List of Outputs

Chapter 42: The LOGISTIC Procedure

Output 42.1.1: Startup Model

Output 42.1.2: Step 1 of the Stepwise Analysis

Output 42.1.3: Step 2 of the Stepwise Analysis

Output 42.1.4: Step 3 of the Stepwise Analysis

Output 42.1.5: Summary of the Stepwise Selection

Output 42.1.6: Display of the LACKFIT Option

Output 42.1.7: Data Set of Estimates and Covariances

Output 42.1.8: Predicted Probabilities and Confidence Intervals

Output 42.1.9: Initial Step in Backward Elimination

Output 42.1.10: Fast Elimination Step

Output 42.1.11: Classifying Input Observations

Output 42.2.1: Effect Coding of CLASS Variables

Output 42.2.2: Wald Tests of Individual Effects

Output 42.2.3: Parameter Estimates with Effect Coding

Output 42.2.4: Effects Selected into the Model

Output 42.2.5: Type 3 Effects and Parameter Estimates with Effect Coding

Output 42.2.6: Reference Coding of CLASS Variables

Output 42.2.7: Type 3 Effects and Parameter Estimates with Reference Coding

Output 42.2.8: Results of CONTRAST Statements

Output 42.3.1: Proportional Odds Model Regression Analysis

Output 42.3.2: Estimated Covariance Matrix

Output 42.4.1: Analysis of Saturated Model

Output 42.4.2: Fit Statistics

Output 42.4.3: Tests

Output 42.4.4: Estimates

Output 42.4.5: Analysis of Main Effects Model

Output 42.4.6: Estimates

Output 42.5.1: False Positive and False Negative Rates

Output 42.6.1: Logistic Regression Analysis for Vaso-Constriction Data

Output 42.6.3: Residuals, Hat Matrix, and CI Displacement C (Experimental)

Output 42.6.2: Regression Diagnostics from the INFLUENCE Option (Experimental)

Output 42.6.4: CI Displacement CBar, Change in Deviance and Pearson 2, and DFBETAS for the Intercept (Experimental)

Output 42.6.5: DFBETAS for LogRate and LogVolume (Experimental)

Output 42.7.2: R-Square, Confidence Intervals, and Customized Odds Ratio

Output 42.7.1: Deviance and Pearson Goodness-of-Fit Analysis

Output 42.7.3: Receiver Operating Characteristic Curve (Experimental)

Output 42.7.4: Estimated Probability and 95% Prediction Limits (Experimental)

Output 42.8.1: Full Model Fit

Output 42.8.2: Reduced Model Fit

Output 42.9.1: Results of the Model Fit for the Two-Way Layout

Output 42.9.2: Williams' Model for Overdispersion

Output 42.9.3: Reduced Model with Overdispersion Controlled

Output 42.10.1: Conditional Logistic Regression (Gall as Risk Factor)

Output 42.10.2: Conditional Logistic Regression (Gall and Hyper as Risk Factors)

Output 42.10.3: Exact Conditional Logistic Regression (Gall as Risk Factor)

Output 42.10.4: Exact Conditional Logistic Regression (Gall and Hyper as Risk Factors)

