Acknowledgments- What's New in SAS/STAT 14.1
- Overview
- Enhancements
- BCHOICE Procedure
- CALIS Procedure
- FACTOR Procedure
- FMM Procedure
- FREQ Procedure
- GEE Procedure
- GENMOD Procedure
- GLIMMIX Procedure
- GLMSELECT Procedure
- HPGENSELECT Procedure
- HPLOGISTIC Procedure
- HPPRINCOMP Procedure
- HPSPLIT Procedure
- ICLIFETEST Procedure
- ICPHREG Procedure
- IRT Procedure
- LIFETEST Procedure
- LOGISTIC Procedure
- MCMC Procedure
- MI Procedure
- MIXED Procedure
- NLMIXED Procedure
- NPAR1WAY Procedure
- PHREG Procedure
- POWER Procedure
- QUANTREG Procedure
- QUANTSELECT Procedure
- SPP Procedure
- SURVEYLOGISTIC Procedure
- SURVEYMEANS Procedure
- SURVEYPHREG Procedure
- What’s Changed
- Random Number Generator Change
- CALIS Procedure
- LOGISTIC Procedure
- MI Procedure
- SPP Procedure
- SURVEYLOGISTIC Procedure
- SURVEYMEANS Procedure
- References
- Getting Started/Overview
- Overview of SAS/STAT Software
- Experimental Software
- About This Book
- Where to Turn for More Information
- Accessing the SAS/STAT Sample Library
- Sashelp Data Sets
- Online Documentation
- SAS Technical Support Services
- Related SAS Software
- Introductions
- Introduction to Statistical Modeling with SAS/STAT Software
- Overview: Statistical Modeling
- Statistical Models
- Classes of Statistical Models
- Linear and Nonlinear Models
- Regression Models and Models with Classification Effects
- Univariate and Multivariate Models
- Fixed, Random, and Mixed Models
- Generalized Linear Models
- Latent Variable Models
- Bayesian Models
- Classical Estimation Principles
- Statistical Background
- Hypothesis Testing and Power
- Important Linear Algebra Concepts
- Expectations of Random Variables and Vectors
- Mean Squared Error
- Linear Model Theory
- References
- Introduction to Regression Procedures
- Overview: Regression Procedures
- Introduction
- Introductory Example: Linear Regression
- Model Selection Methods
- Linear Regression: The REG Procedure
- Model Selection: The GLMSELECT Procedure
- Response Surface Regression: The RSREG Procedure
- Partial Least Squares Regression: The PLS Procedure
- Generalized Linear Regression
- Contingency Table Data: The CATMOD Procedure
- Generalized Linear Models: The GENMOD Procedure
- Generalized Linear Mixed Models: The GLIMMIX Procedure
- Logistic Regression: The LOGISTIC Procedure
- Discrete Event Data: The PROBIT Procedure
- Correlated Data: The GENMOD and GLIMMIX Procedures
- Ill-Conditioned Data: The ORTHOREG Procedure
- Quantile Regression: The QUANTREG and QUANTSELECT Procedures
- Nonlinear Regression: The NLIN and NLMIXED Procedures
- Nonparametric Regression
- Adaptive Regression: The ADAPTIVEREG Procedure
- Local Regression: The LOESS Procedure
- Thin Plate Smoothing Splines: The TPSPLINE Procedure
- Generalized Additive Models: The GAM Procedure
- Robust Regression: The ROBUSTREG Procedure
- Regression with Transformations: The TRANSREG Procedure
- Interactive Features in the CATMOD, GLM, and REG Procedures
- Statistical Background in Linear Regression
- Linear Regression Models
- Parameter Estimates and Associated Statistics
- Predicted and Residual Values
- Testing Linear Hypotheses
- Multivariate Tests
- Comments on Interpreting Regression Statistics
- References
- Introduction to Analysis of Variance Procedures
- Overview: Analysis of Variance Procedures
- Procedures That Perform Sum of Squares Analysis of Variance
- Procedures That Perform General Analysis of Variance
- Statistical Details for Analysis of Variance
- Analysis of Variance for Fixed-Effect Models
- PROC GLM for General Linear Models
- PROC ANOVA for Balanced Designs
- Comparing Group Means
- PROC TTEST for Comparing Two Groups
- Analysis of Variance for Categorical Data and Generalized Linear Models
- Nonparametric Analysis of Variance
- Constructing Analysis of Variance Designs
- For More Information
- References
- Introduction to Mixed Modeling Procedures
- Overview: Mixed Modeling Procedures
- Types of Mixed Models
- Linear, Generalized Linear, and Nonlinear Mixed Models
- Models for Clustered and Hierarchical Data
- Models with Subjects and Groups
- Linear Mixed Models
- Generalized Linear Mixed Models
- Nonlinear Mixed Models: The NLMIXED Procedure
- References
- Introduction to Bayesian Analysis Procedures
- Overview
- Introduction
- Background in Bayesian Statistics
- Prior Distributions
- Bayesian Inference
- Bayesian Analysis: Advantages and Disadvantages
- Markov Chain Monte Carlo Method
- Assessing Markov Chain Convergence
- Summary Statistics
- A Bayesian Reading List
- References
- Introduction to Categorical Data Analysis Procedures
- Overview: Categorical Data Analysis Procedures
- Introduction
- Sampling Frameworks and Distribution Assumptions
- Simple Random Sampling: One Population
- Stratified Simple Random Sampling: Multiple Populations
- Observational Data: Analyzing the Entire Population
- Randomized Experiments
- Relaxation of Sampling Assumptions
- Comparison of PROC FREQ and the Modeling Procedures
- Comparison of Modeling Procedures
- References
- Introduction to Multivariate Procedures
- Overview: Multivariate Procedures
- Comparison of the PRINCOMP and FACTOR Procedures
- Comparison of the PRINCOMP and PRINQUAL Procedures
- Comparison of the PRINCOMP and CORRESP Procedures
- Comparison of the PRINQUAL and CORRESP Procedures
- Comparison of the TRANSREG and PRINQUAL Procedures
- References
- Introduction to Discriminant Procedures
- Overview: Discriminant Procedures
- Background: Discriminant Procedures
- Example: Contrasting Univariate and Multivariate Analyses
- References
- Introduction to Clustering Procedures
- Overview: Clustering Procedures
- Clustering Variables
- Clustering Observations
- Characteristics of Methods for Clustering Observations
- Well-Separated Clusters
- Poorly Separated Clusters
- Multinormal Clusters of Unequal Size and Dispersion
- Elongated Multinormal Clusters
- Nonconvex Clusters
- The Number of Clusters
- References
- Introduction to Scoring, Standardization, and Ranking Procedures
- Introduction to Survival Analysis Procedures
- Overview
- Survival Analysis Procedures
- Parametric Accelerated Failure Time Models: The LIFEREG Procedure
- Nonparametric Methods for Right-Censored Data: The LIFETEST Procedure
- Nonparametric Methods for Interval-Censored Data: The ICLIFETEST Procedure
- Proportional Hazards Regression for Interval-Censored Data: The ICPHREG Procedure
- Quantile Regression: The QUANTLIFE Procedure
- Cox Regression and Extensions: The PHREG Procedure
- Cox Regression for Survey Data: The SURVEYPHREG Procedure
- Survival Analysis with SAS/STAT Procedures
- Bayesian Survival Analysis with SAS/STAT Procedures
- References
- Introduction to Survey Sampling and Analysis Procedures
- Overview: Survey Sampling and Analysis Procedures
- The Survey Procedures
- PROC SURVEYSELECT
- PROC SURVEYIMPUTE
- PROC SURVEYMEANS
- PROC SURVEYFREQ
- PROC SURVEYREG
- PROC SURVEYLOGISTIC
- PROC SURVEYPHREG
- Survey Design Specification
- Population
- Stratification
- Clustering
- Multistage Sampling
- Sampling Weights
- Population Totals and Sampling Rates
- Variance Estimation
- Example: Survey Sampling and Analysis Procedures
- References
- The Four Types of Estimable Functions
- Overview
- Estimability
- Estimable Functions
- Type I SS and Estimable Functions
- Type II SS and Estimable Functions
- Type III and IV SS and Estimable Functions
- References
- Introduction to Nonparametric Analysis
- Overview: Nonparametric Analysis
- One-Sample Tests
- Two-Sample Tests
- Tests for k Samples
- Measures of Correlation and Associated Tests
- Obtaining Ranks
- Kernel Density Estimation
- References
- Introduction to Structural Equation Modeling with Latent Variables
- Overview of Structural Equation Modeling with Latent Variables
- Testing Covariance Patterns
- Regression with Measurement Errors
- Model Identification
- Path Diagrams and Path Analysis
- Some Measurement Models
- H4: Full Measurement Model for Lord Data
- H3: Congeneric (One-Factor) Model for Lord Data
- H2: Two-Factor Model with Parallel Tests for Lord Data
- H1: One-Factor Model with Parallel Tests for Lord Data
- The FACTOR and RAM Modeling Languages
- Specifying the Full Measurement Model (H4) by the FACTOR Modeling Language: Lord Data
- Specifying the Parallel Tests Model (H2) by the FACTOR Modeling Language: Lord Data
- Specifying the Parallel Tests Model (H2) by the RAM Modeling Language: Lord Data
- A Combined Measurement-Structural Model
- Fitting LISREL Models by the LISMOD Modeling Language
- The Measurement Model for y
- The Measurement Model for x
- The Structural Model
- Fit Summary of the LISMOD Model for Career Aspiration Analysis 3
- Some Important PROC CALIS Features
- Modeling Languages for Specifying Models
- Estimation Methods
- Statistical Inference
- Multiple-Group Analysis
- Goodness-of-Fit Statistics
- Customizable Fit Summary Table
- Standardized Solution
- Statistical Graphics
- Testing Parametric Functions
- Effect Analysis
- Model Modifications
- Optimization Methods
- Other Commonly Used Options
- Comparison of the CALIS and FACTOR Procedures for Exploratory Factor Analysis
- Comparison of the CALIS and SYSLIN Procedures
- References
- Introduction to Power and Sample Size Analysis
- Overview
- Statistical Background
- Power and Study Planning
- SAS/STAT Tools for Power and Sample Size Analysis
- Basic Graphs (POWER, GLMPOWER, Power and Sample Size Application)
- Highly Customized Graphs (POWER, GLMPOWER)
- Formatted Tables (%POWTABLE Macro)
- Narratives and Graphical User Interface (Power and Sample Size Application)
- Customized Power Formulas (DATA Step)
- Empirical Power Simulation (DATA Step, SAS/STAT Software)
- References
- Shared Concepts
- Levelization of Classification Variables
- Parameterization of Model Effects
- 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
- Other Parameterizations
- CODE Statement
- EFFECT Statement
- Collection Effects
- Lag Effects
- Multimember Effects
- Polynomial Effects
- Spline Effects
- Splines and Spline Bases
- EFFECTPLOT Statement
- ESTIMATE Statement
- Syntax: ESTIMATE Statement
- Positional and Nonpositional Syntax for Coefficients in Linear Functions
- Joint Hypothesis Tests with Complex Alternatives, the Chi-Bar-Square Statistic
- ODS Table Names: ESTIMATE Statement
- ODS Graphics: ESTIMATE Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- Syntax: LSMESTIMATE Statement
- ODS Table Names: LSMESTIMATE Statement
- ODS Graphics: LSMESTIMATE Statement
- NLOPTIONS Statement
- SLICE Statement
- STORE Statement
- TEST Statement
- Programming Statements
- References
- Using the Output Delivery System
- Overview: Using the Output Delivery System
- Output Defaults
- HTML Output in the SAS Windowing Environment
- LISTING Output in the SAS Windowing Environment
- Assumptions about ODS Defaults in This Chapter
- The HTMLBLUE Style
- Default Open Destination
- Setting the Default Destination in the Results Tab
- Setting the Default Destination in the SAS Registry
- Setting the Default Destination in SAS System Options
- Setting the Destination in ODS Statements
- Output Objects and ODS Destinations
- The ODS Statement
- Paths and Selection
- RUN-Group Processing
- The SAS Results Window
- The ODS PATH Statement
- The Master Template Store
- Controlling Output Appearance with Templates
- ODS and the NOPRINT Option
- Examples: Using the Output Delivery System
- Creating HTML Output with ODS
- Selecting ODS Tables for Display
- Excluding ODS Tables from Display
- Creating an Output Data Set from an ODS Table
- Creating an Output Data Set: Subsetting the Data
- RUN-Group Processing
- ODS Output Data Sets and Using PROC TEMPLATE to Customize Output
- HTML Output with Hyperlinks between Tables
- HTML Output with Graphics and Hyperlinks
- Correlation and Covariance Matrices
- References
- Statistical Graphics Using ODS
- Statistical Graphics Using ODS
- Introduction
- Getting Started with ODS Statistical Graphics
- Default Plots for Simple Linear Regression with PROC REG
- Survival Estimate Plot with PROC LIFETEST
- Contour and Surface Plots with PROC KDE
- Contour Plots with PROC KRIGE2D
- Partial Least Squares Plots with PROC PLS
- Box-Cox Transformation Plot with PROC TRANSREG
- LS-Means Diffogram with PROC GLIMMIX
- Principal Component Analysis Plots with PROC PRINCOMP
- Grouped Scatter Plot with PROC SGPLOT
- A Primer on ODS Statistical Graphics
- Enabling and Disabling ODS Graphics
- ODS Styles
- ODS Destinations
- Accessing Individual Graphs
- Specifying the Size and Resolution of Graphs
- Modifying Your Graphs
- Procedures That Support ODS Graphics
- Procedures That Support ODS Graphics and Traditional Graphics
- Syntax
- Selecting and Viewing Graphs
- Specifying an ODS Destination for Graphics
- Viewing Your Graphs in the SAS Windowing Environment
- Determining Graph Names and Labels
- Selecting and Excluding Graphs
- Graphic Image Files
- Image File Types
- Scalable Vector Graphics
- Naming Graphic Image Files
- Saving Graphic Image Files
- Creating Graphs in Multiple Destinations
- Graph Size and Resolution
- ODS Graphics Editor
- The Default Template Stores and the Template Search Path
- ODS Styles
- An Overview of ODS Styles
- Attribute Priorities
- Overriding How Groups Are Distinguished
- ODS Style Elements and Attributes
- Style Templates and Colors
- Some Common ODS Style Elements
- ODS Style Comparisons
- Modifying the HTMLBLUE Style
- ODS Style Template Modification Macro
- Varying Colors and Markers but Not Lines
- Changing the Default Markers and Lines
- Changing the Default Style
- Statistical Graphics Procedures
- Examples of ODS Statistical Graphics
- Creating Graphs with Tooltips in HTML
- Creating Graphs for a Presentation
- Creating Graphs in PostScript Files
- Displaying Graphs Using the DOCUMENT Procedure
- Customizing the Style for Box Plots
- References
- ODS Graphics Template Modification
- Graph Templates
- The Graph Template Language
- Locating Templates
- Displaying Templates
- Editing Templates
- Saving Customized Templates
- Using Customized Templates
- Reverting to the Default Templates
- Graph Template Modification Macro
- Examples of ODS Graphics Template Modification
- Customizing Graphs Through Template Changes
- Modifying Graph Titles and Axis Labels
- Modifying Colors, Line Styles, and Markers
- Modifying Tick Marks and Grid Lines
- Modifying the Style to Show Grid Lines
- Adding Equations and Special Characters to Fit Plots
- Customizing Panels
- Customizing Axes and Reference Lines
- Adding Text to Every Graph
- Adding a Date and Project Stamp to a Few Graphs
- Adding Data Set Information to a Graph
- Adding a Date and Project Stamp to All Graphs
- PROC TEMPLATE Statement Order and Primary Plots
- Marginal Model Plots
- References
- Customizing the Kaplan-Meier Survival Plot
