SAS/STAT 15.1 User's Guide

Examples from Documentation

Select a procedure PROC ACECLUS PROC ADAPTIVEREG PROC ANOVA PROC BCHOICE PROC BGLIMM (NEW) PROC BOXPLOT PROC CALIS PROC CANCORR PROC CANDISC PROC CATMOD PROC CAUSALGRAPH (NEW) PROC CAUSALMED PROC CAUSALTRT PROC CLUSTER PROC CORR PROC CORRESP PROC DISCRIM PROC DISTANCE PROC FACTOR PROC FASTCLUS PROC FMM PROC FREQ PROC GAM PROC GAMPL PROC GEE PROC GENMOD PROC GLIMMIX PROC GLM PROC GLMMOD PROC GLMPOWER PROC GLMSELECT PROC HPCANDISC PROC HPFMM PROC HPGENSELECT PROC HPLMIXED PROC HPLOGISTIC PROC HPMIXED PROC HPNLMOD PROC HPPLS PROC HPPRINCOMP PROC HPQUANTSELECT PROC HPREG PROC HPSPLIT PROC ICLIFETEST PROC ICPHREG PROC INBREED PROC IRT PROC KDE PROC KRIGE2D PROC LATTICE PROC LIFEREG PROC LIFETEST PROC LOESS PROC LOGISTIC PROC MCMC PROC MDS PROC MI PROC MIANALYZE PROC MIXED PROC MODECLUS PROC MULTTEST PROC NESTED PROC NLIN PROC NLMIXED PROC NPAR1WAY PROC ORTHOREG PROC PHREG PROC PLAN PROC PLM PROC PLS PROC POWER PROC PRINCOMP PROC PRINQUAL PROC PROBIT PROC PSMATCH PROC QUANTLIFE PROC QUANTREG PROC QUANTSELECT PROC REG PROC RMSTREG (NEW) PROC ROBUSTREG PROC RSREG PROC SCORE PROC SEQDESIGN PROC SEQTEST PROC SIM2D PROC SIMNORMAL PROC SPP PROC STDRATE PROC STEPDISC PROC SURVEYFREQ PROC SURVEYIMPUTE PROC SURVEYLOGISTIC PROC SURVEYMEANS PROC SURVEYPHREG PROC SURVEYREG PROC SURVEYSELECT PROC TCALIS PROC TPSPLINE PROC TRANSREG PROC TREE PROC TTEST PROC UNIVARIATE PROC VARCLUS PROC VARCOMP PROC VARIOGRAM

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ACECLUS Procedure

• Getting Started: ACECLUS Procedure

• Example 24.1: Transformation and Cluster Analysis of Fisher Iris Data

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ADAPTIVEREG Procedure

• Getting Started: ADAPTIVEREG Procedure

• Example 25.1: Surface Fitting with Many Noisy Variables
• Example 25.2: Fitting Data with Mixture Structures
• Example 25.3: Predicting E-Mail Spam
• Example 25.4: Nonparametric Poisson Model for Mackerel Egg Density

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ANOVA Procedure

• One-Way Layout with Means Comparisons
• Randomized Complete Block with One Factor

• Example 26.1: Randomized Complete Block With Factorial Treatment Structure
• Example 26.2: Alternative Multiple Comparison Procedures
• Example 26.3: Split Plot
• Example 26.4: Latin Square Split Plot
• Example 26.5: Strip-Split Plot

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BCHOICE Procedure

• A Simple Logit Model Example
• A Logit Model Example with Random Effects

• Example 27.1: Alternative-Specific and Individual-Specific Effects
• Example 27.2: Nested Logit Modeling
• Example 27.3: Probit Modeling
• Example 27.4: A Random-Effects-Only Logit Model
• Example 27.5: Heterogeneity Affected by Individual Characteristics
• Example 27.6: Quantities of Interest and Willingness To Pay
• Example 27.7: Predict the Choice Probabilities
• Example 27.8: MaxDiff Choice Example
• Example 27.9: Allocation Choice Example

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BGLIMM Procedure New Procedure!

• Logistic Regression with Random Intercepts

• Example 28.1: Normal Regression with Repeated Measurements
• Example 28.2: Mating Experiment with Crossed Random Effects
• Example 28.3: Poisson Regression with Random Effects
• Example 28.4: Repeated Growth Measurements with Group Difference

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BOXPLOT Procedure

• Creating Box Plots from Raw Data
• Creating Box Plots from Summary Data
• Saving Summary Data with Outliers

• Example 29.1: Displaying Summary Statistics in a Box Plot
• Example 29.2: Using Box Plots to Compare Groups
• Example 29.3: Creating Various Styles of Box-and-Whiskers Plots
• Example 29.4: Creating Notched Box-and-Whiskers Plots
• Example 29.5: Creating Box-and-Whiskers Plots with Varying Widths
• Example 29.6: Creating Horizontal Box-and-Whiskers Plots

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CALIS Procedure

• A Structural Equation Example
• A Factor Model Example
• Direct Covariance Structures Analysis
• Which Modeling Language?

• Example 30.1: Estimating Covariances and Correlations
• Example 30.2: Estimating Covariances and Means Simultaneously
• Example 30.3: Testing Uncorrelatedness of Variables
• Example 30.4: Testing Covariance Patterns
• Example 30.5: Testing Some Standard Covariance Pattern Hypotheses
• Example 30.6: Linear Regression Model
• Example 30.7: Multivariate Regression Models
• Example 30.8: Measurement Error Models
• Example 30.9: Testing Specific Measurement Error Models
• Example 30.10: Measurement Error Models with Multiple Predictors
• Example 30.11: Measurement Error Models Specified As Linear Equations
• Example 30.12: Confirmatory Factor Models
• Example 30.13: Confirmatory Factor Models: Some Variations
• Example 30.14: Residual Diagnostics and Robust Estimation
• Example 30.15: The Full Information Maximum Likelihood Method
• Example 30.16: Comparing the ML and FIML Estimation
• Example 30.17: Path Analysis: Stability of Alienation
• Example 30.18: Simultaneous Equations with Mean Structures and Reciprocal Paths
• Example 30.19: Fitting Direct Covariance Structures
• Example 30.20: Confirmatory Factor Analysis: Cognitive Abilities
• Example 30.21: Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
• Example 30.22: Testing Equality of Covariance and Mean Matrices between Independent Groups
• Example 30.23: Illustrating Various General Modeling Languages
• Example 30.24: Testing Competing Path Models for the Career Aspiration Data
• Example 30.25: Fitting a Latent Growth Curve Model
• Example 30.26: Higher-Order and Hierarchical Factor Models
• Example 30.27: Linear Relations among Factor Loadings
• Example 30.28: Multiple-Group Model for Purchasing Behavior
• Example 30.29: Fitting the RAM and EQS Models by the COSAN Modeling Language
• Example 30.30: Second-Order Confirmatory Factor Analysis
• Example 30.31: Linear Relations among Factor Loadings: COSAN Model Specification
• Example 30.32: Ordinal Relations among Factor Loadings
• Example 30.33: Longitudinal Factor Analysis

