DOE

Example 1

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the data from the 'Investment Castings' table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X( :X1, :X2, :X3 )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.

Example 2

Summary: Opens a data table, defines a full factorial design with continuous and categorical factors, simulates responses using the REML method, and generates a table for analysis.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Full Factorial Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous,
        {-1, 1},
        "X1",
        0
    ),
    Add Factor(
        Categorical,
        {"x", "y", "z"},
        "X2",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B", "C", "D"},
        "X3",
        0
    ), Set Random Seed( 151647685 ),
    Make Design, Simulate Responses( 0 ),
    Set Run Order( Sort Left to Right ),
    Make Table}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Define full factorial design.
4. Add maximize response "Y".
5. Add continuous factor X1 with levels -1, 1.
6. Add categorical factor X2 with levels x, y, z.
7. Add categorical factor X3 with levels A, B, C, D.
8. Set random seed for reproducibility.
9. Generate design and simulate responses.
10. Set run order and make table.

Example 3

Summary: Generates a space-filling design using the DOE dialog, with factors for Alpha, Beta, Gamma, Algorithm, and Compiler, and optimizes the design type to Fast Flexible Filling.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Space Filling Design,
    {
    Add Response(
        Maximize, "CPU Time", ., ., .
    ),
    Change Factor Settings(
        1, -1, 1, "Alpha"
    ),
    Change Factor Settings(
        2, -1, 1, "Beta"
    ),
    Add Factor(
        Continuous, -1, 1, "Gamma", 0
    ),
    Add Factor(
        Categorical,
        {"Dynamic", "Greedy", "Transform"
        },
        "Algorithm",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Compiler",
        0
    ), Set Random Seed( 12345 ),
    FFF Optimality Criterion( "MaxPro" ),
    Space Filling Design Type(
        Fast Flexible Filling, 50
    ), Make Table}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Add maximize response.
4. Set Alpha factor settings.
5. Set Beta factor settings.
6. Add Gamma continuous factor.
7. Add Algorithm categorical factor.
8. Add Compiler categorical factor.
9. Set random seed.
10. Configure FFF optimality.
11. Select space filling design type.
12. Generate and make table.

Example 4

Summary: Opens a data table, loads factors from the current data table, and creates a space filling design using the DOE function.

Code:

// Load and Edit in Space Filling Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Space Filling Design
DOE(
    Space Filling Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Load factors from table.
3. Create space filling design.

Example 5

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing Custom Design and Load Factors from the provided data table.

Code:

// Load and Edit in Custom Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Custom Design
DOE(
    Custom Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Load factors from table.
3. Start Custom Design.
4. Edit design using loaded factors.

Example 6

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Evaluate Design function to specify factors.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Whole Plots, :Subplots, :A1, :A2,
        :A3, :A4, :C1, :C2
    )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.
3. Specify factors: Whole Plots.
4. Specify factors: Subplots.
5. Specify factors: A1.
6. Specify factors: A2.
7. Specify factors: A3.
8. Specify factors: A4.
9. Specify factors: C1.
10. Specify factors: C2.

Example 7

Summary: Generates a custom design of experiments (DOE) using the DOE Dialog in JMP, with the goal of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Minimize, "OCV", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "A1", 2
    ),
    Add Factor(
        Continuous, -1, 1, "A2", 2
    ),
    Add Factor(
        Continuous, -1, 1, "A3", 2
    ),
    Add Factor(
        Continuous, -1, 1, "A4", 2
    ),
    Add Factor(
        Continuous, -1, 1, "C1", 1
    ),
    Add Factor(
        Continuous, -1, 1, "C2", 1
    ), Set Random Seed( 1866762673 ),
    Number of Starts( 21 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {1, 1}, {5, 1} ),
    Add Term( {1, 1}, {6, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {2, 1}, {5, 1} ),
    Add Term( {2, 1}, {6, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {3, 1}, {5, 1} ),
    Add Term( {3, 1}, {6, 1} ),
    Add Term( {4, 1}, {5, 1} ),
    Add Term( {4, 1}, {6, 1} ),
    Add Term( {5, 1}, {6, 1} ),
    Set N Whole Plots( 16 ),
    Make Strip Plot Design,
    Set N Subplots( 6 ),
    Set Sample Size( 48 ),
    Optimality Criterion(
        "Make D-Optimal Design"
    ), Make Design}
);

Code Explanation:

1. Open data table.
2. Start DOE Custom Design.
3. Add response "OCV" to minimize.
4. Add factor "A1" as continuous.
5. Add factor "A2" as continuous.
6. Add factor "A3" as continuous.
7. Add factor "A4" as continuous.
8. Add factor "C1" as continuous.
9. Add factor "C2" as continuous.
10. Configure design settings and generate.

Example 8

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Evaluate Design function to specify factors.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X( :X1, :X2, :X3, :X4, :X5, :X6 )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.

Example 9

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to define custom design parameters.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
d = DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X5", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X6", 0
    ), Set Random Seed( 12345 ),
    Number of Starts( 1 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {1, 1}, {6, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {6, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ),
    Add Alias Term( {3, 1}, {6, 1} ),
    Add Alias Term( {4, 1}, {5, 1} ),
    Add Alias Term( {4, 1}, {6, 1} ),
    Add Alias Term( {5, 1}, {6, 1} ),
    Set Sample Size( 60 ),
    Simulate Responses, Make Design}
);
rep = d << Report;
rep["Design"] << Close( 1 );

Code Explanation:

1. Open data table.
2. Define DOE parameters.
3. Add response variable.
4. Add continuous factors.
5. Set random seed.
6. Specify number of starts.
7. Add main effects.
8. Add alias terms.
9. Set sample size.
10. Simulate responses.
11. Make design.
12. Generate report.
13. Close "Design" section.

Example 10

Summary: Evaluates a design experiment with factors Silica, Sulfur, and Silane using the REML method in the Standard Least Squares personality.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X( :Silica, :Sulfur, :Silane )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.
3. Specify factors: Silica, Sulfur, Silane.

Example 11

Summary: Opens a data table, defines a response surface design using the DOE dialog, and generates a table with specified factor settings and random seed.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Response Surface Design,
    {
    Add Response(
        Match Target, "Stretch", 350, 550,
        1
    ),
    Change Factor Settings(
        1, 0.7, 1.7, "Silica"
    ),
    Change Factor Settings(
        2, 1.8, 2.8, "Sulfur"
    ),
    Add Factor(
        Continuous, 40, 60, "Silane", 0
    ), Set Random Seed( 12345 ),
    Make Design( 1 ), Center Points( 3 ),
    Make Table}
);

Code Explanation:

1. Open table.
2. Start DOE dialog.
3. Define response surface design.
4. Add response variable.
5. Set target range for stretch.
6. Configure silica factor settings.
7. Configure sulfur factor settings.
8. Add continuous factor Silane.
9. Set random seed.
10. Generate and make table.

Example 12

Summary: Opens a data table, loads and edits in Response Surface Design, and applies a custom design with load factors from the current data table.

Code:

// Load and Edit in Response Surface Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Response Surface Design
DOE(
    Response Surface Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Load Response Surface Design.
3. Load factors from current table.

Example 13

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Response Surface Design feature to load responses from the current data table.

Code:

// Load and Edit in Response Surface Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Response Surface Design
DOE(
    Response Surface Design,
    Load Responses(
        Current Data Table()
    )
);

Code Explanation:

1. Open data table.
2. Define DOE object.
3. Specify Response Surface Design.
4. Load responses from current table.

Example 14

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Custom Design platform to load responses from a data table.

Code:

// Load and Edit in Custom Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Custom Design
DOE(
    Custom Design,
    Load Responses(
        Current Data Table()
    )
);

Code Explanation:

1. Open table.
2. Load Custom Design.
3. Load responses from table.

Example 15

Summary: This JSL script evaluates a design using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the data from the 'Investment Castings' table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Whole Plots, :Furnace Temp,
        :Coating
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.

Example 16

Summary: Generates a custom design of experiments (DOE) using the DOE Dialog in JMP, with two categorical factors and a response variable, to analyze corrosion resistance.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Corrosion Resistance",
        ., ., .
    ),
    Add Factor(
        Categorical,
        {"360", "370", "380"},
        "Furnace Temp",
        1
    ),
    Add Factor(
        Categorical,
        {"C1", "C2", "C3", "C4"},
        "Coating",
        0
    ), Set Random Seed( 15973514 ),
    Number of Starts( 3 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Set Sample Size( 24 ),
    Set N Whole Plots( 6 ), Make Design}
);

Code Explanation:

1. Open table.
2. Define DOE parameters.
3. Add response variable.
4. Add categorical factor.
5. Add another categorical factor.
6. Set random seed.
7. Specify number of starts.
8. Add main effect term.
9. Add interaction term.
10. Add second interaction term.
11. Set sample size.
12. Set number of whole plots.
13. Generate design.

Example 17

Summary: Loads a data table, applies Taguchi Arrays to load factors from the current table, and enables variance component analysis using the REML method with random effects in the Standard Least Squares personality.

Code:

// Load and Edit in Taguchi Arrays
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Taguchi Arrays
DOE(
    Taguchi Arrays,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Load Taguchi Arrays.
3. Load factors from current table.

Example 18

Summary: Evaluates a design using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing factors such as Cocoa, Sugar, Flour, Butter, Milk, and Eggs.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Cocoa, :Sugar, :Flour, :Butter,
        :Milk, :Eggs
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors: Cocoa, Sugar, Flour, Butter, Milk, Eggs.

Example 19

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to define a custom design with mixture factors.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Taste", 1, 10, .
    ),
    Add Factor(
        Mixture, 0.1, 0.2, "Cocoa", 0
    ),
    Add Factor(
        Mixture, 0, 0.15, "Sugar", 0
    ),
    Add Factor(
        Mixture, 0.2, 0.3, "Flour", 0
    ),
    Add Factor(
        Mixture, 0.1, 0.2, "Butter", 0
    ),
    Add Factor(
        Mixture, 0.25, 0.35, "Milk", 0
    ),
    Add Factor(
        Mixture, 0.05, 0.2, "Eggs", 0
    ), Set Random Seed( 12345 ),
    Number of Starts( 40 ),
    Add Constraint(
        [1 1 1 0 0 0 0.45,
        -1 -1 -1 0 0 0 -0.45]
    ), Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {1, 1}, {6, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {6, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ),
    Add Alias Term( {3, 1}, {6, 1} ),
    Add Alias Term( {4, 1}, {5, 1} ),
    Add Alias Term( {4, 1}, {6, 1} ),
    Add Alias Term( {5, 1}, {6, 1} ),
    Set Sample Size( 10 )}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Define response variable.
4. Add mixture factor: Cocoa.
5. Add mixture factor: Sugar.
6. Add mixture factor: Flour.
7. Add mixture factor: Butter.
8. Add mixture factor: Milk.
9. Add mixture factor: Eggs.
10. Configure design settings.

Example 20

Summary: Generates a MaxDiff design for a data table, utilizing the DOE dialog to configure the number of profiles and choice sets.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    MaxDiff Design,
    X( :Candy ),
    {Set Number of Profiles( 4 ),
    Set Number of Choice Sets( 7 ),
    Make Design, Simulate Responses( 0 )}
);

Code Explanation:

1. Open table.
2. Start DOE dialog.
3. Select MaxDiff Design.
4. Set X variable.
5. Configure number of profiles.
6. Configure number of choice sets.
7. Generate design.
8. Simulate responses off.

Example 21

Summary: This JSL script evaluates a design of experiments (DOE) using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing factors such as temperature, time, and catalyst.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        Whole Plots, Subplots,
        :Temperature, :Time, :Catalyst
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.
4. Include whole plots.
5. Include subplots.
6. Add temperature factor.
7. Add time factor.
8. Add catalyst factor.

Example 22

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to simulate responses and create a design.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "Temperature",
        2
    ),
    Add Factor(
        Continuous, -1, 1, "Time", 1
    ),
    Add Factor(
        Continuous, -1, 1, "Catalyst", 0
    ), Set Random Seed( 12345 ),
    Number of Starts( 1000 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set N Whole Plots( 4 ),
    Set N Subplots( 8 ),
    Set Sample Size( 24 ),
    Simulate Responses, Make Design}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Add response variable.
4. Add continuous factor: Temperature.
5. Add continuous factor: Time.
6. Add continuous factor: Catalyst.
7. Set random seed.
8. Define number of starts.
9. Add main effects.
10. Add interaction terms.
11. Set whole plots.
12. Set subplots.
13. Set sample size.
14. Simulate responses.
15. Create design.

Example 23

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Choice Design platform to load factors from a data table.

Code:

// Load and Edit in Choice Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Choice Design
DOE(
    Choice Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Launch Choice Design.
3. Load factors from table.

Example 24

Summary: This JSL script evaluates a design experiment with random effects in the Standard Least Squares personality, using the REML method to perform variance component analysis and fit a model for shrinkage.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Grind, :Temperature, :Time,
        :Charge, :Station
    )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.
3. Specify variables: Grind, Temperature, Time.
4. Specify variables: Charge, Station.

Example 25

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to design an experiment.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Match Target, "Strength", 1.2,
        1.4, .
    ),
    Add Factor(
        Categorical,
        {"Coarse", "Medium"},
        "Grind",
        0
    ),
    Add Factor(
        Continuous, 195, 205,
        "Temperature", 0
    ),
    Add Factor(
        Continuous, 3, 4, "Time", 0
    ),
    Add Factor(
        Continuous, 1.6, 2.4, "Charge", 0
    ),
    Add Factor( Blocking, 4, "Station" ),
    Set Random Seed( 569534903 ),
    Number of Starts( 100 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Set Sample Size( 12 ), Make Design}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Add response variable.
4. Add categorical factor.
5. Add continuous factor.
6. Add continuous factor.
7. Add continuous factor.
8. Add blocking factor.
9. Set random seed.
10. Set number of starts.
11. Add intercept term.
12. Add main effects terms.
13. Add alias terms.
14. Set sample size.
15. Generate design.

Example 26

Summary: Generates a custom design for a response variable using the DOE dialog, incorporating continuous factors X1, X2, and X3, with polynomial terms and random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Match Target, "Y", 54, 56, .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ), Set Random Seed( 929281409 ),
    Number of Starts( 40 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 2 ),
    Make Design}
);

Code Explanation:

1. Open table.
2. Define DOE dialog.
3. Add response variable.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Set random seed.
8. Set number of starts.
9. Add polynomial terms.
10. Set sample size.
11. Set optimality criterion.
12. Generate design.

Example 27

Summary: This JSL script evaluates a design experiment using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the data from an open data table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X( :Operator, :Speed, :Current )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.

Example 28

Summary: Generates a full factorial design using the DOE dialog in JMP, with three factors: categorical 'Operator', continuous 'Speed', and continuous 'Current'.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Full Factorial Design,
    {
    Add Response(
        Match Target, "Depth", 0.12, 0.22,
        .
    ),
    Add Factor(
        Categorical,
        {"John", "Mary"},
        "Operator",
        0
    ),
    Add Factor(
        Continuous,
        {3, 5},
        "Speed",
        0
    ),
    Add Factor(
        Continuous,
        {150, 165},
        "Current",
        0
    ), Set Random Seed( 1344277810 ),
    Make Design}
);

Code Explanation:

1. Open table.
2. Start DOE dialog.
3. Define response variable.
4. Add categorical factor.
5. Add continuous factor.
6. Add continuous factor.
7. Set random seed.
8. Generate design.

Example 29

Summary: Evaluates a design experiment using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the data from the 'Investment Castings' data table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Methanol, :Ethanol, :Propanol,
        :Butanol, :pH, :Time
    )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.