- Overview
- Controlling the Survival Plot by Specifying Procedure Options
- Enabling ODS Graphics and the Default Kaplan-Meier Plot
- Individual Survival Plots
- Hall-Wellner Confidence Bands and Homogeneity Test
- Equal-Precision Bands
- Displaying the Patients-at-Risk Table inside the Plot
- Displaying the Patients-at-Risk Table outside the Plot
- Modifying At-Risk Table Times
- Reordering the Groups
- Suppressing the Censored Observations
- Failure Plots
- Controlling the Survival Plot by Modifying Graph Templates
- The Modularized Templates
- Changing the Plot Title
- Modifying the Axis
- Changing the Line Thickness
- Changing the Group Color
- Changing the Line Pattern
- Changing the Font
- Changing the Legend and Inset Position
- Changing How the Censored Points Are Displayed
- Adding a Y-Axis Reference Line
- Changing the Homogeneity Test Inset
- Suppressing the Second Title and Adding a Footnote
- Adding a Small Inset Table with Event Information
- Adding an External Table with Event Information
- Suppressing the Legend
- Kaplan-Meier Plot with Event Table and Other Customizations
- Compiled Template Cleanup
- Graph Templates, Macros, and Macro Variables
- Dynamic Variables
- Style Templates
- Changing the Style
- Color Priority Styles
- Displaying a Style and Extracting Color Lists
- Modifying Color Lists
- Swapping Colors among Style Elements
- Displaying a Style and Extracting Font Information
- Displaying Other Style Elements
- SAS Item Stores
- References
- Procedures
- The ACECLUS Procedure
- Overview: ACECLUS Procedure
- Getting Started: ACECLUS Procedure
- Syntax: ACECLUS Procedure
- Details: ACECLUS Procedure
- Example: ACECLUS Procedure
- References
- The ADAPTIVEREG Procedure
- Overview: ADAPTIVEREG Procedure
- Getting Started: ADAPTIVEREG Procedure
- Syntax: ADAPTIVEREG Procedure
- PROC ADAPTIVEREG Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- MODEL Statement
- OUTPUT Statement
- PARTITION Statement
- SCORE Statement
- WEIGHT Statement
- Details: ADAPTIVEREG Procedure
- Fitting Algorithms
- Missing Values
- ANOVA Decomposition
- Computational Resources
- ODS Table Names
- ODS Graphics
- Examples: ADAPTIVEREG Procedure
- Surface Fitting with Many Noisy Variables
- Fitting Data with Mixture Structures
- Predicting E-Mail Spam
- Nonparametric Poisson Model for Mackerel Egg Density
- References
- The ANOVA Procedure
- Overview: ANOVA Procedure
- Getting Started: ANOVA Procedure
- Syntax: ANOVA Procedure
- PROC ANOVA Statement
- ABSORB Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- MANOVA Statement
- MEANS Statement
- MODEL Statement
- REPEATED Statement
- TEST Statement
- Details: ANOVA Procedure
- Specification of Effects
- Using PROC ANOVA Interactively
- Missing Values
- Output Data Set
- Computational Method
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: ANOVA Procedure
- Randomized Complete Block With Factorial Treatment Structure
- Alternative Multiple Comparison Procedures
- Split Plot
- Latin Square Split Plot
- Strip-Split Plot
- References
- The BCHOICE Procedure
- Overview: BCHOICE Procedure
- Getting Started: BCHOICE Procedure
- Syntax: BCHOICE Procedure
- PROC BCHOICE Statement
- BY Statement
- CLASS Statement
- MODEL Statement
- PREDDIST Statement
- RANDOM Statement
- RESTRICT Statement
- Details: BCHOICE Procedure
- Discrete Choice Models
- Types of Choice Models
- Random Effects
- Identification and Specification
- Gamerman Algorithm
- Tuning the Random Walk Metropolis in Logit Models
- Autocall Macros for Postprocessing
- Regenerating Diagnostics Plots
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: BCHOICE Procedure
- Alternative-Specific and Individual-Specific Effects
- Nested Logit Modeling
- Probit Modeling
- A Random-Effects-Only Logit Model
- Heterogeneity Affected by Individual Characteristics
- Inference on Quantities of Interest
- Predict the Choice Probabilities
- References
- The BOXPLOT Procedure
- Overview: BOXPLOT Procedure
- Getting Started: BOXPLOT Procedure
- Creating Box Plots from Raw Data
- Creating Box Plots from Summary Data
- Saving Summary Data with Outliers
- Syntax: BOXPLOT Procedure
- Details: BOXPLOT Procedure
- Summary Statistics Represented by Box Plots
- Output Data Sets
- Input Data Sets
- Styles of Box Plots
- Percentile Definitions
- Missing Values
- Continuous Group Variables
- Positioning Insets
- Displaying Blocks of Data
- Clipping Extreme Values
- ODS Graphics
- Examples: BOXPLOT Procedure
- Displaying Summary Statistics in a Box Plot
- Using Box Plots to Compare Groups
- Creating Various Styles of Box-and-Whiskers Plots
- Creating Notched Box-and-Whiskers Plots
- Creating Box-and-Whiskers Plots with Varying Widths
- Creating Horizontal Box-and-Whiskers Plots
- References
- The CALIS Procedure
- Overview: CALIS Procedure
- Compatibility with the CALIS Procedure in SAS/STAT 9.2 or Earlier
- Compatibility with the TCALIS Procedure in SAS/STAT 9.2
- A Guide to the PROC CALIS Documentation
- Getting Started: CALIS Procedure
- A Structural Equation Example
- A Factor Model Example
- Direct Covariance Structures Analysis
- Which Modeling Language?
- Syntax: CALIS Procedure
- Classes of Statements in PROC CALIS
- Single-Group Analysis Syntax
- Multiple-Group Multiple-Model Analysis Syntax
- PROC CALIS Statement
- BOUNDS Statement
- BY Statement
- COSAN Statement
- COV Statement
- DETERM Statement
- EFFPART Statement
- FACTOR Statement
- FITINDEX Statement
- FREQ Statement
- GROUP Statement
- LINCON Statement
- LINEQS Statement
- LISMOD Statement
- LMTESTS Statement
- MATRIX Statement
- MEAN Statement
- MODEL Statement
- MSTRUCT Statement
- NLINCON Statement
- NLOPTIONS Statement
- OUTFILES Statement
- PARAMETERS Statement
- PARTIAL Statement
- PATH Statement
- PATHDIAGRAM Statement
- PCOV Statement
- PVAR Statement
- RAM Statement
- REFMODEL Statement
- RENAMEPARM Statement
- SAS Programming Statements
- SIMTESTS Statement
- STD Statement
- STRUCTEQ Statement
- TESTFUNC Statement
- VAR Statement
- VARIANCE Statement
- VARNAMES Statement
- WEIGHT Statement
- Details: CALIS Procedure
- Input Data Sets
- Output Data Sets
- Default Analysis Type and Default Parameterization
- The COSAN Model
- The FACTOR Model
- The LINEQS Model
- The LISMOD Model and Submodels
- The MSTRUCT Model
- The PATH Model
- The RAM Model
- Naming Variables and Parameters
- Setting Constraints on Parameters
- Automatic Variable Selection
- Path Diagrams: Layout Algorithms, Default Settings, and Customization
- Estimation Criteria
- Relationships among Estimation Criteria
- Gradient, Hessian, Information Matrix, and Approximate Standard Errors
- Counting the Degrees of Freedom
- Assessment of Fit
- Case-Level Residuals, Outliers, Leverage Observations, and Residual Diagnostics
- Latent Variable Scores
- Total, Direct, and Indirect Effects
- Standardized Solutions
- Modification Indices
- Missing Values and the Analysis of Missing Patterns
- Measures of Multivariate Kurtosis
- Initial Estimates
- Use of Optimization Techniques
- Computational Problems
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: CALIS Procedure
- Estimating Covariances and Correlations
- Estimating Covariances and Means Simultaneously
- Testing Uncorrelatedness of Variables
- Testing Covariance Patterns
- Testing Some Standard Covariance Pattern Hypotheses
- Linear Regression Model
- Multivariate Regression Models
- Measurement Error Models
- Testing Specific Measurement Error Models
- Measurement Error Models with Multiple Predictors
- Measurement Error Models Specified As Linear Equations
- Confirmatory Factor Models
- Confirmatory Factor Models: Some Variations
- Residual Diagnostics and Robust Estimation
- The Full Information Maximum Likelihood Method
- Comparing the ML and FIML Estimation
- Path Analysis: Stability of Alienation
- Simultaneous Equations with Mean Structures and Reciprocal Paths
- Fitting Direct Covariance Structures
- Confirmatory Factor Analysis: Cognitive Abilities
- Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
- Testing Equality of Covariance and Mean Matrices between Independent Groups
- Illustrating Various General Modeling Languages
- Testing Competing Path Models for the Career Aspiration Data
- Fitting a Latent Growth Curve Model
- Higher-Order and Hierarchical Factor Models
- Linear Relations among Factor Loadings
- Multiple-Group Model for Purchasing Behavior
- Fitting the RAM and EQS Models by the COSAN Modeling Language
- Second-Order Confirmatory Factor Analysis
- Linear Relations among Factor Loadings: COSAN Model Specification
- Ordinal Relations among Factor Loadings
- Longitudinal Factor Analysis
- References
- The CANCORR Procedure
- Overview: CANCORR Procedure
- Getting Started: CANCORR Procedure
- Syntax: CANCORR Procedure
- PROC CANCORR Statement
- BY Statement
- FREQ Statement
- PARTIAL Statement
- VAR Statement
- WEIGHT Statement
- WITH Statement
- Details: CANCORR Procedure
- Example: CANCORR Procedure
- References
- The CANDISC Procedure
- Overview: CANDISC Procedure
- Getting Started: CANDISC Procedure
- Syntax: CANDISC Procedure
- Details: CANDISC Procedure
- Missing Values
- Computational Details
- Input Data Set
- Output Data Sets
- Computational Resources
- Displayed Output
- ODS Table Names
- Example: CANDISC Procedure
- References
- The CATMOD Procedure
- Overview: CATMOD Procedure
- Types of Input Data
- Types of Statistical Analyses
- Background: The Underlying Model
- Linear Models Contrasted with Log-Linear Models
- Using PROC CATMOD Interactively
- Getting Started: CATMOD Procedure
- Syntax: CATMOD Procedure
- PROC CATMOD Statement
- BY Statement
- CONTRAST Statement
- DIRECT Statement
- FACTORS Statement
- LOGLIN Statement
- MODEL Statement
- POPULATION Statement
- REPEATED Statement
- RESPONSE Statement
- RESTRICT Statement
- WEIGHT Statement
- Details: CATMOD Procedure
- Missing Values
- Input Data Sets
- Ordering of Populations and Responses
- Specification of Effects
- Output Data Sets
- Logistic Analysis
- Log-Linear Model Analysis
- Repeated Measures Analysis
- Generation of the Design Matrix
- Cautions
- Computational Method
- Computational Formulas
- Memory and Time Requirements
- Displayed Output
- ODS Table Names
- Examples: CATMOD Procedure
- Linear Response Function, r=2 Responses
- Mean Score Response Function, r=3 Responses
- Logistic Regression, Standard Response Function
- Log-Linear Model, Three Dependent Variables
- Log-Linear Model, Structural and Sampling Zeros
- Repeated Measures, 2 Response Levels, 3 Populations
- Repeated Measures, 4 Response Levels, 1 Population
- Repeated Measures, Logistic Analysis of Growth Curve
- Repeated Measures, Two Repeated Measurement Factors
- Direct Input of Response Functions and Covariance Matrix
- Predicted Probabilities
- References
- The CLUSTER Procedure
- Overview: CLUSTER Procedure
- Getting Started: CLUSTER Procedure
- Syntax: CLUSTER Procedure
- PROC CLUSTER Statement
- BY Statement
- COPY Statement
- FREQ Statement
- ID Statement
- RMSSTD Statement
- VAR Statement
- Details: CLUSTER Procedure
- Clustering Methods
- Miscellaneous Formulas
- Ultrametrics
- Algorithms
- Computational Resources
- Missing Values
- Ties
- Size, Shape, and Correlation
- Output Data Set
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: CLUSTER Procedure
- Cluster Analysis of Flying Mileages between 10 American Cities
- Crude Birth and Death Rates
- Cluster Analysis of Fisher’s Iris Data
- Evaluating the Effects of Ties
- References
- The CORRESP Procedure
- Overview: CORRESP Procedure
- Getting Started: CORRESP Procedure
- Syntax: CORRESP Procedure
- PROC CORRESP Statement
- BY Statement
- ID Statement
- SUPPLEMENTARY Statement
- TABLES Statement
- VAR Statement
- WEIGHT Statement
- Details: CORRESP Procedure
- Input Data Set
- Using the TABLES Statement
- Using the VAR Statement
- Missing and Invalid Data
- Coding, Fuzzy Coding, and Doubling
- Creating a Data Set Containing the Crosstabulation
- Output Data Sets
- Computational Resources
- Algorithm and Notation
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: CORRESP Procedure
- Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
- Simple Correspondence Analysis of U.S. Population
- References
- The DISCRIM Procedure
- Overview: DISCRIM Procedure
- Getting Started: DISCRIM Procedure
- Syntax: DISCRIM Procedure
- PROC DISCRIM Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- ID Statement
- PRIORS Statement
- TESTCLASS Statement
- TESTFREQ Statement
- TESTID Statement
- VAR Statement
- WEIGHT Statement
- Details: DISCRIM Procedure
- Missing Values
- Background
- Posterior Probability Error-Rate Estimates
- Saving and Using Calibration Information
- Input Data Sets
- Output Data Sets
- Computational Resources
- Displayed Output
- ODS Table Names
- Examples: DISCRIM Procedure
- Univariate Density Estimates and Posterior Probabilities
- Bivariate Density Estimates and Posterior Probabilities
- Normal-Theory Discriminant Analysis of Iris Data
- Linear Discriminant Analysis of Remote-Sensing Data on Crops
- References
- The DISTANCE Procedure
- Overview: DISTANCE Procedure
- Getting Started: DISTANCE Procedure
- Syntax: DISTANCE Procedure
- PROC DISTANCE Statement
- VAR Statement
- ID Statement
- COPY Statement
- BY Statement
- FREQ Statement
- WEIGHT Statement
- Details: DISTANCE Procedure
- Examples: DISTANCE Procedure
- References
- The FACTOR Procedure
- Overview: FACTOR Procedure
- Getting Started: FACTOR Procedure
- Syntax: FACTOR Procedure
- PROC FACTOR Statement
- BY Statement
- FREQ Statement
- PARTIAL Statement
- PATHDIAGRAM Statement
- PRIORS Statement
- VAR Statement
- WEIGHT Statement
- Details: FACTOR Procedure
- Input Data Set
- Output Data Sets
- Confidence Intervals and the Salience of Factor Loadings
- Simplicity Functions for Rotations
- Missing Values
- Cautions
- Factor Scores
- Variable Weights and Variance Explained
- Heywood Cases and Other Anomalies about Communality Estimates
- Time Requirements
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: FACTOR Procedure
- Principal Component Analysis
- Principal Factor Analysis
- Maximum Likelihood Factor Analysis
- Using Confidence Intervals to Locate Salient Factor Loadings
- Creating Path Diagrams for Factor Solutions
- References
- The FASTCLUS Procedure
- Overview: FASTCLUS Procedure
- Getting Started: FASTCLUS Procedure
- Syntax: FASTCLUS Procedure
- Details: FASTCLUS Procedure
- Updates in the FASTCLUS Procedure
- Missing Values
- Output Data Sets
- Computational Resources
- Using PROC FASTCLUS
- Displayed Output
- ODS Table Names
- Examples: FASTCLUS Procedure
- References
- The FMM Procedure
- Overview: FMM Procedure
- Basic Features
- Assumptions
- Notation for the Finite Mixture Model
- PROC FMM Contrasted with Other SAS Procedures
- Getting Started: FMM 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?