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CANCORR Procedure

• Getting Started: CANCORR Procedure

• Example 31.1: Canonical Correlation Analysis of Fitness Club Data

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CANDISC Procedure

• Getting Started: CANDISC Procedure

• Example 32.1: Analyzing Iris Data by Using PROC CANDISC

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The CATMOD Procedure

• Weighted Least Squares Analysis of Mean Response
• Generalized Logits Model

• Example 33.1: Linear Response Function, r=2 Responses
• Example 33.2: Mean Score Response Function, r=3 Responses
• Example 33.3: Logistic Regression, Standard Response Function
• Example 33.4: Log-Linear Model, Three Dependent Variables
• Example 33.5: Log-Linear Model, Structural and Sampling Zeros
• Example 33.6: Repeated Measures, 2 Response Levels, 3 Populations
• Example 33.7: Repeated Measures, 4 Response Levels, 1 Population
• Example 33.8: Repeated Measures, Logistic Analysis of Growth Curve
• Example 33.9: Repeated Measures, Two Repeated Measurement Factors
• Example 33.10: Direct Input of Response Functions and Covariance Matrix
• Example 33.11: Predicted Probabilities

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The CAUSALGRAPH Procedure New Procedure!

• Getting Started: CAUSALGRAPH Procedure

• Example 34.1: Constructing Adjustment Sets
• Example 34.2: Searching Efficiently for an Adjustment Set
• Example 34.3: Testing Adjustments and Enumerating Paths
• Example 34.4: Finding Adjustment Sets Common to Multiple Models
• Example 34.5: Identifying a Causal Effect by Using Instrumental Variables
• Example 34.6: Distinguishing Models with Data
• Example 34.7: Applying an Adjustment Set to Estimate a Causal Effect from Data

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The CAUSALMED Procedure

• Getting Started: CAUSALMED Procedure

• Example 35.1: Mediation Analysis with Interaction Effects and Four-Way Decomposition
• Example 35.2: Evaluating Controlled Direct Effects and Conditional Mediation Effects
• Example 35.3: Smoking Effect on Infant Mortality
• Example 35.4: Mediation Analysis by Linear Structural Equation Modeling

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The CAUSALTRT Procedure

• Getting Started: CAUSALTRT Procedure

• Example 36.1: Estimating the ATE Using Inverse Probability Weights
• Example 36.2: Estimating the ATE Using Augmented Inverse Probability Weights
• Example 36.3: Estimating Treatment Effects Using Regression Adjustment

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CLUSTER Procedure

• Getting Started: CLUSTER Procedure

• Example 37.1: Cluster Analysis of Flying Mileages between 10 American Cities
• Example 37.2: Crude Birth and Death Rates
• Example 37.3: Cluster Analysis of Fisher’s Iris Data
• Example 37.4: Evaluating the Effects of Ties

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CORR Procedure

• Getting Started: CORR Procedure

• Example 2.1: Computing Four Measures of Association
• Example 2.2: Computing Correlations between Two Sets of Variables
• Example 2.3: Analysis Using Fisher’s
• Example 2.4: Applications of Fisher’s
• Example 2.5: Computing Polyserial Correlations
• Example 2.6: Computing Cronbach’s Coefficient Alpha
• Example 2.7: Saving Correlations in an Output Data Set
• Example 2.8: Creating Scatter Plots
• Example 2.9: Computing Partial Correlations

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CORRESP Procedure

• Getting Started: CORRESP Procedure

• Example 38.1: Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
• Example 38.2: Simple Correspondence Analysis of U.S. Population

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DISCRIM Procedure

• Getting Started: DISCRIM Procedure

• Example 39.1: Univariate Density Estimates and Posterior Probabilities
• Example 39.2: Bivariate Density Estimates and Posterior Probabilities
• Example 39.3: Normal-Theory Discriminant Analysis of Iris Data
• Example 39.4: Linear Discriminant Analysis of Remote-Sensing Data on Crops

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DISTANCE Procedure

• Creating a Distance Matrix as Input for a Subsequent Cluster Analysis

• Example 40.1: Divorce Grounds – the Jaccard Coefficient
• Example 40.2: Financial Data – Stock Dividends

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FACTOR Procedure

• Getting Started: FACTOR Procedure

• Example 41.1: Principal Component Analysis
• Example 41.2: Principal Factor Analysis
• Example 41.3: Determining the Number of Factors to Retain
• Example 41.4: Maximum Likelihood Factor Analysis
• Example 41.5: Using Confidence Intervals to Locate Salient Factor Loadings
• Example 41.6: Creating Path Diagrams for Factor Solutions

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FASTCLUS Procedure

• Getting Started: FASTCLUS Procedure

• Example 42.1: Fisher’s Iris Data
• Example 42.2: Outliers

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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?

