for Bayes Estimation
      • ODS Table Names
      • ODS Graphics
    • Examples: HPFMM Procedure
      • Modeling Mixing Probabilities: All Mice Are Created Equal, but Some Are More Equal
      • The Usefulness of Custom Starting Values: When Do Cows Eat?
      • Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!
    • References
  • The GAMPL Procedure
    • Overview: GAMPL Procedure
      • PROC GAMPL Features
      • PROC GAMPL Contrasted with PROC GAM
    • Getting Started: GAMPL Procedure
    • Syntax: GAMPL Procedure
      • PROC GAMPL Statement
      • CLASS Statement
      • FREQ Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PERFORMANCE Statement
      • WEIGHT Statement
    • Details: GAMPL Procedure
      • Missing Values
      • Thin-Plate Regression Splines
      • Generalized Additive Models
      • Model Evaluation Criteria
      • Fitting Algorithms
      • Degrees of Freedom
      • Model Inference
      • Dispersion Parameter
      • Tests for Smoothing Components
      • Computational Method: Multithreading
      • Choosing an Optimization Technique
        • First- or Second-Order Techniques
        • Technique Descriptions
      • Displayed Output
      • ODS Table Names
      • ODS Graphics
    • Examples: GAMPL Procedure
      • Scatter Plot Smoothing
      • Nonparametric Logistic Regression
      • Nonparametric Negative Binomial Model for Mackerel Egg Density
    • References
  • The HPGENSELECT Procedure
    • Overview: HPGENSELECT Procedure
      • PROC HPGENSELECT Features
      • PROC HPGENSELECT Contrasted with PROC GENMOD
    • Getting Started: HPGENSELECT Procedure
    • Syntax: HPGENSELECT Procedure
      • PROC HPGENSELECT Statement
      • BY Statement
      • CLASS Statement
      • CODE Statement
      • FREQ Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
      • RESTRICT Statement
      • SELECTION Statement
      • WEIGHT Statement
      • ZEROMODEL Statement
    • Details: HPGENSELECT Procedure
      • Missing Values
      • Exponential Family Distributions
      • Response Distributions
      • Response Probability Distribution Functions
      • Log-Likelihood Functions
      • The LASSO Method of Model Selection
      • Using Validation and Test Data
      • Computational Method: Multithreading
      • Choosing an Optimization Algorithm
        • First- or Second-Order Algorithms
        • Algorithm Descriptions
      • Displayed Output
      • ODS Table Names
    • Examples: HPGENSELECT Procedure
      • Model Selection
      • Modeling Binomial Data
      • Tweedie Model
      • Model Selection by the LASSO Method
    • References
  • The HPLMIXED Procedure
    • Overview: HPLMIXED Procedure
      • PROC HPLMIXED Features
      • Notation for the Mixed Model
      • PROC HPLMIXED Contrasted with Other SAS Procedures
    • Getting Started: HPLMIXED Procedure
      • Mixed Model Analysis of Covariance with Many Groups
    • Syntax: HPLMIXED Procedure
      • PROC HPLMIXED Statement
      • CLASS Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARMS Statement
      • PERFORMANCE Statement
      • RANDOM Statement
      • REPEATED Statement
    • Details: HPLMIXED Procedure
      • Linear Mixed Models Theory
        • Matrix Notation
        • Formulation of the Mixed Model
        • Estimating Covariance Parameters in the Mixed Model
        • Estimating Fixed and Random Effects in the Mixed Model
        • Statistical Properties
      • Computational Method
        • Distributed Computing
        • Multithreading
      • Displayed Output
        • Performance Information
        • Model Information
        • Class Level Information
        • Dimensions
        • Number of Observations
        • Optimization Information
        • Iteration History
        • Convergence Status
        • Covariance Parameter Estimates
        • Fit Statistics
        • Timing Information
      • ODS Table Names
    • Examples: HPLMIXED Procedure
      • Computing BLUPs for a Large Number of Subjects
    • References
  • The HPLOGISTIC Procedure
    • Overview: HPLOGISTIC Procedure
      • PROC HPLOGISTIC Features
      • PROC HPLOGISTIC Contrasted with Other SAS Procedures
    • Getting Started: HPLOGISTIC Procedure
      • Binary Logistic Regression
    • Syntax: HPLOGISTIC Procedure
      • PROC HPLOGISTIC Statement
      • BY Statement
      • CLASS Statement
      • CODE Statement
      • FREQ Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
