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SAS/STAT 13.2 User's Guide

Examples from Documentation

Select an example PROC ACECLUS PROC ADAPTIVEREG PROC ANOVA PROC BCHOICE PROC BOXPLOT PROC CALIS PROC CANCORR PROC CANDISC PROC CATMOD PROC CLUSTER PROC CORRESP PROC DISCRIM PROC DISTANCE PROC FACTOR PROC FASTCLUS PROC FMM PROC FREQ PROC GAM PROC GEE (New) PROC GENMOD PROC GLIMMIX PROC GLM PROC GLMMOD PROC GLMPOWER PROC GLMSELECT PROC HPFMM PROC HPMIXED PROC ICLIFETEST PROC ICPHREG (New) 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 QUANTLIFE PROC QUANTREG PROC QUANTSELECT PROC REG PROC ROBUSTREG PROC RSREG PROC SCORE PROC SEQDESIGN PROC SEQTEST PROC SIM2D PROC SIMNORMAL PROC SPP (New) PROC STDRATE PROC STEPDISC PROC SURVEYFREQ PROC SURVEYLOGISTIC PROC SURVEYMEANS PROC SURVEYPHREG PROC SURVEYREG PROC SURVEYSELECT PROC TCALIS PROC TPSPLINE PROC TRANSREG PROC TREE PROC TTEST 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
• A Logit Model 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: Inference on Quantities of Interest
• Example 27.7: Predict the Choice Probabilities

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BOXPLOT Procedure

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

• Example 28.1: Displaying Summary Statistics in a Box Plot
• Example 28.2: Using Box Plots to Compare Groups
• Example 28.3: Creating Various Styles of Box-and-Whiskers Plots
• Example 28.4: Creating Notched Box-and-Whiskers Plots
• Example 28.5: Creating Box-and-Whiskers Plots with Varying Widths
• Example 28.6: 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 29.1: Estimating Covariances and Correlations
• Example 29.2: Estimating Covariances and Means Simultaneously
• Example 29.3: Testing Uncorrelatedness of Variables
• Example 29.4: Testing Covariance Patterns
• Example 29.5: Testing Some Standard Covariance Pattern Hypotheses
• Example 29.6: Linear Regression Model
• Example 29.7: Multivariate Regression Models
• Example 29.8: Measurement Error Models
• Example 29.9: Testing Specific Measurement Error Models
• Example 29.10: Measurement Error Models with Multiple Predictors
• Example 29.11: Measurement Error Models Specified As Linear Equations
• Example 29.12: Confirmatory Factor Models
• Example 29.13: Confirmatory Factor Models: Some Variations
• Example 29.14: Residual Diagnostics and Robust Estimation
• Example 29.15: The Full Information Maximum Likelihood Method
• Example 29.16: Comparing the ML and FIML Estimation
• Example 29.17: Path Analysis: Stability of Alienation
• Example 29.18: Simultaneous Equations with Mean Structures and Reciprocal Paths
• Example 29.19: Fitting Direct Covariance Structures
• Example 29.20: Confirmatory Factor Analysis: Cognitive Abilities
• Example 29.21: Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
• Example 29.22: Testing Equality of Covariance and Mean Matrices between Independent Groups
• Example 29.23: Illustrating Various General Modeling Languages
• Example 29.24: Testing Competing Path Models for the Career Aspiration Data
• Example 29.25: Fitting a Latent Growth Curve Model
• Example 29.26: Higher-Order and Hierarchical Factor Models
• Example 29.27: Linear Relations among Factor Loadings
• Example 29.28: Multiple-Group Model for Purchasing Behavior
• Example 29.29: Fitting the RAM and EQS Models by the COSAN Modeling Language
• Example 29.30: Second-Order Confirmatory Factor Analysis
• Example 29.31: Linear Relations among Factor Loadings: COSAN Model Specification
• Example 29.32: Ordinal Relations among Factor Loadings
• Example 29.33: Longitudinal Factor Analysis