- Syntax: FMM Procedure
- PROC FMM Statement
- BAYES Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- PERFORMANCE Statement
- PROBMODEL Statement
- RESTRICT Statement
- WEIGHT Statement
- Details: FMM 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
- Default Output
- Model Information
- Class Level Information
- Number of Observations
- Response Profile
- Default Output for Maximum Likelihood
- Default Output for Bayesian Estimation
- ODS Table Names
- ODS Graphics
- Examples: FMM 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!
- Modeling Multinomial Overdispersion: Town and Country
- References
- The FREQ Procedure
- Overview: FREQ Procedure
- Getting Started: FREQ Procedure
- Syntax: FREQ Procedure
- PROC FREQ Statement
- BY Statement
- EXACT Statement
- OUTPUT Statement
- TABLES Statement
- TEST Statement
- WEIGHT Statement
- Details: FREQ Procedure
- Inputting Frequency Counts
- Grouping with Formats
- Missing Values
- In-Database Computation
- Statistical Computations
- Definitions and Notation
- Chi-Square Tests and Statistics
- Measures of Association
- Binomial Proportion
- Risks and Risk Differences
- Common Risk Difference
- Odds Ratio and Relative Risks for 2 x 2 Tables
- Cochran-Armitage Test for Trend
- Jonckheere-Terpstra Test
- Tests and Measures of Agreement
- Cochran-Mantel-Haenszel Statistics
- Gail-Simon Test for Qualitative Interactions
- Exact Statistics
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: FREQ Procedure
- Output Data Set of Frequencies
- Frequency Dot Plots
- Chi-Square Goodness-of-Fit Tests
- Binomial Proportions
- Analysis of a 2x2 Contingency Table
- Output Data Set of Chi-Square Statistics
- Cochran-Mantel-Haenszel Statistics
- Cochran-Armitage Trend Test
- Friedman’s Chi-Square Test
- Cochran’s Q Test
- References
- The GAM Procedure
- Overview: GAM Procedure
- Getting Started: GAM Procedure
- Syntax: GAM Procedure
- PROC GAM Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- MODEL Statement
- OUTPUT Statement
- SCORE Statement
- Details: GAM Procedure
- Missing Values
- Nonparametric Regression
- Additive Models and Generalized Additive Models
- Forms of Additive Models
- Estimates from PROC GAM
- Backfitting and Local Scoring Algorithms
- Smoothers
- Selection of Smoothing Parameters
- Confidence Intervals for Smoothers
- Distribution Family and Canonical Link
- Dispersion Parameter
- Computational Resources
- ODS Table Names
- ODS Graphics
- Examples: GAM Procedure
- Generalized Additive Model with Binary Data
- Poisson Regression Analysis of Component Reliability
- Comparing PROC GAM with PROC LOESS
- References
- The GAMPL Procedure
- Overview: GAMPL Procedure
- 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
- 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 GEE Procedure
- Overview: GEE Procedure
- Getting Started
- Syntax: GEE Procedure
- PROC GEE Statement
- BY Statement
- CLASS Statement
- ESTIMATE Statement
- FREQ Statement
- LSMEANS Statement
- MISSMODEL Statement
- MODEL Statement
- OUTPUT Statement
- REPEATED Statement
- WEIGHT Statement
- Details: GEE Procedure
- Generalized Estimating Equations
- Alternating Logistic Regression
- Weighted Generalized Estimating Equations under the MAR Assumption
- ODS Table Names
- ODS Graphics
- Examples: GEE Procedure
- Comparison of the Marginal and Random Effect Models for Binary Data
- Log-Linear Model for Count Data
- Weighted GEE for Longitudinal Data That Have Missing Values
- GEE for Binary Data with Logit Link Function
- Alternating Logistic Regression for Ordinal Multinomial Data
- GEE for Nominal Multinomial Data
- References
- The GENMOD Procedure
- Overview: GENMOD Procedure
- Getting Started: GENMOD Procedure
- Syntax: GENMOD Procedure
- PROC GENMOD Statement
- ASSESS Statement
- BAYES Statement
- BY Statement
- CLASS Statement
- CODE Statement
- CONTRAST Statement
- DEVIANCE Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- EXACT Statement
- EXACTOPTIONS Statement
- FREQ Statement
- FWDLINK Statement
- INVLINK Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- Programming Statements
- REPEATED Statement
- SLICE Statement
- STORE Statement
- STRATA Statement
- VARIANCE Statement
- WEIGHT Statement
- ZEROMODEL Statement
- Details: GENMOD Procedure
- Generalized Linear Models Theory
- Specification of Effects
- Parameterization Used in PROC GENMOD
- Type 1 Analysis
- Type 3 Analysis
- Confidence Intervals for Parameters
- F Statistics
- Lagrange Multiplier Statistics
- Predicted Values of the Mean
- Residuals
- Multinomial Models
- Zero-Inflated Models
- Tweedie Distribution For Generalized Linear Models
- Generalized Estimating Equations
- Assessment of Models Based on Aggregates of Residuals
- Case Deletion Diagnostic Statistics
- Bayesian Analysis
- Exact Logistic and Exact Poisson Regression
- Response Level Ordering
- Missing Values
- Displayed Output for Classical Analysis
- Displayed Output for Bayesian Analysis
- Displayed Output for Exact Analysis
- ODS Table Names
- ODS Graphics
- Examples: GENMOD Procedure
- Logistic Regression
- Normal Regression, Log Link
- Gamma Distribution Applied to Life Data
- Ordinal Model for Multinomial Data
- GEE for Binary Data with Logit Link Function
- Log Odds Ratios and the ALR Algorithm
- Log-Linear Model for Count Data
- Model Assessment of Multiple Regression Using Aggregates of Residuals
- Assessment of a Marginal Model for Dependent Data
- Bayesian Analysis of a Poisson Regression Model
- Exact Poisson Regression
- Tweedie Regression
- References
- The GLIMMIX Procedure
- Overview: GLIMMIX Procedure
- Basic Features
- Assumptions
- Notation for the Generalized Linear Mixed Model
- The Basic Model
- G-Side and R-Side Random Effects and Covariance Structures
- Relationship with Generalized Linear Models
- PROC GLIMMIX Contrasted with Other SAS Procedures
- Getting Started: GLIMMIX Procedure
- Syntax: GLIMMIX Procedure
- PROC GLIMMIX Statement
- BY Statement
- CLASS Statement
- CODE Statement
- CONTRAST Statement
- COVTEST Statement
- EFFECT Statement
- ESTIMATE Statement
- FREQ Statement
- ID Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- NLOPTIONS Statement
- OUTPUT Statement
- PARMS Statement
- RANDOM Statement
- SLICE Statement
- STORE Statement
- WEIGHT Statement
- Programming Statements
- User-Defined Link or Variance Function
- Details: GLIMMIX Procedure
- Generalized Linear Models Theory
- Maximum Likelihood
- Scale and Dispersion Parameters
- Quasi-likelihood for Independent Data
- Effects of Adding Overdispersion
- Generalized Linear Mixed Models Theory
- Model or Integral Approximation
- Pseudo-likelihood Estimation Based on Linearization
- Maximum Likelihood Estimation Based on Laplace Approximation
- Maximum Likelihood Estimation Based on Adaptive Quadrature
- Aspects Common to Adaptive Quadrature and Laplace Approximation
- Notes on Bias of Estimators
- Pseudo-likelihood Estimation for Weighted Multilevel Models
- GLM Mode or GLMM Mode
- Statistical Inference for Covariance Parameters
- The Likelihood Ratio Test
- One- and Two-Sided Testing, Mixture Distributions
- Handling the Degenerate Distribution
- Wald Versus Likelihood Ratio Tests
- Confidence Bounds Based on Likelihoods
- Degrees of Freedom Methods
- Between-Within Degrees of Freedom Approximation
- Containment Degrees of Freedom Approximation
- Satterthwaite Degrees of Freedom Approximation
- Kenward-Roger Degrees of Freedom Approximation
- Empirical Covariance ("Sandwich") Estimators
- Exploring and Comparing Covariance Matrices
- Processing by Subjects
- Radial Smoothing Based on Mixed Models
- Odds and Odds Ratio Estimation
- Parameterization of Generalized Linear Mixed Models
- Intercept
- Interaction Effects
- Nested Effects
- Implications of the Non-Full-Rank Parameterization
- Missing Level Combinations
- Notes on the EFFECT Statement
- Positional and Nonpositional Syntax for Contrast Coefficients
- Response-Level Ordering and Referencing
- Comparing the GLIMMIX and MIXED Procedures
- Singly or Doubly Iterative Fitting
- Default Estimation Techniques
- Default Output
- Model Information
- Class Level Information
- Number of Observations
- Response Profile
- Dimensions
- Optimization Information
- Iteration History
- Convergence Status
- Fit Statistics
- Covariance Parameter Estimates
- Type III Tests of Fixed Effects
- Notes on Output Statistics
- ODS Table Names
- ODS Graphics
- Examples: GLIMMIX Procedure
- Binomial Counts in Randomized Blocks
- Mating Experiment with Crossed Random Effects
- Smoothing Disease Rates; Standardized Mortality Ratios
- Quasi-likelihood Estimation for Proportions with Unknown Distribution
- Joint Modeling of Binary and Count Data
- Radial Smoothing of Repeated Measures Data
- Isotonic Contrasts for Ordered Alternatives
- Adjusted Covariance Matrices of Fixed Effects
- Testing Equality of Covariance and Correlation Matrices
- Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
- Maximum Likelihood in Proportional Odds Model with Random Effects
- Fitting a Marginal (GEE-Type) Model
- Response Surface Comparisons with Multiplicity Adjustments
- Generalized Poisson Mixed Model for Overdispersed Count Data
- Comparing Multiple B-Splines
- Diallel Experiment with Multimember Random Effects
- Linear Inference Based on Summary Data
- Weighted Multilevel Model for Survey Data
- Quadrature Method for Multilevel Model
- References
- The GLM Procedure
- Overview: GLM Procedure
- Getting Started: GLM Procedure
- Syntax: GLM Procedure
- PROC GLM Statement
- ABSORB Statement
- BY Statement
- CLASS Statement
- CODE Statement
- CONTRAST Statement
- ESTIMATE Statement
- FREQ Statement
- ID Statement
- LSMEANS Statement
- MANOVA Statement
- MEANS Statement
- MODEL Statement
- OUTPUT Statement
- RANDOM Statement
- REPEATED Statement
- STORE Statement
- TEST Statement
- WEIGHT Statement
- Details: GLM Procedure
- Statistical Assumptions for Using PROC GLM
- Specification of Effects
- Using PROC GLM Interactively
- Parameterization of PROC GLM Models
- Hypothesis Testing in PROC GLM
- Effect Size Measures for F Tests in GLM
- Absorption
- Specification of ESTIMATE Expressions
- Comparing Groups
- Means versus LS-Means
- Multiple Comparisons
- Simple Effects
- Homogeneity of Variance in One-Way Models
- Weighted Means
- Construction of Least Squares Means
- Multivariate Analysis of Variance
- Repeated Measures Analysis of Variance
- Random-Effects Analysis
- Missing Values
- Computational Resources
- Computational Method
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: GLM Procedure
- Randomized Complete Blocks with Means Comparisons and Contrasts
- Regression with Mileage Data
- Unbalanced ANOVA for Two-Way Design with Interaction
- Analysis of Covariance
- Three-Way Analysis of Variance with Contrasts
- Multivariate Analysis of Variance
- Repeated Measures Analysis of Variance
- Mixed Model Analysis of Variance with the RANDOM Statement
- Analyzing a Doubly Multivariate Repeated Measures Design
- Testing for Equal Group Variances
- Analysis of a Screening Design
- References
- The GLMMOD Procedure
- Overview: GLMMOD Procedure
- Getting Started: GLMMOD Procedure
- Syntax: GLMMOD Procedure
- Details: GLMMOD Procedure
- Examples: GLMMOD Procedure
- References
- The GLMPOWER Procedure
- Overview: GLMPOWER Procedure
- Getting Started: GLMPOWER Procedure
- Syntax: GLMPOWER Procedure
- PROC GLMPOWER Statement
- BY Statement
- CLASS Statement
- CONTRAST Statement
- MANOVA Statement
- MODEL Statement
- PLOT Statement
- POWER Statement
- REPEATED Statement
- WEIGHT Statement
- Details: GLMPOWER Procedure
- Specifying Value Lists in the POWER Statement
- Sample Size Adjustment Options
- Error and Information Output
- Displayed Output
- ODS Table Names
- Computational Methods and Formulas
- Contrasts in Fixed-Effect Univariate Models
- Adjustments for Covariates in Univariate Models
- Contrasts in Fixed-Effect Multivariate Models
- ODS Graphics
- Examples: GLMPOWER Procedure
- References
- The GLMSELECT Procedure
- Overview: GLMSELECT Procedure
- Getting Started: GLMSELECT Procedure
- Syntax: GLMSELECT Procedure
- PROC GLMSELECT Statement
- BY Statement
- CLASS Statement
- CODE Statement
- EFFECT Statement
- FREQ Statement
- MODEL Statement
- MODELAVERAGE Statement
- OUTPUT Statement
- PARTITION Statement
- PERFORMANCE Statement
- SCORE Statement
- STORE Statement
- WEIGHT Statement
- Details: GLMSELECT Procedure
- Model-Selection Methods
- Full Model Fitted (NONE)
- Forward Selection (FORWARD)
- Backward Elimination (BACKWARD)
- Stepwise Selection(STEPWISE)
- Least Angle Regression (LAR)
- Lasso Selection (LASSO)
- Adaptive LASSO Selection
- Elastic Net Selection (ELASTICNET)
- Group LASSO Selection (GROUPLASSO)
- Model Selection Issues
- Criteria Used in Model Selection Methods
- CLASS Variable Parameterization and the SPLIT Option
- Macro Variables Containing Selected Models
- Using the STORE Statement
- Building the SSCP Matrix
- Model Averaging
- Using Validation and Test Data
- Cross Validation
- External Cross Validation
- Screening
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: GLMSELECT Procedure
- Modeling Baseball Salaries Using Performance Statistics
- Using Validation and Cross Validation
- Scatter Plot Smoothing by Selecting Spline Functions
- Multimember Effects and the Design Matrix
- Model Averaging
- Elastic Net and External Cross Validation
- LASSO with Screening
- Group LASSO Selection
- References
- The HPCANDISC Procedure
- Overview: HPCANDISC Procedure
- 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
- 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
- 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?