Output 42.11.1: Modeling Constant Risk of Infection

Output 42.11.2: Modeling Separate Risk of Infection

Output 42.12.1: An Observation with Time=3 in Data Set Beetles

Output 42.12.2: Corresponding Beetle-day Observations in Days

Output 42.12.3: Parameter Estimates for the Grouped Proportional Hazards Model

Output 42.12.4: Predicted Survival at Concentrations of 0.20 and 0.80 mg/cm ²

Output 42.13.1: Classification of Data used for Scoring

Output 42.13.2: Classification of Test Data

Chapter 43: The MDS Procedure

Output 43.1.1: Iteration History and Final Estimates for Body Parts Data

Output 43.1.2: Plot of Over-All Fit for Body Parts Data

Output 43.1.4: Plot of Dimension Coefficients for Body Parts Data

Chapter 44: The MI Procedure

Output 44.1.1: Model Information

Output 44.1.2: Missing Data Patterns

Output 44.1.3: Univariate Statistics

Output 44.1.4: Pairwise Correlations

Output 44.1.5: Initial Parameter Estimates for EM

Output 44.1.6: EM (MLE) Iteration History

Output 44.1.7: EM (MLE) Parameter Estimates

Output 44.1.8: EM Estimates

Output 44.2.1: Model Information

Output 44.2.2: Monotone Model Specification

Output 44.2.3: Missing Data Patterns

Output 44.2.4: Variance Information

Output 44.2.5: Parameter Estimates

Output 44.2.6: Imputed Data Set

Output 44.3.1: Monotone Model Specification

Output 44.3.2: Regression Model

Output 44.3.3: Regression Predicted Mean Matching Model

Output 44.3.4: Variance Information

Output 44.3.5: Parameter Estimates

Output 44.3.6: Imputed Data Set

Output 44.4.1: Model Information

Output 44.4.2: Monotone Model Specification

Output 44.4.3: Missing Data Patterns

Output 44.4.4: Logistic Regression Model

Output 44.4.5: Imputed Data Set

Output 44.5.1: Model Information

Output 44.5.2: Monotone Model Specification

Output 44.5.3: Missing Data Patterns

Output 44.5.4: Discriminant Model

Output 44.5.5: Imputed Data Set

Output 44.6.1: Model Information

Output 44.6.2: Missing Data Patterns

Output 44.6.3: EM (Posterior Mode) Iteration History

Output 44.6.4: Initial Parameter Estimates

Output 44.6.5: Variance Information

Output 44.6.6: Parameter Estimates

Output 44.7.1: Model Information

Output 44.7.2: Missing Data Pattern

Output 44.7.3: Monotone Missing Data Pattern

Output 44.8.1: Time-Series Plot for Oxygen

Output 44.8.2: Autocorrelation Function Plot for Oxygen

Output 44.8.3: Autocorrelation Function Plot for Oxygen

Output 44.8.4: Time-Series Plot for Oxygen (Experimental)

Output 44.8.5: Autocorrelation Function Plot for Oxygen (Experimental)

Output 44.9.1: OUTEST Data Set

Output 44.9.2: Model Information

Output 44.10.1: Missing Data Pattern

Output 44.10.2: Variable Transformations

Output 44.10.3: Initial Parameter Estimates

Output 44.10.4: Variance Information

Output 44.10.5: Parameter Estimates

Output 44.10.6: Imputed Data Set in Original Scale

Output 44.11.1: Missing Data Pattern

Output 44.11.2: Model Information

Output 44.11.3: Monotone Model Specification

Output 44.11.4: Missing Data Pattern

Output 44.11.5: Imputed Data Set

Chapter 45: The MIANALYZE Procedure

Output 45.1.1: UNIVARIATE Output Data Set

Output 45.1.2: Multiple Imputation Variance Information

Output 45.2.1: COV Data Set

Output 45.1.3: Multiple Imputation Parameter Estimates

Output 45.2.2: Covariance Matrices

Output 45.2.3: Multiple Imputation Multivariate Inference

Output 45.3.1: EST Type Data Set

Output 45.3.2: Multiple Imputation Variance Information

Output 45.3.3: Multiple Imputation Parameter Estimates

Output 45.4.1: PROC MIXED Model Coefficients

Output 45.4.2: PROC MIXED Covariance Matrices

Output 45.4.3: Multiple Imputation Variance Information

Output 45.4.4: Multiple Imputation Parameter Estimates

Output 45.5.1: PROC GENMOD Model Coefficients

Output 45.5.2: PROC GENMOD Model Information

Output 45.5.3: PROC GENMOD Covariance Matrices

Output 45.6.1: PROC GLM Model Coefficients

Output 45.6.2: PROC GLM (X X) ^{ˆ 1} Matrices

Output 45.7.1: PROC LOGISTIC Model Coefficients

Output 45.7.2: PROC LOGISTIC Covariance Matrices

Output 45.7.3: Multiple Imputation Variance Information

Output 45.7.4: Multiple Imputation Parameter Estimates

Output 45.8.1: PROC MIXED Model Coefficients

Output 45.8.2: Multiple Imputation Variance Information

Output 45.8.3: Multiple Imputation Parameter Estimates

Output 45.9.1: Test Specification

Output 45.9.2: Multiple Imputation Variance Information

Output 45.9.3: Multiple Imputation Parameter Estimates

Output 45.9.4: Multiple Imputation Multivariate Inference

Output 45.10.1: Output z Statistics

Output 45.10.2: Combining Fisher's z statistics

Output 45.10.3: Parameter Estimates with 95% Confidence Limits

Output 45.10.4: Estimated Correlation Coefficient

Chapter 46: The MIXED Procedure

Output 46.1.1: Split-Plot Example

Output 46.1.2: Inference Space Results

Output 46.2.1: Repeated Measures with Unstructured Covariance Matrix

Output 46.2.2: Repeated Measures with Compound Symmetry Structure

Output 46.2.3: Repeated Measures with Heterogeneous Structures

Output 46.3.1: Plotting the Likelihood

Output 46.3.2: Plot of Likelihood Surface

Output 46.4.1: Known G and R

Output 46.5.1: Random Coefficients Analysis

Output 46.5.2: Random Coefficients with Nested Errors Analysis

Output 46.6.1: Line-Source Sprinkler Irrigation Analysis

Output 46.7.1: Heterogeneous Variance Analysis

Output 46.7.2: Leave-One-Out Estimates

Output 46.7.3: Fixed Effects Delete Estimates (Experimental)