Example 30

Summary: Opens a data table, initiates the DOE dialog, and defines a Definitive Screening Design with multiple factors to optimize yield. The design is then generated using the REML method.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Factor(
        Continuous, 0, 10, "Methanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Ethanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Propanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Butanol", 0
    ),
    Add Factor(
        Continuous, 6, 9, "pH", 0
    ),
    Add Factor(
        Continuous, 1, 2, "Time", 0
    ), Number of Extra Runs( 0 ),
    Set Random Seed( 123 ), Make Design}
);

Code Explanation:

1. Open data table.
2. Initiate DOE dialog.
3. Define Definitive Screening Design.
4. Add response variable "Yield".
5. Add factor "Methanol".
6. Add factor "Ethanol".
7. Add factor "Propanol".
8. Add factor "Butanol".
9. Add factor "pH".
10. Add factor "Time".

Example 31

Summary: Loads a data table and performs a Definitive Screening Design using the Load Factors from the current data table, enabling variance component analysis with REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// Load and Edit in Definitive Screening Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Definitive Screening Design
DOE(
    Definitive Screening Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open table.
2. Load DOE platform.
3. Select Definitive Screening Design.
4. Load factors from current table.

Example 32

Summary: Evaluates a design experiment using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the data from the 'Investment Castings' table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Lot, :Methanol, :Ethanol,
        :Propanol, :Butanol, :pH, :Time
    )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.

Example 33

Summary: Generates a Definitive Screening Design (DSD) using the DOE Dialog in JMP, with the goal of maximizing a response variable 'Y' and exploring the effects of six continuous factors: Methanol, Ethanol, Propanol, Butanol, pH, and Time.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, 0, 10, "Methanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Ethanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Propanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Butanol", 0
    ),
    Add Factor(
        Continuous, 6, 9, "pH", 0
    ),
    Add Factor(
        Continuous, 1, 2, "Time", 0
    ), Number of Extra Runs( 0 ),
    Show Blocking Options( 1, 2 ),
    Make Design}
);

Code Explanation:

1. Open table.
2. Define DOE.
3. Set response to maximize.
4. Add continuous factor: Methanol.
5. Add continuous factor: Ethanol.
6. Add continuous factor: Propanol.
7. Add continuous factor: Butanol.
8. Add continuous factor: pH.
9. Add continuous factor: Time.
10. Generate design.

Example 34

Summary: Opens a data table, initiates the DOE dialog, and defines a Definitive Screening Design with continuous factors for Methanol, Ethanol, Propanol, Butanol, pH, and Time, as well as a response variable Yield.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Factor(
        Continuous, 0, 10, "Methanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Ethanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Propanol", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Butanol", 0
    ),
    Add Factor(
        Continuous, 6, 9, "pH", 0
    ),
    Add Factor(
        Continuous, 1, 2, "Time", 0
    ), Show Blocking Options( 1, 2 ),
    Number of Extra Runs( 4 ),
    Set Random Seed( 123 ), Make Design}
);

Code Explanation:

1. Open data table.
2. Initiate DOE dialog.
3. Define Definitive Screening Design.
4. Add response variable "Yield".
5. Add continuous factor "Methanol".
6. Add continuous factor "Ethanol".
7. Add continuous factor "Propanol".
8. Add continuous factor "Butanol".
9. Add continuous factor "pH".
10. Add continuous factor "Time".

Example 35

Summary: This JSL script evaluates a design experiment using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing variables Dichloromethane, Methanol, and Sample Volume from an open data table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Dichloromethane, :Methanol,
        :Sample Volume
    )
);

Code Explanation:

1. Open data table.
2. Evaluate design experiment.
3. Specify variables: Dichloromethane, Methanol, Sample Volume.

Example 36

Summary: Generates a custom design for a DOE (Design of Experiments) analysis using the REML method, fitting a model with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Metacrate", ., ., ., 1,
        99
    ),
    Add Factor(
        Continuous, 110, 150,
        "Dichloromethane", 0
    ),
    Add Factor(
        Continuous, 400, 600, "Methanol",
        0
    ),
    Add Factor(
        Continuous, 3, 7, "Sample Volume",
        0
    ), Set Random Seed( 981216 ),
    Number of Starts( 6776 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 32 ),
    Optimality Criterion(
        "Make I-Optimal Design"
    ), Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open data table.
2. Start DOE dialog.
3. Add response variable.
4. Add factor: Dichloromethane.
5. Add factor: Methanol.
6. Add factor: Sample Volume.
7. Set random seed.
8. Define number of starts.
9. Add polynomial terms.
10. Set sample size.
11. Choose optimality criterion.
12. Generate design.

Example 37

Summary: Evaluates design using the DOE function to analyze the relationship between pH, Water Temp, Extraction Time, Ratio, Agitation Speed, Hydrolyze, and Pre-Soak in a data table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :pH, :Water Temp,
        :Extraction TIme, :Ratio,
        :Agitation Speed, :Hydrolyze,
        :"Pre-Soak"n
    )
);

Code Explanation:

1. Open data table.
2. Define variables for evaluation.
3. Evaluate design using DOE function.

Example 38

Summary: Opens a data table, defines a DOE (Design of Experiments) with various factors and response variables, and generates a design using the Definitive Screening Design method.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Solids", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "pH", 0
    ),
    Add Factor(
        Continuous, -1, 1, "Water Temp",
        0
    ),
    Add Factor(
        Continuous, -1, 1,
        "Extraction TIme", 0
    ),
    Add Factor(
        Continuous, -1, 1, "Ratio", 0
    ),
    Add Factor(
        Continuous, -1, 1,
        "Agitation Speed", 0
    ),
    Add Factor(
        Categorical,
        {"L1", "L2"},
        "Hydrolyze",
        0
    ),
    Add Factor(
        Categorical,
        {"L1", "L2"},
        "Pre-Soak",
        0
    ), Show Blocking Options( 0, 0 ),
    Number of Extra Runs( 4 ),
    Set Random Seed( 12345 ), Make Design,
    Simulate Responses( 0 ),
    Save X Matrix( 0 )}
);

Code Explanation:

1. Open table.
2. Define DOE parameters.
3. Add response variable.
4. Add continuous factor: pH.
5. Add continuous factor: Water Temp.
6. Add continuous factor: Extraction Time.
7. Add continuous factor: Ratio.
8. Add continuous factor: Agitation Speed.
9. Add categorical factor: Hydrolyze.
10. Add categorical factor: Pre-Soak.

Example 39

Summary: Generates a custom design for a DOE (Design of Experiments) analysis using the Covering Array method, incorporating categorical factors and disallowed combinations to optimize experimental runs.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Covering Array,
    {
    Add Factor(
        Categorical,
        {"USA", "UK", "Canada", "France",
        "Mexico"},
        "Market",
        0
    ),
    Add Factor(
        Categorical,
        {"ISDN", "Bus", "Coin", "Res"},
        "Near Phone",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Near Interface",
        0
    ),
    Add Factor(
        Categorical,
        {"ISDN", "Bus", "Coin", "Res"},
        "Far Phone",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Far Interface",
        0
    ), Set Strength( 2 ),
    Disallowed Combinations(
        Near Phone == "ISDN" &
        Near Interface == "A" | Far Phone
         == "ISDN" & Far Interface == "A"
         | Near Phone == "Bus" &
        Near Interface == "B" |
        Near Phone == "Res" &
        Near Interface == "B"
    ), Set Random Seed( 632 )}
);

Code Explanation:

1. Open table.
2. Start DOE dialog.
3. Add categorical factor "Market".
4. Add categorical factor "Near Phone".
5. Add categorical factor "Near Interface".
6. Add categorical factor "Far Phone".
7. Add categorical factor "Far Interface".
8. Set strength to 2.
9. Define disallowed combinations.
10. Set random seed to 632.

Example 40

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Covering Array design to load factors from the data table.

Code:

// Load and Edit in Covering Array
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Covering Array
DOE(
    Covering Array,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open table.
2. Load DOE platform.
3. Use Covering Array design.
4. Load factors from table.

Example 41

Summary: Opens a data table, initiates the DOE dialog, and designs an experiment using Mixture Design with Extreme Vertices. It sets factor ranges, adds constraints, and selects the response to maximize.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Mixture Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Change Factor Settings(
        1, 0.1, 0.5, "X1"
    ),
    Change Factor Settings(
        2, 0.1, 0.7, "X2"
    ),
    Change Factor Settings(
        3, 0, 0.7, "X3"
    ), Set Random Seed( 205900700 ),
    Add Constraint(
        [-85 -90 -100 -90,
        85 90 100 95,
        -0.7 0 -1 -0.4]
    ),
    Mixture Design Type(
        Extreme Vertices, 2
    )}
);

Code Explanation:

1. Open data table.
2. Initiate DOE dialog.
3. Select Mixture Design.
4. Add response: Maximize Y.
5. Set factor X1 range.
6. Set factor X2 range.
7. Set factor X3 range.
8. Set random seed.
9. Add design constraints.
10. Choose Extreme Vertices design.

Example 42

Summary: Evaluates a design using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing factors such as Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors: Feed Rate, Catalyst, Stir Rate, Temperature, Concentration.

Example 43

Summary: Generates a custom design of experiments (DOE) using the DOE Dialog in JMP, with factors such as Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration, to analyze the response variable Percent Reacted.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Screening Design,
    {
    Add Response(
        Maximize, "Percent Reacted", 90,
        100, 1
    ),
    Add Factor(
        Continuous, 10, 15, "Feed Rate",
        0
    ),
    Add Factor(
        Continuous, 1, 2, "Catalyst", 0
    ),
    Add Factor(
        Continuous, 100, 120, "Stir Rate",
        0
    ),
    Add Factor(
        Continuous, 140, 180,
        "Temperature", 0
    ),
    Add Factor(
        Continuous, 3, 6, "Concentration",
        0
    ), Set Random Seed( 34567 ),
    Make Design( 3 )}
);

Code Explanation:

1. Open table.
2. Define DOE parameters.
3. Add response variable.
4. Add factor: Feed Rate.
5. Add factor: Catalyst.
6. Add factor: Stir Rate.
7. Add factor: Temperature.
8. Add factor: Concentration.
9. Set random seed.
10. Generate design.

Example 44

Summary: Loads a data table and creates a mixture design using the Load Factors from the current data table, enabling variance component analysis with REML method and model fitting for shrinkage with random effects in Standard Least Squares personality.

Code:

// Load and Edit in Mixture Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Mixture Design
DOE(
    Mixture Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open table.
2. Create mixture design.
3. Load factors from table.

Example 45

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to design an experiment.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        "Maximize", "Number Popped", ., .,
        .
    ),
    Add Response(
        "None", "Total Kernals", ., ., .
    ),
    Add Factor(
        Categorical,
        {"Top Secret", "Wilbur"},
        "Brand",
        0
    ),
    Add Factor(
        Continuous, 3, 5, "Time", 0
    ),
    Add Factor(
        Continuous, 5, 10, "Power", 0
    ), Set Random Seed( 151747005 ),
    Number of Starts( 40 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 2} ),
    Add Term( {3, 2} ),
    Set Sample Size( 16 ),
    Add Constraint( [1 1 13, -1 -1 -10] ),
    Make Design}
);

Code Explanation:

1. Open data table.
2. Initiate DOE dialog.
3. Add maximize response.
4. Add none response.
5. Add categorical factor.
6. Add continuous factor.
7. Add continuous factor.
8. Set random seed.
9. Define number of starts.
10. Add polynomial terms.
11. Set sample size.
12. Add constraint.
13. Generate design.

Example 46

Summary: Generates a custom design of experiments (DOE) using the Custom Design platform in JMP, with specified factors and interactions to analyze the effect of various process variables on a response variable.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Percent Reacted", 90,
        99, 1
    ),
    Add Factor(
        Continuous, 10, 15, "Feed Rate",
        0
    ),
    Add Factor(
        Continuous, 1, 2, "Catalyst", 0
    ),
    Add Factor(
        Continuous, 100, 120, "Stir Rate",
        0
    ),
    Add Factor(
        Continuous, 140, 180,
        "Temperature", 0
    ),
    Add Factor(
        Continuous, 3, 6, "Concentration",
        0
    ), Set Random Seed( 2024677686 ),
    Number of Starts( 200 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {1, 1}, {5, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {2, 1}, {5, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {3, 1}, {5, 1} ),
    Add Term( {4, 1}, {5, 1} ),
    Set Sample Size( 20 ), Make Design}
);

Code Explanation:

1. Open table.
2. Define DOE parameters.
3. Add response variable.
4. Add continuous factors.
5. Set random seed.
6. Specify number of starts.
7. Add main effects.
8. Add interaction terms.
9. Set sample size.
10. Generate design.

Example 47

Summary: Generates a full factorial design with six continuous factors and one response variable, utilizing the DOE dialog to create a table for analysis.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Full Factorial Design,
    {
    Add Response(
        Maximize, "Percent Reacted", 90,
        100, 1
    ),
    Add Factor(
        Continuous,
        {10, 15},
        "Feed Rate",
        0
    ),
    Add Factor(
        Continuous,
        {1, 2},
        "Catalyst",
        0
    ),
    Add Factor(
        Continuous,
        {100, 120},
        "Stir Rate",
        0
    ),
    Add Factor(
        Continuous,
        {140, 180},
        "Temperature",
        0
    ),
    Add Factor(
        Continuous,
        {3, 6},
        "Concentration",
        0
    ), Set Random Seed( 12345 ),
    Make Design, Make Table}
);

Code Explanation:

1. Open data table.
2. Define DOE parameters.
3. Add response variable.
4. Add continuous factor: Feed Rate.
5. Add continuous factor: Catalyst.
6. Add continuous factor: Stir Rate.
7. Add continuous factor: Temperature.
8. Add continuous factor: Concentration.
9. Set random seed.
10. Create design and table.

Example 48

Summary: Generates a DOE (Design of Experiments) dialog in JMP, specifying a screening design with multiple continuous factors and a response variable. The script sets up the experiment to simulate responses using REML (Restricted Maximum Likelihood) method.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Screening Design,
    {
    Add Response(
        Maximize, "Percent Reacted", 90,
        100, 1
    ),
    Add Factor(
        Continuous, 10, 15, "Feed Rate",
        0
    ),
    Add Factor(
        Continuous, 1, 2, "Catalyst", 0
    ),
    Add Factor(
        Continuous, 100, 120, "Stir Rate",
        0
    ),
    Add Factor(
        Continuous, 140, 180,
        "Temperature", 0
    ),
    Add Factor(
        Continuous, 3, 6, "Concentration",
        0
    ), Set Random Seed( 23456 ),
    Make Design( 1 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 )}
);

Code Explanation:

1. Open data table.
2. Initiate DOE dialog.
3. Set response type.
4. Define response name.
5. Set response target.
6. Set response lower limit.
7. Set response upper limit.
8. Set response standard deviation.
9. Add continuous factor.
10. Define factor name.
11. Set factor minimum value.
12. Set factor maximum value.
13. Add continuous factor.
14. Define factor name.
15. Set factor minimum value.
16. Set factor maximum value.
17. Add continuous factor.
18. Define factor name.
19. Set factor minimum value.
20. Set factor maximum value.
21. Add continuous factor.
22. Define factor name.
23. Set factor minimum value.
24. Set factor maximum value.
25. Add continuous factor.
26. Define factor name.
27. Set factor minimum value.
28. Set factor maximum value.
29. Set random seed.
30. Generate design.
31. Disable response simulation.
32. Disable saving X matrix.