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CANCORR Procedure

• Getting Started: CANCORR Procedure

• Example 30.1: Canonical Correlation Analysis of Fitness Club Data

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CANDISC Procedure

• Getting Started: CANDISC Procedure

• Example 31.1: 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 32.1: Linear Response Function, r=2 Responses
• Example 32.2: Mean Score Response Function, r=3 Responses
• Example 32.3: Logistic Regression, Standard Response Function
• Example 32.4: Log-Linear Model, Three Dependent Variables
• Example 32.5: Log-Linear Model, Structural and Sampling Zeros
• Example 32.6: Repeated Measures, 2 Response Levels, 3 Populations
• Example 32.7: Repeated Measures, 4 Response Levels, 1 Population
• Example 32.8: Repeated Measures, Logistic Analysis of Growth Curve
• Example 32.9: Repeated Measures, Two Repeated Measurement Factors
• Example 32.10: Direct Input of Response Functions and Covariance Matrix
• Example 32.11: Predicted Probabilities

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CLUSTER Procedure

• Getting Started: CLUSTER Procedure

• Example 33.1: Cluster Analysis of Flying Mileages between 10 American Cities
• Example 33.2: Crude Birth and Death Rates
• Example 33.3: Cluster Analysis of Fisher’s Iris Data
• Example 33.4: Evaluating the Effects of Ties

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CORRESP Procedure

• Getting Started: CORRESP Procedure

• Example 34.1: Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
• Example 34.2: Simple Correspondence Analysis of U.S. Population

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DISCRIM Procedure

• Getting Started: DISCRIM Procedure

• Example 35.1: Univariate Density Estimates and Posterior Probabilities
• Example 35.2: Bivariate Density Estimates and Posterior Probabilities
• Example 35.3: Normal-Theory Discriminant Analysis of Iris Data
• Example 35.4: 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 36.1: Divorce Grounds – the Jaccard Coefficient
• Example 36.2: Financial Data – Stock Dividends

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FACTOR Procedure

• Getting Started: FACTOR Procedure

• Example 37.1: Principal Component Analysis
• Example 37.2: Principal Factor Analysis
• Example 37.3: Maximum Likelihood Factor Analysis
• Example 37.4: Using Confidence Intervals to Locate Salient Factor Loadings
• Example 37.5: Creating Path Diagrams for Factor Solutions

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FASTCLUS Procedure

• Getting Started: FASTCLUS Procedure

• Example 38.1: Fisher’s Iris Data
• Example 38.2: 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 39.1: Modeling Mixing Probabilities: All Mice Are Created Equal, but Some Are More Equal
• Example 39.2: The Usefulness of Custom Starting Values: When Do Cows Eat?
• Example 39.3: Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!
• Example 39.4: Modeling Multinomial Overdispersion: Town and Country

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FREQ Procedure

• Frequency Tables and Statistics
• Agreement Study

• Example 40.1: Output Data Set of Frequencies
• Example 40.2: Frequency Dot Plots
• Example 40.3: Chi-Square Goodness-of-Fit Tests
• Example 40.4: Binomial Proportions
• Example 40.5: Analysis of a 2x2 Contingency Table
• Example 40.6: Output Data Set of Chi-Square Statistics
• Example 40.7: Cochran-Mantel-Haenszel Statistics
• Example 40.8: Cochran-Armitage Trend Test
• Example 40.9: Friedman’s Chi-Square Test
• Example 40.10: Cochran’s Q Test

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GAM Procedure

• Getting Started: GAM Procedure

• Example 41.1: Generalized Additive Model with Binary Data
• Example 41.2: Poisson Regression Analysis of Component Reliability
• Example 41.3: Comparing PROC GAM with PROC LOESS

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GEE Procedure (Experimental) New Procedure!

• Example 42.1: Comparison of the Marginal and Random Effect Models for Binary Data
• Example 42.2: Log-Linear Model for Count Data
• Example 42.3: Weighted GEE for Longitudinal Data That Have Missing Values

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GENMOD Procedure

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

• Example 43.1: Logistic Regression
• Example 43.2: Normal Regression, Log Link
• Example 43.3: Gamma Distribution Applied to Life Data
• Example 43.4: Ordinal Model for Multinomial Data
• Example 43.5: GEE for Binary Data with Logit Link Function
• Example 43.6: Log Odds Ratios and the ALR Algorithm
• Example 43.7: Log-Linear Model for Count Data
• Example 43.8: Model Assessment of Multiple Regression Using Aggregates of Residuals
• Example 43.9: Assessment of a Marginal Model for Dependent Data
• Example 43.10: Bayesian Analysis of a Poisson Regression Model
• Example 43.11: Exact Poisson Regression
• Example 43.12: Tweedie Regression