• Example 43.1: Modeling Mixing Probabilities: All Mice Are Equal, but Some Mice Are More Equal Than Others
• Example 43.2: The Usefulness of Custom Starting Values: When Do Cows Eat?
• Example 43.3: Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!
• Example 43.4: Modeling Multinomial Overdispersion: Town and Country

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FREQ Procedure

• Frequency Tables and Statistics
• Agreement Study

• Example 44.1: Output Data Set of Frequencies
• Example 44.2: Frequency Dot Plots
• Example 44.3: Chi-Square Goodness-of-Fit Tests
• Example 44.4: Binomial Proportions
• Example 44.5: Analysis of a 2x2 Contingency Table
• Example 44.6: Output Data Set of Chi-Square Statistics
• Example 44.7: Cochran-Mantel-Haenszel Statistics
• Example 44.8: Cochran-Armitage Trend Test
• Example 44.9: Friedman’s Chi-Square Test
• Example 44.10: Cochran’s Q Test

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GAM Procedure

• Getting Started: GAM Procedure

• Example 45.1: Generalized Additive Model with Binary Data
• Example 45.2: Poisson Regression Analysis of Component Reliability
• Example 45.3: Comparing PROC GAM with PROC LOESS

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GAMPL Procedure

• Getting Started: GAMPL Procedure

• Example 4.1: Scatter Plot Smoothing
• Example 4.2: Nonparametric Logistic Regression
• Example 4.3: Semiparametric Negative Binomial Model for Mackerel Egg Density
• Example 4.4: Nonparametric Tweedie Regression

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GEE Procedure

• Example 47.1: Comparison of the Marginal and Random Effect Models for Binary Data
• Example 47.2: Log-Linear Model for Count Data
• Example 47.3: Weighted GEE for Longitudinal Data That Have Missing Values
• Example 47.4: GEE for Binary Data with Logit Link Function
• Example 47.5: Alternating Logistic Regression for Ordinal Multinomial Data
• Example 47.6: GEE for Nominal Multinomial Data

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GENMOD Procedure

• Poisson Regression
• Bayesian Analysis of a Linear Regression Model
• Generalized Estimating Equations

• Example 48.1: Logistic Regression
• Example 48.2: Normal Regression, Log Link
• Example 48.3: Gamma Distribution Applied to Life Data
• Example 48.4: Ordinal Model for Multinomial Data
• Example 48.5: GEE for Binary Data with Logit Link Function
• Example 48.6: Log Odds Ratios and the ALR Algorithm
• Example 48.7: Log-Linear Model for Count Data
• Example 48.8: Model Assessment of Multiple Regression Using Aggregates of Residuals
• Example 48.9: Assessment of a Marginal Model for Dependent Data
• Example 48.10: Bayesian Analysis of a Poisson Regression Model
• Example 48.11: Exact Poisson Regression
• Example 48.12: Tweedie Regression

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GLIMMIX Procedure

• Logistic Regressions with Random Intercepts

• Example 49.1: Binomial Counts in Randomized Blocks
• Example 49.2: Mating Experiment with Crossed Random Effects
• Example 49.3: Smoothing Disease Rates; Standardized Mortality Ratios
• Example 49.4: Quasi-likelihood Estimation for Proportions with Unknown Distribution
• Example 49.5: Joint Modeling of Binary and Count Data
• Example 49.6: Radial Smoothing of Repeated Measures Data
• Example 49.7: Isotonic Contrasts for Ordered Alternatives
• Example 49.8: Adjusted Covariance Matrices of Fixed Effects
• Example 49.9: Testing Equality of Covariance and Correlation Matrices
• Example 49.10: Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
• Example 49.11: Maximum Likelihood in Proportional Odds Model with Random Effects
• Example 49.12: Fitting a Marginal (GEE-Type) Model
• Example 49.13: Response Surface Comparisons with Multiplicity Adjustments
• Example 49.14: Generalized Poisson Mixed Model for Overdispersed Count Data
• Example 49.15: Comparing Multiple B-Splines
• Example 49.16: Diallel Experiment with Multimember Random Effects
• Example 49.17: Linear Inference Based on Summary Data
• Example 49.18: Weighted Multilevel Model for Survey Data
• Example 49.19: Quadrature Method for Multilevel Model

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GLM Procedure

• PROC GLM for Unbalanced ANOVA
• PROC GLM for Quadratic Least Squares Regression

• Example 50.1: Randomized Complete Blocks with Means Comparisons and Contrasts
• Example 50.2: Regression with Mileage Data
• Example 50.3: Unbalanced ANOVA for Two-Way Design with Interaction
• Example 50.4: Analysis of Covariance
• Example 50.5: Three-Way Analysis of Variance with Contrasts
• Example 50.6: Multivariate Analysis of Variance
• Example 50.7: Repeated Measures Analysis of Variance
• Example 50.8: Mixed Model Analysis of Variance with the RANDOM Statement
• Example 50.9: Analyzing a Doubly Multivariate Repeated Measures Design
• Example 50.10: Testing for Equal Group Variances
• Example 50.11: Analysis of a Screening Design

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GLMMOD Procedure

• A One-Way Design

• Example 51.1: A Two-Way Design
• Example 51.2: Factorial Screening

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GLMPOWER Procedure

• Simple Two-Way ANOVA
• Incorporating Contrasts, Unbalanced Designs, and Multiple Means Scenarios

• Example 52.1: One-Way ANOVA
• Example 52.2: Two-Way ANOVA with Covariate
• Example 52.3: Repeated Measures ANOVA

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GLMSELECT Procedure

• Getting Started: GLMSELECT Procedure

• Example 53.1: Modeling Baseball Salaries Using Performance Statistics
• Example 53.2: Using Validation and Cross Validation
• Example 53.3: Scatter Plot Smoothing by Selecting Spline Functions
• Example 53.4: Multimember Effects and the Design Matrix
• Example 53.5: Model Averaging
• Example 53.6: Elastic Net and External Cross Validation
• Example 53.7: LASSO with Screening
• Example 53.8: Group LASSO Selection

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HPCANDISC Procedure

• Getting Started: HPCANDISC Procedure

• Example 5.1: Analyzing Iris Data with PROC HPCANDISC
• Example 5.2: Performing Canonical Discriminant Analysis in Single-Machine and Distributed Modes

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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?

• Example 6.1: Modeling Mixing Probabilities: All Mice Are Equal, but Some Mice Are More Equal Than Others
• Example 6.2: The Usefulness of Custom Starting Values: When Do Cows Eat?
• Example 6.3: Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!