Example 49

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to generate an augmented design.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
dt = Current Data Table();
dt2 = dt <<
run script( "Original Data Table" );
Current Data Table( dt2 );
DOE(
    Augment Design,
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    ),
    Y( :Percent Reacted ),
    {Augment Method( Augment ),
    Set Random Seed( 456 ),
    Number of Starts( 10 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {1, 1}, {5, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {2, 1}, {5, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {3, 1}, {5, 1} ),
    Add Term( {4, 1}, {5, 1} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 1 ),
    Make Design, Save X Matrix( 0 ),
    Simulate Responses( 0 ), Make Table}
);

Code Explanation:

1. Open data table.
2. Set current data table.
3. Run script on data table.
4. Set new current data table.
5. Start DOE dialog.
6. Specify factors: Feed Rate, Catalyst, Stir Rate, Temperature, Concentration.
7. Specify response: Percent Reacted.
8. Configure augment method.
9. Set random seed.
10. Define design terms and criteria.
11. Generate and save design.

Example 50

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Screening Design feature to load factors from the current data table.

Code:

// Load and Edit in Screening Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Screening Design
DOE(
    Screening Design,
    Load Factors( Current Data Table() )
);

Code Explanation:

1. Open data table.
2. Load Screening Design.
3. Use current data table.

Example 51

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the Screening Design feature to load responses from the current data table.

Code:

// Load and Edit in Screening Design
// Open data table
dt = Open("data_table.jmp");
// Load and Edit in Screening Design
DOE(
    Screening Design,
    Load Responses(
        Current Data Table()
    )
);

Code Explanation:

1. Open data table.
2. Load Screening Design.
3. Load responses from current data table.

Example 52

Summary: Opens a data table, initiates the DOE dialog, and adds categorical factors for Web Browser, Operating System, RAM, and Connection Speed with a strength of 3.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Covering Array,
    {
    Add Factor(
        Categorical,
        {"Safari", "IE", "Firefox",
        "Chrome", "Other"},
        "Web Browser",
        0
    ),
    Add Factor(
        Categorical,
        {"Macintosh", "Windows"},
        "Operating System",
        0
    ),
    Add Factor(
        Categorical,
        {"16 MB", "4 MB", "8 MB"},
        "RAM",
        0
    ),
    Add Factor(
        Categorical,
        {"0-1 Mbps", "1-5 Mbps",
        ">5 Mbps"},
        "Connection Speed",
        0
    ), Set Strength( 3 )}
);

Code Explanation:

1. Open table.
2. Initiate DOE dialog.
3. Add categorical factor.
4. Define factor levels.
5. Name factor "Web Browser".
6. Add categorical factor.
7. Define factor levels.
8. Name factor "Operating System".
9. Add categorical factor.
10. Define factor levels.
11. Name factor "RAM".
12. Add categorical factor.
13. Define factor levels.
14. Name factor "Connection Speed".
15. Set strength to 3.

Example 53

Summary: This JSL script evaluates a design of experiments (DOE) using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing factors from an open data table.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Rater, :Variety, :Field,
        :"De-Stem"n, :Yeast, :Temperature,
        :Press, :Barrel Age,
        :Barrel Seasoning, :Filtering
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.

Example 54

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to evaluate design factors.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :Liquid, :Sugar, :Flour, :Sifted,
        :Type, :Temp, :Salt, :Clamp,
        :Coat
    ),
    {Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Term( {7, 1} ),
    Add Term( {8, 1} ),
    Add Term( {9, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {1, 1}, {6, 1} ),
    Add Alias Term( {1, 1}, {7, 1} ),
    Add Alias Term( {1, 1}, {8, 1} ),
    Add Alias Term( {1, 1}, {9, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {6, 1} ),
    Add Alias Term( {2, 1}, {7, 1} ),
    Add Alias Term( {2, 1}, {8, 1} ),
    Add Alias Term( {2, 1}, {9, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ),
    Add Alias Term( {3, 1}, {6, 1} ),
    Add Alias Term( {3, 1}, {7, 1} ),
    Add Alias Term( {3, 1}, {8, 1} ),
    Add Alias Term( {3, 1}, {9, 1} ),
    Add Alias Term( {4, 1}, {5, 1} ),
    Add Alias Term( {4, 1}, {6, 1} ),
    Add Alias Term( {4, 1}, {7, 1} ),
    Add Alias Term( {4, 1}, {8, 1} ),
    Add Alias Term( {4, 1}, {9, 1} ),
    Add Alias Term( {5, 1}, {6, 1} ),
    Add Alias Term( {5, 1}, {7, 1} ),
    Add Alias Term( {5, 1}, {8, 1} ),
    Add Alias Term( {5, 1}, {9, 1} ),
    Add Alias Term( {6, 1}, {7, 1} ),
    Add Alias Term( {6, 1}, {8, 1} ),
    Add Alias Term( {6, 1}, {9, 1} ),
    Add Alias Term( {7, 1}, {8, 1} ),
    Add Alias Term( {7, 1}, {9, 1} ),
    Add Alias Term( {8, 1}, {9, 1} )}
);

Code Explanation:

1. Open data table;
2. Start DOE evaluation.
3. Set factors: Liquid, Sugar, Flour, Sifted, Type, Temp, Salt, Clamp, Coat.
4. Add main effects for all factors.
5. Add interaction terms for all factors.
6. Define alias terms for all factor pairs.
7. Complete DOE evaluation.

Example 55

Summary: Evaluates a design of experiments (DOE) for the specified factors: Solvent, Active, and Water in a data table using the REML method.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X( :Solvent, :Active, :Water )
);

Code Explanation:

1. Open data table.
2. Evaluate design for DOE.
3. Specify factors: Solvent, Active, Water.

Example 56

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to design an experiment.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Mixture, 0.26, 0.8, "Solvent", 0
    ),
    Add Factor(
        Mixture, 0.18, 0.4, "Active", 0
    ),
    Add Factor(
        Mixture, 0.02, 0.56, "Water", 0
    ), Set Random Seed( 625641159 ),
    Number of Starts( 6371 ),
    Add Constraint(
        [-1 1.4 0 0, 0 0.1 -1 0]
    ), Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 16 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open table.
2. Start DOE dialog.
3. Define response variable.
4. Add mixture factor "Solvent".
5. Add mixture factor "Active".
6. Add mixture factor "Water".
7. Set random seed.
8. Specify number of starts.
9. Add constraints.
10. Define terms for model.

Example 57

Summary: Evaluates a design with factors %Beads, %Strength, Flow(g/min), and T(ºC) using the REML method in the Standard Least Squares personality.

Code:

// Evaluate Design
// Open data table
dt = Open("data_table.jmp");
// Evaluate Design
DOE(
    Evaluate Design,
    X(
        :"%Beads"n, :"%Strength"n,
        :"Flow(g/min)"n, :"T(ºC)"n
    )
);

Code Explanation:

1. Open table.
2. Evaluate design.
3. Specify factors.
4. Include "%Beads".
5. Include "%Strength".
6. Include "Flow(g/min)".
7. Include "T(ºC)".

Example 58

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE dialog to define factors and responses.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
dt = Current Data Table();
dt2 = dt <<
run script( "Original Data Table" );
Current Data Table( dt2 );
DOE(
    Augment Design,
    X(
        :"%Beads"n, :"%Strength"n,
        :"Flow(g/min)"n, :"T(ºC)"n
    ),
    Y( :"Size/nm"n ),
    {Augment Method( Augment ),
    Set Random Seed( 2137584708 ),
    Number of Starts( 25507 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Potential Term( {1, 1}, {2, 1} ),
    Add Potential Term( {1, 1}, {3, 1} ),
    Add Potential Term( {1, 1}, {4, 1} ),
    Add Potential Term( {2, 1}, {3, 1} ),
    Add Potential Term( {2, 1}, {4, 1} ),
    Add Potential Term( {3, 1}, {4, 1} ),
    Add Potential Term( {1, 2} ),
    Add Potential Term( {2, 2} ),
    Add Potential Term( {3, 2} ),
    Add Potential Term( {4, 2} ),
    Add Term( {1, 3} ),
    Add Term( {2, 3} ),
    Add Term( {3, 3} ),
    Add Term( {4, 3} ),
    Set Sample Size( 22 ),
    D Efficiency Weight( 0 ), Make Design,
    Save X Matrix( 0 ),
    Simulate Responses( 0 ), Make Table}
);

Code Explanation:

1. Open data table.
2. Set current data table.
3. Run script "Original Data Table".
4. Set new current data table.
5. Start DOE dialog.
6. Define factors: %Beads, %Strength, Flow(g/min), T(ºC).
7. Define response: Size/nm.
8. Set augment method.
9. Configure random seed.
10. Set number of starts.
11. Add main effects.
12. Add two-way interactions.
13. Add three-way interactions.
14. Set sample size.
15. Disable efficiency weight.
16. Create design.
17. Disable X matrix save.
18. Disable response simulation.
19. Generate final table.

Example 59

Summary: Generates a custom design for a DOE analysis using the REML method, fitting a model with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ), Set Random Seed( 78379455 ),
    Number of Starts( 1 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 24 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open table.
2. Create DOE dialog.
3. Add response variable Y.
4. Add factor X1.
5. Add factor X2.
6. Add factor X3.
7. Set random seed.
8. Define number of starts.
9. Add main effects.
10. Add interaction terms.
11. Set sample size.
12. Disable response simulation.
13. Disable X matrix saving.
14. Generate design.

Example 60

Summary: Generates a custom design of experiments (DOE) using the DOE Dialog in JMP, with factors including temperature, gl ratio, and ht, to analyze the relationship between these variables.

Code:

// DOE Dialog
// Open data table
dt = Open("data_table.jmp");
// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Minimize, "Odor", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "temp", 0
    ),
    Add Factor(
        Continuous, -1, 1, "gl ratio", 0
    ),
    Add Factor(
        Continuous, -1, 1, "ht", 0
    ), Set Random Seed( 904322795 ),
    Number of Starts( 21 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 2 ),
    Make Design, Make Table}
);

Code Explanation:

1. Open data table.
2. Define DOE dialog.
3. Add response variable.
4. Add continuous factor: temp.
5. Add continuous factor: gl ratio.
6. Add continuous factor: ht.
7. Set random seed.
8. Specify number of starts.
9. Add main effects.
10. Add interaction terms.
11. Set sample size.
12. Choose optimality criterion.
13. Generate design.
14. Create design table.

Example 61

Summary: Generates a custom design of experiments (DOE) using the Custom Design platform in JMP, with a response variable, two continuous factors, and one categorical factor. The script also sets various DOE parameters and generates a design table.

Code:

// Basis for Design
// Open data table
dt = Open("data_table.jmp");
// Basis for Design
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 2
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 1
    ),
    Add Factor(
        Categorical,
        {"L1", "L2", "L3", "L4", "L5",
        "L6", "L7", "L8", "L9", "L10"},
        "X3",
        0
    ), Set Random Seed( 117724710 ),
    Number of Starts( 253 ),
    Add Term( {1, 0} ),
    Add Term( {3, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Set N Whole Plots( 4 ),
    Set N Subplots( 16 ),
    Set Sample Size( 48 ), Make Design,
    Make Table}
);

Code Explanation:

1. Open table.
2. Define DOE parameters.
3. Add response variable.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add categorical factor X3.
7. Set random seed.
8. Specify number of starts.
9. Add main effects.
10. Add interaction terms.
11. Set whole plots.
12. Set subplots.
13. Set sample size.
14. Generate design.
15. Create design table.

Example 62

Summary: Performs a space filling design using the Fast Flexible Filling method to optimize the number of trees in a forest, terms sampled per split, bootstrap sample rate, minimum splits per tree, and minimum split size for maximum response Y.

Code:

// DOE Dialog
DOE(
    Space Filling Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Change Factor Settings(
        1, 3, 1000,
        "Number of Trees in the Forest"
    ),
    Change Factor Settings(
        2, 1, 15,
        "Number of Terms Sampled per Split"
    ),
    Add Factor(
        Continuous, 0.1, 1,
        "Bootstrap Sample Rate", 0
    ),
    Add Factor(
        Continuous, 1, 10,
        "Minimum Splits per Tree",
        0
    ),
    Add Factor(
        Continuous, 3, 1000,
        "Maximum Splits per Tree",
        0
    ),
    Add Factor(
        Continuous, 1, 25,
        "Minimum Split Size", 0
    ),
    Set Random Seed( 503612986 ),
    FFF Optimality Criterion(
        MaxPro
    ),
    Space Filling Design Type(
        Fast Flexible Filling, 60
    ), Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Start DOE process.
2. Select Space Filling Design.
3. Add response variable Y for maximization.
4. Set factor 1: Number of Trees in the Forest, range 1-1000.
5. Set factor 2: Number of Terms Sampled per Split, range 1-15.
6. Add continuous factor: Bootstrap Sample Rate, range 0.1-1.
7. Add continuous factor: Minimum Splits per Tree, range 1-10.
8. Add continuous factor: Maximum Splits per Tree, range 3-1000.
9. Add continuous factor: Minimum Split Size, range 1-25.
10. Set random seed to 503612986.
11. Use MaxPro optimality criterion.
12. Choose Fast Flexible Filling design type with 60 iterations.
13. Randomize run order.
14. Generate and make design table.

Example 63

Summary: Performs a custom design of experiments (DOE) to optimize yield, match target MFI, and maximize CI in an investment casting process. It defines multiple continuous factors and sets a random seed for the simulation.

Code:

// V19: Custom DOE Settings
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ),
    Add Factor(
        Continuous, 3.5, 4.5, "pH", 0
    ),
    Add Factor(
        Continuous, 8, 10, "Viscosity", 0
    ), Set Random Seed( 103517925 )}
);

Code Explanation:

1. Start DOE process.
2. Select Custom Design.
3. Define response "Yield" to maximize.
4. Define response "MFI" to match target 198.
5. Define response "CI" to maximize, lower bound 80.
6. Add continuous factor "SA" from 55 to 65.
7. Add continuous factor "M%" from 0.3 to 1.3.
8. Add continuous factor "Xf" from 14 to 17.
9. Add continuous factor "Ambient Temp" from 0.02 to 1.4.
10. Add continuous factor "pH" from 3.5 to 4.5.
11. Add continuous factor "Viscosity" from 8 to 10.
12. Set random seed to 103517925.

Example 64

Summary: This JSL script defines a custom design of experiments (DOE) to optimize yield, match target MFI, and maximize CI in an investment casting process. The script uses the REML method for variance component analysis and fits a model with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ), Set Random Seed( 103517925 ),
    Number of Starts( 800 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Set Sample Size( 16 ), Make Design,
    Simulate Responses}
);

Code Explanation:

1. Define DOE parameters.
2. Add response: Maximize Yield.
3. Add response: Match Target MFI.
4. Add response: Maximize CI.
5. Add factor SA.
6. Add factor M%.
7. Add factor Xf.
8. Add factor Ambient Temp.
9. Set random seed.
10. Configure design and simulate responses.

Example 65

Summary: Generates a custom design for a DOE experiment using the REML method, with random effects in the Standard Least Squares personality. The script optimizes three responses: Yield, MFI, and CI, while controlling four continuous factors.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ), Set Random Seed( 103517925 ),
    Number of Starts( 6 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {4, 2} ),
    Set Sample Size( 21 ),
    Optimality Criterion( 2 ),
    Make Design, Simulate Responses}
);

Code Explanation:

1. Start DOE Custom Design.
2. Add response "Yield" to maximize.
3. Set "Yield" target to 93.
4. Add response "MFI" to match target.
5. Set "MFI" target to 198.
6. Add response "CI" to maximize.
7. Set "CI" target to 80.
8. Add continuous factor "SA" from 55 to 65.
9. Add continuous factor "M%" from 0.3 to 1.3.
10. Add continuous factor "Xf" from 14 to 17.
11. Add continuous factor "Ambient Temp" from 0.02 to 1.4.
12. Set random seed to 103517925.
13. Set number of starts to 6.
14. Add main effect for all factors.
15. Add interaction terms between factors.
16. Set sample size to 21.
17. Use D-optimality criterion.
18. Generate design and simulate responses.

