OUP Companion web site: SAS programs

	OUP.COM Home Page
	OUP UK
	Companion Web Sites

	Atkinson: Optimum Experimental Design
	SAS programs

COMPANION WEB SITE

Atkinson: Optimum Experimental Design
SAS programs

These SAS programs accompany Atkinson, Donev, and Tobias (2007), reproducing the calculations underlying most of the tables and figures in the book. The programs use the following SAS products for the purposes listed:

Base SAS Data processing, manipulation, and display
SAS/STAT Fitting and analyzing statistical models
SAS/QC Constructing experimental designs, including optimal exact designs
SAS/IML Matrix calculations and optimization, including finding optimal continuous designs
SAS/ETS Fitting differential equation models

You will need all these products in order to run all of the SAS programs. Note that most universities and business organizations license a bundle of SAS products that includes these tools.

The programs were developed using SAS 9.1, released in 2004, but most of the SAS facilities used are available in any recent version of the software. The most significant exception is the ODS Graphics facility, demonstrated in Chapter 8.

Chapter 1 - Introduction

Chapter 3 - Experimental Strategies

Data for Table 3.2 - Purification of nickel sulphate

Chapter 4 - The Choice of a Model

Example 4.1 - Response surface model

Chapter 5 - Models and Least Squares

Table 5.4 - Determinants and variances for designs for a single quantitative factor

Chapter 6 - Criteria for a Good Experiment

Chapter 8 - The Analysis of Experiments

Chapter 9 - Optimum Design Theory

Example 9.1 - Quadratic regression through the origin

Example 9.2 - Quadratic regression: Variance function

Optimum design measure for quadratic design on [-1,1]

Example 9.3 - Quadratic regression: Variance function

Chapter 10 - Criteria of Optimality

Figure 10.1 - Quadratic regression through the origin

Chapter 11 - D-optimum Designs

Example 11.1 - Cubic regression through the origin
Example 11.1 - Cubic regression through the origin
Table 11.2 - Efficiencies for Example 1.1
Table 11.3 - Polynomial regression in one variable
Table 11.7 - Optimal quadratic designs

Chapter 12 - Algorithms for the Construction of Exact D-optimum Designs

Example 12.1 - Exact optimal quadratic designs on square

Equation 12.2 - Exact design weights over [-1,0,1]2
Example 12.1 - Exact optimal quadratic designs

Example 12.2 - Performance of an Internal Combustion Engine

Chapter 13 - Optimum Experimental Design with SAS

SAS Task 13.1 - Exact optimal quadratic designs

SAS Task 13.2 - Exact optimal quadratic designs on square

SAS Task 13.3 - Exact optimal quadratic designs on square

Chapter 14 - Experiments with Both Qualitative and Quantitative Factors

Example 14.1 - Second-order response surface with one qualitative factor
Table 14.1 - D-optimum exact N-trial designs for a second-order model in m quantitative factors with one qualitative factor at B levels with one qualitative factor

Example 14.3 - Second-order response surface with two qualitative factors

Chapter 15 - Blocking Response Surface Designs

Example 15.1 - Second-order block designs with variable block sizes
Example 15.2 - Second-order designs with random effects
SAS Task 15.1 - Blocked second-order designs
SAS Task 15.2 - Trend-free second-order design
SAS Task 5.3 - Second-order designs with random effects
Section 15.5.5 - Db-optimum design for clinical trial

Chapter 16 - Mixture Experiments

Figures 16.1 & 16.2 - Optimal mixture designs

Example 16.1 Modification of an Acrylonitrile Powder

Table 16.2. Example 16.2: mixture experiment with linear constraints

Example 16.3: quadratic constraints for a mixture experiment

Fig. 16.5. Example 16.4: second-order model for a three-component mixture with a qualitative factor at three levels

Fig. 16.6. Three-component mixture in two blocks

Fig. 16.7 & 16.8 - 16-trial D-optimum designs in 6 blocks for a 4-component mixture

Fig. 16.9-16.11 - Optimally blocked and orthogonally blocked mixture designs

Chapter 17 - Non-linear Models

Fig. 17.2 & 17.4 - Examples 17.1 and 17.3: Computing Jacobians for directly specified nonlinear models
Fig. 17.6 - Optimum design for two consecutive first-order models
Fig. 17.7 & 17.8 - Sampling window for two consecutive first-order reactions
Tables 17.1 and 17.2 - D- and c-optimum designs for a compartmental model
Example 17.5 - Sequential nonlinear optimum design
Example 17.6 and SAS Task 17.5 - Generalized consecutive reaction
Example 17.7 - Multivariate nonlinear design

SAS Tasks 17.1 & 17.2 - Nonlinear fitting

Chapter 18 - Bayesian Optimum Designs

Example 18.1: Truncated quadratic model
Example 18.2: Exponential decay
Tables 18.5 - 18.7: Exponential decay, optimum designs for various priors

Tables 18.8-18.11 - Bayesian D- and c-optimum designs for compartmental model

Tables 18.12 - Local and Bayesian D-optimum designs for reversible reaction

how the computations are performed,
how the optimization is initialized, and
what values are used for consolidation

Chapter 19 - Design Augmentation

Chapter 20 - Model Checking and Designs for Discriminating Between Models

Example 20.1 & SAS Task 20.1 - Checking for second-order departures from a first-order model

Example 2.2 & SAS Task 20.2 - Second-order response surface: constrained design region

Example 20.3 & SAS Task 20.3 - A Non-linear Model for crop yield and plant density

SAS Task 20.4 - Resolution IV designs as Bayesian D-optimum
Figure 20.4 - Derivative function for T-optimum design
Figure 20.6 - Sequential T-optimum design
SAS Task 20.4 - Resolution IV designs as Bayesian D-optimum

Chapter 21 - Compound Design Criteria

Section 21.4 and SAS Task 21.1 - Compound optimum designs for polynomials in one factor
Table 21.2 - Linear or quadratic regression
Example 21.2 and SAS Task 21.2-21.3: Response surface in four factors

Example 21.3: First-order growth model
Example 21.4: Two linear models
Example 21.6: Constant or quadratic model?
Example 21.7: Compartmental model

Chapter 22 - Generalized Linear Models

Sections 22.4.2 and 22.4.3 and SAS Tasks 22.1-22.4 - One-variable regression
Two-variable logistic regression

Second-order response surface

Section 22.5 - Optimum design for gamma models

Chapter 23 - Response Transformation and Structured Variances

Example 23.1 - Box and Cox wool data

Example 23.2 - Exponential decay

Chapter 24 - Time-dependent Models with Correlated Observations

Example 24.1 - Two consecutive first-order reactions

Example 24.2 - Compartmental model

Privacy Policy and Legal Notice
Content and Graphics copyright Oxford University Press, 2008. All rights reserved.