- Syntax: HPFMM Procedure
- PROC HPFMM Statement
- BAYES Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- ID Statement
- MODEL Statement
- 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
- Choosing an Optimization Algorithm
- 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 HPGENSELECT Procedure
- Overview: HPGENSELECT Procedure
- 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
- Displayed Output
- ODS Table Names
- Examples: HPGENSELECT Procedure
- 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
- 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
- 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
- References
- The HPLOGISTIC Procedure
- Overview: HPLOGISTIC Procedure
- Getting Started: HPLOGISTIC Procedure
- 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
- Displayed Output
- ODS Table Names
- Examples: HPLOGISTIC Procedure
- References
- The HPMIXED Procedure
- Overview: HPMIXED Procedure
- Basic Features
- Assumptions and Notation
- Computational Approach
- The HPMIXED Procedure Contrasted with the MIXED Procedure
- Getting Started: HPMIXED Procedure
- Syntax: HPMIXED Procedure
- PROC HPMIXED Statement
- BY Statement
- CLASS Statement
- CONTRAST Statement
- EFFECT Statement
- ESTIMATE Statement
- ID Statement
- LSMEANS Statement
- MODEL Statement
- NLOPTIONS Statement
- OUTPUT Statement
- PARMS Statement
- RANDOM Statement
- REPEATED Statement
- TEST Statement
- WEIGHT Statement
- Details: HPMIXED Procedure
- Model Assumptions
- Computing and Maximizing the Likelihood
- Computing Starting Values by EM-REML
- Sparse Matrix Techniques
- Hypothesis Tests for Fixed Effects
- Default Output
- ODS Table Names
- Examples: HPMIXED Procedure
- Ranking Many Random-Effect Coefficients
- Comparing Results from PROC HPMIXED and PROC MIXED
- Using PROC GLIMMIX for Further Analysis of PROC HPMIXED Fit
- Mixed Model Analysis of Microarray Data
- Repeated Measures
- References
- The HPNLMOD Procedure
- Overview: HPNLMOD Procedure
- Getting Started: HPNLMOD Procedure
- 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
- References
- The HPPLS Procedure
- Overview: HPPLS Procedure
- Getting Started: HPPLS Procedure
- 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
- 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
- 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
- Missing Values
- Output Data Sets
- Computational Method
- 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
- 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
- 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
- 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
- References
- The HPREG Procedure
- Overview: HPREG Procedure
- 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
- 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
- The ICLIFETEST Procedure
- Overview: ICLIFETEST Procedure
- Getting Started: ICLIFETEST Procedure
- Syntax: ICLIFETEST Procedure
- Details: ICLIFETEST Procedure
- Examples: ICLIFETEST Procedure
- Analyzing Data with Observations below a Limit of Detection
- Controlling the Plotting of Survival Estimates
- Plotting Kernel-Smoothed Hazard Functions
- Outputting Scores for Permutation Tests
- References
- The ICPHREG Procedure
- Overview: ICPHREG Procedure
- Getting Started: ICPHREG Procedure
- Syntax: ICPHREG Procedure
- PROC ICPHREG Statement
- BASELINE Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- HAZARDRATIO Statement
- MODEL Statement
- OUTPUT Statement
- STRATA Statement
- TEST Statement
- Details: ICPHREG Procedure
- Model and Likelihood
- Baseline Parameterization
- Specification of Effects
- Computational Details
- Predicted Values
- Hazard Ratios
- Left-Truncation of Failure Times
- Residuals and Diagnostic Statistics
- Input and Output Data Sets
- Missing Values
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: ICPHREG Procedure
- Fitting Cubic Spline Models
- Plotting Predicted Survival and Cumulative Hazard Functions
- Fitting Stratified Weibull Models
- References
- The INBREED Procedure
- Overview: INBREED Procedure
- Getting Started: INBREED Procedure
- Syntax: INBREED Procedure
- Details: INBREED Procedure
- Examples: INBREED Procedure
- References
- The IRT Procedure
- Overview: IRT Procedure
- Getting Started: IRT Procedure
- Syntax: IRT Procedure
- PROC IRT Statement
- BY Statement
- COV Statement
- EQUALITY Statement
- FACTOR Statement
- FREQ Statement
- GROUP Statement
- MODEL Statement
- VAR Statement
- VARIANCE Statement
- WEIGHT Statement
- Details: IRT Procedure
- Notation for the Item Response Theory Model
- Assumptions
- PROC IRT Contrasted with Other SAS Procedures
- Response Models
- Marginal Likelihood
- Approximating the Marginal Likelihood
- Maximizing the Marginal Likelihood
- Factor Score Estimation
- Model and Item Fit
- Item and Test information
- Missing Values
- Output Data Sets
- ODS Table Names
- ODS Graphics
- Examples: IRT Procedure
- Unidimensional IRT Models
- Multidimensional Exploratory and Confirmatory IRT Models
- Multiple-Group Analysis
- Item Selection Using Item and Test Information
- Subject Scoring
- References
- The KDE Procedure
- Overview: KDE Procedure
- Getting Started: KDE Procedure
- Syntax: KDE Procedure
- Details: KDE Procedure
- Computational Overview
- Kernel Density Estimates
- Binning
- Convolutions
- Fast Fourier Transform
- Bandwidth Selection
- ODS Table Names
- ODS Graphics
- Examples: KDE Procedure
- Computing a Basic Kernel Density Estimate
- Changing the Bandwidth
- Changing the Bandwidth (Bivariate)
- Requesting Additional Output Tables
- Univariate KDE Graphics
- Bivariate KDE Graphics
- References
- The KRIGE2D Procedure
- Overview: KRIGE2D Procedure
- Getting Started: KRIGE2D Procedure
- Syntax: KRIGE2D Procedure
- PROC KRIGE2D Statement
- BY Statement
- COORDINATES Statement
- GRID Statement
- ID Statement
- PREDICT Statement
- MODEL Statement
- RESTORE Statement
- Details: KRIGE2D Procedure
- Theoretical Semivariogram Models
- The Nugget Effect
- Anisotropic Models
- Details of Ordinary Kriging
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: KRIGE2D Procedure
- Spatial Prediction of Pollutant Concentration
- Investigating the Effect of Model Specification on Spatial Prediction
- Data Quality and Prediction with Missing Values
- References
- The LATTICE Procedure
- Overview: LATTICE Procedure
- Getting Started: LATTICE Procedure
- Syntax: LATTICE Procedure
- Details: LATTICE Procedure
- Example: LATTICE Procedure
- References
- The LIFEREG Procedure
- Overview: LIFEREG Procedure
- Getting Started: LIFEREG Procedure
- Syntax: LIFEREG Procedure
- PROC LIFEREG Statement
- BAYES Statement
- BY Statement
- CLASS Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- INSET Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- PROBPLOT Statement
- SLICE Statement
- STORE Statement
- TEST Statement
- WEIGHT Statement
- Details: LIFEREG Procedure
- Missing Values
- Model Specification
- Computational Method
- Supported Distributions
- Predicted Values
- Confidence Intervals
- Fit Statistics
- Probability Plotting
- INEST= Data Set
- OUTEST= Data Set
- XDATA= Data Set
- Computational Resources
- Bayesian Analysis
- Displayed Output for Classical Analysis
- Displayed Output for Bayesian Analysis
- ODS Table Names
- ODS Graphics
- Examples: LIFEREG Procedure
- Motorette Failure
- Computing Predicted Values for a Tobit Model
- Overcoming Convergence Problems by Specifying Initial Values
- Analysis of Arbitrarily Censored Data with Interaction Effects
- Probability Plotting—Right Censoring
- Probability Plotting—Arbitrary Censoring
- Bayesian Analysis of Clinical Trial Data
- Model Postfitting Analysis
- References
- The LIFETEST Procedure
- Overview: LIFETEST Procedure
- Getting Started: LIFETEST Procedure
- Syntax: LIFETEST Procedure
- PROC LIFETEST Statement
- BY Statement
- FREQ Statement
- ID Statement
- STRATA Statement
- TEST Statement
- TIME Statement
- WEIGHT Statement
- Details: LIFETEST Procedure
- Missing Values
- Computational Formulas
- Breslow, Fleming-Harrington, and Kaplan-Meier Methods
- Life-Table Method
- Pointwise Confidence Limits in the OUTSURV= Data Set
- Simultaneous Confidence Intervals for Kaplan-Meier Curves
- Kernel-Smoothed Hazard Estimate
- Comparison of Two or More Groups of Survival Data
- Rank Tests for the Association of Survival Time with Covariates
- Analysis of Competing-Risks Data
- Computer Resources
- Output Data Sets
- Displayed Output
- Plot Options Superseded by ODS Graphics
- ODS Table Names
- ODS Graphics
- Modifying the Survival Plots
- Examples: LIFETEST Procedure
- Product-Limit Estimates and Tests of Association
- Enhanced Survival Plot and Multiple-Comparison Adjustments
- Life-Table Estimates for Males with Angina Pectoris
- Nonparametric Analysis of Competing-Risks Data
- References
- The LOESS Procedure
- Overview: LOESS Procedure
- Getting Started: LOESS Procedure
- Syntax: LOESS Procedure
- PROC LOESS Statement
- BY Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- SCORE Statement
- WEIGHT Statement
- Details: LOESS Procedure
- Missing Values
- Output Data Sets
- Data Scaling
- Direct versus Interpolated Fitting
- k-d Trees and Blending
- Local Weighting
- Iterative Reweighting
- Specifying the Local Polynomials
- Smoothing Matrix
- Model Degrees of Freedom
- Statistical Inference and Lookup Degrees of Freedom
- Automatic Smoothing Parameter Selection
- Sparse and Approximate Degrees of Freedom Computation
- Scoring Data Sets
- ODS Table Names
- ODS Graphics
- Examples: LOESS Procedure
- Engine Exhaust Emissions
- Sulfate Deposits in the U.S. for 1990
- Catalyst Experiment
- El Niño Southern Oscillation
- References
- The LOGISTIC Procedure
- Overview: LOGISTIC Procedure
- Getting Started: LOGISTIC Procedure
- Syntax: LOGISTIC Procedure
- PROC LOGISTIC Statement
- BY Statement
- CLASS Statement
- CODE Statement
- CONTRAST Statement
- EFFECT Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- EXACT Statement
- EXACTOPTIONS Statement
- FREQ Statement
- ID Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- NLOPTIONS Statement
- ODDSRATIO Statement
- OUTPUT Statement
- ROC Statement
- ROCCONTRAST Statement
- SCORE Statement
- SLICE Statement
- STORE Statement
- STRATA Statement
- TEST Statement
- UNITS Statement
- WEIGHT Statement
- Details: LOGISTIC Procedure
- Missing Values
- Response Level Ordering
- Link Functions and the Corresponding Distributions
- Determining Observations for Likelihood Contributions
- Iterative Algorithms for Model Fitting
- Convergence Criteria
- Existence of Maximum Likelihood Estimates
- Effect-Selection Methods
- Model Fitting Information
- Generalized Coefficient of Determination
- Score Statistics and Tests
- Confidence Intervals for Parameters
- Odds Ratio Estimation
- Rank Correlation of Observed Responses and Predicted Probabilities
- Linear Predictor, Predicted Probability, and Confidence Limits
- Classification Table
- Overdispersion
- The Hosmer-Lemeshow Goodness-of-Fit Test
- Receiver Operating Characteristic Curves
- Testing Linear Hypotheses about the Regression Coefficients
- Joint Tests and Type 3 Tests
- Regression Diagnostics
- Scoring Data Sets
- Conditional Logistic Regression
- Exact Conditional Logistic Regression
- Input and Output Data Sets
- Computational Resources
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: LOGISTIC Procedure
- Stepwise Logistic Regression and Predicted Values
- Logistic Modeling with Categorical Predictors
- Ordinal Logistic Regression
- Nominal Response Data: Generalized Logits Model
- Stratified Sampling
- Logistic Regression Diagnostics
- ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
- Comparing Receiver Operating Characteristic Curves
- Goodness-of-Fit Tests and Subpopulations
- Overdispersion
- Conditional Logistic Regression for Matched Pairs Data
- Exact Conditional Logistic Regression
- Firth’s Penalized Likelihood Compared with Other Approaches
- Complementary Log-Log Model for Infection Rates
- Complementary Log-Log Model for Interval-Censored Survival Times
- Scoring Data Sets
- Using the LSMEANS Statement
- Partial Proportional Odds Model
- References
- The MCMC Procedure
- Overview: MCMC Procedure
- Getting Started: MCMC Procedure
- Syntax: MCMC Procedure
- PROC MCMC Statement
- ARRAY Statement
- BEGINCNST/ENDCNST Statement
- BEGINNODATA/ENDNODATA Statements
- BY Statement
- MODEL Statement
- PARMS Statement
- PREDDIST Statement
- PRIOR/HYPERPRIOR Statement
- Programming Statements
- RANDOM Statement