Example 66

Summary: Generates a custom design for a DOE experiment using the REML method, fitting a model with random effects in the Standard Least Squares personality. The script optimizes responses 'Yield', 'MFI', and 'CI' while controlling factors 'SA', 'M%', 'Xf', 'Ambient Temp', 'pH', and 'Viscosity'.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ),
    Add Factor(
        Continuous, 3.5, 4.5, "pH", 0
    ),
    Add Factor(
        Continuous, 8, 10, "Viscosity", 0
    ), Set Random Seed( 103517925 ),
    Number of Starts( 2 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {4, 2} ),
    Add Term( {1, 1}, {5, 1} ),
    Add Term( {2, 1}, {5, 1} ),
    Add Term( {3, 1}, {5, 1} ),
    Add Term( {4, 1}, {5, 1} ),
    Add Term( {5, 2} ),
    Add Term( {1, 1}, {6, 1} ),
    Add Term( {2, 1}, {6, 1} ),
    Add Term( {3, 1}, {6, 1} ),
    Add Term( {4, 1}, {6, 1} ),
    Add Term( {5, 1}, {6, 1} ),
    Add Term( {6, 2} ),
    Set Sample Size( 34 ),
    Optimality Criterion( 2 ),
    Make Design, Simulate Responses}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response "Yield" to maximize.
4. Add response "MFI" to match target 198.
5. Add response "CI" to maximize with lower bound 80.
6. Add factor "SA" as continuous with range 55-65.
7. Add factor "M%" as continuous with range 0.3-1.3.
8. Add factor "Xf" as continuous with range 14-17.
9. Add factor "Ambient Temp" as continuous with range 0.02-1.4.
10. Add factor "pH" as continuous with range 3.5-4.5.
11. Add factor "Viscosity" as continuous with range 8-10.
12. Set random seed to 103517925.
13. Set number of starts to 2.
14. Add main effects for all factors.
15. Add interaction terms between factors.
16. Set sample size to 34.
17. Set optimality criterion to 2.
18. Make design.
19. Simulate responses.

Example 67

Summary: Generates a Definitive Screening Design using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing various factors and responses to optimize yield, MFI, and CI.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Quarry",
        0
    ),
    Add Factor(
        Continuous, 2.5, 7, "pH", 0
    ),
    Add Factor(
        Continuous, 1000, 3000,
        "Vessel Size", 0
    ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Definitive Screening Design.
3. Add response "Yield" to maximize.
4. Set "Yield" target to 93.
5. Add response "MFI" to match target.
6. Set "MFI" target to 198.
7. Add response "CI" to maximize.
8. Set "CI" target to 80.
9. Add continuous factor "SA" range 55-65.
10. Add continuous factor "M%" range 0.3-1.3.
11. Add continuous factor "Xf" range 14-17.
12. Add categorical factor "Quarry" levels A, B.
13. Add continuous factor "pH" range 2.5-7.
14. Add continuous factor "Vessel Size" range 1000-3000.
15. Generate design.

Example 68

Summary: This JSL script defines a Definitive Screening Design to optimize three responses: Yield, MFI, and CI. The design is generated using continuous factors SA, M%, Xf, and Ambient Temp.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ), Make Design, Simulate Responses}
);

Code Explanation:

1. Define DOE dialog.
2. Use Definitive Screening Design.
3. Add response "Yield" to maximize.
4. Set "Yield" target to 93.
5. Add response "MFI" to match target.
6. Set "MFI" target to 198.
7. Add response "CI" to maximize.
8. Set "CI" target to 80.
9. Add continuous factor "SA" from 55 to 65.
10. Add continuous factor "M%" from 0.3 to 1.3.
11. Add continuous factor "Xf" from 14 to 17.
12. Add continuous factor "Ambient Temp" from 0.02 to 1.4.
13. Generate design.
14. Simulate responses.

Example 69

Summary: Generates a Definitive Screening Design using the DOE dialog, with responses to maximize Yield and match Target MFI, as well as continuous factors for SA, M%, Xf, Ambient Temp, pH, and Viscosity.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Continuous, 0.02, 1.4,
        "Ambient Temp", 0
    ),
    Add Factor(
        Continuous, 3.5, 4.5, "pH", 0
    ),
    Add Factor(
        Continuous, 8, 10, "Viscosity", 0
    ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Definitive Screening Design.
3. Add response: Maximize Yield.
4. Add response: Match Target MFI.
5. Add response: Maximize CI.
6. Add continuous factor SA (55-65).
7. Add continuous factor M% (0.3-1.3).
8. Add continuous factor Xf (14-17).
9. Add continuous factor Ambient Temp (0.02-1.4).
10. Add continuous factor pH (3.5-4.5).
11. Add continuous factor Viscosity (8-10).
12. Generate design.

Example 70

Summary: This JSL script defines a Definitive Screening Design (DSD) experiment with multiple response variables and factors, including continuous and categorical variables. The script sets up the design for subsequent analysis using REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE: DSD for Follow-up Experiment
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", 192, 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Quarry",
        0
    ),
    Add Factor(
        Continuous, 2.5, 7, "pH", 0
    ),
    Add Factor(
        Continuous, 1000, 3000,
        "Vessel Size", 0
    ), Show Blocking Options( 0, 0 )}
);

Code Explanation:

1. Define DOE experiment.
2. Set design type to DSD.
3. Add response variable "Yield" for maximization.
4. Add response variable "MFI" for target matching.
5. Add response variable "CI" for maximization.
6. Add continuous factor "SA" with range 55-65.
7. Add continuous factor "M%" with range 0.3-1.3.
8. Add continuous factor "Xf" with range 14-17.
9. Add categorical factor "Quarry" with levels A, B.
10. Add continuous factor "pH" with range 2.5-7.

Example 71

Summary: Generates a Definitive Screening Design for an optimization problem, incorporating multiple responses and factors to maximize Yield, match Target MFI, and optimize CI, while also considering continuous and categorical factors.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", 93, ., .
    ),
    Add Response(
        Match Target, "MFI", 192, 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 55, 65, "SA", 0
    ),
    Add Factor(
        Continuous, 0.3, 1.3, "M%", 0
    ),
    Add Factor(
        Continuous, 14, 17, "Xf", 0
    ),
    Add Factor(
        Categorical,
        {"A", "B"},
        "Quarry",
        0
    ),
    Add Factor(
        Continuous, 2.5, 7, "pH", 0
    ),
    Add Factor(
        Continuous, 1000, 3000,
        "Vessel Size", 0
    ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Definitive Screening Design.
3. Add response: Maximize Yield.
4. Set Yield target to 93.
5. Add response: Match Target MFI.
6. Set MFI target range 192 to 198.
7. Add response: Maximize CI.
8. Set CI target to 80.
9. Add continuous factor SA, range 55 to 65.
10. Add continuous factor M%, range 0.3 to 1.3.
11. Add continuous factor Xf, range 14 to 17.
12. Add categorical factor Quarry, levels A and B.
13. Add continuous factor pH, range 2.5 to 7.
14. Add continuous factor Vessel Size, range 1000 to 3000.
15. Generate design.

Example 72

Summary: Generates a custom design for a DOE (Design of Experiments) using the REML method, with random effects in the Standard Least Squares personality. The script optimizes multiple responses and includes continuous factors, interaction terms, and alias terms.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", 80, ., 1
    ),
    Add Response(
        Maximize, "Assay", 98, ., 1
    ),
    Add Response(
        Minimize, "Haze", ., 10, 1
    ),
    Add Response(
        Minimize, "Cost", ., ., 1
    ),
    Add Factor(
        Continuous, -1, 1, "Base", 0
    ),
    Add Factor(
        Continuous, -1, 1, "Carbamate", 0
    ),
    Add Factor(
        Continuous, -1, 1, "Toluene", 0
    ),
    Add Factor(
        Continuous, -1, 1, "Temperature",
        0
    ),
    Add Factor(
        Continuous, -1, 1, "Time", 0
    ), Set Random Seed( 13174183 ),
    Number of Starts( 40 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {1, 1}, {5, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {2, 1}, {5, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {3, 1}, {5, 1} ),
    Add Term( {4, 1}, {5, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ),
    Add Alias Term( {4, 1}, {5, 1} ),
    Set Sample Size( 16 ), Make Design,
    Center Points( 3 )}
);

Code Explanation:

1. Start DOE Custom Design.
2. Add response "Yield" to maximize.
3. Set "Yield" lower bound to 80.
4. Add response "Assay" to maximize.
5. Set "Assay" upper bound to 98.
6. Add response "Haze" to minimize.
7. Set "Haze" upper bound to 10.
8. Add response "Cost" to minimize.
9. Add continuous factor "Base".
10. Add continuous factor "Carbamate".
11. Add continuous factor "Toluene".
12. Add continuous factor "Temperature".
13. Add continuous factor "Time".
14. Set random seed to 13174183.
15. Set number of starts to 40.
16. Add main effect for each factor.
17. Add interaction terms between factors.
18. Add alias terms between factors.
19. Set sample size to 16.
20. Generate design.
21. Add 3 center points.

Example 73

Summary: Generates a custom design for a DOE experiment using the REML method, fitting a model with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Minimize, "Bin 10", ., ., .
    ),
    Add Factor(
        Categorical,
        {"L1", "L2", "L3", "L4"},
        "Split",
        0
    ),
    Add Factor(
        Continuous, -1, 1,
        "451 Param Avg.", 0
    ),
    Add Factor(
        Continuous, -1, 1,
        "164 Param Avg.", 0
    ), Set Random Seed( 20699087 ),
    Number of Starts( 7393 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 2 ),
    Simulate Responses, Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response for minimization.
4. Add categorical factor with levels.
5. Add continuous factor "451 Param Avg."
6. Add continuous factor "164 Param Avg."
7. Set random seed for reproducibility.
8. Specify number of starts.
9. Add intercept term.
10. Add main effects terms.
11. Add interaction terms.
12. Set sample size.
13. Choose optimality criterion.
14. Simulate responses.
15. Generate design.

Example 74

Summary: Generates a custom design for a response maximization problem using the REML method, incorporating continuous and categorical factors, with random seed control and alias terms.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 2
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 1
    ),
    Add Factor(
        Categorical,
        {"L1", "L2", "L3", "L4", "L5",
        "L6", "L7", "L8", "L9", "L10"},
        "X3",
        0
    ), Set Random Seed( 117724710 ),
    Number of Starts( 253 ),
    Add Term( {1, 0} ),
    Add Term( {3, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Set N Whole Plots( 4 ),
    Set N Subplots( 16 ),
    Set Sample Size( 48 ), Make Design,
    Make Table}
);

Code Explanation:

1. Initiate DOE dialog.
2. Select Custom Design.
3. Define response Y for maximization.
4. Add continuous factor X1 with range -1 to 1.
5. Add continuous factor X2 with range -1 to 1.
6. Add categorical factor X3 with 10 levels.
7. Set random seed for reproducibility.
8. Specify number of design starts.
9. Include intercept term in model.
10. Include interaction term between X3 and response.
11. Define alias terms for model terms.
12. Set whole plots and subplots.
13. Set sample size for design.
14. Generate design.
15. Create design table.

Example 75

Summary: Generates a custom design for an experiment using the DOE dialog, with responses to maximize yield and assay, minimize impurity, and factors including continuous temperature, time, and amount, as well as categorical seed and mixture water, alcohol, and ether. The script also sets random seeds, number of starts, sample size, and adds constraints.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        "Maximize", "Yield", 90, ., 1
    ),
    Add Response(
        "Maximize", "Assay", 98, ., 2
    ),
    Add Response(
        "Minimize", "Impurity", ., 1, 3
    ),
    Add Factor(
        Continuous, 0, 20, "Temperature",
        0
    ),
    Add Factor(
        Continuous, 1, 2, "Time", 0
    ),
    Add Factor(
        Continuous, 1, 2, "Amount", 0
    ),
    Add Factor(
        Categorical,
        {"No", "Yes"},
        "Seed",
        0
    ),
    Add Factor(
        Mixture, 0.2, 1, "Water", 0
    ),
    Add Factor(
        Mixture, 0.15, 1, "Alcohol", 0
    ),
    Add Factor(
        Mixture, 0.2, 0.6, "Ether", 0
    ), Add Factor( Blocking, 6, "Day" ),
    Set Random Seed( 3833733 ),
    Number of Starts( 40 ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Term( {7, 1} ),
    Add Term( {8, 1} ),
    Set Sample Size( 12 ),
    Add Constraint(
        [0 0 0 0 -1 -1 -0.7]
    ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add Yield response for maximization.
4. Add Assay response for maximization.
5. Add Impurity response for minimization.
6. Add Temperature continuous factor.
7. Add Time continuous factor.
8. Add Amount continuous factor.
9. Add Seed categorical factor.
10. Add Water mixture factor.
11. Add Alcohol mixture factor.
12. Add Ether mixture factor.
13. Add Day blocking factor.
14. Set random seed.
15. Set number of starts.
16. Add main effects for all factors.
17. Set sample size.
18. Add constraint.
19. Make design.

Example 76

Summary: Creates a space filling design using the Fast Flexible Filling method, with factors set for maximum split per tree, row and column sampling rates, and number of trees. The script also sets a random seed and optimizes the design for maximum probability.

Code:

// DOE Dialog
DOE(
    Space Filling Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Change Factor Settings(
        1, 1, 10, "Number Terms"
    ),
    Change Factor Settings(
        2, 5, 15,
        "Minimum Split Per Tree"
    ),
    Add Factor(
        Continuous, 100, 2000,
        "Maximum Split Per Tree", 0
    ),
    Add Factor(
        Continuous, 0.4, 1,
        "Row Sampling Rate", 0
    ),
    Add Factor(
        Continuous, 0.4, 1,
        "Column Sampling Rate", 0
    ),
    Add Factor(
        Continuous, 100, 1000,
        "Number Trees", 0
    ), Set Random Seed( 548678189 ),
    FFF Optimality Criterion( MaxPro ),
    Space Filling Design Type(
        Fast Flexible Filling, 60
    ), Make Table}
);

Code Explanation:

1. Create DOE dialog.
2. Define space filling design.
3. Add maximize response "Y".
4. Set factor 1 range 1-10, name "Number Terms".
5. Set factor 2 range 5-15, name "Minimum Split Per Tree".
6. Add continuous factor, range 100-2000, name "Maximum Split Per Tree".
7. Add continuous factor, range 0.4-1, name "Row Sampling Rate".
8. Add continuous factor, range 0.4-1, name "Column Sampling Rate".
9. Add continuous factor, range 100-1000, name "Number Trees".
10. Set random seed 548678189.

Example 77

Summary: Process of generating a Space Filling Design for a DOE (Design of Experiments) using the Fast Flexible Filling method, with specified factor settings and response maximization.

Code:

// DOE Dialog
DOE(
    Space Filling Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Change Factor Settings(
        1, 1, 10, "Number Terms"
    ),
    Change Factor Settings(
        2, 5, 15,
        "Minimum Split Per Tree"
    ),
    Add Factor(
        Continuous, 100, 2000,
        "Maximum Split Per Tree", 0
    ),
    Add Factor(
        Continuous, 0.4, 1,
        "Row Sampling Rate", 0
    ),
    Add Factor(
        Continuous, 0.4, 1,
        "Column Sampling Rate", 0
    ),
    Add Factor(
        Continuous, 100, 1000,
        "Number Trees", 0
    ), Set Random Seed( 588569099 ),
    FFF Optimality Criterion( MaxPro ),
    Space Filling Design Type(
        Fast Flexible Filling, 10
    ), Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Start DOE process.
2. Use Space Filling Design.
3. Add response for maximization.
4. Set factor 1 settings.
5. Set factor 2 settings.
6. Add continuous factor "Maximum Split Per Tree".
7. Add continuous factor "Row Sampling Rate".
8. Add continuous factor "Column Sampling Rate".
9. Add continuous factor "Number Trees".
10. Generate and display design table.