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HPGENSELECT Procedure

• Getting Started: HPGENSELECT Procedure

• Example 7.1: Model Selection
• Example 7.2: Modeling Binomial Data
• Example 7.3: Tweedie Model
• Example 7.4: Model Selection by the LASSO Method

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HPLMIXED Procedure

• Mixed Model Analysis of Covariance with Many Groups

• Example 8.1: Computing BLUPs for a Large Number of Subjects

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HPLOGISTIC Procedure

• Binary Logistic Regression

• Example 9.1: Model Selection
• Example 9.2: Modeling Binomial Data
• Example 9.3: Ordinal Logistic Regression
• Example 9.4: Partitioning Data

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HPMIXED Procedure

• Mixed Model with Large Number of Fixed and Random Effects

• Example 59.1: Ranking Many Random-Effect Coefficients
• Example 59.2: Comparing Results from PROC HPMIXED and PROC MIXED
• Example 59.3: Using PROC GLIMMIX for Further Analysis of PROC HPMIXED Fit
• Example 59.4: Mixed Model Analysis of Microarray Data
• Example 59.5: Repeated Measures

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HPNLMOD Procedure

• Least Squares Model
• Binomial Model

• Example 10.1: Segmented Model

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HPPLS Procedure

• Spectrometric Calibration

• Example 11.1: Choosing a PLS Model by Test Set Validation
• Example 11.2: Fitting a PLS Model in Single-Machine and Distributed Modes

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HPPRINCOMP Procedure

• Getting Started: HPPRINCOMP Procedure

• Example 12.1: Analyzing Mean Temperatures of US Cities
• Example 12.2: Computing Principal Components in Single-Machine and Distributed Modes
• Example 12.3: Extracting Principal Components with NIPALS

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HPQUANTSELECT Procedure

• Getting Started: HPQUANTSELECT Procedure

• Example 13.1: Simulation Study
• Example 13.2: Growth Charts for Body Mass Index

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HPREG Procedure

• Getting Started: HPREG Procedure

• Example 14.1: Model Selection with Validation
• Example 14.2: Backward Selection in Single-Machine and Distributed Modes
• Example 14.3: Forward-Swap Selection
• Example 14.4: Forward Selection with Screening

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HPSPLIT Procedure

• Getting Started: HPSPLIT Procedure

• Example 15.1: Building a Classification Tree for a Binary Outcome
• Example 15.2: Cost-Complexity Pruning with Cross Validation
• Example 15.3: Creating a Regression Tree
• Example 15.4: Creating a Binary Classification Tree with Validation Data
• Example 15.5: Assessing Variable Importance

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ICLIFETEST Procedure

• Getting Started: ICLIFETEST Procedure

• Example 66.1: Analyzing Data with Observations below a Limit of Detection
• Example 66.2: Controlling the Plotting of Survival Estimates
• Example 66.3: Plotting Kernel-Smoothed Hazard Functions
• Example 66.4: Outputting Scores for Permutation Tests

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ICPHREG Procedure

• Getting Started: ICPHREG Procedure

• Example 67.1: Fitting Cubic Spline Models
• Example 67.2: Plotting Predicted Survival and Cumulative Hazard Functions
• Example 67.3: Fitting Stratified Weibull Models
• Example 67.4: Fitting Semiparametric Proportional Hazards Models
• Example 67.5: Fitting Semiparametric Models with Time-Dependent Covariates

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INBREED Procedure

• The Format of the Input Data Set
• Performing the Analysis

• Example 68.1: Monoecious Population Analysis
• Example 68.2: Pedigree Analysis
• Example 68.3: Pedigree Analysis with BY Groups

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IRT Procedure

• Getting Started: IRT Procedure

• Example 69.1: Unidimensional IRT Models
• Example 69.2: Multidimensional Exploratory and Confirmatory IRT Models
• Example 69.3: Multiple-Group Analysis
• Example 69.4: Item Selection Using Item and Test Information
• Example 69.5: Subject Scoring

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KDE Procedure

• Getting Started: KDE Procedure

• Example 70.1: Computing a Basic Kernel Density Estimate
• Example 70.2: Changing the Bandwidth
• Example 70.3: Changing the Bandwidth (Bivariate)
• Example 70.4: Requesting Additional Output Tables
• Example 70.5: Univariate KDE Graphics
• Example 70.6: Bivariate KDE Graphics

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KRIGE2D Procedure

• Spatial Prediction Using Kriging, Contour Plots

• Example 71.1: Spatial Prediction of Pollutant Concentration
• Example 71.2: Investigating the Effect of Model Specification on Spatial Prediction
• Example 71.3: Data Quality and Prediction with Missing Values

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LATTICE Procedure

• Getting Started: LATTICE Procedure

• Example 72.1: Analysis of Variance through PROC LATTICE

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LIFEREG Procedure

• Modeling Right-Censored Failure Time Data
• Bayesian Analysis of Right-Censored Data

• Example 73.1: Motorette Failure
• Example 73.2: Computing Predicted Values for a Tobit Model
• Example 73.3: Overcoming Convergence Problems by Specifying Initial Values
• Example 73.4: Analysis of Arbitrarily Censored Data with Interaction Effects
• Example 73.5: Probability Plotting—Right Censoring
• Example 73.6: Probability Plotting—Arbitrary Censoring
• Example 73.7: Bayesian Analysis of Clinical Trial Data
• Example 73.8: Model Postfitting Analysis

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LIFETEST Procedure

• Getting Started: LIFETEST Procedure

• Example 74.1: Product-Limit Estimates and Tests of Association
• Example 74.2: Enhanced Survival Plot and Multiple-Comparison Adjustments
• Example 74.3: Life-Table Estimates for Males with Angina Pectoris
• Example 74.4: Nonparametric Analysis of Competing-Risks Data
• Example 74.5: Restricted Mean Analysis

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LOESS Procedure

• Scatter Plot Smoothing

• Example 75.1: Engine Exhaust Emissions
• Example 75.2: Sulfate Deposits in the U.S. for 1990
• Example 75.3: Catalyst Experiment
• Example 75.4: El Niño Southern Oscillation