Output 46.7.4: Covariance Parameter Delete Estimates (Experimental)

Output 46.7.5: Residual Diagnostics

Output 46.7.6: Restricted Likelihood Distance and Fixed Effects Diagnostics

Output 46.7.7: REML Distance and Fixed Effects Diagnostics (Experimental)

Output 46.7.8: Covariance Parameter Diagnostics

Output 46.7.9: Covariance Parameter Diagnostics (Experimental)

Output 46.8.1: Default Influence Statistics in Noniterative Analysis

Output 46.8.2: Overall and Fixed Effects Diagnostics (Experimental)

Output 46.8.3: Covariance Parameter Diagnostics (Experimental)

Output 46.8.4: Fixed Effects Delete Estimates (Experimental)

Output 46.8.5: Covariance Parameter Delete Estimates (Experimental)

Output 46.8.6: Distribution of Observed Values (Experimental)

Output 46.8.7: Distribution of Marginal Residuals (Experimental)

Output 46.8.8: Distribution of Conditional Residuals (Experimental)

Output 46.9.1: Coefficients of Type 3 Estimable Functions

Output 46.9.2: Type 3 Tests in Split-Plot Example

Output 46.9.3: Type 3 LComponents Table

Chapter 47: The MODECLUS Procedure

Output 47.1.1: Cluster Analysis of Sample from a Uniform Distribution

Output 47.1.2: Cluster Analysis of Sample from an Exponential Distribution

Output 47.1.3: Cluster Analysis of Sample from a Bimodal Mixture of Two Normal Distributions

Output 47.2.1: Clustering with K-Nearest-Neighbor Density Estimates

Output 47.2.2: Clustering with Uniform Kernel Density Estimates

Outptt 47.2.3: Uniform Kernel Density Estimates, Clustering Neighborhoods Extended to Nearest Neighbor

Output 47.3.1: Significance Tests with the JOIN=20 and SHORT Options

Output 47.3.2: Significance Tests with the JOIN Option

Output 47.3.3: Scatter Plots of Cluster Memberships by _NJOIN_

Output 47.4.1: Scatter Plot of Data

Output 47.4.2: Results from PROC MODECLUS

Output 47.4.3: Scatter Plots of Cluster Memberships by _R_

Output 47.5.1: Partial Output of METHOD=6 with TRACE and Default THRESHOLD=

Output 47.5.2: Density Plot

Output 47.5.3: Partial Output of METHOD=6 with TRACE and THRESHOLD=.55

Output 47.5.4: Density Plot

Chapter 48: The MULTTEST Procedure

Output 48.1.1: Cochran-Armitage Test with Permutation Resampling

Output 48.1.2: Contrast Coefficients

Output 48.1.3: Summary Statistics

Output 48.1.4: Resulting p-Values

Output 48.1.5: Exact Permutation Distribution

Output 48.2.1: FT and t-tests with Bootstrap Resampling

Output 48.2.2: Contrast Coefficients

Output 48.2.3: Summary Statistics

Output 48.2.4: p-Values

Output 48.2.5: Resampling Data Set

Output 48.3.1: Peto Test

Output 48.3.2: Contrast Coefficients

Output 48.3.3: p-Values

Output 48.3.4: OUT= Data Set

Output 48.4.1: Fisher Test with Permutation Resampling

Output 48.4.2: Default Contrast Coefficients

Output 48.4.3: p-Values

Output 48.4.4: OUT= Data Set

Output 48.5.1: Inputting Raw p-Values

Chapter 49: The NESTED Procedure

Output 49.1.1: Analysis of Calcium Concentration in Turnip Greens Using PROC NESTED

Chapter 50: The NLIN Procedure

Output 50.1.2: Least Squares Analysis for the Quadratic Model

Output 50.1.1: Nonlinear Least Squares Iterative Phase

Output 50.1.3: Observed and Predicted Values for the Quadratic Model

Output 50.2.1: Nonlinear Least Squares Analysis

Output 50.2.2: Listing of Computed Weights from PROC NLIN

Output 50.3.1: Nonlinear Least Squares Analysis from PROC NLIN