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GLIMMIX Procedure

• Logistic Regressions with Random Intercepts

• Example 44.1: Binomial Counts in Randomized Blocks
• Example 44.2: Mating Experiment with Crossed Random Effects
• Example 44.3: Smoothing Disease Rates; Standardized Mortality Ratios
• Example 44.4: Quasi-likelihood Estimation for Proportions with Unknown Distribution
• Example 44.5: Joint Modeling of Binary and Count Data
• Example 44.6: Radial Smoothing of Repeated Measures Data
• Example 44.7: Isotonic Contrasts for Ordered Alternatives
• Example 44.8: Adjusted Covariance Matrices of Fixed Effects
• Example 44.9: Testing Equality of Covariance and Correlation Matrices
• Example 44.10: Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
• Example 44.11: Maximum Likelihood in Proportional Odds Model with Random Effects
• Example 44.12: Fitting a Marginal (GEE-Type) Model
• Example 44.13: Response Surface Comparisons with Multiplicity Adjustments
• Example 44.14: Generalized Poisson Mixed Model for Overdispersed Count Data
• Example 44.15: Comparing Multiple B-Splines
• Example 44.16: Diallel Experiment with Multimember Random Effects
• Example 44.17: Linear Inference Based on Summary Data
• Example 44.18: Weighted Multilevel Model for Survey Data

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GLM Procedure

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

• Example 45.1: Randomized Complete Blocks with Means Comparisons and Contrasts
• Example 45.2: Regression with Mileage Data
• Example 45.3: Unbalanced ANOVA for Two-Way Design with Interaction
• Example 45.4: Analysis of Covariance
• Example 45.5: Three-Way Analysis of Variance with Contrasts
• Example 45.6: Multivariate Analysis of Variance
• Example 45.7: Repeated Measures Analysis of Variance
• Example 45.8: Mixed Model Analysis of Variance with the RANDOM Statement
• Example 45.9: Analyzing a Doubly Multivariate Repeated Measures Design
• Example 45.10: Testing for Equal Group Variances
• Example 45.11: Analysis of a Screening Design

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GLMMOD Procedure

• A One-Way Design

• Example 46.1: A Two-Way Design
• Example 46.2: Factorial Screening

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GLMPOWER Procedure

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

• Example 47.1: One-Way ANOVA
• Example 47.2: Two-Way ANOVA with Covariate
• Example 47.3: Repeated Measures ANOVA

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GLMSELECT Procedure

• Getting Started: GLMSELECT Procedure

• Example 48.1: Modeling Baseball Salaries Using Performance Statistics
• Example 48.2: Using Validation and Cross Validation
• Example 48.3: Scatter Plot Smoothing by Selecting Spline Functions
• Example 48.4: Multimember Effects and the Design Matrix
• Example 48.5: Model Averaging
• Example 48.6: Elastic Net and External Cross Validation
• Example 48.7: LASSO with Screening

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HPMIXED Procedure

• Mixed Model with Large Number of Fixed and Random Effects

• Example 49.1: Ranking Many Random-Effect Coefficients
• Example 49.2: Comparing Results from PROC HPMIXED and PROC MIXED
• Example 49.3: Using PROC GLIMMIX for Further Analysis of PROC HPMIXED Fit
• Example 49.4: Mixed Model Analysis of Microarray Data
• Example 49.5: Repeated Measures

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ICLIFETEST Procedure

• Getting Started: ICLIFETEST Procedure

• Example 50.1: Analyzing Data with Observations below a Limit of Detection
• Example 50.2: Controlling the Plotting of Survival Estimates
• Example 50.3: Plotting Kernel-Smoothed Hazard Functions

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ICPHREG Procedure New Procedure!

• Getting Started: ICPHREG Procedure

• Example 51.1: Fitting Cubic Spline Models
• Example 51.2: Plotting Predicted Survival and Cumulative Hazard Functions

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INBREED Procedure

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

• Example 52.1: Monoecious Population Analysis
• Example 52.2: Pedigree Analysis
• Example 52.3: Pedigree Analysis with BY Groups

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IRT Procedure

• Getting Started: IRT Procedure

• Example 53.1: Unidimensional IRT Models
• Example 53.2: Multidimensional Exploratory and Confirmatory IRT Models
• Example 53.3: Multiple-Group Analysis
• Example 53.4: Quality of Life Survey