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LOGISTIC Procedure

• Getting Started: LOGISTIC Procedure

• Example 76.1: Stepwise Logistic Regression and Predicted Values
• Example 76.2: Logistic Modeling with Categorical Predictors
• Example 76.3: Ordinal Logistic Regression
• Example 76.4: Nominal Response Data: Generalized Logits Model
• Example 76.5: Stratified Sampling
• Example 76.6: Logistic Regression Diagnostics
• Example 76.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
• Example 76.8: Comparing Receiver Operating Characteristic Curves
• Example 76.9: Goodness-of-Fit Tests and Subpopulations
• Example 76.10: Overdispersion
• Example 76.11: Conditional Logistic Regression for Matched Pairs Data
• Example 76.12: Exact Conditional Logistic Regression
• Example 76.13: Firth’s Penalized Likelihood Compared with Other Approaches
• Example 76.14: Complementary Log-Log Model for Infection Rates
• Example 76.15: Complementary Log-Log Model for Interval-Censored Survival Times
• Example 76.16: Scoring Data Sets
• Example 76.17: Using the LSMEANS Statement
• Example 76.18: Partial Proportional Odds Model
• Example 76.19: Goodness-of-Fit Tests and Calibration

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MCMC Procedure

• Simple Linear Regression
• The Behrens-Fisher Problem
• Random-Effects Model

• Example 77.1: Simulating Samples From a Known Density
• Example 77.2: Box-Cox Transformation
• Example 77.3: Logistic Regression Model with a Diffuse Prior
• Example 77.4: Logistic Regression Model with Jeffreys’ Prior
• Example 77.5: Poisson Regression
• Example 77.6: Nonlinear Poisson Regression Models
• Example 77.7: Logistic Regression Random-Effects Model
• Example 77.8: Nonlinear Poisson Regression Multilevel Random-Effects Model
• Example 77.9: Multivariate Normal Random-Effects Model
• Example 77.10: Missing at Random Analysis
• Example 77.11: Nonignorably Missing Data (MNAR) Analysis
• Example 77.12: Change Point Models
• Example 77.13: Exponential and Weibull Survival Analysis
• Example 77.14: Time Independent Cox Model
• Example 77.15: Time Dependent Cox Model
• Example 77.16: Piecewise Exponential Frailty Model
• Example 77.17: Normal Regression with Interval Censoring
• Example 77.18: Constrained Analysis
• Example 77.19: Implement a New Sampling Algorithm
• Example 77.20: Using a Transformation to Improve Mixing
• Example 77.21: Gelman-Rubin Diagnostics
• Example 77.22: One-Compartment Model with Pharmacokinetic Data

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MDS Procedure

• Getting Started: MDS Procedure

• Example 78.1: Jacobowitz Body Parts Data from Children and Adults

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MI Procedure

• Getting Started: MI Procedure

• Example 79.1: EM Algorithm for MLE
• Example 79.2: Monotone Propensity Score Method
• Example 79.3: Monotone Regression Method
• Example 79.4: Monotone Logistic Regression Method for CLASS Variables
• Example 79.5: Monotone Discriminant Function Method for CLASS Variables
• Example 79.6: FCS Methods for Continuous Variables
• Example 79.7: FCS Method for CLASS Variables
• Example 79.8: FCS Method with Trace Plot
• Example 79.9: MCMC Method
• Example 79.10: Producing Monotone Missingness with MCMC
• Example 79.11: Checking Convergence in MCMC
• Example 79.12: Saving and Using Parameters for MCMC
• Example 79.13: Transforming to Normality
• Example 79.14: Multistage Imputation
• Example 79.15: Creating Control-Based Pattern Imputation in Sensitivity Analysis
• Example 79.16: Adjusting Imputed Continuous Values in Sensitivity Analysis
• Example 79.17: Adjusting Imputed Classification Levels in Sensitivity Analysis
• Example 79.18: Adjusting Imputed Values with Parameters in a Data Set

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MIANALYZE Procedure

• Getting Started: MIANALYZE Procedure

• Example 80.1: Reading Means and Standard Errors from a DATA= Data Set
• Example 80.2: Reading Means and Covariance Matrices from a DATA= COV Data Set
• Example 80.3: Reading Regression Results from a DATA= EST Data Set
• Example 80.4: Reading Mixed Model Results from PARMS= and COVB= Data Sets
• Example 80.5: Reading Generalized Linear Model Results
• Example 80.6: Reading GLM Results from PARMS= and XPXI= Data Sets
• Example 80.7: Reading Logistic Model Results from a PARMS= Data Set
• Example 80.8: Reading Mixed Model Results with Classification Covariates
• Example 80.9: Reading Nominal Logistic Model Results
• Example 80.10: Using a TEST statement
• Example 80.11: Combining Correlation Coefficients
• Example 80.12: Sensitivity Analysis with Control-Based Pattern Imputation
• Example 80.13: Sensitivity Analysis with the Tipping-Point Approach

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MIXED Procedure

• Clustered Data Example

• Example 81.1: Split-Plot Design
• Example 81.2: Repeated Measures
• Example 81.3: Plotting the Likelihood
• Example 81.4: Known G and R
• Example 81.5: Random Coefficients
• Example 81.6: Line-Source Sprinkler Irrigation
• Example 81.7: Influence in Heterogeneous Variance Model
• Example 81.8: Influence Analysis for Repeated Measures Data
• Example 81.9: Examining Individual Test Components
• Example 81.10: Isotonic Contrasts for Ordered Mean Values

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MODECLUS Procedure

• Getting Started: MODECLUS Procedure

• Example 82.1: Cluster Analysis of Samples from Univariate Distributions
• Example 82.2: Cluster Analysis of Flying Mileages between Ten American Cities
• Example 82.3: Cluster Analysis with Significance Tests
• Example 82.4: Cluster Analysis: Hertzsprung-Russell Plot
• Example 82.5: Using the TRACE Option When METHOD=6

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MULTTEST Procedure

• Drug Example

• Example 83.1: Cochran-Armitage Test with Permutation Resampling
• Example 83.2: Freeman-Tukey and t Tests with Bootstrap Resampling
• Example 83.3: Peto Mortality-Prevalence Test
• Example 83.4: Fisher Test with Permutation Resampling
• Example 83.5: Inputting Raw p-Values
• Example 83.6: Adaptive Adjustments and ODS Graphics

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NESTED Procedure

• Reliability of Automobile Models

• Example 84.1: Variability of Calcium Concentration in Turnip Greens

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NLIN Procedure

• Nonlinear or Linear Model
• Notation for Nonlinear Regression Models
• Estimating the Parameters in the Nonlinear Model