      • SELECTION Statement
      • WEIGHT Statement
    • Details: HPLOGISTIC Procedure
      • Missing Values
      • Response Distributions
      • Log-Likelihood Functions
      • Existence of Maximum Likelihood Estimates
      • Using Validation and Test Data
      • Model Fit and Assessment Statistics
      • The Hosmer-Lemeshow Goodness-of-Fit Test
      • Computational Method: Multithreading
      • Choosing an Optimization Algorithm
        • First- or Second-Order Algorithms
        • Algorithm Descriptions
      • Displayed Output
      • ODS Table Names
    • Examples: HPLOGISTIC Procedure
      • Model Selection
      • Modeling Binomial Data
      • Ordinal Logistic Regression
      • Partitioning Data
    • References
  • The HPNLMOD Procedure
    • Overview: HPNLMOD Procedure
      • PROC HPNLMOD Features
      • PROC HPNLMOD Contrasted with the NLIN and NLMIXED Procedures
    • Getting Started: HPNLMOD Procedure
      • Least Squares Model
      • Binomial Model
    • Syntax: HPNLMOD Procedure
      • PROC HPNLMOD Statement
      • BOUNDS Statement
      • BY Statement
      • ESTIMATE Statement
      • MODEL Statement
      • PARAMETERS Statement
      • PERFORMANCE Statement
      • PREDICT Statement
      • RESTRICT Statement
      • Programming Statements
    • Details: HPNLMOD Procedure
      • Least Squares Estimation
      • Built-In Log-Likelihood Functions
      • Computational Method
      • Choosing an Optimization Algorithm
      • Displayed Output
      • ODS Table Names
    • Examples: HPNLMOD Procedure
      • Segmented Model
    • References
  • The HPPLS Procedure
    • Overview: HPPLS Procedure
      • PROC HPPLS Features
      • PROC HPPLS Contrasted with PROC PLS
    • Getting Started: HPPLS Procedure
      • Spectrometric Calibration
        • Fitting a PLS Model
        • Selecting the Number of Factors by Test Set Validation
        • Predicting New Observations
    • Syntax: HPPLS Procedure
      • PROC HPPLS Statement
      • BY Statement
      • CLASS Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
    • Details: HPPLS Procedure
      • Regression Methods
        • Partial Least Squares
        • SIMPLS
        • Principal Components Regression
        • Reduced Rank Regression
        • Relationships between Methods
      • Test Set Validation
      • Centering and Scaling
      • Missing Values
      • Computational Method
        • Multithreading
      • Output Data Set
      • Displayed Output
        • Performance Information
        • Data Access Information
        • Centering and Scaling Information
        • Model Information
        • Number of Observations
        • Class Level Information
        • Dimensions
        • Test Set Validation
        • Percent Variation Accounted for by Extracted Factors
        • Model Details
        • Parameter Estimates
        • Timing Information
      • ODS Table Names
    • Examples: HPPLS Procedure
      • Choosing a PLS Model by Test Set Validation
      • Fitting a PLS Model in Single-Machine and Distributed Modes
    • References
  • The HPPRINCOMP Procedure
    • Overview: HPPRINCOMP Procedure
      • PROC HPPRINCOMP Features
      • PROC HPPRINCOMP Contrasted with PROC PRINCOMP
    • Getting Started: HPPRINCOMP Procedure
    • Syntax: HPPRINCOMP Procedure
      • PROC HPPRINCOMP Statement
      • BY Statement
      • CODE Statement
      • FREQ Statement
      • ID Statement
      • OUTPUT Statement
      • PARTIAL Statement
      • PERFORMANCE Statement
      • VAR Statement
      • WEIGHT Statement
    • Details: HPPRINCOMP Procedure
      • Computing Principal Components
        • Eigenvalue Decomposition
        • NIPALS
        • ITERGS
      • Missing Values
      • Output Data Sets
        • OUT= Data Set
        • OUTSTAT= Data Set
      • Computational Method
        • Multithreading
      • Displayed Output
        • Performance Information
        • Data Access Information
        • Model Information
        • Number of Observations
        • Number of Variables
        • Simple Statistics
        • Centering and Scaling Information
        • Explained Variation of Variables
        • Correlation Matrix
        • Regression Statistics
        • Regression Coefficients
        • Partial Correlation Matrix
        • Total Variance
        • Eigenvalues
        • Eigenvectors
        • Loadings
        • Timing Information
