SAS.STAT 9.1 Users Guide (Vol. 5)
Chapter 51: The NLMIXED Procedure
- Output 51.5.1: Analysis Results for Failure Time Model
- Output 51.5.2: Estimated Cumulative Distribution Function
- Output 51.5.3: Analysis Results for Frailty Model
- Output 51.5.4: Patient-Specific CDFs and Predicted Values. Pain Reliever 1: Solid Line, Closed Circles. Pain Reliever 2: Dashed Lines, Open Circles.
Chapter 52: The NPAR1WAY Procedure
- Output 52.1.1: Wilcoxon Two-Sample Test
- Output 52.1.2: Median Two-Sample Test
- Output 52.1.3: Empirical Distribution Function Statistics
- Output 52.2.1: Wilcoxon Two-Sample Test
- Output 52.3.1: Savage Multisample Test
Chapter 53: The ORTHOREG Procedure
- Output 53.1.1: PROC ORTHOREG Results for Atomic Weight Example
- Output 53.2.1: Wampler data: Deviations from Certified Values
Chapter 54: The PHREG Procedure
- Output 54.1.1: Individual Score Test Results for All Variables
- Output 54.1.2: First Model in the Stepwise Selection Process
- Output 54.1.3: Score Tests Adjusted for the Variable LogBUN
- Output 54.1.4: Second Model in the Stepwise Selection Process
- Output 54.1.5: Third Model in the Stepwise Regression
- Output 54.1.6: Final Model in the Stepwise Regression
- Output 54.1.7: Model Selection Summary
- Output 54.2.1: Best Variable Combinations
- Output 54.3.1: Summary of Number of Case and Controls
- Output 54.3.2: Conditional Logistic Regression Analysis for the Low Birth-Weight Study
- Output 54.4.1: Heart Transplant Study Analysis I
- Output 54.4.2: Heart Transplant Study Analysis II
- Output 54.5.1: Cox Regression Analysis on the Survival of Rodents
- Output 54.5.2: Plot of DFBETA Statistic for DOSE versus Subject Number
- Output 54.5.3: Plot of DFBETA Statistic for NPAP versus Subject Number
- Output 54.6.1: Survivor Function Estimates for LogBUN=1.0 and HGB=10.0
- Output 54.6.2: Survival Curves for Specific Covariate Patterns
- Output 54.7.1: Martingale Residual Plot
- Output 54.7.2: Deviance Residual Plot
- Output 54.8.1: Analysis of the Intensity Model
- Output 54.8.2: Analysis of the Proportional Means Model
- Output 54.8.3: Analysis of the PWP Total Time Model with Noncommon Effects
- Output 54.8.4: Analysis of the PWP Gap Time Model with Noncommon Effects
- Output 54.8.5: Summary of Bladder Tumor Recurrences in 86 Patients
- Output 54.8.6: Analysis of Marginal Cox Models
- Output 54.8.7: Tests of Treatment Effects
- Output 54.9.1: Breakdown of Blindness in the Control and Treated Groups
- Output 54.9.2: Inference Based on the Robust Sandwich Covariance
- Output 54.10.1: Cox Model with Bilirubin as a Covariate
- Output 54.10.2: Cumulative Martingale Residuals vs Bilirubin (Experimental)
- Output 54.10.3: Typical Cumulative Residual Plot Patterns
- Output 54.10.4: Model with log(Bilirubin) as a Covariate
- Output 54.10.5: Panel Plot of Cumulative Martingale Residuals vs log(Bilirubin) (Experimental)
- Output 54.10.6: Cumulative Martingale Residuals vs log(Bilirubin) (Experimental)
- Output 54.10.7: Standardized Score Process for log(Bilirubin) (Experimental)
- Output 54.10.8: Standardized Score Process for log(Protime) (Experimental)
- Output 54.10.9: Kolmogorov-type Supremum Tests for Proportional Hazards Assumption
Chapter 55: The PLAN Procedure
- Output 55.1.1: A Split-Plot Design
- Output 55.2.1: A Hierarchical Design
- Output 55.3.1: A Generalized Cyclic Block Design
- Output 55.3.2: A Generalized Cyclic Block Design