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KDE Procedure

• Getting Started: KDE Procedure

• Example 54.1: Computing a Basic Kernel Density Estimate
• Example 54.2: Changing the Bandwidth
• Example 54.3: Changing the Bandwidth (Bivariate)
• Example 54.4: Requesting Additional Output Tables
• Example 54.5: Univariate KDE Graphics
• Example 54.6: Bivariate KDE Graphics

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KRIGE2D Procedure

• Spatial Prediction Using Kriging, Contour Plots

• Example 55.1: Spatial Prediction of Pollutant Concentration
• Example 55.2: Investigating the Effect of Model Specification on Spatial Prediction
• Example 55.3: Data Quality and Prediction with Missing Values

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LATTICE Procedure

• Getting Started: LATTICE Procedure

• Example 56.1: 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 57.1: Motorette Failure
• Example 57.2: Computing Predicted Values for a Tobit Model
• Example 57.3: Overcoming Convergence Problems by Specifying Initial Values
• Example 57.4: Analysis of Arbitrarily Censored Data with Interaction Effects
• Example 57.5: Probability Plotting—Right Censoring
• Example 57.6: Probability Plotting—Arbitrary Censoring
• Example 57.7: Bayesian Analysis of Clinical Trial Data
• Example 57.8: Model Postfitting Analysis

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LIFETEST Procedure

• Getting Started: LIFETEST Procedure

• Example 58.1: Product-Limit Estimates and Tests of Association
• Example 58.2: Enhanced Survival Plot and Multiple-Comparison Adjustments
• Example 58.3: Life-Table Estimates for Males with Angina Pectoris

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LOESS Procedure

• Scatter Plot Smoothing

• Example 59.1: Engine Exhaust Emissions
• Example 59.2: Sulfate Deposits in the U.S. for 1990
• Example 59.3: Catalyst Experiment
• Example 59.4: El Niño Southern Oscillation

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LOGISTIC Procedure

• Getting Started: LOGISTIC Procedure

• Example 60.1: Stepwise Logistic Regression and Predicted Values
• Example 60.2: Logistic Modeling with Categorical Predictors
• Example 60.3: Ordinal Logistic Regression
• Example 60.4: Nominal Response Data: Generalized Logits Model
• Example 60.5: Stratified Sampling
• Example 60.6: Logistic Regression Diagnostics
• Example 60.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
• Example 60.8: Comparing Receiver Operating Characteristic Curves
• Example 60.9: Goodness-of-Fit Tests and Subpopulations
• Example 60.10: Overdispersion
• Example 60.11: Conditional Logistic Regression for Matched Pairs Data
• Example 60.12: Exact Conditional Logistic Regression
• Example 60.13: Firth’s Penalized Likelihood Compared with Other Approaches
• Example 60.14: Complementary Log-Log Model for Infection Rates
• Example 60.15: Complementary Log-Log Model for Interval-Censored Survival Times
• Example 60.16: Scoring Data Sets
• Example 60.17: Using the LSMEANS Statement
• Example 60.18: Partial Proportional Odds Model

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MCMC Procedure

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

• Example 61.1: Simulating Samples From a Known Density
• Example 61.2: Box-Cox Transformation
• Example 61.3: Logistic Regression Model with a Diffuse Prior
• Example 61.4: Logistic Regression Model with Jeffreys’ Prior
• Example 61.5: Poisson Regression
• Example 61.6: Nonlinear Poisson Regression Models
• Example 61.7: Logistic Regression Random-Effects Model
• Example 61.8: Nonlinear Poisson Regression Multilevel Random-Effects Model
• Example 61.9: Multivariate Normal Random-Effects Model
• Example 61.10: Missing at Random Analysis
• Example 61.11: Nonignorably Missing Data (MNAR) Analysis
• Example 61.12: Change Point Models
• Example 61.13: Exponential and Weibull Survival Analysis
• Example 61.14: Time Independent Cox Model
• Example 61.15: Time Dependent Cox Model
• Example 61.16: Piecewise Exponential Frailty Model
• Example 61.17: Normal Regression with Interval Censoring
• Example 61.18: Constrained Analysis
• Example 61.19: Implement a New Sampling Algorithm
• Example 61.20: Using a Transformation to Improve Mixing
• Example 61.21: Gelman-Rubin Diagnostics