Example 78

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a custom design with continuous factors X1-X4.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ), Set Random Seed( 3803118 ),
    Number of Starts( 1 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Set Sample Size( 12 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response: Maximize Y.
4. Add factor X1, continuous, range -1 to 1.
5. Add factor X2, continuous, range -1 to 1.
6. Add factor X3, continuous, range -1 to 1.
7. Add factor X4, continuous, range -1 to 1.
8. Set random seed 3803118.
9. Set number of starts to 1.
10. Define design terms and aliases, set sample size 12.

Example 79

Summary: This script initiates a custom design of experiments (DOE) process to analyze the relationship between four continuous factors (X1, X2, X3, and X4) and their interactions, using the REML method for variance component analysis and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ), Set Random Seed( 1533566117 ),
    Number of Starts( 141 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Set Sample Size( 14 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Initiate DOE process.
2. Use Custom Design method.
3. Define response to maximize ("Y").
4. Add continuous factor "X1".
5. Add continuous factor "X2".
6. Add continuous factor "X3".
7. Add continuous factor "X4".
8. Set random seed for reproducibility.
9. Specify number of design starts.
10. Include main effects for all factors.
11. Include alias terms for interactions.
12. Set sample size.
13. Disable response simulation.
14. Disable saving X matrix.
15. Generate design.
16. Randomize run order.
17. Create final design table.

Example 80

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a Custom Design in JMP.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ), Set Random Seed( 1906185792 ),
    Number of Starts( 1 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Set Sample Size( 16 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Start DOE dialog.
2. Select Custom Design.
3. Add response to maximize.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Add continuous factor X4.
8. Set random seed.
9. Set number of starts.
10. Add main effects and interactions.
11. Add alias terms.
12. Set sample size.
13. Disable response simulation.
14. Disable saving X matrix.
15. Generate design.
16. Randomize run order.
17. Create data table.

Example 81

Summary: Performs a custom design of experiments (DOE) to analyze the relationship between four continuous factors (X1, X2, X3, and X4) using the REML method in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ), Set Random Seed( 2076006612 ),
    Number of Starts( 91 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Alias Term( {1, 1}, {2, 1} ),
    Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ),
    Set Sample Size( 18 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Start DOE process.
2. Select Custom Design.
3. Define response to maximize.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Add continuous factor X4.
8. Set random seed for reproducibility.
9. Specify number of design starts.
10. Add main effects for all factors.
11. Add alias terms for interaction effects.
12. Set sample size.
13. Disable response simulation.
14. Disable X matrix saving.
15. Generate the design.
16. Randomize run order.
17. Create the design table.

Example 82

Summary: This JSL script defines a Definitive Screening Design (DSD) using the DOE Dialog, specifying continuous factors X1 to X5 and maximizing response Y. The design is then simulated with random responses.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X5", 0
    ), Show Blocking Options( 0, 0 ),
    Number of Extra Runs( 4 ),
    Set Random Seed( 131982 ),
    Make Design,
    Simulate Responses(
        1,
        Random Seed( -1 )
    ), Save X Matrix( 0 )}
);

Code Explanation:

1. Define DOE parameters.
2. Set response type to maximize.
3. Add continuous factor X1.
4. Add continuous factor X2.
5. Add continuous factor X3.
6. Add continuous factor X4.
7. Add continuous factor X5.
8. Disable blocking options.
9. Specify 4 extra runs.
10. Set random seed to 131982.

Example 83

Summary: Performs a custom design of experiments (DOE) to analyze the relationship between various factors and their impact on NOx emissions, utilizing the REML method for variance component analysis and fitting a model with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "NOx", ., ., .
    ),
    Add Factor(
        Continuous, 0, 6,
        "Hydrogen Fraction", 0
    ),
    Add Factor(
        Continuous, 1.05, 1.3,
        "Air/Fuel Ratio", 0
    ),
    Add Factor(
        Continuous, 21, 25,
        "Lance Position X", 0
    ),
    Add Factor(
        Continuous, 5, 10,
        "Lance Position Y", 0
    ),
    Add Factor(
        Continuous, 0, 6,
        "Secondary Fuel Fraction", 0
    ),
    Add Factor(
        Continuous, 0.5, 1.5,
        "Dispersant", 0
    ),
    Add Factor(
        Continuous, 0, 10, "Ethanol", 0
    ), Set Random Seed( 338785861 ),
    Number of Starts( 1468 ),
    Add Potential Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {5, 1} ),
    Add Term( {6, 1} ),
    Add Term( {7, 1} ),
    Add Term( {1, 2} ),
    Add Potential Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Potential Term( {1, 1}, {3, 1} ),
    Add Potential Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Add Potential Term( {1, 1}, {4, 1} ),
    Add Potential Term( {2, 1}, {4, 1} ),
    Add Potential Term( {3, 1}, {4, 1} ),
    Add Term( {4, 2} ),
    Add Potential Term( {1, 1}, {5, 1} ),
    Add Potential Term( {2, 1}, {5, 1} ),
    Add Potential Term( {3, 1}, {5, 1} ),
    Add Potential Term( {4, 1}, {5, 1} ),
    Add Potential Term( {5, 2} ),
    Add Potential Term( {1, 1}, {6, 1} ),
    Add Potential Term( {2, 1}, {6, 1} ),
    Add Potential Term( {3, 1}, {6, 1} ),
    Add Potential Term( {4, 1}, {6, 1} ),
    Add Potential Term( {5, 1}, {6, 1} ),
    Add Potential Term( {6, 2} ),
    Add Potential Term( {1, 1}, {7, 1} ),
    Add Potential Term( {2, 1}, {7, 1} ),
    Add Potential Term( {3, 1}, {7, 1} ),
    Add Potential Term( {4, 1}, {7, 1} ),
    Add Potential Term( {5, 1}, {7, 1} ),
    Add Potential Term( {6, 1}, {7, 1} ),
    Add Potential Term( {7, 2} ),
    Center Points( 2 ),
    Set Sample Size( 32 ),
    Optimality Criterion( 2 ),
    Simulate Responses}
);

Code Explanation:

1. Start DOE process.
2. Select Custom Design.
3. Define response variable NOx.
4. Add Hydrogen Fraction factor.
5. Add Air/Fuel Ratio factor.
6. Add Lance Position X factor.
7. Add Lance Position Y factor.
8. Add Secondary Fuel Fraction factor.
9. Add Dispersant factor.
10. Add Ethanol factor.
11. Set random seed.
12. Specify number of starts.
13. Add potential interaction terms.
14. Add main effect terms.
15. Add squared terms.
16. Add two center points.
17. Set sample size.
18. Choose optimality criterion.
19. Simulate responses.

Example 84

Summary: Initiates a custom design of experiments (DOE) process to optimize a response variable Y, with factors Dichloromethane, Methanol, and Sample Volume. The script uses the REML method for variance component analysis and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, 110, 150,
        "Dichloromethane", 0
    ),
    Add Factor(
        Continuous, 400, 600, "Methanol",
        0
    ),
    Add Factor(
        Continuous, 3, 7, "Sample Volume",
        0
    ), Set Random Seed( 1145917887 ),
    Number of Starts( 13717 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 32 ),
    Optimality Criterion( 2 ),
    Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Initiate DOE process.
2. Select Custom Design.
3. Define response: Maximize Y.
4. Add factor: Dichloromethane, range 110-150.
5. Add factor: Methanol, range 400-600.
6. Add factor: Sample Volume, range 3-7.
7. Set random seed for reproducibility.
8. Specify number of starts for optimization.
9. Add main effects terms.
10. Add interaction and quadratic terms.
11. Set sample size to 32.
12. Choose optimality criterion.
13. Generate design.
14. Randomize run order.
15. Create data table.

Example 85

Summary: Generates a full factorial design with six continuous factors and one response variable, using the REML method to perform variance component analysis and fit a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Full Factorial Design,
    {
    Add Response(
        Maximize, "Proportion Reacted",
        90, 100, 1
    ),
    Add Factor(
        Continuous,
        {10, 15},
        "Feed Rate",
        0
    ),
    Add Factor(
        Continuous,
        {1, 2},
        "Catalyst",
        0
    ),
    Add Factor(
        Continuous,
        {100, 120},
        "Stir Rate",
        0
    ),
    Add Factor(
        Continuous,
        {140, 180},
        "Temperature",
        0
    ),
    Add Factor(
        Continuous,
        {3, 6},
        "Concentration",
        0
    ), Set Random Seed( 12345 ),
    Make Design, Make Table}
);

Code Explanation:

1. Open DOE dialog.
2. Choose Full Factorial Design.
3. Add response variable.
4. Set response goal to maximize.
5. Name response "Proportion Reacted".
6. Define response limits 90 to 100.
7. Set response standard deviation to 1.
8. Add continuous factor "Feed Rate".
9. Define "Feed Rate" range 10 to 15.
10. Add continuous factor "Catalyst".
11. Define "Catalyst" range 1 to 2.
12. Add continuous factor "Stir Rate".
13. Define "Stir Rate" range 100 to 120.
14. Add continuous factor "Temperature".
15. Define "Temperature" range 140 to 180.
16. Add continuous factor "Concentration".
17. Define "Concentration" range 3 to 6.
18. Set random seed to 12345.
19. Generate design.
20. Create data table.

Example 86

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a custom design with continuous factors X1-X4.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ), Set Random Seed( 308514798 ),
    Number of Starts( 31958 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Add Term( {4, 2} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 2 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response to maximize.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Add continuous factor X4.
8. Set random seed.
9. Set number of starts.
10. Define model terms.
11. Set sample size.
12. Set optimality criterion.
13. Disable response simulation.
14. Disable X matrix saving.
15. Create design.

Example 87

Summary: Generates a custom design for a mixture experiment using the DOE dialog, with factors for Solvent, Active, and Water, and performs a variance component analysis using the REML method.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Mixture, 0.26, 0.8, "Solvent", 0
    ),
    Add Factor(
        Mixture, 0.18, 0.4, "Active", 0
    ),
    Add Factor(
        Mixture, 0.02, 0.56, "Water", 0
    ), Set Random Seed( 1544088351 ),
    Number of Starts( 2072 ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 32 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response variable "Y".
4. Define "Solvent" factor.
5. Define "Active" factor.
6. Define "Water" factor.
7. Set random seed.
8. Set number of starts.
9. Add main effects terms.
10. Add interaction terms.
11. Set sample size.
12. Disable response simulation.
13. Disable X matrix saving.
14. Generate design.
15. Randomize run order.
16. Create design table.

Example 88

Summary: Generates a custom design for an experiment using the DOE Dialog, optimizing for I-optimality and specifying random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor( Mixture, 0, 1, "A", 0 ),
    Add Factor( Mixture, 0, 1, "B", 0 ),
    Add Factor( Mixture, 0, 1, "C", 0 ),
    Set Random Seed( 802670610 ),
    Number of Starts( 3198 ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 16 ),
    Optimality Criterion(
        "Make I-Optimal Design"n
    ), Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design,
    Set Run Order( Randomize ),
    Make Table}
);

Code Explanation:

1. Start DOE process.
2. Use Custom Design.
3. Add response to maximize.
4. Define factor A as mixture.
5. Define factor B as mixture.
6. Define factor C as mixture.
7. Set random seed for reproducibility.
8. Specify number of starts.
9. Add main effect terms.
10. Add interaction terms.
11. Set sample size.
12. Optimize design for I-optimality.
13. Do not simulate responses.
14. Do not save X matrix.
15. Generate design.
16. Randomize run order.
17. Create data table.

Example 89

Summary: Initiates a custom design of experiments (DOE) process to optimize a response variable, incorporating mixture factors for Solvent, Active, and Water, while applying random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Mixture, 0.26, 0.8, "Solvent", 0
    ),
    Add Factor(
        Mixture, 0.18, 0.4, "Active", 0
    ),
    Add Factor(
        Mixture, 0.02, 0.56, "Water", 0
    ), Set Random Seed( 625641159 ),
    Number of Starts( 6371 ),
    Add Constraint(
        [-1 1.4 0 0, 0 0.1 -1 0]
    ), Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1}, {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 16 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Initiate DOE process.
2. Select Custom Design.
3. Define response to maximize.
4. Add mixture factor "Solvent".
5. Add mixture factor "Active".
6. Add mixture factor "Water".
7. Set random seed for reproducibility.
8. Specify number of design starts.
9. Add design constraints.
10. Include all main effects and interactions.

Example 90

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a custom design with continuous factors X1, X2, and X3.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ), Set Random Seed( 78379455 ),
    Number of Starts( 1 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Set Sample Size( 24 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Start DOE dialog.
2. Select Custom Design.
3. Add response "Y" to maximize.
4. Add continuous factor "X1".
5. Add continuous factor "X2".
6. Add continuous factor "X3".
7. Set random seed.
8. Specify number of starts.
9. Add main effect terms.
10. Add interaction terms.
11. Set sample size.
12. Disable response simulation.
13. Disable X matrix saving.
14. Generate design.

Example 91

Summary: Generates a Definitive Screening Design for a six-factor experiment, maximizing the response variable 'Y', and displays the design table.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X5", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X6", 0
    ), Show Blocking Options( 0, 0 ),
    Number of Extra Runs( 0 ),
    Set Random Seed( 524050865 ),
    Make Design, Make Table}
);

Code Explanation:

1. Open DOE dialog.
2. Select Definitive Screening Design.
3. Add response "Y" to maximize.
4. Add continuous factor "X1".
5. Add continuous factor "X2".
6. Add continuous factor "X3".
7. Add continuous factor "X4".
8. Add continuous factor "X5".
9. Add continuous factor "X6".
10. Generate and display design table.

Example 92

Summary: Generates a Definitive Screening Design using the DOE dialog, with six continuous factors and one response variable, to perform a variance component analysis using the REML method and fit a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, -1, 1, "X1", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X2", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X3", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X4", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X5", 0
    ),
    Add Factor(
        Continuous, -1, 1, "X6", 0
    ), Show Blocking Options( 0, 0 ),
    Number of Extra Runs( 4 ),
    Set Random Seed( 1217955066 ),
    Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Definitive Screening Design.
3. Add response variable Y.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Add continuous factor X4.
8. Add continuous factor X5.
9. Add continuous factor X6.
10. Generate design with settings.

Example 93

Summary: Generates a custom design for a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous, 100, 1000,
        "Number Trees", 0
    ),
    Add Factor(
        Continuous, 2, 5, "Number Terms",
        0
    ), Set Random Seed( 1335293222 ),
    Number of Starts( 34388 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Set Sample Size( 12 ),
    Optimality Criterion( 2 ),
    Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response variable Y.
4. Add continuous factor Number Trees.
5. Add continuous factor Number Terms.
6. Set random seed.
7. Define number of starts.
8. Add linear term for Number Trees.
9. Add quadratic term for Number Trees.
10. Add interaction term between factors.
11. Set sample size.
12. Set optimality criterion.
13. Generate design.

Example 94

Summary: Initiates a custom design of experiments (DOE) process to optimize yield, match target MFI, and maximize CI in an investment casting application. The script defines various factors, including continuous and categorical variables, and sets a random seed.