- Output 55.4.1: A Randomized Latin Square Design
- Output 55.5.1: A Generalized Cyclic Incomplete Block Design
- Output 55.6.1: List of Permutations
- Output 55.6.2: List of Permutations
- Output 55.6.3: Randomized Permutations
- Output 55.6.4: List of Combinations
- Output 55.6.5: Combinations Data Set Created by ODS
Chapter 56: The PLS Procedure
- Output 56.1.1: Amount of Training Set Variation Explained
- Output 56.1.2: First X- and Y-scores for Penta-Peptide Model 1
- Output 56.1.3: Second X- and Y-scores for Penta-Peptide Model 1
- Output 56.1.4: First and Second X-scores for Penta-Peptide Model 1
- Output 56.1.5: First and Second X-weights for Penta-Peptide Model 1
- Output 56.1.6: Estimated PLS Regression Coefficients and VIP (Model 1)
- Output 56.2.1: Distances from the X-variables to the Model (Training Set)
- Output 56.2.2: Distances from the Y-variables to the Model (Training Set)
- Output 56.3.1: Spectra for Three Samples of Tyrosine and Tryptophan
- Output 56.3.2: Amount of Training Set Variation Explained
- Output 56.3.3: Test Set Validation for the Number of PLS Factors
- Output 56.3.4: Predictor Loadings Across Frequencies
Chapter 57: The POWER Procedure
- Output 57.1.1: Sample Sizes for One-Way ANOVA Contrasts
- Output 57.1.2: Plot of Sample Size versus Power for One-Way ANOVA Contrasts
- Output 57.1.3: Plot of Power versus Sample Size for One-Way ANOVA Contrasts
- Output 57.2.1: Approximate Sample Size for z Test of a Proportion
- Output 57.2.2: Approximate Sample Size for z Test with Continuity Correction
- Output 57.2.3: Plot of Power versus Sample Size for Exact Binomial Test
- Output 57.2.4: Plot for Assessing Sensitivity to True Proportion Value
- Output 57.2.5: Numerical Content of Plot
- Output 57.2.6: Plot of Power versus Sample Size for Another 1-Sided Test
- Output 57.2.7: Plot of Power versus Sample Size for a 2-Sided Test
- Output 57.3.1: Power for Paired t Analysis of Crossover Design
- Output 57.3.2: Plot of Power versus Sample Size for Paired t Analysis of Crossover Design
- Output 57.3.3: Power for Paired Equivalence Test for Crossover Design
- Output 57.4.1: Power for Noninferiority Test of Ratio
- Output 57.4.2: Plot of Power versus Mean Ratio for Noninferiority Test
- Output 57.5.1: Power Analysis for Multiple Regression
- Output 57.5.2: Plot of Power versus Sample Size for Multiple Regression
- Output 57.5.3: Power Analysis for Fishers z Test
- Output 57.5.4: Sample Size Determination for Fishers z Test
- Output 57.6.1: Survival Curves
- Output 57.6.2: Sample Size Determination for Log-Rank Test
- Output 57.7.1: Sample Size Determination for Confidence Interval Precision
- Output 57.7.2: Plot of Sample Size vs. Confidence Interval Half-Width
- Output 57.8.1: Computed Sample Sizes
- Output 57.8.2: Plot of Sample Size versus Power
- Output 57.8.3: Plot of Power versus Sample Size using First Strategy
- Output 57.8.4: Computed Sample Sizes
- Output 57.8.5: Plot of Power versus Sample Size Using Second Strategy
- Output 57.8.6: Plot of Sample Size versus Mean Difference
- Output 57.8.7: Plot with Overlapping Points
- Output 57.8.8: Sample Sizes
- Output 57.8.9: Plot with Unequally Spaced Points
- Output 57.8.10: Plot with Fractional Sample Sizes
- Output 57.8.11: Plot with Simple Reference Lines on Y-Axis
- Output 57.8.12: Plot with CROSSREF=YES Style Reference Lines from Y-Axis
- Output 57.8.13: Plot with CROSSREF=YES Style Reference Lines from X-Axis