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MDS Procedure

• Getting Started: MDS Procedure

• Example 62.1: Jacobowitz Body Parts Data from Children and Adults

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MI Procedure

• Getting Started: MI Procedure

• Example 63.1: EM Algorithm for MLE
• Example 63.2: Monotone Propensity Score Method
• Example 63.3: Monotone Regression Method
• Example 63.4: Monotone Logistic Regression Method for CLASS Variables
• Example 63.5: Monotone Discriminant Function Method for CLASS Variables
• Example 63.6: FCS Method for Continuous Variables
• Example 63.7: FCS Method for CLASS Variables
• Example 63.8: FCS Method with Trace Plot
• Example 63.9: MCMC Method
• Example 63.10: Producing Monotone Missingness with MCMC
• Example 63.11: Checking Convergence in MCMC
• Example 63.12: Saving and Using Parameters for MCMC
• Example 63.13: Transforming to Normality
• Example 63.14: Multistage Imputation
• Example 63.15: Creating Control-Based Pattern Imputation in Sensitivity Analysis
• Example 63.16: Adjusting Imputed Continuous Values in Sensitivity Analysis
• Example 63.17: Adjusting Imputed Classification Levels in Sensitivity Analysis
• Example 63.18: Adjusting Imputed Values with Parameters in a Data Set

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MIANALYZE Procedure

• Getting Started: MIANALYZE Procedure

• Example 64.1: Reading Means and Standard Errors from a DATA= Data Set
• Example 64.2: Reading Means and Covariance Matrices from a DATA= COV Data Set
• Example 64.3: Reading Regression Results from a DATA= EST Data Set
• Example 64.4: Reading Mixed Model Results from PARMS= and COVB= Data Sets
• Example 64.5: Reading Generalized Linear Model Results
• Example 64.6: Reading GLM Results from PARMS= and XPXI= Data Sets
• Example 64.7: Reading Logistic Model Results from a PARMS= Data Set
• Example 64.8: Reading Mixed Model Results with Classification Covariates
• Example 64.9: Reading Nominal Logistic Model Results
• Example 64.10: Using a TEST statement
• Example 64.11: Combining Correlation Coefficients
• Example 64.12: Sensitivity Analysis with Control-Based Pattern Imputation
• Example 64.13: Sensitivity Analysis with Tipping-Point Approach

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MIXED Procedure

• Clustered Data Example

• Example 65.1: Split-Plot Design
• Example 65.2: Repeated Measures
• Example 65.3: Plotting the Likelihood
• Example 65.4: Known G and R
• Example 65.5: Random Coefficients
• Example 65.6: Line-Source Sprinkler Irrigation
• Example 65.7: Influence in Heterogeneous Variance Model
• Example 65.8: Influence Analysis for Repeated Measures Data
• Example 65.9: Examining Individual Test Components
• Example 65.10: Isotonic Contrasts for Ordered Mean Values

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MODECLUS Procedure

• Getting Started: MODECLUS Procedure

• Example 66.1: Cluster Analysis of Samples from Univariate Distributions
• Example 66.2: Cluster Analysis of Flying Mileages between Ten American Cities
• Example 66.3: Cluster Analysis with Significance Tests
• Example 66.4: Cluster Analysis: Hertzsprung-Russell Plot
• Example 66.5: Using the TRACE Option When METHOD=6

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MULTTEST Procedure

• Drug Example

• Example 67.1: Cochran-Armitage Test with Permutation Resampling
• Example 67.2: Freeman-Tukey and and t Tests with Bootstrap Resampling
• Example 67.3: Peto Mortality-Prevalence Test
• Example 67.4: Fisher Test with Permutation Resampling
• Example 67.5: Inputting Raw p-Values
• Example 67.6: Adaptive Adjustments and ODS Graphics

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NESTED Procedure

• Reliability of Automobile Models

• Example 68.1: 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 69.1: Segmented Model
• Example 69.2: Iteratively Reweighted Least Squares
• Example 69.3: Probit Model with Likelihood Function
• Example 69.4: Affecting Curvature through Parameterization
• Example 69.5: Comparing Nonlinear Trends among Groups
• Example 69.6: ODS Graphics and Diagnostics
• Example 69.7: Parameter Profiling and Bootstrapping