• Example 85.1: Segmented Model
• Example 85.2: Iteratively Reweighted Least Squares
• Example 85.3: Probit Model with Likelihood Function
• Example 85.4: Affecting Curvature through Parameterization
• Example 85.5: Comparing Nonlinear Trends among Groups
• Example 85.6: ODS Graphics and Diagnostics
• Example 85.7: Parameter Profiling and Bootstrapping

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NLMIXED Procedure

• Nonlinear Growth Curves with Gaussian Data
• Logistic-Normal Model with Binomial Data

• Example 86.1: One-Compartment Model with Pharmacokinetic Data
• Example 86.2: Probit-Normal Model with Binomial Data
• Example 86.3: Probit-Normal Model with Ordinal Data
• Example 86.4: Poisson-Normal Model with Count Data
• Example 86.5: Failure Time and Frailty Model
• Example 86.6: Simulated Nested Linear Random-Effects Model
• Example 86.7: Overdispersion Hierarchical Nonlinear Mixed Model

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NPAR1WAY Procedure

• Getting Started: NPAR1WAY Procedure

• Example 87.1: Two-Sample Location Tests and Plots
• Example 87.2: EDF Statistics and EDF Plot
• Example 87.3: Exact Wilcoxon Two-Sample Test
• Example 87.4: Hodges-Lehmann Estimation
• Example 87.5: Exact Savage Multisample Test

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ORTHOREG Procedure

• Longley Data

• Example 88.1: Precise Analysis of Variance
• Example 88.2: Wampler Data
• Example 88.3: Fitting Polynomials

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PHREG Procedure

• Classical Method of Maximum Likelihood
• Bayesian Analysis

• Example 89.1: Stepwise Regression
• Example 89.2: Best Subset Selection
• Example 89.3: Modeling with Categorical Predictors
• Example 89.4: Firth’s Correction for Monotone Likelihood
• Example 89.5: Conditional Logistic Regression for m:n Matching
• Example 89.6: Model Using Time-Dependent Explanatory Variables
• Example 89.7: Time-Dependent Repeated Measurements of a Covariate
• Example 89.8: Survival Curves
• Example 89.9: Analysis of Residuals
• Example 89.10: Analysis of Recurrent Events Data
• Example 89.11: Analysis of Clustered Data
• Example 89.12: Model Assessment Using Cumulative Sums of Martingale Residuals
• Example 89.13: Bayesian Analysis of the Cox Model
• Example 89.14: Bayesian Analysis of Piecewise Exponential Model
• Example 89.15: Analysis of Competing-Risks Data
• Example 89.16: Concordance and ROC Curves

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PLAN Procedure

• Three Replications with Four Factors
• Randomly Assigning Subjects to Treatments

• Example 90.1: A Split-Plot Design
• Example 90.2: A Hierarchical Design
• Example 90.3: An Incomplete Block Design
• Example 90.4: A Latin Square Design
• Example 90.5: A Generalized Cyclic Incomplete Block Design
• Example 90.6: Permutations and Combinations
• Example 90.7: Crossover Designs

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PLM Procedure

• Getting Started: PLM Procedure

• Example 91.1: Scoring with PROC PLM
• Example 91.2: Working with Item Stores
• Example 91.3: Group Comparisons in an Ordinal Model
• Example 91.4: Posterior Inference for Binomial Data
• Example 91.5: BY-Group Processing
• Example 91.6: Comparing Multiple B-Splines
• Example 91.7: Linear Inference with Arbitrary Estimates

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PLS Procedure

• Spectrometric Calibration

• Example 92.1: Examining Model Details
• Example 92.2: Examining Outliers
• Example 92.3: Choosing a PLS Model by Test Set Validation
• Example 92.4: Partial Least Squares Spline Smoothing

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POWER Procedure

• Computing Power for a One-Sample t Test
• Determining Required Sample Size for a Two-Sample t Test

• Example 93.1: One-Way ANOVA
• Example 93.2: The Sawtooth Power Function in Proportion Analyses
• Example 93.3: Simple AB/BA Crossover Designs
• Example 93.4: Noninferiority Test with Lognormal Data
• Example 93.5: Multiple Regression and Correlation
• Example 93.6: Comparing Two Survival Curves
• Example 93.7: Confidence Interval Precision
• Example 93.8: Customizing Plots
• Example 93.9: Binary Logistic Regression with Independent Predictors
• Example 93.10: Wilcoxon-Mann-Whitney Test
• Example 93.11: Logistic Regression Using the CUSTOM Statement

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PRINCOMP Procedure

• Getting Started: PRINCOMP Procedure

• Example 95.1: Analyzing Mean Temperatures of US Cities
• Example 95.2: Analyzing Rankings of US College Basketball Teams
• Example 95.3: Analyzing Job Ratings of Police Officers

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PRINQUAL Procedure

• Getting Started: PRINQUAL Procedure

• Example 96.1: Multidimensional Preference Analysis of Automobile Data
• Example 96.2: Principal Components of Basketball Rankings

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PROBIT Procedure

• Estimating the Natural Response Threshold Parameter

• Example 97.1: Dosage Levels
• Example 97.2: Multilevel Response
• Example 97.3: Logistic Regression and Scoring New Data
• Example 97.4: An Epidemiology Study
• Example 97.5: Model Postfitting Analysis

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PSMATCH Procedure New Procedure!