- Output 57.8.14: Plot with Default VARY SettingsPanel 1 of 2
- Output 57.8.15: Plot with Default VARY SettingsPanel 2 of 2
- Output 57.8.16: Plot with Varying Color Instead of Panel
- Output 57.8.17: Plot with Features Explicitly Linked to ParametersPanel 1 of 2
- Output 57.8.18: Plot with Features Explicitly Linked to ParametersPanel 2 of 2
- Output 57.8.19: Plot with a By-Feature Key Inside the Plotting Region
- Output 57.8.20: Plot with a Numbered By-Curve Key
- Output 57.8.21: Plot with a Nonnumbered By-Curve Key
- Output 57.8.22: Plot with Directly Labeled Curves
- Output 57.8.23: Plot with MARKERS=ANALYSIS
- Output 57.8.24: Plot with MARKERS=NICE
Chapter 58: The PRINCOMP Procedure
- Output 58.1.1: Plot of Raw Data
- Output 58.1.2: Results of Principal Component Analysis
- Output 58.1.3: Plot of Principal Components
- Output 58.2.1: Results of Principal Component AnalysisPROC PRINCOMP
- Output 58.2.2: OUT= Data Set Sorted by First Principal Component
- Output 58.2.3: OUT= Data Set Sorted by Second Principal Component
- Output 58.2.4: Plot of the First Two Principal Components
- Output 58.2.5: Plot of the First and Third Principal Components
- Output 58.3.1: Summary Statistics for Basketball Rankings Using PROC MEANS
- Output 58.3.2: Principal Components Analysis of Basketball Rankings Using PROC PRINCOMP
- Output 58.3.3: Basketball Rankings Using PROC PRINCOMP
- Output 58.4.1: Eigenvalue Scatter Plot (Experimental)
- Output 58.4.2: Component Scores Matrix Plot (Experimental)
- Output 58.4.3: Component Pattern Plot (Experimental)
- Output 58.4.4: Component Scores Plot1st versus 2nd (Experimental)
- Output 58.4.5: Component Scores Plot1st versus 3rd (Experimental)
- Output 58.4.6: Painted Components Scores Plot2nd versus 3rd, Painted by 1st (Experimental)
Chapter 59: The PRINQUAL Procedure
- Output 59.1.1: Principal Component Analysis of Original Data
- Output 59.1.2: Transformation of Automobile Preference Data
- Output 59.1.3: Principal Components of Transformed Data
- Output 59.1.4: Preference Ratings for Automobiles Manufactured in 1980
- Output 59.2.1: Multidimensional Preference Analysis (Experimental)
- Output 59.3.1: Transformation of Basketball Team Rankings
- Output 59.3.2: Alternative Approach for Analyzing Basketball Rankings
- Output 59.3.3: Monotonic Transformation for Each News Service
Chapter 60: The PROBIT Procedure
- Example 11.1: Plot of Observed and Fitted Probabilities
- Example 11.2: Dosage Levels: PROC PROBIT
- Example 11.3: Multilevel Response: PROC PROBIT
- Output 60.2.2: Plot of Predicted Probilities for the Test Preparation Group
- Output 60.2.3: Plot of Predicted Probabilities for the Standard Preparation Group
- Output 60.3.1: Logistic Regression: PROC PROBIT
- Output 60.4.1: Class Level Information
- Output 60.4.2: Parameter Information
- Output 60.4.3: Model Information
- Output 60.4.4: Goodness-of-Fit Tests and Response-Covariate Profile
- Output 60.4.5: Type III Tests
- Output 60.4.6: Analysis of Parameter Estimates
- Output 60.4.7: Estimated Covariance Matrix
- Output 60.4.8: Estimated Correlation Matrix
- Output 60.4.9: Probit Analysis on Dose
- Output 60.4.10: Outest Data Set for Epidemiology Study
- Output 60.4.11: Predicted Probability Plot
- Output 60.4.12: Inverse Predicted Probability Plot
- Output 60.4.13: Linear Predictor Plot
- Output 60.4.14: Out2
- Output 60.4.15: Out3
- Output 60.4.16: Out4
- Output 60.4.17: Goodness-of-Fit Table