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NLMIXED Procedure

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

• Example 70.1: One-Compartment Model with Pharmacokinetic Data
• Example 70.2: Probit-Normal Model with Binomial Data
• Example 70.3: Probit-Normal Model with Ordinal Data
• Example 70.4: Poisson-Normal Model with Count Data
• Example 70.5: Failure Time and Frailty Model
• Example 70.6: Simulated Nested Linear Random-Effects Model
• Example 70.7: Overdispersion Hierarchical Nonlinear Mixed Model

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NPAR1WAY Procedure

• Getting Started: NPAR1WAY Procedure

• Example 71.1: Two-Sample Location Tests and Plots
• Example 71.2: EDF Statistics and EDF Plot
• Example 71.3: Exact Wilcoxon Two-Sample Test
• Example 71.4: Hodges-Lehmann Estimation
• Example 71.5: Exact Savage Multisample Test

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ORTHOREG Procedure

• Longley Data

• Example 72.1: Precise Analysis of Variance
• Example 72.2: Wampler Data
• Example 72.3: Fitting Polynomials

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PHREG Procedure

• Classical Method of Maximum Likelihood
• Bayesian Analysis

• Example 73.1: Stepwise Regression
• Example 73.2: Best Subset Selection
• Example 73.3: Modeling with Categorical Predictors
• Example 73.4: Firth’s Correction for Monotone Likelihood
• Example 73.5: Conditional Logistic Regression for m:n Matching
• Example 73.6: Model Using Time-Dependent Explanatory Variables
• Example 73.7: Time-Dependent Repeated Measurements of a Covariate
• Example 73.8: Survival Curves
• Example 73.9: Analysis of Residuals
• Example 73.10: Analysis of Recurrent Events Data
• Example 73.11: Analysis of Clustered Data
• Example 73.12: Model Assessment Using Cumulative Sums of Martingale Residuals
• Example 73.13: Bayesian Analysis of the Cox Model
• Example 73.14: Bayesian Analysis of Piecewise Exponential Model
• Example 73.15: Analysis of Competing-Risks Data

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PLAN Procedure

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

• Example 74.1: A Split-Plot Design
• Example 74.2: A Hierarchical Design
• Example 74.3: An Incomplete Block Design
• Example 74.4: A Latin Square Design
• Example 74.5: A Generalized Cyclic Incomplete Block Design
• Example 74.6: Permutations and Combinations
• Example 74.7: Crossover Designs

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PLM Procedure

• Getting Started: PLM Procedure

• Example 75.1: Scoring with PROC PLM
• Example 75.2: Working with Item Stores
• Example 75.3: Group Comparisons in an Ordinal Model
• Example 75.4: Posterior Inference for Binomial Data
• Example 75.5: BY-Group Processing
• Example 75.6: Comparing Multiple B-Splines
• Example 75.7: Linear Inference with Arbitrary Estimates

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PLS Procedure

• Spectrometric Calibration

• Example 76.1: Examining Model Details
• Example 76.2: Examining Outliers
• Example 76.3: Choosing a PLS Model by Test Set Validation
• Example 76.4: 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 77.1: One-Way ANOVA
• Example 77.2: The Sawtooth Power Function in Proportion Analyses
• Example 77.3: Simple AB/BA Crossover Designs
• Example 77.4: Noninferiority Test with Lognormal Data
• Example 77.5: Multiple Regression and Correlation
• Example 77.6: Comparing Two Survival Curves
• Example 77.7: Confidence Interval Precision
• Example 77.8: Customizing Plots
• Example 77.9: Binary Logistic Regression with Independent Predictors
• Example 77.10: Wilcoxon-Mann-Whitney Test

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PRINCOMP Procedure

• Getting Started: PRINCOMP Procedure

• Example 79.1: Analyzing Mean Temperatures of US Cities
• Example 79.2: Analyzing Rankings of US College Basketball Teams
• Example 79.3: Analyzing Job Ratings of Police Officers

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PRINQUAL Procedure

• Getting Started: PRINQUAL Procedure

• Example 80.1: Multidimensional Preference Analysis of Automobile Data
• Example 80.2: Principal Components of Basketball Rankings

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PROBIT Procedure

• Estimating the Natural Response Threshold Parameter

• Example 81.1: Dosage Levels
• Example 81.2: Multilevel Response
• Example 81.3: Logistic Regression
• Example 81.4: An Epidemiology Study
• Example 81.5: Model Postfitting Analysis