• Getting Started: PSMATCH Procedure

• Example 98.1: Propensity Score Weighting
• Example 98.2: Propensity Score Stratification
• Example 98.3: Optimal Variable Ratio Matching
• Example 98.4: Greedy Nearest Neighbor Matching
• Example 98.5: Outcome Analysis after Matching
• Example 98.6: Matching with Replacement
• Example 98.7: Mahalanobis Distance Matching
• Example 98.8: Matching with Precomputed Propensity Scores
• Example 98.9: Sensitivity Analysis after One-to-One Matching

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QUANTLIFE Procedure

• Getting Started: QUANTLIFE Procedure

• Example 99.1: Primary Biliary Cirrhosis Study
• Example 99.2: Drug Abuse Study

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QUANTREG Procedure

• Analysis of Fish-Habitat Relationships
• Growth Charts for Body Mass Index

• Example 100.1: Comparison of Algorithms
• Example 100.2: Quantile Regression for Econometric Growth Data
• Example 100.3: Quantile Regression Analysis of Birth-Weight Data
• Example 100.4: Nonparametric Quantile Regression for Ozone Levels
• Example 100.5: Quantile Polynomial Regression for Salary Data
• Example 100.6: Counterfactual Analysis of Smoking-Weight Data

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QUANTSELECT Procedure

• Getting Started: QUANTSELECT Procedure

• Example 101.1: Simulation Study
• Example 101.2: Econometric Growth Data
• Example 101.3: Pollution and Mortality
• Example 101.4: Surface Fitting with Many Noisy Variables
• Example 101.5: Quantile Process Regression

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REG Procedure

• Simple Linear Regression
• Polynomial Regression
• Using PROC REG Interactively

• Example 102.1: Modeling Salaries of Major League Baseball Players
• Example 102.2: Aerobic Fitness Prediction
• Example 102.3: Predicting Weight by Height and Age
• Example 102.4: Regression with Quantitative and Qualitative Variables
• Example 102.5: Ridge Regression for Acetylene Data
• Example 102.6: Chemical Reaction Response

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RMSTREG Procedure New Procedure!

• Getting Started: RMSTREG Procedure

• Example 103.1: Comparison of PROC RMSTREG with Other Procedures
• Example 103.2: Comparing the RMSTs of Two Groups
• Example 103.3: Making Model-Based Inferences

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ROBUSTREG Procedure

• M Estimation
• LTS Estimation

• Example 104.1: Comparison of Robust Estimates
• Example 104.2: Robust ANOVA
• Example 104.3: Growth Study of De Long and Summers
• Example 104.4: Constructed Effects
• Example 104.5: Robust Diagnostics

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RSREG Procedure

• A Response Surface with a Simple Optimum

• Example 105.1: A Saddle Surface Response Using Ridge Analysis
• Example 105.2: Response Surface Analysis with Covariates

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SEQDESIGN Procedure

• Getting Started: SEQDESIGN Procedure

• Example 107.1: Creating Fixed-Sample Designs
• Example 107.2: Creating a One-Sided O’Brien-Fleming Design
• Example 107.3: Creating Two-Sided Pocock and O’Brien-Fleming Designs
• Example 107.4: Generating Graphics Display for Sequential Designs
• Example 107.5: Creating Designs Using Haybittle-Peto Methods
• Example 107.6: Creating Designs with Various Stopping Criteria
• Example 107.7: Creating Whitehead’s Triangular Designs
• Example 107.8: Creating a One-Sided Error Spending Design
• Example 107.9: Creating Designs with Various Number of Stages
• Example 107.10: Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper beta Boundaries
• Example 107.11: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject H0
• Example 107.12: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept H0
• Example 107.13: Creating a Design with a Nonbinding Beta Boundary
• Example 107.14: Computing Sample Size for Survival Data That Have Uniform Accrual
• Example 107.15: Computing Sample Size for Survival Data with Truncated Exponential Accrual

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SEQTEST Procedure

• Getting Started: SEQTEST Procedure

• Example 108.1: Testing the Difference between Two Proportions
• Example 108.2: Testing an Effect in a Regression Model
• Example 108.3: Testing an Effect with Early Stopping to Accept H0
• Example 108.4: Testing a Binomial Proportion
• Example 108.5: Comparing Two Proportions with a Log Odds Ratio Test
• Example 108.6: Comparing Two Survival Distributions with a Log-Rank Test
• Example 108.7: Testing an Effect in a Proportional Hazards Regression Model
• Example 108.8: Testing an Effect in a Logistic Regression Model
• Example 108.9: Conducting a Trial with a Nonbinding Acceptance Boundary

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SCORE Procedure

• Getting Started: SCORE Procedure

• Example 106.1: Factor Scoring Coefficients
• Example 106.2: Regression Parameter Estimates
• Example 106.3: Custom Scoring Coefficients

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SIM2D Procedure

• Preliminary Spatial Data Analysis
• Investigating Variability by Simulation

• Example 109.1: Simulation and Economic Feasibility
• Example 109.2: Variability at Selected Locations
• Example 109.3: Risk Analysis with Simulation

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SIMNORMAL Procedure

• Getting Started: SIMNORMAL Procedure

• Example : Example: SIMNORM Procedure

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SPP Procedure

• Getting Started: SPP Procedure

• Example 111.1: Exploration of a Multitype Point Pattern
• Example 111.2: Testing Covariate Dependence of a Point Pattern
• Example 111.3: Intensity Model Validation Diagnostics

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STDIZE Procedure

• Getting Started: STDIZE Procedure

• Example 112.1: Standardization of Variables in Cluster Analysis

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STDRATE Procedure

• Getting Started: STDRATE Procedure

• Example 113.1: Comparing Directly Standardized Rates
• Example 113.2: Computing Mantel-Haenszel Risk Estimation
• Example 113.3: Computing Attributable Fraction Estimates
• Example 113.4: Displaying SMR Results from BY Groups

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STEPDISC Procedure

• Getting Started: STEPDISC Procedure

• Example 114.1: Performing a Stepwise Discriminant Analysis

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SURVEYFREQ Procedure

• Getting Started: SURVEYFREQ Procedure

• Example 115.1: Two-Way Tables
• Example 115.2: Multiway Tables (Domain Analysis)
• Example 115.3: Output Data Sets

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SURVEYIMPUTE Procedure

• Getting Started: SURVEYIMPUTE Procedure

• Example 116.1: Approximate Bayesian Bootstrap Imputation
• Example 116.2: Fully Efficient Fractional Imputation
• Example 116.3: Fully Efficient Fractional Imputation, Fay’s Balanced Repeated Replication, and Domain Analysis
• Example 116.4: Fractional Hot-Deck Imputation for Mixed Variables

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SURVEYLOGISTIC Procedure

• Getting Started: SURVEYLOGISTIC Procedure

• Example 117.1: Stratified Cluster Sampling
• Example 117.2: The Medical Expenditure Panel Survey (MEPS)