- UDS Statement
- Details: MCMC Procedure
- How PROC MCMC Works
- Blocking of Parameters
- Sampling Methods
- Tuning the Proposal Distribution
- Direct Sampling
- Conjugate Sampling
- Initial Values of the Markov Chains
- Assignments of Parameters
- Standard Distributions
- Usage of Multivariate Distributions
- Specifying a New Distribution
- Using Density Functions in the Programming Statements
- Truncation and Censoring
- Some Useful SAS Functions
- Matrix Functions in PROC MCMC
- Create Design Matrix
- Modeling Joint Likelihood
- Access Lag and Lead Variables
- CALL ODE and CALL QUAD Subroutines
- Regenerating Diagnostics Plots
- Caterpillar Plot
- Autocall Macros for Postprocessing
- Gamma and Inverse-Gamma Distributions
- Posterior Predictive Distribution
- Handling of Missing Data
- Functions of Random-Effects Parameters
- Floating Point Errors and Overflows
- Handling Error Messages
- Computational Resources
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: MCMC Procedure
- Simulating Samples From a Known Density
- Box-Cox Transformation
- Logistic Regression Model with a Diffuse Prior
- Logistic Regression Model with Jeffreys’ Prior
- Poisson Regression
- Nonlinear Poisson Regression Models
- Logistic Regression Random-Effects Model
- Nonlinear Poisson Regression Multilevel Random-Effects Model
- Multivariate Normal Random-Effects Model
- Missing at Random Analysis
- Nonignorably Missing Data (MNAR) Analysis
- Change Point Models
- Exponential and Weibull Survival Analysis
- Time Independent Cox Model
- Time Dependent Cox Model
- Piecewise Exponential Frailty Model
- Normal Regression with Interval Censoring
- Constrained Analysis
- Implement a New Sampling Algorithm
- Using a Transformation to Improve Mixing
- Gelman-Rubin Diagnostics
- One-Compartment Model with Pharmacokinetic Data
- References
- The MDS Procedure
- Overview: MDS Procedure
- Getting Started: MDS Procedure
- Syntax: MDS Procedure
- PROC MDS Statement
- BY Statement
- ID Statement
- INVAR Statement
- MATRIX Statement
- VAR Statement
- WEIGHT Statement
- Details: MDS Procedure
- Formulas
- OUT= Data Set
- OUTFIT= Data Set
- OUTRES= Data Set
- INITIAL= Data Set
- Missing Values
- Normalization of the Estimates
- Comparison with Earlier Procedures
- Displayed Output
- ODS Table Names
- ODS Graphics
- Example: MDS Procedure
- References
- The MI Procedure
- Overview: MI Procedure
- Getting Started: MI Procedure
- Syntax: MI Procedure
- PROC MI Statement
- BY Statement
- CLASS Statement
- EM Statement
- FCS Statement
- FREQ Statement
- MCMC Statement
- MNAR Statement
- MONOTONE Statement
- TRANSFORM Statement
- VAR Statement
- Details: MI Procedure
- Descriptive Statistics
- EM Algorithm for Data with Missing Values
- Statistical Assumptions for Multiple Imputation
- Missing Data Patterns
- Imputation Methods
- Monotone Methods for Data Sets with Monotone Missing Patterns
- Monotone and FCS Regression Methods
- Monotone and FCS Predictive Mean Matching Methods
- Monotone and FCS Discriminant Function Methods
- Monotone and FCS Logistic Regression Methods
- Monotone Propensity Score Method
- FCS Methods for Data Sets with Arbitrary Missing Patterns
- Checking Convergence in FCS Methods
- MCMC Method for Arbitrary Missing Multivariate Normal Data
- Producing Monotone Missingness with the MCMC Method
- MCMC Method Specifications
- Checking Convergence in MCMC
- Input Data Sets
- Output Data Sets
- Combining Inferences from Multiply Imputed Data Sets
- Multiple Imputation Efficiency
- Number of Imputations
- Imputer’s Model Versus Analyst’s Model
- Parameter Simulation versus Multiple Imputation
- Sensitivity Analysis for the MAR Assumption
- Multiple Imputation with Pattern-Mixture Models
- Specifying Sets of Observations for Imputation in Pattern-Mixture Models
- Adjusting Imputed Values in Pattern-Mixture Models
- Summary of Issues in Multiple Imputation
- Plot Options Superseded by ODS Graphics
- ODS Table Names
- ODS Graphics
- Examples: MI Procedure
- EM Algorithm for MLE
- Monotone Propensity Score Method
- Monotone Regression Method
- Monotone Logistic Regression Method for CLASS Variables
- Monotone Discriminant Function Method for CLASS Variables
- FCS Methods for Continuous Variables
- FCS Method for CLASS Variables
- FCS Method with Trace Plot
- MCMC Method
- Producing Monotone Missingness with MCMC
- Checking Convergence in MCMC
- Saving and Using Parameters for MCMC
- Transforming to Normality
- Multistage Imputation
- Creating Control-Based Pattern Imputation in Sensitivity Analysis
- Adjusting Imputed Continuous Values in Sensitivity Analysis
- Adjusting Imputed Classification Levels in Sensitivity Analysis
- Adjusting Imputed Values with Parameters in a Data Set
- References
- The MIANALYZE Procedure
- Overview: MIANALYZE Procedure
- Getting Started: MIANALYZE Procedure
- Syntax: MIANALYZE Procedure
- PROC MIANALYZE Statement
- BY Statement
- CLASS Statement
- MODELEFFECTS Statement
- STDERR Statement
- TEST Statement
- Details: MIANALYZE Procedure
- Input Data Sets
- Combining Inferences from Imputed Data Sets
- Multiple Imputation Efficiency
- Multivariate Inferences
- Testing Linear Hypotheses about the Parameters
- Examples of the Complete-Data Inferences
- ODS Table Names
- Examples: MIANALYZE Procedure
- Reading Means and Standard Errors from a DATA= Data Set
- Reading Means and Covariance Matrices from a DATA= COV Data Set
- Reading Regression Results from a DATA= EST Data Set
- Reading Mixed Model Results from PARMS= and COVB= Data Sets
- Reading Generalized Linear Model Results
- Reading GLM Results from PARMS= and XPXI= Data Sets
- Reading Logistic Model Results from a PARMS= Data Set
- Reading Mixed Model Results with Classification Covariates
- Reading Nominal Logistic Model Results
- Using a TEST statement
- Combining Correlation Coefficients
- Sensitivity Analysis with Control-Based Pattern Imputation
- Sensitivity Analysis with the Tipping-Point Approach
- References
- The MIXED Procedure
- Overview: MIXED Procedure
- Getting Started: MIXED Procedure
- Syntax: MIXED Procedure
- PROC MIXED Statement
- BY Statement
- CLASS Statement
- CODE Statement
- CONTRAST Statement
- ESTIMATE Statement
- ID Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- PARMS Statement
- PRIOR Statement
- RANDOM Statement
- REPEATED Statement
- SLICE Statement
- STORE Statement
- WEIGHT Statement
- Details: MIXED Procedure
- Mixed Models Theory
- Parameterization of Mixed Models
- Residuals and Influence Diagnostics
- Default Output
- ODS Table Names
- ODS Graphics
- Computational Issues
- Examples: MIXED Procedure
- Split-Plot Design
- Repeated Measures
- Plotting the Likelihood
- Known G and R
- Random Coefficients
- Line-Source Sprinkler Irrigation
- Influence in Heterogeneous Variance Model
- Influence Analysis for Repeated Measures Data
- Examining Individual Test Components
- Isotonic Contrasts for Ordered Mean Values
- References
- The MODECLUS Procedure
- Overview: MODECLUS Procedure
- Getting Started: MODECLUS Procedure
- Syntax: MODECLUS Procedure
- Details: MODECLUS Procedure
- Density Estimation
- Clustering Methods
- Significance Tests
- Computational Resources
- Missing Values
- Output Data Sets
- Displayed Output
- ODS Table Names
- Examples: MODECLUS Procedure
- Cluster Analysis of Samples from Univariate Distributions
- Cluster Analysis of Flying Mileages between Ten American Cities
- Cluster Analysis with Significance Tests
- Cluster Analysis: Hertzsprung-Russell Plot
- Using the TRACE Option When METHOD=6
- References
- The MULTTEST Procedure
- Overview: MULTTEST Procedure
- Getting Started: MULTTEST Procedure
- Syntax: MULTTEST Procedure
- PROC MULTTEST Statement
- BY Statement
- CLASS Statement
- CONTRAST Statement
- FREQ Statement
- ID Statement
- STRATA Statement
- TEST Statement
- Details: MULTTEST Procedure
- Statistical Tests
- p-Value Adjustments
- Missing Values
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: MULTTEST Procedure
- Cochran-Armitage Test with Permutation Resampling
- Freeman-Tukey and t Tests with Bootstrap Resampling
- Peto Mortality-Prevalence Test
- Fisher Test with Permutation Resampling
- Inputting Raw p-Values
- Adaptive Adjustments and ODS Graphics
- References
- The NESTED Procedure
- Overview: NESTED Procedure
- Getting Started: NESTED Procedure
- Syntax: NESTED Procedure
- Details: NESTED Procedure
- Missing Values
- Unbalanced Data
- General Random-Effects Model
- Analysis of Covariation
- Error Terms in F Tests
- Computational Method
- Displayed Output
- ODS Table Names
- Example: NESTED Procedure
- References
- The NLIN Procedure
- Overview: NLIN Procedure
- Getting Started: NLIN Procedure
- Nonlinear or Linear Model
- Notation for Nonlinear Regression Models
- Estimating the Parameters in the Nonlinear Model
- Syntax: NLIN Procedure
- PROC NLIN Statement
- BOOTSTRAP Statement
- BOUNDS Statement
- BY Statement
- CONTROL Statement
- DER Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- PARAMETERS Statement
- PROFILE Statement
- RETAIN Statement
- Other Programming Statements
- Details: NLIN Procedure
- Automatic Derivatives
- Measures of Nonlinearity, Diagnostics and Inference
- Missing Values
- Special Variables
- Troubleshooting
- Computational Methods
- Output Data Sets
- Confidence Intervals
- Covariance Matrix of Parameter Estimates
- Convergence Measures
- Displayed Output
- Incompatibilities with SAS 6.11 and Earlier Versions of PROC NLIN
- ODS Table Names
- ODS Graphics
- Examples: NLIN Procedure
- Segmented Model
- Iteratively Reweighted Least Squares
- Probit Model with Likelihood Function
- Affecting Curvature through Parameterization
- Comparing Nonlinear Trends among Groups
- ODS Graphics and Diagnostics
- Parameter Profiling and Bootstrapping
- References
- The NLMIXED Procedure
- Overview: NLMIXED Procedure
- Introduction
- Literature on Nonlinear Mixed Models
- PROC NLMIXED Compared with Other SAS Procedures and Macros
- Getting Started: NLMIXED Procedure
- Syntax: NLMIXED Procedure
- PROC NLMIXED Statement
- ARRAY Statement
- BOUNDS Statement
- BY Statement
- CONTRAST Statement
- ESTIMATE Statement
- ID Statement
- MODEL Statement
- PARMS Statement
- PREDICT Statement
- RANDOM Statement
- REPLICATE Statement
- Programming Statements
- Details: NLMIXED Procedure
- Modeling Assumptions and Notation
- Integral Approximations
- Built-in Log-Likelihood Functions
- Hierarchical Model Specification
- Optimization Algorithms
- Finite-Difference Approximations of Derivatives
- Hessian Scaling
- Active Set Methods
- Line-Search Methods
- Restricting the Step Length
- Computational Problems
- Covariance Matrix
- Prediction
- Computational Resources
- Displayed Output
- ODS Table Names
- Examples: NLMIXED Procedure
- One-Compartment Model with Pharmacokinetic Data
- Probit-Normal Model with Binomial Data
- Probit-Normal Model with Ordinal Data
- Poisson-Normal Model with Count Data
- Failure Time and Frailty Model
- Simulated Nested Linear Random-Effects Model
- Overdispersion Hierarchical Nonlinear Mixed Model
- References
- The NPAR1WAY Procedure
- Overview: NPAR1WAY Procedure
- Getting Started: NPAR1WAY Procedure
- Syntax: NPAR1WAY Procedure
- PROC NPAR1WAY Statement
- BY Statement
- CLASS Statement
- EXACT Statement
- FREQ Statement
- OUTPUT Statement
- STRATA Statement
- VAR Statement
- Details: NPAR1WAY Procedure
- Missing Values
- Tied Values
- Statistical Computations
- Simple Linear Rank Tests for Two-Sample Data
- One-Way ANOVA Tests
- Scores for Linear Rank and One-Way ANOVA Tests
- Stratified Analysis
- Hodges-Lehmann Estimation of Location Shift
- Fligner-Policello Test
- Multiple Comparisons Based on Pairwise Rankings
- Tests Based on the Empirical Distribution Function
- Exact Tests
- Contents of the Output Data Set
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: NPAR1WAY Procedure
- Two-Sample Location Tests and Plots
- EDF Statistics and EDF Plot
- Exact Wilcoxon Two-Sample Test
- Hodges-Lehmann Estimation
- Exact Savage Multisample Test
- References
- The ORTHOREG Procedure
- Overview: ORTHOREG Procedure
- Getting Started: ORTHOREG Procedure
- Syntax: ORTHOREG Procedure
- PROC ORTHOREG Statement
- BY Statement
- CLASS Statement
- EFFECT Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- SLICE Statement
- STORE Statement
- TEST Statement
- WEIGHT Statement
- Details: ORTHOREG Procedure
- Examples: ORTHOREG Procedure
- References
- The PHREG Procedure
- Overview: PHREG Procedure
- Getting Started: PHREG Procedure
- Syntax: PHREG Procedure