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QUANTLIFE Procedure

• Getting Started: QUANTLIFE Procedure

• Example 82.1: Primary Biliary Cirrhosis Study
• Example 82.2: Drug Abuse Study

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QUANTREG Procedure

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

• Example 83.1: Comparison of Algorithms
• Example 83.2: Quantile Regression for Econometric Growth Data
• Example 83.3: Quantile Regression Analysis of Birth-Weight Data
• Example 83.4: Nonparametric Quantile Regression for Ozone Levels
• Example 83.5: Quantile Polynomial Regression for Salary Data

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QUANTSELECT Procedure

• Getting Started: QUANTSELECT Procedure

• Example 84.1: Simulation Study
• Example 84.2: Econometric Growth Data
• Example 84.3: Pollution and Mortality
• Example 84.4: Surface Fitting with Many Noisy Variables

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REG Procedure

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

• Example 85.1: Modeling Salaries of Major League Baseball Players
• Example 85.2: Aerobic Fitness Prediction
• Example 85.3: Predicting Weight by Height and Age
• Example 85.4: Regression with Quantitative and Qualitative Variables
• Example 85.5: Ridge Regression for Acetylene Data
• Example 85.6: Chemical Reaction Response

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ROBUSTREG Procedure

• M Estimation
• LTS Estimation

• Example 86.1: Comparison of Robust Estimates
• Example 86.2: Robust ANOVA
• Example 86.3: Growth Study of De Long and Summers
• Example 86.4: Constructed Effects
• Example 86.5: Robust Diagnostics

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RSREG Procedure

• A Response Surface with a Simple Optimum

• Example 87.1: A Saddle Surface Response Using Ridge Analysis
• Example 87.2: Response Surface Analysis with Covariates

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SEQDESIGN Procedure

• Getting Started: SEQDESIGN Procedure

• Example 89.1: Creating Fixed-Sample Designs
• Example 89.2: Creating a One-Sided O’Brien-Fleming Design
• Example 89.3: Creating Two-Sided Pocock and O’Brien-Fleming Designs
• Example 89.4: Generating Graphics Display for Sequential Designs
• Example 89.5: Creating Designs Using Haybittle-Peto Methods
• Example 89.6: Creating Designs with Various Stopping Criteria
• Example 89.7: Creating Whitehead’s Triangular Designs
• Example 89.8: Creating a One-Sided Error Spending Design
• Example 89.9: Creating Designs with Various Number of Stages
• Example 89.10: Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper
• Example 89.11: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject
• Example 89.12: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept
• Example 89.13: Creating a Design with a Nonbinding Beta Boundary
• Example 89.14: Computing Sample Size for Survival Data That Have Uniform Accrual
• Example 89.15: Computing Sample Size for Survival Data with Truncated Exponential Accrual

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SEQTEST Procedure

• Getting Started: SEQTEST Procedure

• Example 90.1: Testing the Difference between Two Proportions
• Example 90.2: Testing an Effect in a Regression Model
• Example 90.3: Testing an Effect with Early Stopping to Accept
• Example 90.4: Testing a Binomial Proportion
• Example 90.5: Comparing Two Proportions with a Log Odds Ratio Test
• Example 90.6: Comparing Two Survival Distributions with a Log-Rank Test
• Example 90.7: Testing an Effect in a Proportional Hazards Regression Model
• Example 90.8: Testing an Effect in a Logistic Regression Model
• Example 90.9: Conducting a Trial with a Nonbinding Acceptance Boundary

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SCORE Procedure

• Getting Started: SCORE Procedure

• Example 88.1: Factor Scoring Coefficients
• Example 88.2: Regression Parameter Estimates
• Example 88.3: Custom Scoring Coefficients

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SIM2D Procedure

• Preliminary Spatial Data Analysis
• Investigating Variability by Simulation

• Example 91.1: Simulation and Economic Feasibility
• Example 91.2: Variability at Selected Locations
• Example 91.3: Risk Analysis with Simulation

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SIMNORMAL Procedure

• Getting Started: SIMNORMAL Procedure

• Example: SIMNORM Procedure

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SPP Procedure New Procedure!

