## D-Optimal Designs

### Introduction to D-Optimal Designs

Traditional experimental designs (Full Factorial Designs, Fractional Factorial Designs, and Response Surface Designs) are appropriate for calibrating linear models in experimental settings where factors are relatively unconstrained in the region of interest. In some cases, however, models are necessarily nonlinear. In other cases, certain treatments (combinations of factor levels) may be expensive or infeasible to measure. D-optimal designs are model-specific designs that address these limitations of traditional designs.

A D-optimal design is generated by an iterative search algorithm and seeks to minimize the covariance of the parameter estimates for a specified model. This is equivalent to maximizing the determinant D = |XTX|, where X is the design matrix of model terms (the columns) evaluated at specific treatments in the design space (the rows). Unlike traditional designs, D-optimal designs do not require orthogonal design matrices, and as a result, parameter estimates may be correlated. Parameter estimates may also be locally, but not globally, D-optimal.

There are several Statistics and Machine Learning Toolbox™ functions for generating D-optimal designs:

FunctionDescription
candexch

Uses a row-exchange algorithm to generate a D-optimal design with a specified number of runs for a specified model and a specified candidate set. This is the second component of the algorithm used by rowexch.

candgen

Generates a candidate set for a specified model. This is the first component of the algorithm used by rowexch.

cordexch

Uses a coordinate-exchange algorithm to generate a D-optimal design with a specified number of runs for a specified model.

daugment

Uses a coordinate-exchange algorithm to augment an existing D-optimal design with additional runs to estimate additional model terms.

dcovary

Uses a coordinate-exchange algorithm to generate a D-optimal design with fixed covariate factors.

rowexch

Uses a row-exchange algorithm to generate a D-optimal design with a specified number of runs for a specified model. The algorithm calls candgen and then candexch. (Call candexch separately to specify a candidate set.)

The following sections explain how to use these functions to generate D-optimal designs.

Note

The function rsmdemo generates simulated data for experimental settings specified by either the user or by a D-optimal design generated by cordexch. It uses the rstool interface to visualize response surface models fit to the data, and it uses the nlintool interface to visualize a nonlinear model fit to the data.

### Generate D-Optimal Designs

Two Statistics and Machine Learning Toolbox algorithms generate D-optimal designs:

• The cordexch function uses a coordinate-exchange algorithm

• The rowexch function uses a row-exchange algorithm

Both cordexch and rowexch use iterative search algorithms. They operate by incrementally changing an initial design matrix X to increase D = |XTX| at each step. In both algorithms, there is randomness built into the selection of the initial design and into the choice of the incremental changes. As a result, both algorithms may return locally, but not globally, D-optimal designs. Run each algorithm multiple times and select the best result for your final design. Both functions have a 'tries' parameter that automates this repetition and comparison.

At each step, the row-exchange algorithm exchanges an entire row of X with a row from a design matrix C evaluated at a candidate set of feasible treatments. The rowexch function automatically generates a C appropriate for a specified model, operating in two steps by calling the candgen and candexch functions in sequence. Provide your own C by calling candexch directly. In either case, if C is large, its static presence in memory can affect computation.

The coordinate-exchange algorithm, by contrast, does not use a candidate set. (Or rather, the candidate set is the entire design space.) At each step, the coordinate-exchange algorithm exchanges a single element of X with a new element evaluated at a neighboring point in design space. The absence of a candidate set reduces demands on memory, but the smaller scale of the search means that the coordinate-exchange algorithm is more likely to become trapped in a local minimum than the row-exchange algorithm.

For example, suppose you want a design to estimate the parameters in the following three-factor, seven-term interaction model:

$y={\beta }_{0}+{\beta }_{1}x{}_{1}+{\beta }_{2}x{}_{2}+{\beta }_{3}x{}_{3}+{\beta }_{12}x{}_{1}x{}_{2}+{\beta }_{13}x{}_{1}x{}_{3}+{\beta }_{23}x{}_{2}x{}_{3}+\epsilon$

Use cordexch to generate a D-optimal design with seven runs:

nfactors = 3;
nruns = 7;
[dCE,X] = cordexch(nfactors,nruns,'interaction','tries',10)
dCE =
-1     1     1
-1    -1    -1
1     1     1
-1     1    -1
1    -1     1
1    -1    -1
-1    -1     1
X =
1    -1     1     1    -1    -1     1
1    -1    -1    -1     1     1     1
1     1     1     1     1     1     1
1    -1     1    -1    -1     1    -1
1     1    -1     1    -1     1    -1
1     1    -1    -1    -1    -1     1
1    -1    -1     1     1    -1    -1

Columns of the design matrix X are the model terms evaluated at each row of the design dCE. The terms appear in order from left to right:

1. Constant term

2. Linear terms (1, 2, 3)

3. Interaction terms (12, 13, 23)

Use X in a linear regression model fit to response data measured at the design points in dCE.

Use rowexch in a similar fashion to generate an equivalent design:

[dRE,X] = rowexch(nfactors,nruns,'interaction','tries',10)
dRE =
-1    -1     1
1    -1     1
1    -1    -1
1     1     1
-1    -1    -1
-1     1    -1
-1     1     1
X =
1    -1    -1     1     1    -1    -1
1     1    -1     1    -1     1    -1
1     1    -1    -1    -1    -1     1
1     1     1     1     1     1     1
1    -1    -1    -1     1     1     1
1    -1     1    -1    -1     1    -1
1    -1     1     1    -1    -1     1

### Augment D-Optimal Designs

In practice, you may want to add runs to a completed experiment to learn more about a process and estimate additional model coefficients. The daugment function uses a coordinate-exchange algorithm to augment an existing D-optimal design.