Code:

// Custom Design
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 50, 80, "SA", 0
    ),
    Add Factor(
        Continuous, 0, 3.7, "M%", 0
    ),
    Add Factor(
        Continuous, 13, 19, "Xf", 0
    ),
    Add Factor(
        Categorical,
        {"Umbogo A", "Kuanga B",
        "Kuanga A"},
        "Quarry",
        0
    ),
    Add Factor(
        Continuous, 2, 6.5, "pH", 0
    ),
    Add Factor(
        Continuous, 6.5, 7.5, "Viscosity",
        0
    ),
    Add Factor(
        Continuous, 5, 25, "Ambient Temp",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B", "C", "D"},
        "Shift",
        0
    ), Set Random Seed( 103517925 )}
);

Code Explanation:

1. Initiate DOE process.
2. Define Custom Design.
3. Add response: Maximize Yield.
4. Add response: Match Target MFI.
5. Add response: Maximize CI.
6. Add continuous factor SA.
7. Add continuous factor M%.
8. Add continuous factor Xf.
9. Add categorical factor Quarry.
10. Add continuous factor pH.
11. Add continuous factor Viscosity.
12. Add continuous factor Ambient Temp.
13. Add categorical factor Shift.
14. Set random seed 103517925.

Example 95

Summary: Creates a Definitive Screening Design (DSD) using the DOE function, defining multiple responses and factors to optimize yield, match target MFI, and maximize CI while controlling various continuous and categorical factors.

Code:

// DSD
DOE(
    Definitive Screening Design,
    {
    Add Response(
        Maximize, "Yield", ., ., .
    ),
    Add Response(
        Match Target, "MFI", ., 198, .
    ),
    Add Response(
        Maximize, "CI", 80, ., .
    ),
    Add Factor(
        Continuous, 50, 80, "SA", 0
    ),
    Add Factor(
        Continuous, 0, 3.7, "M%", 0
    ),
    Add Factor(
        Continuous, 13, 19, "Xf", 0
    ),
    Add Factor(
        Categorical,
        {"Umbogo A", "Kuanga B",
        "Kuanga A"},
        "Quarry",
        0
    ),
    Add Factor(
        Continuous, 2, 6.5, "pH", 0
    ),
    Add Factor(
        Continuous, 6.5, 7.5, "Viscosity",
        0
    ),
    Add Factor(
        Continuous, 5, 25, "Ambient Temp",
        0
    ),
    Add Factor(
        Categorical,
        {"A", "B", "C", "D"},
        "Shift",
        0
    ), Set Random Seed( 103517925 )}
);

Code Explanation:

1. Create a new DOE.
2. Define a Definitive Screening Design.
3. Add response "Yield" to maximize.
4. Add response "MFI" to match target 198.
5. Add response "CI" to maximize, lower bound 80.
6. Add continuous factor "SA" range 50-80.
7. Add continuous factor "M%" range 0-3.7.
8. Add continuous factor "Xf" range 13-19.
9. Add categorical factor "Quarry" with levels.
10. Add continuous factor "pH" range 2-6.5.
11. Add continuous factor "Viscosity" range 6.5-7.5.
12. Add continuous factor "Ambient Temp" range 5-25.
13. Add categorical factor "Shift" with levels.
14. Set random seed to 103517925.

Example 96

Summary: Performs a custom design of experiments (DOE) to analyze the impact of factors 'Brand', 'Time', and 'Power' on responses 'Number Popped' and 'Total Kernels'. The script uses the REML method for variance component analysis and fits a model with random effects in the Standard Least Squares personality.

Code:

// 
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Number Popped", ., .,
        .
    ),
    Add Response(
        Maximize, "Total Kernels", ., .,
        .
    ),
    Add Factor(
        Categorical,
        {"Top Secret", "Wilbur"},
        "Brand",
        0
    ),
    Add Factor(
        Continuous, 3, 5, "Time", 0
    ),
    Add Factor(
        Continuous, 5, 10, "Power", 0
    ), Set Random Seed( 1117081326 ),
    Add Constraint( [1 1 13, -1 -1 -10] ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Set Sample Size( 16 ),
    Optimality Criterion( 2 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 )}
);

Code Explanation:

1. Define DOE with Custom Design.
2. Add response "Number Popped" to maximize.
3. Add response "Total Kernels" to maximize.
4. Add categorical factor "Brand" with levels "Top Secret", "Wilbur".
5. Add continuous factor "Time" from 3 to 5.
6. Add continuous factor "Power" from 5 to 10.
7. Set random seed for reproducibility.
8. Add constraint on factors.
9. Add main effects for all factors.
10. Add interaction terms between factors.

Example 97

Summary: Performs a custom design of experiments (DOE) to optimize the conversion rate, utilizing a combination of continuous and categorical factors, with random effects modeling and REML method for variance component analysis.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Maximize, "Conversion", ., ., .
    ),
    Add Factor(
        Continuous, 40, 60, "Temp", 0
    ),
    Add Factor(
        Continuous, 2, 20, "Pd (eq)", 0
    ),
    Add Factor(
        Continuous, 100, 1500,
        "Base (eq)", 0
    ),
    Add Factor(
        Categorical,
        {"pyrimidyl", "pyridyl"},
        "Boronic Acid Type",
        0
    ), Set Random Seed( 346162045 ),
    Number of Starts( 10000 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Add Term( {4, 1} ),
    Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ),
    Add Term( {2, 2} ),
    Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ),
    Add Term( {3, 2} ),
    Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {4, 1} ),
    Add Term( {3, 1}, {4, 1} ),
    Set Sample Size( 20 ),
    Optimality Criterion( 2 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Initiate DOE process.
2. Select Custom Design.
3. Define response: Maximize Conversion.
4. Add continuous factor Temp (40-60).
5. Add continuous factor Pd (eq) (2-20).
6. Add continuous factor Base (eq) (100-1500).
7. Add categorical factor Boronic Acid Type.
8. Set random seed for reproducibility.
9. Specify number of design starts.
10. Configure model terms for interactions.
11. Set sample size.
12. Choose optimality criterion.
13. Disable response simulation.
14. Disable X matrix saving.
15. Generate and save design.

Example 98

Summary: Performs a full factorial design of experiments (DOE) to analyze the relationships between continuous factors 'Discharge', 'Gradient', and 'Clutter' with response variable 'Y', using the REML method for variance component analysis.

Code:

// DOE Dialog
DOE(
    Full Factorial Design,
    {
    Add Response(
        Maximize, "Y", ., ., .
    ),
    Add Factor(
        Continuous,
        {-1, 1},
        "Discharge",
        0
    ),
    Add Factor(
        Continuous,
        {-1, 1},
        "Gradient",
        0
    ),
    Add Factor(
        Continuous,
        {-1, 1},
        "Clutter",
        0
    ), Set Random Seed( 782 ),
    Make Design, Replicates( 1 ),
    Simulate Responses( 0 ),
    Set Run Order( Sort Left to Right ),
    Make Table}
);

Code Explanation:

1. Start DOE process.
2. Choose Full Factorial Design.
3. Add response "Y" for maximization.
4. Add continuous factor "Discharge".
5. Set "Discharge" range -1 to 1.
6. Add continuous factor "Gradient".
7. Set "Gradient" range -1 to 1.
8. Add continuous factor "Clutter".
9. Set "Clutter" range -1 to 1.
10. Generate design with specified settings.

Example 99

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a custom design with categorical factors X1 and X2.

Code:

// DOE Dialog
DOE(
    Custom Design,
    {
    Add Response(
        Minimize, "Y", ., ., .
    ),
    Add Factor(
        Categorical,
        {"Low", "High"},
        "X1",
        1
    ),
    Add Factor(
        Categorical,
        {"Low", "Medium", "High"},
        "X2",
        0
    ), Set Random Seed( 1590851790 ),
    Number of Starts( 16493 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {1, 1}, {2, 1} ),
    Set N Whole Plots( 10 ),
    Set Sample Size( 30 ),
    Simulate Responses( 1 ),
    Save X Matrix( 0 ), Make Design}
);

Code Explanation:

1. Open DOE dialog.
2. Select Custom Design.
3. Add response: Minimize Y.
4. Add factor X1: Categorical, levels Low, High.
5. Add factor X2: Categorical, levels Low, Medium, High.
6. Set random seed.
7. Set number of starts.
8. Add intercept term.
9. Add main effect X1.
10. Add main effect X2.
11. Add interaction X1*X2.
12. Set whole plots to 10.
13. Set sample size to 30.
14. Simulate responses.
15. Do not save X matrix.
16. Make design.

Example 100

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

Open("data_table.jmp");
doe = DOE();

Code Explanation:

1. Open data table;
2. Create DOE object.

Example 101

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing a DOE object to load responses from a data table.

Code:

dt = Open("data_table.jmp");
doe = DOE();
doe << Load Responses;

Code Explanation:

1. Open data table.
2. Create new DOE object.
3. Load responses into DOE.

Example 102

Summary: Generates a custom design of experiments (DOE) to analyze the impact of multiple factors on a response variable, utilizing the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt1 = Open("data_table.jmp");
d = DOE(
    Evaluate Design,
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    {Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ),
    Add Term( {1, 1}, {2, 1} ), Add Term( {1, 1}, {3, 1} ), Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} )}
);
d << Change Anticipated Coefficients( [1 2 3 4 1 2 3 4] );
d << Set RMSE( 1.5 );
Close( dt1, no save );
rpt = d << report;

Code Explanation:

1. Open data table.
2. Define DOE parameters.
3. Add main effects.
4. Add interaction terms.
5. Add alias terms.
6. Change anticipated coefficients.
7. Set root mean square error.
8. Close original data table.
9. Generate DOE report.

Example 103

Summary: Generates a custom design of experiments (DOE) to analyze the relationship between Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration using the REML method in JMP.

Code:

dt1 = Open("data_table.jmp");
d = DOE(
    Evaluate Design,
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    {Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ),
    Add Term( {1, 1}, {2, 1} ), Add Term( {1, 1}, {3, 1} ), Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ),
    Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} )}
);
d << Change Anticipated Coefficients( [1 2 3 4 1 2 3 4] );
d << Set RMSE( 1.5 );

Code Explanation:

1. Open data table.
2. Define DOE settings.
3. Specify factors: Feed Rate, Catalyst, Stir Rate, Temperature, Concentration.
4. Add main effects for all factors.
5. Add two-way interactions for all factor pairs.
6. Add alias terms for selected interactions.
7. Change anticipated coefficients.
8. Set RMSE value.

Example 104

Summary: Process of opening a data table, setting it as current, launching the Compare Designs platform, and generating a report before closing the window.

Code:

dt = Open("data_table.jmp");
Current Data Table( dt );
cd = DOE( Compare Designs );
d4 = Window( "Report: Compare Designs " );
cd << close window;

Code Explanation:

1. Open data table.
2. Set current data table.
3. Launch Compare Designs platform.
4. Get Compare Designs report window.
5. Close Compare Designs window.

Example 105

Summary: Creates a MaxDiff design with 3 profiles and 7 choice sets, generating a table from the design and closing the design window.

Code:

dtProf = Open("data_table.jmp");
d = DOE( MaxDiff Design, X( :Candy ), {Set Number of Profiles( 3 ), Set Number of Choice Sets( 7 )} );
dt = d << Make Table;
d << close window;

Code Explanation:

1. Open data table;
2. Create MaxDiff design.
3. Set number of profiles to 3.
4. Set number of choice sets to 7.
5. Make table from design.
6. Close design window.

Example 106

Summary: Compares two designs in a DOE analysis, specifying reference and additional design factors for a variance component analysis using REML.

Code:

dt_1 = Open("data_table.jmp");
dt_2 = Open("data_table.jmp");
DOE(
    Compare Designs,
    Reference Design( "data_table1", X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ) ),
    Additional Designs( "data_table2", X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ) )
);
Window( "Report: Compare Designs " ) << close window;

Code Explanation:

1. Open data table;
2. Open data table;
3. Initiate DOE analysis.
4. Select Compare Designs option.
5. Set reference design.
6. Specify reference design factors.
7. Add additional design.
8. Specify additional design factors.
9. Close report window.

Example 107

Summary: Compares two data tables using Design of Experiments (DOE) analysis, loading factors from the current data table.

Code:

dt1 = Open("data_table.jmp");
dt2 = Open("data_table.jmp");
Current Data Table( dt1 );
DOE( Compare Designs );

Code Explanation:

1. Open data table;
2. Open data table;
3. Set current data table to dt1.
4. Initiate DOE analysis.
5. Compare designs from both tables.

Example 108

Summary: Runs the creation and closure of a DOE (Design of Experiments) object using the Custom Design method, loading factors from the current data table.

Code:

dt = Open("data_table.jmp");
d = DOE( Compare Designs );
d << close window;

Code Explanation:

1. Open table.
2. Create DOE object.
3. Close DOE window.

Example 109

Summary: Runs a custom design experiment to analyze the relationship between continuous factors X1 and X2, covariates sex, weight, and age, using REML method for variance component analysis and fitting a model with random effects in Standard Least Squares personality.

Code:

dtCov = Open("data_table.jmp");
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Covariate, sex, 0 ), Add Factor( Covariate, weight, 0 ), Add Factor( Covariate, age, 0 ), Set Random Seed( 1475678972 ),
    Number of Starts( 10791 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ),
    Add Term( {5, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Add Alias Term( {1, 1}, {3, 1} ), Add Alias Term( {1, 1}, {4, 1} ),
    Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ),
    Add Alias Term( {3, 1}, {4, 1} ), Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} ), Set Sample Size( 10 ),
    Simulate Responses( 0 ), Save X Matrix( 0 )}
);
rpt = d << report;

Code Explanation:

1. Open data table.
2. Create custom design experiment.
3. Define response variable to maximize.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add covariate factor sex.
7. Add covariate factor weight.
8. Add covariate factor age.
9. Set random seed for reproducibility.
10. Configure design settings and terms.

Example 110

Summary: Creates a Design of Experiments (DOE) using Easy DOE and performs a variance component analysis with random effects in the Standard Least Squares personality.

Code:

dtResponses = Open("data_table.jmp");
d = DOE( Easy DOE );

Code Explanation:

1. Open data table.
2. Create new DOE design.

Example 111

Summary: Creates and modifies custom design experiments for variance component analysis using REML method in JMP, incorporating covariates, factors, and random seed settings.

Code:

dtCov = Open("data_table.jmp");
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 11055475 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} )}
);
d << Add Factor( Covariate, sex, 0 );
d << Add Term( {1, 1}, {2, 1} );
d << close window;
Close( dtCov, nosave );
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Mixture, 0, 1, "X1", 0 ), Add Factor( Mixture, 0, 1, "X2", 0 ),
    Set Random Seed( 11055475 ), Add Term( {1, 1} ), Add Term( {2, 1} )}
);
d << Add Factor( Mixture, 0, 1, "X3", 0 );
d << Add Term( {1, 1}, {2, 1} );
d << close window;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Constant, 0, "X1" ), Add Factor( Constant, 0, "X2" ), Set Random Seed( 11055475 ),
    Add Term( {1, 0} )}
);
d << Add Factor( Constant, 0, "X3" );
d << Add Term( {1, 1}, {2, 1} );
d << close window;
factorTypes = {"Continuous", "Categorical", "Discrete Numeric", "Blocking", "Covariate", "Mixture", "Constant"};
makeLevels = Function( {nLevels, type = "Character"},
    {mylist, i},
    If( type == "Character",
        mylist = {};
        For( i = 1, i <= nLevels, i++,
            Insert Into( mylist, "L" || Char( i ) )
        );
        Return( mylist );
    );
    If( type == "Discrete Numeric",
        mylist = {};
        For( i = 1, i <= nLevels, i++,
            Insert Into( mylist, Random Integer( 1, 100 ) )
        );
        Return( Sort Ascending( mylist ) );
    );
);
Random Reset( 123456789 );

Code Explanation:

1. Open data table.
2. Create custom design experiment.
3. Add response variable Y.
4. Add covariates height and weight.
5. Set random seed.
6. Add intercept term.
7. Add interaction terms.
8. Add covariate sex.
9. Add interaction terms.
10. Close experiment window.