      • ODS Table Names
    • Examples: HPPRINCOMP Procedure
      • Analyzing Mean Temperatures of US Cities
      • Computing Principal Components in Single-Machine and Distributed Modes
      • Extracting Principal Components with NIPALS
    • References
  • The HPQUANTSELECT Procedure
    • Overview: HPQUANTSELECT Procedure
      • PROC HPQUANTSELECT Features
      • PROC HPQUANTSELECT Contrasted with Other SAS Procedures
    • Getting Started: HPQUANTSELECT Procedure
    • Syntax: HPQUANTSELECT Procedure
      • PROC HPQUANTSELECT Statement
      • BY Statement
      • CLASS Statement
      • CODE Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
      • SELECTION Statement
      • WEIGHT Statement
    • Details: HPQUANTSELECT Procedure
      • Quantile Regression
        • Linear Model with iid Errors
        • Linear-in-Parameter Model with Non-iid Settings
        • More Statistics for Parameter Estimates
      • Criteria Used in Model Selection
        • Quasi-likelihood Information Criteria
        • Statistical Tests for Significance Level
      • Diagnostic Statistics
      • Classification Variables and the SPLIT Option
      • Macro Variables That Contain Selected Effects
      • Using Validation and Test Data
        • Using the Validation ACL as the STOP= Criterion
        • Using the Validation ACL as the CHOOSE= Criterion
        • Using the Validation ACL as the SELECT= Criterion
      • Computational Method
        • Multithreading
      • Output Data Set
      • Displayed Output
        • Performance Information
        • Data Access Information
        • Model Information
        • Selection Information
        • Number of Observations
        • Class Level Information
        • Dimensions
        • Entry and Removal Candidates
        • Selection Summary
        • Stop Reason
        • Selection Reason
        • Selected Effects
        • Fit Statistics
        • Parameter Estimates
        • Timing Information
      • ODS Table Names
    • Examples: HPQUANTSELECT Procedure
      • Simulation Study
      • Growth Charts for Body Mass Index
    • References
  • The HPREG Procedure
    • Overview: HPREG Procedure
      • PROC HPREG Features
      • PROC HPREG Contrasted with Other SAS Procedures
    • Getting Started: HPREG Procedure
    • Syntax: HPREG Procedure
      • PROC HPREG Statement
      • BY Statement
      • CLASS Statement
      • CODE Statement
      • FREQ Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
      • SELECTION Statement
      • WEIGHT Statement
    • Details: HPREG Procedure
      • Criteria Used in Model Selection
      • Diagnostic Statistics
      • Classification Variables and the SPLIT Option
      • Using Validation and Test Data
      • Computational Method
      • Output Data Set
      • Screening
      • Displayed Output
      • ODS Table Names
    • Examples: HPREG Procedure
      • Model Selection with Validation
      • Backward Selection in Single-Machine and Distributed Modes
      • Forward-Swap Selection
      • Forward Selection with Screening
    • References
  • The HPSPLIT Procedure
    • Overview: HPSPLIT Procedure
      • PROC HPSPLIT Features
    • Getting Started: HPSPLIT Procedure
    • Syntax: HPSPLIT Procedure
      • PROC HPSPLIT Statement
      • CLASS Statement
      • CODE Statement
      • GROW Statement
      • ID Statement
      • MODEL Statement
      • OUTPUT Statement
      • PARTITION Statement
      • PERFORMANCE Statement
      • PRUNE Statement
      • RULES Statement
    • Details: HPSPLIT Procedure
      • Building a Decision Tree
      • Splitting Criteria
      • Splitting Strategy
      • Pruning
      • Memory Considerations
      • Primary and Surrogate Splitting Rules
      • Handling Missing Values
      • Unknown Values of Categorical Predictors
      • Scoring
      • Measures of Model Fit
      • Variable Importance
      • ODS Table Names
      • ODS Graphics
      • SAS Enterprise Miner Syntax and Notes
    • Examples: HPSPLIT Procedure
      • Building a Classification Tree for a Binary Outcome
      • Cost-Complexity Pruning with Cross Validation
      • Creating a Regression Tree
      • Creating a Binary Classification Tree with Validation Data
      • Assessing Variable Importance
      • Applying Breiman’s 1-SE Rule with Misclassification Rate
    • References


ProductRelease
SAS/STAT14.1
Type
Usage and Reference
Copyright Date
July 2015
Last Updated
14Jul2015