- PROC PHREG Statement
- ASSESS Statement
- BASELINE Statement
- BAYES Statement
- BY Statement
- CLASS Statement
- CONTRAST Statement
- EFFECT Statement
- ESTIMATE Statement
- FREQ Statement
- HAZARDRATIO Statement
- ID Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- Programming Statements
- RANDOM Statement
- STRATA Statement
- SLICE Statement
- STORE Statement
- TEST Statement
- WEIGHT Statement
- Details: PHREG Procedure
- Failure Time Distribution
- Time and CLASS Variables Usage
- Partial Likelihood Function for the Cox Model
- Counting Process Style of Input
- Left-Truncation of Failure Times
- The Multiplicative Hazards Model
- Proportional Rates/Means Models for Recurrent Events
- The Frailty Model
- Proportional Subdistribution Hazards Model for Competing-Risks Data
- Hazard Ratios
- Newton-Raphson Method
- Firth’s Modification for Maximum Likelihood Estimation
- Robust Sandwich Variance Estimate
- Testing the Global Null Hypothesis
- Type 3 Tests and Joint Tests
- Confidence Limits for a Hazard Ratio
- Using the TEST Statement to Test Linear Hypotheses
- Analysis of Multivariate Failure Time Data
- Model Fit Statistics
- Schemper-Henderson Predictive Measure
- Residuals
- Diagnostics Based on Weighted Residuals
- Influence of Observations on Overall Fit of the Model
- Survivor Function Estimators
- Caution about Using Survival Data with Left Truncation
- Effect Selection Methods
- Assessment of the Proportional Hazards Model
- The Penalized Partial Likelihood Approach for Fitting Frailty Models
- Specifics for Bayesian Analysis
- Computational Resources
- Input and Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: PHREG Procedure
- Stepwise Regression
- Best Subset Selection
- Modeling with Categorical Predictors
- Firth’s Correction for Monotone Likelihood
- Conditional Logistic Regression for m:n Matching
- Model Using Time-Dependent Explanatory Variables
- Time-Dependent Repeated Measurements of a Covariate
- Survival Curves
- Analysis of Residuals
- Analysis of Recurrent Events Data
- Analysis of Clustered Data
- Model Assessment Using Cumulative Sums of Martingale Residuals
- Bayesian Analysis of the Cox Model
- Bayesian Analysis of Piecewise Exponential Model
- Analysis of Competing-Risks Data
- References
- The PLAN Procedure
- Overview: PLAN Procedure
- Getting Started: PLAN Procedure
- Syntax: PLAN Procedure
- Details: PLAN Procedure
- Using PROC PLAN Interactively
- Output Data Sets
- Specifying Factor Structures
- Randomizing Designs
- Displayed Output
- ODS Table Names
- Examples: PLAN Procedure
- A Split-Plot Design
- A Hierarchical Design
- An Incomplete Block Design
- A Latin Square Design
- A Generalized Cyclic Incomplete Block Design
- Permutations and Combinations
- Crossover Designs
- References
- The PLM Procedure
- Overview: PLM Procedure
- Getting Started: PLM Procedure
- Syntax: PLM Procedure
- PROC PLM Statement
- CODE Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- FILTER Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- SCORE Statement
- SHOW Statement
- SLICE Statement
- TEST Statement
- WHERE Statement
- Details: PLM Procedure
- BY Processing and the PLM Procedure
- Analysis Based on Posterior Estimates
- Scoring Data Sets for Zero-Inflated Models
- User-Defined Formats and the PLM Procedure
- ODS Table Names
- ODS Graphics
- Examples: PLM Procedure
- Scoring with PROC PLM
- Working with Item Stores
- Group Comparisons in an Ordinal Model
- Posterior Inference for Binomial Data
- BY-Group Processing
- Comparing Multiple B-Splines
- Linear Inference with Arbitrary Estimates
- References
- The PLS Procedure
- Overview: PLS Procedure
- Getting Started: PLS Procedure
- Syntax: PLS Procedure
- PROC PLS Statement
- BY Statement
- CLASS Statement
- EFFECT Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- Details: PLS Procedure
- Regression Methods
- Cross Validation
- Centering and Scaling
- Missing Values
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: PLS Procedure
- Examining Model Details
- Examining Outliers
- Choosing a PLS Model by Test Set Validation
- Partial Least Squares Spline Smoothing
- References
- The POWER Procedure
- Overview: POWER Procedure
- Getting Started: POWER Procedure
- Syntax: POWER Procedure
- PROC POWER Statement
- COXREG Statement
- LOGISTIC Statement
- MULTREG Statement
- ONECORR Statement
- ONESAMPLEFREQ Statement
- ONESAMPLEMEANS Statement
- ONEWAYANOVA Statement
- PAIREDFREQ Statement
- PAIREDMEANS Statement
- PLOT Statement
- TWOSAMPLEFREQ Statement
- TWOSAMPLEMEANS Statement
- TWOSAMPLESURVIVAL Statement
- TWOSAMPLEWILCOXON Statement
- Details: POWER Procedure
- Overview of Power Concepts
- Summary of Analyses
- Specifying Value Lists in Analysis Statements
- Sample Size Adjustment Options
- Error and Information Output
- Displayed Output
- ODS Table Names
- Computational Resources
- Computational Methods and Formulas
- Common Notation
- Analyses in the COXREG Statement
- Analyses in the LOGISTIC Statement
- Analyses in the MULTREG Statement
- Analyses in the ONECORR Statement
- Analyses in the ONESAMPLEFREQ Statement
- Analyses in the ONESAMPLEMEANS Statement
- Analyses in the ONEWAYANOVA Statement
- Analyses in the PAIREDFREQ Statement
- Analyses in the PAIREDMEANS Statement
- Analyses in the TWOSAMPLEFREQ Statement
- Analyses in the TWOSAMPLEMEANS Statement
- Analyses in the TWOSAMPLESURVIVAL Statement
- Analyses in the TWOSAMPLEWILCOXON Statement
- ODS Graphics
- Examples: POWER Procedure
- One-Way ANOVA
- The Sawtooth Power Function in Proportion Analyses
- Simple AB/BA Crossover Designs
- Noninferiority Test with Lognormal Data
- Multiple Regression and Correlation
- Comparing Two Survival Curves
- Confidence Interval Precision
- Customizing Plots
- Assigning Analysis Parameters to Axes
- Fine-Tuning a Sample Size Axis
- Adding Reference Lines
- Linking Plot Features to Analysis Parameters
- Choosing Key (Legend) Styles
- Modifying Symbol Locations
- Binary Logistic Regression with Independent Predictors
- Wilcoxon-Mann-Whitney Test
- References
- The Power and Sample Size Application
- Overview: PSS Application
- Getting Started: PSS Application
- How to Use: PSS Application
- Overview
- SAS Connections
- Setting Preferences
- Creating and Editing PSS Projects
- Importing and Exporting Projects
- Details: PSS Application
- Example: Two-Sample t Test
- Overview
- Test of Two Independent Means for Equal Variances
- Test of Two Independent Means for Unequal Variances
- Test of Mean Ratios
- Additional Topics
- Example: Analysis of Variance
- Example: Two-Sample Survival Rank Tests
- The PRINCOMP Procedure
- Overview: PRINCOMP Procedure
- Getting Started: PRINCOMP Procedure
- Syntax: PRINCOMP Procedure
- PROC PRINCOMP Statement
- BY Statement
- FREQ Statement
- ID Statement
- PARTIAL Statement
- VAR Statement
- WEIGHT Statement
- Details: PRINCOMP Procedure
- Examples: PRINCOMP Procedure
- Analyzing Mean Temperatures of US Cities
- Analyzing Rankings of US College Basketball Teams
- Analyzing Job Ratings of Police Officers
- References
- The PRINQUAL Procedure
- Overview: PRINQUAL Procedure
- Getting Started: PRINQUAL Procedure
- Syntax: PRINQUAL Procedure
- Details: PRINQUAL Procedure
- The Three Methods of Variable Transformation
- Understanding How PROC PRINQUAL Works
- Splines
- Missing Values
- Controlling the Number of Iterations
- Performing a Principal Component Analysis of Transformed Data
- Using the MAC Method
- Output Data Set
- Avoiding Constant Transformations
- Constant Variables
- Character OPSCORE Variables
- REITERATE Option Usage
- Passive Observations
- Computational Resources
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: PRINQUAL Procedure
- References
- The PROBIT Procedure
- Overview: PROBIT Procedure
- Getting Started: PROBIT Procedure
- Syntax: PROBIT Procedure
- PROC PROBIT Statement
- BY Statement
- CDFPLOT Statement
- CLASS Statement
- EFFECTPLOT Statement
- ESTIMATE Statement
- INSET Statement
- IPPPLOT Statement
- LPREDPLOT Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- PREDPPLOT Statement
- SLICE Statement
- STORE Statement
- TEST Statement
- WEIGHT Statement
- Details: PROBIT Procedure
- Missing Values
- Response Level Ordering
- Computational Method
- Distributions
- INEST= SAS-data-set
- Model Specification
- Lack-of-Fit Tests
- Rescaling the Covariance Matrix
- Tolerance Distribution
- Inverse Confidence Limits
- OUTEST= SAS-data-set
- XDATA= SAS-data-set
- Traditional High-Resolution Graphics
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: PROBIT Procedure
- Dosage Levels
- Multilevel Response
- Logistic Regression and Scoring New Data
- An Epidemiology Study
- Model Postfitting Analysis
- References
- The QUANTLIFE Procedure
- Overview: QUANTLIFE Procedure
- Getting Started: QUANTLIFE Procedure
- Syntax: QUANTLIFE Procedure
- PROC QUANTLIFE Statement
- BASELINE Statement
- BY Statement
- CLASS Statement
- EFFECT Statement
- MODEL Statement
- OUTPUT Statement
- TEST Statement
- WEIGHT Statement
- Details: QUANTLIFE Procedure
- Notation for Censored Quantile Regression
- Kaplan-Meier-Type Estimator for Censored Quantile Regression
- Nelson-Aalen-Type Estimator for Censored Quantile Regression
- Relationship of Quantile Function and Survival Function
- Confidence Interval
- Output Data Sets
- ODS Table Names
- ODS Graphics
- Examples: QUANTLIFE Procedure
- References
- The QUANTREG Procedure
- Overview: QUANTREG Procedure
- Getting Started: QUANTREG Procedure
- Syntax: QUANTREG Procedure
- PROC QUANTREG Statement
- BY Statement
- CLASS Statement
- EFFECT Statement
- ESTIMATE Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- PERFORMANCE Statement
- TEST Statement
- WEIGHT Statement
- Details: QUANTREG Procedure
- Quantile Regression as an Optimization Problem
- Optimization Algorithms
- Confidence Interval
- Covariance-Correlation
- Linear Test
- Leverage Point and Outlier Detection
- INEST= Data Set
- OUTEST= Data Set
- Computational Resources
- ODS Table Names
- ODS Graphics
- Examples: QUANTREG Procedure
- Comparison of Algorithms
- Quantile Regression for Econometric Growth Data
- Quantile Regression Analysis of Birth-Weight Data
- Nonparametric Quantile Regression for Ozone Levels
- Quantile Polynomial Regression for Salary Data
- References
- The QUANTSELECT Procedure
- Overview: QUANTSELECT Procedure
- Getting Started: QUANTSELECT Procedure
- Syntax: QUANTSELECT Procedure
- PROC QUANTSELECT Statement
- BY Statement
- CLASS Statement
- CODE Statement
- EFFECT Statement
- MODEL Statement
- OUTPUT Statement
- PARTITION Statement
- WEIGHT Statement
- Details: QUANTSELECT Procedure
- Quantile Regression
- Quasi-Likelihood Information Criteria
- Quasi-Likelihood Ratio Tests
- Quantile Process Regression
- Observation Quantile Level
- Quantile Regression for Extremal Quantile Levels
- Effect Selection Methods
- Full Model Fitted (NONE)
- Forward Selection (FORWARD)
- Backward Elimination (BACKWARD)
- Stepwise Selection (STEPWISE)
- LASSO Method (LASSO)
- Criteria Used in Model Selection Methods
- Macro Variables That Contain Selected Models
- Using Validation and Test Data
- Displayed Output
- ODS Table Names
- ODS Graphics
- Example: QUANTSELECT Procedure
- Simulation Study
- Econometric Growth Data
- Pollution and Mortality
- Surface Fitting with Many Noisy Variables
- Quantile Process Regression
- References
- The REG Procedure
- Overview: REG Procedure
- Getting Started: REG Procedure
- Syntax: REG Procedure
- PROC REG Statement
- ADD Statement
- BY Statement
- CODE Statement
- DELETE Statement
- FREQ Statement
- ID Statement
- MODEL Statement
- MTEST Statement
- OUTPUT Statement
- PRINT Statement
- REFIT Statement
- RESTRICT Statement
- REWEIGHT Statement
- STORE Statement
- TEST Statement
- VAR Statement
- WEIGHT Statement
- Details: REG Procedure
- Missing Values
- Input Data Sets
- Output Data Sets
- Interactive Analysis
- Model-Selection Methods
- Criteria Used in Model-Selection Methods
- Limitations in Model-Selection Methods
- Parameter Estimates and Associated Statistics
- Predicted and Residual Values
- Models of Less Than Full Rank
- Collinearity Diagnostics
- Model Fit and Diagnostic Statistics
- Influence Statistics
- Reweighting Observations in an Analysis
- Testing for Heteroscedasticity
- Testing for Lack of Fit
- Multivariate Tests
- Autocorrelation in Time Series Data
- Computations for Ridge Regression and IPC Analysis
- Construction of Q-Q and P-P Plots
- Computational Methods
- Computer Resources in Regression Analysis