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SURVEYMEANS Procedure

• Simple Random Sampling
• Stratified Sampling
• Output Data Sets

• Example 118.1: Stratified Cluster Sample Design
• Example 118.2: Domain Analysis
• Example 118.3: Ratio Analysis
• Example 118.4: Analyzing Survey Data with Missing Values
• Example 118.5: Variance Estimation Using Replication Methods
• Example 118.6: Comparing Domain Means

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SURVEYPHREG Procedure

• Getting Started: SURVEYPHREG Procedure

• Example 119.1: Analysis of Clustered Data
• Example 119.2: Stratification, Clustering, and Unequal Weights
• Example 119.3: Domain Analysis
• Example 119.4: Variance Estimation by Using Replicate Weights
• Example 119.5: A Test of the Proportional Hazards Assumption by Using the Programming Statements

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SURVEYREG Procedure

• Simple Random Sampling
• Stratified Sampling
• Output Data Sets

• Example 120.1: Simple Random Sampling
• Example 120.2: Cluster Sampling
• Example 120.3: Regression Estimator for Simple Random Sample
• Example 120.4: Stratified Sampling
• Example 120.5: Regression Estimator for Stratified Sample
• Example 120.6: Stratum Collapse
• Example 120.7: Domain Analysis
• Example 120.8: Comparing Domain Statistics
• Example 120.9: Variance Estimate Using the Jackknife Method

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SURVEYSELECT Procedure

• Simple Random Sampling
• Stratified Sampling
• Stratified Sampling with Control Sorting

• Example 121.1: Replicated Sampling
• Example 121.2: PPS Selection of Two Units per Stratum
• Example 121.3: PPS (Dollar-Unit) Sampling
• Example 121.4: Proportional Allocation

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TPSPLINE Procedure

• Getting Started: TPSPLINE Procedure

• Example 122.1: Partial Spline Model Fit
• Example 122.2: Spline Model with Higher-Order Penalty
• Example 122.3: Multiple Minima of the GCV Function
• Example 122.4: Large Data Set Application
• Example 122.5: Computing a Bootstrap Confidence Interval

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TRANSREG Procedure

• Fitting a Curve through a Scatter Plot
• Main-Effects ANOVA

• Example 123.1: Transformation Regression of Exhaust Emissions Data
• Example 123.2: Box-Cox Transformations
• Example 123.3: Penalized B-Spline
• Example 123.4: Nonmetric Conjoint Analysis of Tire Data
• Example 123.5: Metric Conjoint Analysis of Tire Data
• Example 123.6: Preference Mapping of Automobile Data

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TREE Procedure

• Getting Started: TREE Procedure

• Example 124.1: Mammals’ Teeth
• Example 124.2: Iris Data

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TTEST Procedure

• One-Sample t Test
• Comparing Group Means

• Example 125.1: Using Summary Statistics to Compare Group Means
• Example 125.2: One-Sample Comparison with the FREQ Statement
• Example 125.3: Paired Comparisons
• Example 125.4: AB/BA Crossover Design
• Example 125.5: Equivalence Testing with Lognormal Data
• Example 125.6: Bootstrap with Two-Sample Design

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UNIVARIATE Procedure

• Capabilities of PROC UNIVARIATE
• Summarizing a Data Distribution
• Exploring a Data Distribution
• Modeling a Data Distribution

• Example 4.1: Computing Descriptive Statistics for Multiple Variables
• Example 4.2: Calculating Modes
• Example 4.3: Identifying Extreme Observations and Extreme Values
• Example 4.4: Creating a Frequency Table
• Example 4.5: Creating Basic Summary Plots
• Example 4.6: Analyzing a Data Set With a FREQ Variable
• Example 4.7: Saving Summary Statistics in an OUT= Output Data Set
• Example 4.8: Saving Percentiles in an Output Data Set
• Example 4.9: Computing Confidence Limits for the Mean, Standard Deviation, and Variance
• Example 4.10: Computing Confidence Limits for Quantiles and Percentiles
• Example 4.11: Computing Robust Estimates
• Example 4.12: Testing for Location
• Example 4.13: Performing a Sign Test Using Paired Data
• Example 4.14: Creating a Histogram
• Example 4.15: Creating a One-Way Comparative Histogram
• Example 4.16: Creating a Two-Way Comparative Histogram
• Example 4.17: Adding Insets with Descriptive Statistics
• Example 4.18: Binning a Histogram
• Example 4.19: Adding a Normal Curve to a Histogram
• Example 4.20: Adding Fitted Normal Curves to a Comparative Histogram
• Example 4.21: Fitting a Beta Curve
• Example 4.22: Fitting Lognormal, Weibull, and Gamma Curves
• Example 4.23: Computing Kernel Density Estimates
• Example 4.24: Fitting a Three-Parameter Lognormal Curve
• Example 4.25: Annotating a Folded Normal Curve
• Example 4.26: Creating Lognormal Probability Plots
• Example 4.27: Creating a Histogram to Display Lognormal Fit
• Example 4.28: Creating a Normal Quantile Plot
• Example 4.29: Adding a Distribution Reference Line
• Example 4.30: Interpreting a Normal Quantile Plot
• Example 4.31: Estimating Three Parameters from Lognormal Quantile Plots
• Example 4.32: Estimating Percentiles from Lognormal Quantile Plots
• Example 4.33: Estimating Parameters from Lognormal Quantile Plots
• Example 4.34: Comparing Weibull Quantile Plots
• Example 4.35: Creating a Cumulative Distribution Plot
• Example 4.36: Creating a P-P Plot

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VARCLUS Procedure

• Getting Started: VARCLUS Procedure

• Example 126.1: Correlations among Physical Variables

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VARCOMP Procedure

• Analyzing the Cure Rate of Rubber

• Example 127.1: Using the Four General Estimation Methods
• Example 127.2: Using the GRR Method

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VARIOGRAM Procedure

• Preliminary Spatial Data Analysis
• Empirical Semivariogram Computation
• Autocorrelation Analysis
• Theoretical Semivariogram Model Fitting

• Example 128.1: Aspects of Semivariogram Model Fitting
• Example 128.2: An Anisotropic Case Study with Surface Trend in the Data
• Example 128.3: Analysis without Surface Trend Removal
• Example 128.4: Covariogram and Semivariogram
• Example 128.5: A Box Plot of the Square Root Difference Cloud

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