• Getting Started: SPP Procedure

• Example 93.1: Exploration of a Multitype Point Pattern
• Example 93.2: Testing Covariate Dependence of a Point Pattern
• Example 93.3: Intensity Model Validation Diagnostics

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STDIZE Procedure

• Getting Started: STDIZE Procedure

• Example 94.1: Standardization of Variables in Cluster Analysis

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STDRATE Procedure

• Getting Started: STDRATE Procedure

• Example 95.1: Comparing Directly Standardized Rates
• Example 95.2: Computing Mantel-Haenszel Risk Estimation
• Example 95.3: Computing Attributable Fraction Estimates
• Example 95.4: Displaying SMR Results from BY Groups

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STEPDISC Procedure

• Getting Started: STEPDISC Procedure

• Example 96.1: Performing a Stepwise Discriminant Analysis

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SURVEYFREQ Procedure

• Getting Started: SURVEYFREQ Procedure

• Example 97.1: Two-Way Tables
• Example 97.2: Multiway Tables (Domain Analysis)
• Example 97.3: Output Data Sets

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SURVEYLOGISTIC Procedure

• Getting Started: SURVEYLOGISTIC Procedure

• Example 98.1: Stratified Cluster Sampling
• Example 98.2: The Medical Expenditure Panel Survey (MEPS)

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SURVEYMEANS Procedure

• Simple Random Sampling
• Stratified Sampling
• Output Data Sets

• Example 99.1: Stratified Cluster Sample Design
• Example 99.2: Domain Analysis
• Example 99.3: Ratio Analysis
• Example 99.4: Analyzing Survey Data with Missing Values
• Example 99.5: Variance Estimation Using Replication Methods

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SURVEYPHREG Procedure

• Getting Started: SURVEYPHREG Procedure

• Example 100.1: Analysis of Clustered Data
• Example 100.2: Stratification, Clustering, and Unequal Weights
• Example 100.3: Domain Analysis
• Example 100.4: Variance Estimation by Using Replicate Weights
• Example 100.5: 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 101.1: Simple Random Sampling
• Example 101.2: Cluster Sampling
• Example 101.3: Regression Estimator for Simple Random Sample
• Example 101.4: Stratified Sampling
• Example 101.5: Regression Estimator for Stratified Sample
• Example 101.6: Stratum Collapse
• Example 101.7: Domain Analysis
• Example 101.8: Compare Domain Statistics
• Example 101.9: Variance Estimate Using the Jackknife Method

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SURVEYSELECT Procedure

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

• Example 102.1: Replicated Sampling
• Example 102.2: PPS Selection of Two Units per Stratum
• Example 102.3: PPS (Dollar-Unit) Sampling
• Example 102.4: Proportional Allocation

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TPSPLINE Procedure

• Getting Started: TPSPLINE Procedure

• Example 103.1: Partial Spline Model Fit
• Example 103.2: Spline Model with Higher-Order Penalty
• Example 103.3: Multiple Minima of the GCV Function
• Example 103.4: Large Data Set Application
• Example 103.5: Computing a Bootstrap Confidence Interval

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TRANSREG Procedure

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

• Example 104.1: Transformation Regression of Exhaust Emissions Data
• Example 104.2: Box-Cox Transformations
• Example 104.3: Penalized B-Spline
• Example 104.4: Nonmetric Conjoint Analysis of Tire Data
• Example 104.5: Metric Conjoint Analysis of Tire Data
• Example 104.6: Preference Mapping of Automobile Data

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TREE Procedure

• Getting Started: TREE Procedure

• Example 105.1: Mammals’ Teeth
• Example 105.2: Iris Data

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TTEST Procedure

• One-Sample
• Comparing Group Means

• Example 106.1: Using Summary Statistics to Compare Group Means
• Example 106.2: One-Sample Comparison with the FREQ Statement
• Example 106.3: Paired Comparisons
• Example 106.4: AB/BA Crossover Design
• Example 106.5: Equivalence Testing with Lognormal Data

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VARCLUS Procedure

• Getting Started: VARCLUS Procedure

• Example 107.1: Correlations among Physical Variables

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VARCOMP Procedure

• Analyzing the Cure Rate of Rubber

• Example 108.1: Using the Four General Estimation Methods
• Example 108.2: Using the GRR Method

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VARIOGRAM Procedure

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

• Example 109.1: Aspects of Semivariogram Model Fitting
• Example 109.2: An Anisotropic Case Study with Surface Trend in the Data
• Example 109.3: Analysis without Surface Trend Removal
• Example 109.4: Covariogram and Semivariogram
• Example 109.5: A Box Plot of the Square Root Difference Cloud

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