- Displayed Output
- Plot Options Superseded by ODS Graphics
- ODS Table Names
- ODS Graphics
- Examples: REG Procedure
- Modeling Salaries of Major League Baseball Players
- Aerobic Fitness Prediction
- Predicting Weight by Height and Age
- Regression with Quantitative and Qualitative Variables
- Ridge Regression for Acetylene Data
- Chemical Reaction Response
- References
- The ROBUSTREG Procedure
- Overview: ROBUSTREG Procedure
- Getting Started: ROBUSTREG Procedure
- Syntax: ROBUSTREG Procedure
- PROC ROBUSTREG Statement
- BY Statement
- CLASS Statement
- EFFECT Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- PERFORMANCE Statement
- TEST Statement
- WEIGHT Statement
- Details: ROBUSTREG Procedure
- M Estimation
- High Breakdown Value Estimation
- MM Estimation
- Robust Distance
- Leverage-Point and Outlier Detection
- Implementation of the WEIGHT Statement
- INEST= Data Set
- OUTEST= Data Set
- Computational Resources
- ODS Table Names
- ODS Graphics
- Examples: ROBUSTREG Procedure
- Comparison of Robust Estimates
- Robust ANOVA
- Growth Study of De Long and Summers
- Constructed Effects
- Robust Diagnostics
- References
- The RSREG Procedure
- Overview: RSREG Procedure
- Getting Started: RSREG Procedure
- Syntax: RSREG Procedure
- Details: RSREG Procedure
- Introduction to Response Surface Experiments
- Coding the Factor Variables
- Missing Values
- Plotting the Surface
- Searching for Multiple Response Conditions
- Handling Covariates
- Computational Method
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: RSREG Procedure
- References
- The SCORE Procedure
- Overview: SCORE Procedure
- Getting Started: SCORE Procedure
- Syntax: SCORE Procedure
- Details: SCORE Procedure
- Examples: SCORE Procedure
- References
- The SEQDESIGN Procedure
- Overview: SEQDESIGN Procedure
- Features of the SEQDESIGN Procedure
- Output from the SEQDESIGN Procedure
- Boundaries for Group Sequential Designs
- Group Sequential Methods
- Getting Started: SEQDESIGN Procedure
- Syntax: SEQDESIGN Procedure
- Details: SEQDESIGN Procedure
- Fixed-Sample Clinical Trials
- One-Sided Fixed-Sample Tests in Clinical Trials
- Two-Sided Fixed-Sample Tests in Clinical Trials
- Group Sequential Methods
- Statistical Assumptions for Group Sequential Designs
- Boundary Scales
- Boundary Variables
- Type I and Type II Errors
- Unified Family Methods
- Haybittle-Peto Method
- Whitehead Methods
- Error Spending Methods
- Acceptance (beta) Boundary
- Boundary Adjustments for Overlapping Lower and Upper beta Boundaries
- Specified and Derived Parameters
- Applicable Boundary Keys
- Sample Size Computation
- Applicable One-Sample Tests and Sample Size Computation
- Applicable Two-Sample Tests and Sample Size Computation
- Applicable Regression Parameter Tests and Sample Size Computation
- Aspects of Group Sequential Designs
- Summary of Methods in Group Sequential Designs
- Table Output
- ODS Table Names
- Graphics Output
- ODS Graphics
- Examples: SEQDESIGN Procedure
- Creating Fixed-Sample Designs
- Creating a One-Sided O’Brien-Fleming Design
- Creating Two-Sided Pocock and O’Brien-Fleming Designs
- Generating Graphics Display for Sequential Designs
- Creating Designs Using Haybittle-Peto Methods
- Creating Designs with Various Stopping Criteria
- Creating Whitehead’s Triangular Designs
- Creating a One-Sided Error Spending Design
- Creating Designs with Various Number of Stages
- Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper beta Boundaries
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject H0
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept H0
- Creating a Design with a Nonbinding Beta Boundary
- Computing Sample Size for Survival Data That Have Uniform Accrual
- Computing Sample Size for Survival Data with Truncated Exponential Accrual
- References
- The SEQTEST Procedure
- Overview: SEQTEST Procedure
- Getting Started: SEQTEST Procedure
- Syntax: SEQTEST Procedure
- Details: SEQTEST Procedure
- Input Data Sets
- Boundary Variables
- Information Level Adjustments at Future Stages
- Boundary Adjustments for Information Levels
- Boundary Adjustments for Minimum Error Spending
- Boundary Adjustments for Overlapping Lower and Upper beta Boundaries
- Stochastic Curtailment
- Repeated Confidence Intervals
- Analysis after a Sequential Test
- Available Sample Space Orderings in a Sequential Test
- Applicable Tests and Sample Size Computation
- Table Output
- ODS Table Names
- Graphics Output
- ODS Graphics
- Examples: SEQTEST Procedure
- Testing the Difference between Two Proportions
- Testing an Effect in a Regression Model
- Testing an Effect with Early Stopping to Accept H0
- Testing a Binomial Proportion
- Comparing Two Proportions with a Log Odds Ratio Test
- Comparing Two Survival Distributions with a Log-Rank Test
- Testing an Effect in a Proportional Hazards Regression Model
- Testing an Effect in a Logistic Regression Model
- Conducting a Trial with a Nonbinding Acceptance Boundary
- References
- The SIM2D Procedure
- Overview: SIM2D Procedure
- Getting Started: SIM2D Procedure
- Syntax: SIM2D Procedure
- PROC SIM2D Statement
- BY Statement
- COORDINATES Statement
- GRID Statement
- ID Statement
- RESTORE Statement
- SIMULATE Statement
- MEAN Statement
- Details: SIM2D Procedure
- Computational and Theoretical Details of Spatial Simulation
- Output Data Set
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SIM2D Procedure
- References
- The SIMNORMAL Procedure
- Overview: SIMNORMAL Procedure
- Getting Started: SIMNORMAL Procedure
- Syntax: SIMNORMAL Procedure
- Details: SIMNORMAL Procedure
- Example: SIMNORM Procedure
- References
- The SPP Procedure
- Overview: SPP Procedure
- Getting Started: SPP Procedure
- Syntax: SPP Procedure
- PROC SPP Statement
- BY Statement
- COVTEST Statement
- MODEL Statement
- NLOPTIONS Statement
- PARMS Statement
- PROCESS Statement
- TREND Statement
- Details: SPP Procedure
- Testing for Complete Spatial Randomness
- Exploring Interpoint Interaction
- Distance Functions for Multitype Point Patterns
- Border Edge Correction for Distance Functions
- Confidence Intervals for Summary Statistics
- Ripley-Rasson Window Estimator
- Covariate Dependence Tests
- Nonparametric Intensity Estimation
- Inhomogeneous Poisson Process Model Fitting
- Likelihood Methods for Model Fitting
- Fit Statistics
- Fitted Model Validation That Uses Goodness-of-Fit Tests
- Fitted Model Validation That Uses Residuals
- Negative Binomial Modeling
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SPP Procedure
- Exploration of a Multitype Point Pattern
- Testing Covariate Dependence of a Point Pattern
- Intensity Model Validation Diagnostics
- References
- The STDIZE Procedure
- Overview: STDIZE Procedure
- Getting Started: STDIZE Procedure
- Syntax: STDIZE Procedure
- PROC STDIZE Statement
- BY Statement
- FREQ Statement
- LOCATION Statement
- SCALE Statement
- VAR Statement
- WEIGHT Statement
- Details: STDIZE Procedure
- Standardization Methods
- Computation of the Statistics
- Computing Quantiles
- Constant Data
- Missing Values
- Output Data Sets
- Displayed Output
- ODS Table Names
- Example: STDIZE Procedure
- References
- The STDRATE Procedure
- Overview: STDRATE Procedure
- Getting Started: STDRATE Procedure
- Syntax: STDRATE Procedure
- Details: STDRATE Procedure
- Rate
- Risk
- Direct Standardization
- Mantel-Haenszel Effect Estimation
- Indirect Standardization and Standardized Morbidity/Mortality Ratio
- Attributable Fraction and Population Attributable Fraction
- Applicable Data Sets and Required Variables for Method Specifications
- Applicable Confidence Limits for Rate and Risk Statistics
- Table Output
- ODS Table Names
- Graphics Output
- ODS Graphics
- Examples: STDRATE Procedure
- Comparing Directly Standardized Rates
- Computing Mantel-Haenszel Risk Estimation
- Computing Attributable Fraction Estimates
- Displaying SMR Results from BY Groups
- References
- The STEPDISC Procedure
- Overview: STEPDISC Procedure
- Getting Started: STEPDISC Procedure
- Syntax: STEPDISC Procedure
- Details: STEPDISC Procedure
- Example: STEPDISC Procedure
- References
- The SURVEYFREQ Procedure
- Overview: SURVEYFREQ Procedure
- Getting Started: SURVEYFREQ Procedure
- Syntax: SURVEYFREQ Procedure
- PROC SURVEYFREQ Statement
- BY Statement
- CLUSTER Statement
- REPWEIGHTS Statement
- STRATA Statement
- TABLES Statement
- WEIGHT Statement
- Details: SURVEYFREQ Procedure
- Specifying the Sample Design
- Domain Analysis
- Missing Values
- Statistical Computations
- Variance Estimation
- Definitions and Notation
- Totals
- Covariances of Frequency Estimates
- Proportions
- Row and Column Proportions
- Balanced Repeated Replication (BRR)
- The Jackknife Method
- Confidence Limits for Totals
- Confidence Limits for Proportions
- Degrees of Freedom
- Coefficient of Variation
- Design Effect
- Expected Weighted Frequency
- Risks and Risk Difference
- Odds Ratio and Relative Risks
- Kappa Coefficients
- Rao-Scott Chi-Square Test
- Rao-Scott Likelihood Ratio Chi-Square Test
- Wald Chi-Square Test
- Wald Log-Linear Chi-Square Test
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SURVEYFREQ Procedure
- References
- The SURVEYIMPUTE Procedure
- Overview: SURVEYIMPUTE Procedure
- Getting Started: SURVEYIMPUTE Procedure
- Syntax: SURVEYIMPUTE Procedure
- PROC SURVEYIMPUTE Statement
- BY Statement
- CELLS Statement
- CLASS Statement
- CLUSTER Statement
- ID Statement
- IMPJOINT Statement
- OUTPUT Statement
- REPWEIGHTS Statement
- STRATA Statement
- VAR Statement
- WEIGHT Statement
- Details: SURVEYIMPUTE Procedure
- Specifying the Sample Design
- Missing Values
- Missing Data Patterns
- Random Number Generation
- Fully Efficient Fractional Imputation
- Hot-Deck Imputation
- Replication Variance Estimation
- Output Data Sets
- Displayed Output
- ODS Table Names
- Examples: SURVEYIMPUTE Procedure
- Approximate Bayesian Bootstrap Imputation
- Fully Efficient Fractional Imputation
- Fully Efficient Fractional Imputation, Fay’s Balanced Repeated Replication, and Domain Analysis
- References
- The SURVEYLOGISTIC Procedure
- Overview: SURVEYLOGISTIC Procedure
- Getting Started: SURVEYLOGISTIC Procedure
- Syntax: SURVEYLOGISTIC Procedure
- PROC SURVEYLOGISTIC Statement
- BY Statement
- CLASS Statement
- CLUSTER Statement
- CONTRAST Statement
- DOMAIN Statement
- EFFECT Statement
- ESTIMATE Statement
- FREQ Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- REPWEIGHTS Statement
- SLICE Statement
- STORE Statement
- STRATA Statement
- TEST Statement
- UNITS Statement
- WEIGHT Statement
- Details: SURVEYLOGISTIC Procedure
- Missing Values
- Model Specification
- Model Fitting
- Survey Design Information
- Logistic Regression Models and Parameters
- Variance Estimation
- Domain Analysis
- Hypothesis Testing and Estimation
- Linear Predictor, Predicted Probability, and Confidence Limits
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SURVEYLOGISTIC Procedure
- References
- The SURVEYMEANS Procedure
- Overview: SURVEYMEANS Procedure
- Getting Started: SURVEYMEANS Procedure
- Syntax: SURVEYMEANS Procedure
- PROC SURVEYMEANS Statement
- BY Statement
- CLASS Statement
- CLUSTER Statement
- DOMAIN Statement
- POSTSTRATA Statement
- RATIO Statement
- REPWEIGHTS Statement
- STRATA Statement
- VAR Statement
- WEIGHT Statement
- Details: SURVEYMEANS Procedure
- Missing Values
- Survey Data Analysis
- Statistical Computations
- Replication Methods for Variance Estimation
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SURVEYMEANS Procedure
- Stratified Cluster Sample Design
- Domain Analysis
- Ratio Analysis
- Analyzing Survey Data with Missing Values
- Variance Estimation Using Replication Methods
- References
- The SURVEYPHREG Procedure
- Overview: SURVEYPHREG Procedure
- Getting Started: SURVEYPHREG Procedure
- Syntax: SURVEYPHREG Procedure
- PROC SURVEYPHREG Statement
- BY Statement
- CLASS Statement
- CLUSTER Statement
- DOMAIN Statement
- ESTIMATE Statement
- FREQ Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- NLOPTIONS Statement
- OUTPUT Statement
- Programming Statements
- REPWEIGHTS Statement
- SLICE Statement
- STORE Statement
- STRATA Statement
- TEST Statement
- WEIGHT Statement
- Details: SURVEYPHREG Procedure
- Notation and Estimation
- Failure Time Distribution
- Time and CLASS Variables Usage
- Partial Likelihood Function for the Cox Model
- Specifying the Sample Design
- Missing Values
- Variance Estimation
- Taylor Series Linearization
- Balanced Repeated Replication (BRR) Method
- Jackknife Method