Example 112

Summary: Creates a custom Design of Experiments (DOE) for variance component analysis using the REML method and fitting a model with random effects in the Standard Least Squares personality.

Code:

dtCov = Open("data_table.jmp");
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 11055475 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} )}
);
d << Add Factor( Covariate, sex, 0 );
d << Add Term( {1, 1}, {2, 1} );
d << close window;

Code Explanation:

1. Open data table;
2. Create custom DOE design.
3. Add response variable Y.
4. Add covariate height.
5. Add covariate weight.
6. Set random seed.
7. Add intercept term.
8. Add height term.
9. Add weight term.
10. Add sex covariate.
11. Add interaction terms.
12. Close DOE window.

Example 113

Summary: Generates a custom design of experiments (DOE) for variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
dt[10, "height"] = .;
dt[5, "weight"] = .;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 2616 ), Number of Starts( 80 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ),
    Add Term( {4, 1} ), Add Term( {5, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ), Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} ),
    Set Sample Size( 40 ), Simulate Responses( 0 ), Save X Matrix( 0 )}
);
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << Allow covariate rows to be repeated( 1 );
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << Back up Design;
d << Allow covariate rows to be repeated( 0 );
d << Set Sample Size( 38 );
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << close window;
Close( dt, nosave );
Random Reset( 123 );

Code Explanation:

1. Open data table.
2. Set height of row 10 to missing.
3. Set weight of row 5 to missing.
4. Define custom DOE design.
5. Add response variable Y to maximize.
6. Add three continuous factors X1, X2, X3.
7. Add height and weight as covariates.
8. Set random seed and number of starts.
9. Add main effects and interaction terms.
10. Set sample size to 40, simulate responses, save X matrix.
11. Capture log and collapse whitespace.
12. Allow covariate rows to be repeated.
13. Recapture log and collapse whitespace.
14. Backup design.
15. Disallow covariate rows to be repeated.
16. Set sample size to 38.
17. Recapture log and collapse whitespace.
18. Close design window.
19. Close data table without saving.
20. Reset random seed.

Example 114

Summary: Creates a custom design for a response variable and multiple covariates using the DOE function, with options to set random seed, number of starts, and sample size.

Code:

dtCov = Open("data_table.jmp");
Current Data Table( dtCov );
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Covariate, Silica, 0 ), Add Factor( Covariate, Sulfur, 0 ),
    Add Factor( Covariate, Silane, 0 ), Set Random Seed( 50087686 ), Number of Starts( 10 ), Add Term( {1, 0} ), Add Term( {1, 1} ),
    Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {1, 2} ), Add Term( {1, 1}, {2, 1} ), Add Term( {2, 2} ), Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ), Add Term( {3, 2} ), Enforce Use of Selected Covariate Rows( 1 ),
    Select Covariate Rows( [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] ), Allow covariate rows to be repeated( 1 ), Set Sample Size( 20 ),
    Optimality Criterion( "Make I-Optimal Design" ), Simulate Responses( 0 ), Save X Matrix( 0 ), Make Design}
);
dtCov2 = dtCov << Subset( All Rows( 1 ), All Columns( 1 ), Output Table Name( "data_table Reordered" ) );
dtCov2 << New Column( "Random Uniform", Numeric, "Continuous", Set Values( J( N Row( dtCov ), 1, Random Uniform() ) ) );
dtCov2 << Sort( By( :Random Uniform ), Order( Ascending ), Replace Table );

Code Explanation:

1. Open data table.
2. Set current data table.
3. Define DOE parameters.
4. Create custom design.
5. Add response variable.
6. Add covariates.
7. Set random seed.
8. Specify number of starts.
9. Add polynomial terms.
10. Select covariate rows.
11. Set sample size.
12. Choose optimality criterion.
13. Generate design.
14. Create subset table.
15. Add random uniform column.
16. Sort table randomly.

Example 115

Summary: Generates a custom design of experiments (DOE) for variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
dt[10, "height"] = .;
dt[5, "weight"] = .;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 2616 ), Number of Starts( 80 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ),
    Add Term( {4, 1} ), Add Term( {5, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ), Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} ),
    Set Sample Size( 40 ), Simulate Responses( 0 ), Save X Matrix( 0 )}
);
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << Allow covariate rows to be repeated( 1 );
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << Back up Design;
d << Allow covariate rows to be repeated( 0 );
d << Set Sample Size( 38 );
lc = Collapse Whitespace( Log Capture( d << Make Design ) );
d << close window;

Code Explanation:

1. Open data table;
2. Set height missing for row 10.
3. Set weight missing for row 5.
4. Define custom DOE with response and factors.
5. Set random seed and number of starts.
6. Add terms and alias terms to design.
7. Set sample size to 40.
8. Log capture and collapse whitespace.
9. Allow covariate rows repetition.
10. Log capture, backup design, set sample size to 38, close window.

Example 116

Summary: Generates a custom design of experiments (DOE) to analyze the relationship between continuous factors X1, X2, and X3, while accounting for covariates height and weight, using the REML method in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
dt[10, "height"] = .;
dt[5, "weight"] = .;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 2616 ), Number of Starts( 80 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ),
    Add Term( {4, 1} ), Add Term( {5, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ),
    Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ), Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} ),
    Set Sample Size( 20 ), Simulate Responses( 0 ), Save X Matrix( 0 )}
);

Code Explanation:

1. Open data table;
2. Set height missing for row 10.
3. Set weight missing for row 5.
4. Define custom DOE.
5. Add response variable Y.
6. Add continuous factors X1, X2, X3.
7. Add covariates height, weight.
8. Set random seed to 2616.
9. Set number of starts to 80.
10. Specify model terms and alias terms.

Example 117

Summary: Creates a D-optimal design for a response variable using the Augment Design method, incorporating random effects and specifying linear and interaction terms.

Code:

dt1 = Open("data_table.jmp");
d = DOE(
    Augment Design,
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    Y( :Percent Reacted ),
    {Augment Method( Augment ), Set Random Seed( 282322901 ), Number of Starts( 800 ), Add Term( {1, 0} ), Add Term( {1, 1} ),
    Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ), Add Term( {1, 1}, {2, 1} ), Add Term( {1, 1}, {3, 1} ),
    Add Term( {1, 1}, {4, 1} ), Add Term( {1, 1}, {5, 1} ), Add Term( {2, 1}, {3, 1} ), Add Term( {2, 1}, {4, 1} ),
    Add Term( {2, 1}, {5, 1} ), Add Term( {3, 1}, {4, 1} ), Add Term( {3, 1}, {5, 1} ), Add Term( {4, 1}, {5, 1} ), Set Sample Size( 16 ),
    Optimality Criterion( "Make D-Optimal Design" ), Make Design, Save X Matrix( 0 ), Simulate Responses( 0 )}
);

Code Explanation:

1. Open data table.
2. Define DOE settings.
3. Specify X variables.
4. Specify Y variable.
5. Set augment method.
6. Initialize random seed.
7. Define number of starts.
8. Add linear terms.
9. Add interaction terms.
10. Set sample size.
11. Optimize design for D-optimality.
12. Create design.
13. Do not save X matrix.
14. Do not simulate responses.

Example 118

Summary: Runs the design of experiments for a response variable, utilizing the Augment Design method to generate an experimental design and create a data table.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Augment Design,
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    Y( :Percent Reacted ),
    {Group new runs into separate block, Augment Method( Replicate, 2 ), Save X Matrix( 0 ), Simulate Responses( 0 )}
);
dt2 = d << Make Table;
d << close window;

Code Explanation:

1. Open data table.
2. Define design experiment.
3. Specify input factors.
4. Specify response variable.
5. Configure design options.
6. Generate experimental design.
7. Create data table from design.
8. Close original design window.

Example 119

Summary: Generates a custom design of experiments (DOE) for shrinkage analysis with random effects in the Standard Least Squares personality, utilizing the REML method.

Code:

dt1 = Open("data_table.jmp");
d = DOE(
    Custom Design,
    {Add Response( Minimize, "Shrinkage", ., ., . ), Add Factor( Covariate, Specific Gravity, 0 ),
    Add Factor( Covariate, Tensile Strength, 0 ), Add Factor( Covariate, Supplier, 0 ), Add Factor( Continuous, -1, 1, "Temperature", 0 ),
    Add Factor( Continuous, -1, 1, "Speed", 0 ), Add Factor( Continuous, -1, 1, "Time", 0 ), Set Random Seed( 84951 ),
    Number of Starts( 40 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ),
    Add Term( {5, 1} ), Add Term( {6, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Add Alias Term( {1, 1}, {3, 1} ),
    Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {1, 1}, {6, 1} ), Add Alias Term( {2, 1}, {3, 1} ),
    Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {2, 1}, {6, 1} ), Add Alias Term( {3, 1}, {4, 1} ),
    Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {3, 1}, {6, 1} ), Add Alias Term( {4, 1}, {5, 1} ), Add Alias Term( {4, 1}, {6, 1} ),
    Add Alias Term( {5, 1}, {6, 1} ), Set Sample Size( 12 ), Make Design}
);

Code Explanation:

1. Open table.
2. Define DOE.
3. Add response.
4. Add factors.
5. Set random seed.
6. Configure design settings.
7. Add main effects.
8. Add interaction terms.
9. Define alias terms.
10. Set sample size.
11. Generate design.

Example 120

Summary: Runs the design and execution of a custom experiment with multiple factors, including Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration, to analyze Percent Reacted.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Augment Design,
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    Y( :Percent Reacted ),
    {Group new runs into separate block, Augment Method( Centerpoints, 2 ), Save X Matrix( 0 ), Simulate Responses( 0 )}
);
dt2 = d << Make Table;
d << close window;

Code Explanation:

1. Open data table.
2. Define design of experiment.
3. Specify factors and response.
4. Configure augmentation settings.
5. Generate DOE design.
6. Create new data table.
7. Close original DOE window.

Example 121

Summary: Process of opening a data table, creating a response surface design, and loading factors into the design using the REML method.

Code:

dtFactors = Open("data_table.jmp");
d = DOE( Response Surface Design );
Log Capture( d << Load Factors );

Code Explanation:

1. Open data table.
2. Create response surface design.
3. Load factors into design.

Example 122

Summary: Creates and analyzes a custom design experiment with continuous factors, including response targeting and factor saving, using JMP's DOE platform.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Custom Design,
    Load Responses,
    Add Factor( Continuous, 0.7, 1.7, "Silica", 0 ),
    Add Factor( Continuous, 1.8, 2.8, "Sulfur", 0 ),
    Add Factor( Continuous, 40, 60, "Silane", 0 ),
    Make Design
);
rpt = d << Report;
d << Close Window;
Close( dt, no save );
d = DOE(
    Custom Design,
    Add Response( Match Target, "Stretch", 350, 550, 1 ),
    Add Factor( Continuous, 0.7, 1.7, "Silica", 0 ),
    Add Factor( Continuous, 1.8, 2.8, "Sulfur", 0 ),
    Add Factor( Continuous, 40, 60, "Silane", 0 ),
    Save Factors
);
dt = Current Data Table() << Get as Matrix;
d << Close Window;

Code Explanation:

1. Open data table.
2. Create custom design experiment.
3. Load responses into design.
4. Add continuous factor "Silica".
5. Add continuous factor "Sulfur".
6. Add continuous factor "Silane".
7. Generate the design.
8. Retrieve report from design.
9. Close the design window.
10. Close the original data table without saving.
11. Create another custom design experiment.
12. Add response "Stretch" with target.
13. Add continuous factor "Silica".
14. Add continuous factor "Sulfur".
15. Add continuous factor "Silane".
16. Save the factors.
17. Convert current data table to matrix.
18. Close the design window.

Example 123

Summary: Creates a custom design with continuous factors, response variable, and constraint matrix to perform a variance component analysis using REML method in Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Custom Design, Load Factors );
d << make Design;
rpt = d << report;
d << close window;
Close( dt, no save );
d = DOE(
    Custom Design,
    Add Response( Maximize, "Y", ., ., . ),
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Add Constraint( [1 1 0 1, 1 0 1 1] ),
    Add Term( {1, 0} ),
    Save Constraints
);
dt = Current Data Table();
d << close window;

Code Explanation:

1. Open data table;
2. Create custom design.
3. Make design.
4. Generate report.
5. Close design window.
6. Close original data table without saving.
7. Create new custom design.
8. Add response variable "Y".
9. Add continuous factors X1, X2, X3.
10. Add constraint matrix and terms.

Example 124

Summary: Process of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Power;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table.
2. Define experimental design.
3. Set factors and response.
4. Evaluate design power.
5. Retrieve power results.
6. Close data table.
7. Discard unsaved changes.
8. Close experimental design window.

Example 125

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, leveraging the Custom Design feature of JMP.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :X1, :X2, :X3 ), Y( :Y ) );
X = d << Get Effect Power;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table.
2. Define DOE experiment.
3. Specify factors and response.
4. Evaluate design effect power.
5. Close data table without saving.
6. Close DOE window.

Example 126

Summary: Runs a design of experiments (DOE) analysis using the REML method to evaluate estimation efficiencies in a Standard Least Squares personality, with interactive features for factor selection and response specification.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Estimation Efficiencies;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table.
2. Define design experiment.
3. Specify factors and response.
4. Evaluate design efficiency.
5. Retrieve estimation efficiencies.
6. Close data table without saving.
7. Close design window.

Example 127

Summary: Process of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Design Diagnostics;
X[2] = .;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Set factors and response.
4. Get design diagnostics.
5. Modify diagnostics data.
6. Close data table.
7. Close DOE window.

Example 128

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to generate a design matrix from specified factors.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set Significance Level( 0.10 );
rpt = d << report;
X = rpt[Number Col Edit Box( 1 )] << get as matrix;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table;
2. Create DOE with specified factors.
3. Set significance level to 0.10.
4. Generate DOE report.
5. Extract design matrix from report.
6. Close original data table without saving.
7. Close DOE window.

Example 129

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to create an experimental design.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set RMSE( 1.5 );
rpt = d << report;
X = rpt[Number Col Edit Box( 2 )] << get as matrix;
Close( dt, no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create experimental design.
3. Set root mean square error.
4. Generate report.
5. Extract matrix from report.
6. Close data table without saving.
7. Close design experiment window.

Example 130

Summary: Creates a Design of Experiments (DOE) design using the Augment method, with specified sample size and output table generation.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Augment );
d << Set Sample Size( 24 );
d << Make Design;
d << make table;
Close( dt, no save );
Close( Current Data Table(), no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Set augment method.
4. Define sample size.
5. Generate design.
6. Make design table.
7. Close original table.
8. Close generated table.
9. Close design window.

Example 131

Summary: Creates an augmented design table using the Augment Design method with replicate augmentation, and then generates a table from it.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :X1, :X2, :X3 ), Y( :Y ) );
d << Augment Method( Replicate, 2 );
d << make table;
Close( dt, no save );
Close( Current Data Table(), no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create DOE object.
3. Define factors X1, X2, X3.
4. Set response variable Y.
5. Use Augment Design method.
6. Specify replicate augmentation method.
7. Generate augmented design table.
8. Close original data table.
9. Close generated data table.
10. Close DOE window.

Example 132

Summary: Creates a Design of Experiments (DOE) design table with centerpoints using the Augment Method, and then generates the design table.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Centerpoints, 3 );
d << make table;
Close( dt, no save );
Close( Current Data Table(), no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Specify factors and response.
4. Add centerpoints.
5. Generate design table.
6. Close original data table.
7. Close generated data table.
8. Close DOE window.