For example, the following eight-run design is adequate for estimating main effects in a four-factor model:

dCEmain = cordexch(4,8)
dCEmain =
1    -1    -1     1
-1    -1     1     1
-1     1    -1     1
1     1     1    -1
1     1     1     1
-1     1    -1    -1
1    -1    -1    -1
-1    -1     1    -1

To estimate the six interaction terms in the model, augment the design with eight additional runs:

dCEinteraction = daugment(dCEmain,8,'interaction')
dCEinteraction =
1    -1    -1     1
-1    -1     1     1
-1     1    -1     1
1     1     1    -1
1     1     1     1
-1     1    -1    -1
1    -1    -1    -1
-1    -1     1    -1
-1     1     1     1
-1    -1    -1    -1
1    -1     1    -1
1     1    -1     1
-1     1     1    -1
1     1    -1    -1
1    -1     1     1
1     1     1    -1

The augmented design is full factorial, with the original eight runs in the first eight rows.

The 'start' parameter of the candexch function provides the same functionality as daugment, but uses a row exchange algorithm rather than a coordinate-exchange algorithm.

### Specify Fixed Covariate Factors

In many experimental settings, certain factors and their covariates are constrained to a fixed set of levels or combinations of levels. These cannot be varied when searching for an optimal design. The dcovary function allows you to specify fixed covariate factors in the coordinate exchange algorithm.

For example, suppose you want a design to estimate the parameters in a three-factor linear additive model, with eight runs that necessarily occur at different times. If the process experiences temporal linear drift, you may want to include the run time as a variable in the model. Produce the design as follows:

time = linspace(-1,1,8)';
[dCV,X] = dcovary(3,time,'linear')
dCV =
-1.0000    1.0000    1.0000   -1.0000
1.0000   -1.0000   -1.0000   -0.7143
-1.0000   -1.0000   -1.0000   -0.4286
1.0000   -1.0000    1.0000   -0.1429
1.0000    1.0000   -1.0000    0.1429
-1.0000    1.0000   -1.0000    0.4286
1.0000    1.0000    1.0000    0.7143
-1.0000   -1.0000    1.0000    1.0000
X =
1.0000   -1.0000    1.0000    1.0000   -1.0000
1.0000    1.0000   -1.0000   -1.0000   -0.7143
1.0000   -1.0000   -1.0000   -1.0000   -0.4286
1.0000    1.0000   -1.0000    1.0000   -0.1429
1.0000    1.0000    1.0000   -1.0000    0.1429
1.0000   -1.0000    1.0000   -1.0000    0.4286
1.0000    1.0000    1.0000    1.0000    0.7143
1.0000   -1.0000   -1.0000    1.0000    1.0000

The column vector time is a fixed factor, normalized to values between ±1. The number of rows in the fixed factor specifies the number of runs in the design. The resulting design dCV gives factor settings for the three controlled model factors at each time.

### Specify Categorical Factors

Categorical factors take values in a discrete set of levels. Both cordexch and rowexch have a 'categorical' parameter that allows you to specify the indices of categorical factors and a 'levels' parameter that allows you to specify a number of levels for each factor.

For example, the following eight-run design is for a linear additive model with five factors in which the final factor is categorical with three levels:

dCEcat = cordexch(5,8,'linear','categorical',5,'levels',3)
dCEcat =
-1    -1     1     1     2
-1    -1    -1    -1     3
1     1     1     1     3
1     1    -1    -1     2
1    -1    -1     1     3
-1     1    -1     1     1
-1     1     1    -1     3
1    -1     1    -1     1

### Specify Candidate Sets

The row-exchange algorithm exchanges rows of an initial design matrix X with rows from a design matrix C evaluated at a candidate set of feasible treatments. The rowexch function automatically generates a C appropriate for a specified model, operating in two steps by calling the candgen and candexch functions in sequence. Provide your own C by calling candexch directly.

For example, the following uses rowexch to generate a five-run design for a two-factor pure quadratic model using a candidate set that is produced internally:

dRE1 =
-1     1
0     0
1    -1
1     0
1     1

The same thing can be done using candgen and candexch in sequence:

[dC,C] = candgen(2,'purequadratic') % Candidate set, C
dC =
-1    -1
0    -1
1    -1
-1     0
0     0
1     0
-1     1
0     1
1     1
C =
1    -1    -1     1     1
1     0    -1     0     1
1     1    -1     1     1
1    -1     0     1     0
1     0     0     0     0
1     1     0     1     0
1    -1     1     1     1
1     0     1     0     1
1     1     1     1     1
treatments = candexch(C,5,'tries',10) % D-opt subset
treatments =
2
1
7
3
4
dRE2 = dC(treatments,:) % Display design
dRE2 =
0    -1
-1    -1
-1     1
1    -1
-1     0

You can replace C in this example with a design matrix evaluated at your own candidate set. For example, suppose your experiment is constrained so that the two factors cannot have extreme settings simultaneously. The following produces a restricted candidate set:

constraint = sum(abs(dC),2) < 2; % Feasible treatments
my_dC = dC(constraint,:)
my_dC =
0    -1
-1     0
0     0
1     0
0     1

Use the x2fx function to convert the candidate set to a design matrix:

my_C =
1     0    -1     0     1
1    -1     0     1     0
1     0     0     0     0
1     1     0     1     0
1     0     1     0     1

my_treatments = candexch(my_C,5,'tries',10) % D-opt subset
my_treatments =
2
4
5
1
3
my_dRE = my_dC(my_treatments,:) % Display design
my_dRE =
-1     0
1     0
0     1
0    -1
0     0