- Degrees of Freedom
- Variance Adjustment Factors
- Variance Ratios and Standard Error Ratios
- Domain Analysis
- Hypothesis Tests, Confidence Intervals, and Residuals
- Testing the Global Null Hypothesis
- Model Fit Statistics
- Contrasts
- Confidence Intervals
- Hazard Ratios
- Residuals
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SURVEYPHREG Procedure
- Analysis of Clustered Data
- Stratification, Clustering, and Unequal Weights
- Domain Analysis
- Variance Estimation by Using Replicate Weights
- A Test of the Proportional Hazards Assumption by Using the Programming Statements
- References
- The SURVEYREG Procedure
- Overview: SURVEYREG Procedure
- Getting Started: SURVEYREG Procedure
- Syntax: SURVEYREG Procedure
- PROC SURVEYREG Statement
- BY Statement
- CLASS Statement
- CLUSTER Statement
- CONTRAST Statement
- DOMAIN Statement
- EFFECT Statement
- ESTIMATE Statement
- LSMEANS Statement
- LSMESTIMATE Statement
- MODEL Statement
- OUTPUT Statement
- REPWEIGHTS Statement
- SLICE Statement
- STORE Statement
- STRATA Statement
- TEST Statement
- WEIGHT Statement
- Details: SURVEYREG Procedure
- Missing Values
- Survey Design Information
- Computational Details
- Analysis of Variance (ANOVA)
- Multiple R-Square
- Adjusted R-Square
- Root Mean Square Errors
- Variance Estimation
- Testing
- Domain Analysis
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: SURVEYREG Procedure
- Simple Random Sampling
- Cluster Sampling
- Regression Estimator for Simple Random Sample
- Stratified Sampling
- Regression Estimator for Stratified Sample
- Stratum Collapse
- Domain Analysis
- Compare Domain Statistics
- Variance Estimate Using the Jackknife Method
- References
- The SURVEYSELECT Procedure
- Overview: SURVEYSELECT Procedure
- Getting Started: SURVEYSELECT Procedure
- Syntax: SURVEYSELECT Procedure
- PROC SURVEYSELECT Statement
- CONTROL Statement
- FREQ Statement
- ID Statement
- SAMPLINGUNIT | CLUSTER Statement
- SIZE Statement
- STRATA Statement
- Details: SURVEYSELECT Procedure
- Missing Values
- Sorting by CONTROL Variables
- Random Number Generation
- Sample Selection Methods
- Simple Random Sampling
- Unrestricted Random Sampling
- Systematic Random Sampling
- Sequential Random Sampling
- Bernoulli Sampling
- Poisson Sampling
- PPS Sampling without Replacement
- PPS Sampling with Replacement
- PPS Systematic Sampling
- PPS Sequential Sampling
- Brewer’s PPS Method
- Murthy’s PPS Method
- Sampford’s PPS Method
- Sample Size Allocation
- Secondary Input Data Set
- Sample Output Data Set
- Allocation Output Data Set
- Random Assignment Output Data Set
- Displayed Output
- ODS Table Names
- Examples: SURVEYSELECT Procedure
- Replicated Sampling
- PPS Selection of Two Units per Stratum
- PPS (Dollar-Unit) Sampling
- Proportional Allocation
- References
- The TPSPLINE Procedure
- Overview: TPSPLINE Procedure
- Getting Started: TPSPLINE Procedure
- Syntax: TPSPLINE Procedure
- PROC TPSPLINE Statement
- BY Statement
- FREQ Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- SCORE Statement
- Details: TPSPLINE Procedure
- Examples: TPSPLINE Procedure
- Partial Spline Model Fit
- Spline Model with Higher-Order Penalty
- Multiple Minima of the GCV Function
- Large Data Set Application
- Computing a Bootstrap Confidence Interval
- References
- The TRANSREG Procedure
- Overview: TRANSREG Procedure
- Getting Started: TRANSREG Procedure
- Syntax: TRANSREG Procedure
- PROC TRANSREG Statement
- BY Statement
- FREQ Statement
- ID Statement
- MODEL Statement
- OUTPUT Statement
- WEIGHT Statement
- Details: TRANSREG Procedure
- Model Statement Usage
- Box-Cox Transformations
- Using Splines and Knots
- Scoring Spline Variables
- Linear and Nonlinear Regression Functions
- Simultaneously Fitting Two Regression Functions
- Penalized B-Splines
- Smoothing Splines
- Smoothing Splines Changes and Enhancements
- Iteration History Changes and Enhancements
- ANOVA Codings
- Missing Values
- Missing Values, UNTIE, and Hypothesis Tests
- Controlling the Number of Iterations
- Using the REITERATE Algorithm Option
- Avoiding Constant Transformations
- Constant Variables
- Character OPSCORE Variables
- Convergence and Degeneracies
- Implicit and Explicit Intercepts
- Passive Observations
- Point Models
- Redundancy Analysis
- Optimal Scaling
- OPSCORE, MONOTONE, UNTIE, and LINEAR Transformations
- SPLINE and MSPLINE Transformations
- Specifying the Number of Knots
- SPLINE, BSPLINE, and PSPLINE Comparisons
- Hypothesis Tests
- Output Data Set
- OUTTEST= Output Data Set
- Computational Resources
- Unbalanced ANOVA without CLASS Variables
- Hypothesis Tests for Simple Univariate Models
- Hypothesis Tests with Monotonicity Constraints
- Hypothesis Tests with Dependent Variable Transformations
- Hypothesis Tests with One-Way ANOVA
- Using the DESIGN Output Option
- Discrete Choice Experiments: DESIGN, NORESTORE, NOZERO
- Centering
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: TRANSREG Procedure
- Transformation Regression of Exhaust Emissions Data
- Box-Cox Transformations
- Penalized B-Spline
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
- References
- The TREE Procedure
- Overview: TREE Procedure
- Getting Started: TREE Procedure
- Syntax: TREE Procedure
- PROC TREE Statement
- BY Statement
- COPY Statement
- FREQ Statement
- HEIGHT Statement
- ID Statement
- NAME Statement
- PARENT Statement
- Details: TREE Procedure
- Examples: TREE Procedure
- References
- The TTEST Procedure
- Overview: TTEST Procedure
- Getting Started: TTEST Procedure
- Syntax: TTEST Procedure
- PROC TTEST Statement
- BY Statement
- CLASS Statement
- FREQ Statement
- PAIRED Statement
- VAR Statement
- WEIGHT Statement
- Details: TTEST Procedure
- Input Data Set of Statistics
- Missing Values
- Computational Methods
- Common Notation
- Arithmetic and Geometric Means
- Coefficient of Variation
- One-Sample Design
- Paired Design
- Two-Independent-Sample Design
- AB/BA Crossover Design
- TOST Equivalence Test
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: TTEST Procedure
- Using Summary Statistics to Compare Group Means
- One-Sample Comparison with the FREQ Statement
- Paired Comparisons
- AB/BA Crossover Design
- Equivalence Testing with Lognormal Data
- References
- The VARCLUS Procedure
- Overview: VARCLUS Procedure
- Getting Started: VARCLUS Procedure
- Syntax: VARCLUS Procedure
- PROC VARCLUS Statement
- BY Statement
- FREQ Statement
- PARTIAL Statement
- SEED Statement
- VAR Statement
- WEIGHT Statement
- Details: VARCLUS Procedure
- Missing Values
- Using the VARCLUS procedure
- Output Data Sets
- Computational Resources
- Interpreting VARCLUS Procedure Output
- Displayed Output
- ODS Table Names
- ODS Graphics
- Example: VARCLUS Procedure
- References
- The VARCOMP Procedure
- Overview: VARCOMP Procedure
- Getting Started: VARCOMP Procedure
- Syntax: VARCOMP Procedure
- Details: VARCOMP Procedure
- Missing Values
- Fixed and Random Effects
- Negative Variance Component Estimates
- Computational Methods
- Gauge Repeatability and Reproducibility Analysis
- Confidence Limits
- Displayed Output
- ODS Table Names
- Relationship to PROC MIXED
- Examples: VARCOMP Procedure
- References
- The VARIOGRAM Procedure
- Overview: VARIOGRAM Procedure
- Getting Started: VARIOGRAM Procedure
- Preliminary Spatial Data Analysis
- Empirical Semivariogram Computation
- Autocorrelation Analysis
- Theoretical Semivariogram Model Fitting
- Syntax: VARIOGRAM Procedure
- PROC VARIOGRAM Statement
- BY Statement
- COMPUTE Statement
- COORDINATES Statement
- DIRECTIONS Statement
- ID Statement
- MODEL Statement
- PARMS Statement
- NLOPTIONS Statement
- STORE Statement
- VAR Statement
- Details: VARIOGRAM Procedure
- Theoretical Semivariogram Models
- Theoretical and Computational Details of the Semivariogram
- Stationarity
- Ergodicity
- Anisotropy
- Pair Formation
- Angle Classification
- Distance Classification
- Bandwidth Restriction
- Computation of the Distribution Distance Classes
- Semivariance Computation
- Empirical Semivariograms and Surface Trends
- Theoretical Semivariogram Model Fitting
- Autocorrelation Statistics
- Computational Resources
- Output Data Sets
- Displayed Output
- ODS Table Names
- ODS Graphics
- Examples: VARIOGRAM Procedure
- Aspects of Semivariogram Model Fitting
- An Anisotropic Case Study with Surface Trend in the Data
- Analysis without Surface Trend Removal
- Covariogram and Semivariogram
- A Box Plot of the Square Root Difference Cloud
- References
- Special SAS Data Sets
- Introduction to Special SAS Data Sets
- Special SAS Data Sets
- TYPE=ACE Data Sets
- TYPE=BOXPLOT Data Sets
- TYPE=CALISFIT Data Sets
- TYPE=CALISMDL Data Sets
- TYPE=CHARTSUM Data Sets
- TYPE=CORR Data Sets
- TYPE=COV Data Sets
- TYPE=CSSCP Data Sets
- TYPE=DISTANCE Data Sets
- TYPE=EST Data Sets
- TYPE=FACTOR Data Sets
- TYPE=LINEAR Data Sets
- TYPE=LOGISMOD Data Sets
- TYPE=MIXED Data Sets
- TYPE=QUAD Data Sets
- TYPE=SSCP Data Sets
- TYPE=TREE Data Sets
- TYPE=UCORR Data Sets
- TYPE=UCOV Data Sets
- TYPE=WEIGHT Data Sets
- Definitional Formulas
- Sashelp Data Sets
- Overview of Sashelp Data Sets
- Baseball Data
- Tropical Rain Forest Tree Data
- Body Mass Index for Men
- Isopod Burrows Data
- Bone Marrow Transplant Data
- Birth Weight Data
- Class Data
- Comet Data
- El Nino–Southern Oscillation Data
- Finland’s Lake Laengelmaevesi Fish Catch Data
- Exhaust Emissions Data
- Fisher (1936) Iris Data
- Junk E-mail Data
- Leukemia Data Sets
- Margarine Data
- Flying Mileages between 10 US Cities Data
- Coal Seam Thickness Data
- 1980 US Presidential Election Data
- References
- Analysis of Variance
- The ANOVA Procedure
- The CATMOD Procedure
- The GLM Procedure
- The INBREED Procedure
- The LATTICE Procedure
- The NESTED Procedure
- The PLAN Procedure
- The TTEST Procedure
- Bayesian Analysis
- The BCHOICE Procedure
- The FMM Procedure
- The GENMOD Procedure
- The LIFEREG Procedure
- The MCMC Procedure
- The PHREG Procedure
- Categorical Data Analysis
- The CATMOD Procedure
- The FREQ Procedure
- The FMM Procedure
- The GENMOD Procedure
- The LOGISTIC Procedure
- The PROBIT Procedure
- Cluster Analysis
- The ACECLUS Procedure
- The CLUSTER Procedure
- The DISTANCE Procedure
- The FASTCLUS Procedure
- The MODECLUS Procedure
- The TREE Procedure
- The VARCLUS Procedure
- Descriptive Statistics
- Discriminant Analysis
- Distribution Analysis
- Exact Inference
- The FREQ Procedure
- The GENMOD Procedure
- The LOGISTIC Procedure
- The MULTTEST Procedure
- The NPAR1WAY Procedure
- Finite Mixture Models
- Group Sequential Design and Analysis
- Longitudinal Data Analysis
- Market Research
- The BCHOICE Procedure
- The CORRESP Procedure
- The MDS Procedure
- The PHREG Procedure
- The PRINQUAL Procedure
- The TRANSREG Procedure
- Missing Data Analysis
- The CALIS Procedure
- The GEE Procedure
- The MCMC Procedure
- The MI Procedure
- The MIANALYZE Procedure
- The SURVEYIMPUTE Procedure
- Mixed Models
- The GLIMMIX Procedure
- The HPMIXED Procedure
- The MIXED Procedure
- The NLMIXED Procedure
- The PHREG Procedure
- The VARCOMP Procedure
- Model Selection
- Multivariate Analysis
- The CANCORR Procedure
- The CORRESP Procedure
- The FACTOR Procedure
- The MDS Procedure
- The PRINCOMP Procedure
- The PRINQUAL Procedure
- Nonlinear Regression
- Nonparametric Analysis
- Nonparametric Regression
- The ADAPTIVEREG Procedure
- The GAM Procedure
- The GAMPL Procedure
- The LOESS Procedure
- The TPSPLINE Procedure
- Post Processing
- Power and Sample Size
- Predictive Modeling
- The ADAPTIVEREG Procedure
- The GLMSELECT Procedure
- The HPLOGISTIC Procedure
- The HPGENSELECT Procedure
- The HPREG Procedure
- The PLS Procedure
- The TRANSREG Procedure
- Psychometric Analysis
- The CORRESP Procedure
- The FACTOR Procedure
- The IRT Procedure
- The MDS Procedure
- The PRINCOMP Procedure
- The TRANSREG Procedure
- Quantile Regression
- Regression
- The NLIN Procedure
- The ORTHOREG Procedure
- The PLM Procedure
- The PLS Procedure
- The REG Procedure
- The RSREG Procedure
- The TRANSREG Procedure
- Robust Regression
- Spatial Analysis
- Standardization
- Structural Equations Models
- Survey Sampling and Analysis
- The SURVEYMEANS Procedure
- The SURVEYFREQ Procedure
- The SURVEYIMPUTE Procedure
- The SURVEYLOGISTIC Procedure
- The SURVEYPHREG Procedure
- The SURVEYREG Procedure
- The SURVEYSELECT Procedure
- Survival Analysis
| Product | Release |
|---|---|
| SAS/STAT | 14.1 |
- Type
- Usage and Reference
- Copyright Date
- July 2015
- Last Updated
- 02Nov2015