Example 133

Summary: Creates a design experiment with fold-over augmentation and generates an augmented data table for analysis.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Fold Over, [1 2] );
d << make table;
Close( dt, no save );
Close( Current Data Table(), no save );
d << close window;

Code Explanation:

1. Open data table.
2. Create design experiment.
3. Set factors and response.
4. Augment design using fold over.
5. Specify fold over columns.
6. Make augmented table.
7. Close original data table.
8. Close new data table.
9. Close design window.

Example 134

Summary: Creates and manipulates design tables for mixture experiments, including augmentation, subset selection, and customization.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Add Axial, 1, 2 );
d << make table;
Close( dt, no save );
Close( Current Data Table(), no save );
d << close window;
d = DOE( Mixture Design, Add Factor( Mixture, 0.1, 1, "X4", 0 ) );
d << Mixture Design Type( Extreme Vertices, 3 );
d << Find Subset( 10 );
d << make table;
Log Capture( Close( Current Data Table() ) );
d << close window;
d = DOE( Custom Design );
r = d << report;
t = r[Outline Box( 1 )] << Get Title;
d << close window;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ), Add Factor( Continuous, -1, 1, "X4", 0 ), Add Factor( Continuous, -1, 1, "X5", 0 ),
    Add Factor( Continuous, -1, 1, "X6", 0 ), Add Factor( Continuous, -1, 1, "X7", 0 ), Add Factor( Continuous, -1, 1, "X8", 0 ),
    Add Factor( Continuous, -1, 1, "X9", 0 ), Add Factor( Continuous, -1, 1, "X10", 0 ), Add Factor( Continuous, -1, 1, "X11", 0 ),
    Add Factor( Continuous, -1, 1, "X12", 0 ), Set Random Seed( 219343887 ), Design Search Time( 15 ), Add Term( {1, 0} ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ), Set Sample Size( 13 ), Make Design}
);
time = d << get timing;
d << close window;
isPro = 1;
If( isPro,
    d = DOE(
        Custom Design,
        Add Response( Maximize, "Y", ., ., . ),
        Add Functional Response( "Y", 5, {10, 20, 30, 40, 50} ),
        Set Random Seed( 46055034 ),
        Simulate Responses( 0 ),
        Save X Matrix( 0 )
    );
    d << Make Design;
    dt = d << Make Table;
    colNames = dt << Get Column Names( string );
    Close( dt, nosave );
    d << close window;
);

Code Explanation:

1. Open data table.
2. Create augmented design.
3. Add axial points.
4. Make augmented design table.
5. Close original data table.
6. Close augmented design table.
7. Create mixture design.
8. Add new mixture factor.
9. Set mixture design type.
10. Find subset designs.

Example 135

Summary: Runs the design of experiments (DOE) process to generate a custom design with continuous factors, load responses, and create a report in JMP.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Custom Design,
    Load Responses,
    Add Factor( Continuous, 0.7, 1.7, "Silica", 0 ),
    Add Factor( Continuous, 1.8, 2.8, "Sulfur", 0 ),
    Add Factor( Continuous, 40, 60, "Silane", 0 ),
    Make Design
);
rpt = d << Report;
d << Close Window;

Code Explanation:

1. Open table.
2. Start DOE process.
3. Load responses.
4. Add Silica factor.
5. Add Sulfur factor.
6. Add Silane factor.
7. Generate design.
8. Create report.
9. Close design window.

Example 136

Summary: Runs a custom design experiment with load factors to generate a report, utilizing the REML method for variance component analysis and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Custom Design, Load Factors );
d << make Design;
rpt = d << report;
d << close window;

Code Explanation:

1. Open data table.
2. Create custom design experiment.
3. Load factors into design.
4. Generate the design.
5. Retrieve design report.
6. Close design window.

Example 137

Summary: Runs the design of experiments (DOE) for a custom model with continuous factors X1 and X2, using the REML method to perform variance component analysis.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Term( {1, 0} ),
    Load Constraints
);

Code Explanation:

1. Open data table.
2. Define DOE object.
3. Add continuous factor X1.
4. Add continuous factor X2.
5. Add intercept term.
6. Load design constraints.

Example 138

Summary: Process of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Remove Term( {1, 1}, {3, 1} );
d << Remove Term( {3, 2} );

Code Explanation:

1. Open data table.
2. Define DOE experiment.
3. Specify factors: Silica, Sulfur, Silane.
4. Specify response: Stretch.
5. Evaluate design.
6. Remove interaction term.
7. Remove quadratic term.

Example 139

Summary: Runs the design of experiments (DOE) and retrieves the X matrix for a shrinkage model with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get X Matrix;

Code Explanation:

1. Open data table.
2. Define design experiment.
3. Specify factors and response.
4. Evaluate design.
5. Retrieve X matrix.

Example 140

Summary: Creates a Design of Experiments (DOE) object and retrieves power values for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Power;

Code Explanation:

1. Open data table.
2. Create DOE object.
3. Define factors: Silica, Sulfur, Silane.
4. Define response: Stretch.
5. Evaluate design.
6. Retrieve power values.
7. Assign to variable X.

Example 141

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to design and evaluate an experiment.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :X1, :X2, :X3 ), Y( :Y ) );
X = d << Get Effect Power;

Code Explanation:

1. Open data table.
2. Create design experiment.
3. Specify factors X1, X2, X3.
4. Specify response variable Y.
5. Evaluate design experiment.
6. Retrieve effect power for factors.

Example 142

Summary: Process of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Estimation Efficiencies;

Code Explanation:

1. Open data table.
2. Define DOE experiment.
3. Specify factors and response.
4. Evaluate design efficiencies.

Example 143

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to create a design.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
X = d << Get Design Diagnostics;
X[2] = .;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Specify factors.
4. Specify response.
5. Evaluate design diagnostics.
6. Assign diagnostics to variable.
7. Modify second element.

Example 144

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE design to explore relationships between Silica, Sulfur, Silane, and Stretch.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set Significance Level( 0.10 );
rpt = d << report;
X = rpt[Number Col Edit Box( 1 )] << get as matrix;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Set X factors: Silica, Sulfur, Silane.
4. Set Y factor: Stretch.
5. Set significance level to 0.10.
6. Generate DOE report.
7. Extract report object.
8. Access first number column edit box.
9. Convert to matrix.
10. Assign to variable X.

Example 145

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to define the experiment.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set RMSE( 1.5 );
rpt = d << report;
X = rpt[Number Col Edit Box( 2 )] << get as matrix;

Code Explanation:

1. Open data table.
2. Define DOE experiment.
3. Set X factors.
4. Set Y response.
5. Evaluate design.
6. Set RMSE value.
7. Generate report.
8. Extract report element.
9. Get matrix from edit box.
10. Assign matrix to variable X.

Example 146

Summary: Performs a variance component analysis using the REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to define an experiment.

Code:

dt = Open("data_table.jmp");
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Change Anticipated Coefficients( [1 2 3 4 2 2 2 3 3 3] );
rpt = d << report;
X = rpt["Design Evaluation"]["Power Analysis"][2][2] << get as matrix;

Code Explanation:

1. Open table.
2. Define DOE experiment.
3. Set design factors.
4. Set response variable.
5. Evaluate design.
6. Change anticipated coefficients.
7. Generate report.
8. Extract power analysis section.
9. Convert to matrix.

Example 147

Summary: Process of performing a variance component analysis using the REML method and fitting a model for shrinkage with random effects in the Standard Least Squares personality, utilizing the DOE function to define the experiment.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Group New Runs Into Separate Block;
d << augment method( augment );
d << make design;
d << make table;
x = Column( Current Data Table(), "Block" ) << get as matrix;

Code Explanation:

1. Open data table.
2. Define DOE experiment.
3. Specify factors and response.
4. Group new runs into blocks.
5. Set augmentation method.
6. Generate design.
7. Create design table.
8. Extract block column.
9. Convert to matrix.

Example 148

Summary: Creates a Design of Experiments (DOE) for analyzing the relationship between Silica, Sulfur, and Silane on Stretch, with augmentation and sample size control.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Augment );
d << Set Sample Size( 24 );
d << Make Design;
d << make table;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Specify factors and response.
4. Choose augmentation method.
5. Set sample size.
6. Generate design.
7. Create data table.

Example 149

Summary: Creates a Design of Experiments (DOE) table with replicates, using the Augment Method and specifying factors X1, X2, and X3.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :X1, :X2, :X3 ), Y( :Y ) );
d << Augment Method( Replicate, 2 );
d << make table;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Specify factors X1, X2, X3.
4. Specify response variable Y.
5. Set augment method to replicate.
6. Set number of replicates to 2.
7. Generate DOE table.

Example 150

Summary: Creates a Design of Experiments (DOE) table with centerpoints for analyzing the relationship between Silica, Sulfur, and Silane factors on Stretch response.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Centerpoints, 3 );
d << make table;

Code Explanation:

1. Open data table.
2. Create DOE design.
3. Define factors: Silica, Sulfur, Silane.
4. Define response: Stretch.
5. Set augmentation method.
6. Add centerpoints.
7. Specify 3 centerpoints.
8. Generate DOE table.

Example 151

Summary: Runs the design and augmentation of a custom experiment with random effects using the REML method, generating an augmented design table for analysis.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Fold Over, [1 2] );
d << make table;

Code Explanation:

1. Open data table.
2. Define DOE experiment design.
3. Specify factors and response variable.
4. Apply augment method: fold over.
5. Generate augmented design table.

Example 152

Summary: Creates a design experiment object with an augmented design, utilizing the Augment Method to add axial terms and generating a table for analysis.

Code:

dt = Open("data_table.jmp");
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Add Axial, 1, 2 );
d << make table;

Code Explanation:

1. Open data table.
2. Create design experiment object.
3. Define factors and response.
4. Set augment method.
5. Generate augmented design.
6. Make table from design.

Example 153

Summary: Creates a custom design for a variance component analysis using REML method and fits a model for shrinkage with random effects in the Standard Least Squares personality.

Code:

dt = Open("data_table.jmp");
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Blocking, 2, "X1" ), Add Factor( Categorical, {"L1", "L2", "L3", "L4"}, "X2", 0 ),
    Add Factor( Continuous, 893.874196335673, 2077.16088831704, "X3", 0 ), Add Factor( Covariate, height, 0 ),
    Add Factor( Discrete Numeric, {2, 4, 6}, "X5", 0 ), Add Factor( Discrete Numeric, {2, 4, 6}, "X6", 0 ),
    Add Factor( Continuous, 276.440784335136, 2200.65610105684, "X7", 0 ), Add Factor( Discrete Numeric, {3, 6, 9, 12}, "X8", 0 ),
    Add Factor( Categorical, {"L1", "L2", "L3", "L4"}, "X9", 0 ), Add Factor( Mixture, 0, 1, "X10", 0 ),
    Add Factor( Mixture, 0, 1, "X11", 0 ), Add Factor( Mixture, 0, 1, "X12", 0 ), Set Random Seed( 27882917 )}
);

Code Explanation:

1. Open table.
2. Create custom design.
3. Add response variable.
4. Add blocking factor.
5. Add categorical factor.
6. Add continuous factor.
7. Add covariate factor.
8. Add discrete numeric factor.
9. Add discrete numeric factor.
10. Add continuous factor.
11. Add discrete numeric factor.
12. Add categorical factor.
13. Add mixture factors.
14. Set random seed.

Example 154

Summary: Creates a custom design for a statistical experiment using the DOE function, specifying continuous and categorical factors, and optimizing the design with the REML method.

Code:

Names Default To Here( 1 );
//create basic design
design = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ), Add Factor( Continuous, -1, 1, "X3", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X4", 0 ), Add Factor(
        Categorical,
        {"L1", "L2", "L3"},
        "X5",
        0
    ), Set Random Seed( 1854224195 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ),
    Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ), Add Term( {1, 2} ),
    Add Term( {1, 1}, {2, 1} ), Add Term( {2, 2} ), Add Term( {1, 1}, {3, 1} ),
    Add Term( {2, 1}, {3, 1} ), Add Term( {3, 2} ), Add Term( {1, 1}, {4, 1} ),
    Add Term( {2, 1}, {4, 1} ), Add Term( {3, 1}, {4, 1} ), Add Term( {1, 1}, {5, 1} ),
    Add Term( {2, 1}, {5, 1} ), Add Term( {3, 1}, {5, 1} ), Add Term( {4, 1}, {5, 1} ),
    Set Sample Size( 30 ), Optimality Criterion( "Make I-Optimal Design" ),
    Simulate Responses( 0 ), Save X Matrix( 0 )}
);
//lauch design explorer (is there a better way? Design explorer doesn't appear to be scriptable)
rdesign = report (design);
rdesign[OutlineBox("Design Explorer")][Button Box(1)]<< click;

Code Explanation:

1. Set default names.
2. Create custom design.
3. Add response variable.
4. Add continuous factor X1.
5. Add continuous factor X2.
6. Add continuous factor X3.
7. Add categorical factor X4.
8. Add categorical factor X5.
9. Set random seed.
10. Add terms to model.
11. Set sample size.
12. Optimize design criterion.
13. Disable response simulation.
14. Disable saving X matrix.
15. Launch design explorer.
16. Click button in report.

DOE using Column

Example 1

Summary: Creates a DOE design with multiple factors and a covariate, simulating responses and saving the X matrix for further analysis.

Code:

dt = Open("data_table.jmp");
cov = Column( dt, "height" );
d = doe();
d << add factor << add factor << add factor( covariate, cov );
d << simulate responses;
d << save x matrix;
d << make design;

Code Explanation:

1. Open data table.
2. Assign height column to cov.
3. Create new DOE design.
4. Add first factor to design.
5. Add second factor to design.
6. Add third factor (covariate).
7. Simulate responses for design.
8. Save X matrix of design.
9. Make design executable.
10. Design saved to file.

Example 2

Summary: Creates a design of experiments (DOE) for multivariate correlations analysis with mahalanobis distances, utilizing the Augment Design method and specifying factors for age and sex.

Code:

dt1 = Open("data_table.jmp");
Column( dt1, "age" ) << Set Modeling Type( "Continuous" );
d = DOE(
    Augment Design,
    X( :age, :sex ),
    Y( :height ),
    {Augment Method( Augment ), Set Random Seed( 1280909573 ), Number of Starts( 49249 ), Add Term( {1, 0} ), Add Term( {1, 1} ),
    Add Term( {2, 1} ), Add Alias Term( {1, 1}, {2, 1} ), Set Sample Size( 48 ), D Efficiency Weight( 0 ), Make Design}
);
dt = d << make table;

Code Explanation:

1. Open data table;
2. Set age column as continuous.
3. Create DOE design.
4. Specify age and sex as factors.
5. Set height as response.
6. Use augment design method.
7. Set random seed for reproducibility.
8. Define number of starts.
9. Add intercept term.
10. Add main effects.
11. Add interaction term.
12. Set alias terms.
13. Define sample size.
14. Disable D Efficiency Weight.
15. Generate design.
16. Convert design to data table.

Example 3

Summary: Creates and analyzes a custom design of experiments (DOE) with categorical factors, covariates, and polynomial terms to explore multivariate correlations.

Code:

dta = Open("data_table.jmp");
cov = Column( dta, "height" );
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ), Add Factor(
        Categorical,
        {"L1", "L2"},
        "X2",
        0
    )}
);
d << Add Factor( Covariate, cov );
d << Disallowed Combinations( X1 == 1 & X2 == 1 );
d << Add Term( {1, 0} ) << Add Term( {1, 1} ) << Add Term( {2, 1} ) << Add Term( {3, 1} ) << Add Term( {1, 1}, {3, 1} ) <<
Add Term( {2, 1}, {3, 1} );
d << set random seed( 12345 );
d << Make Design;
rpt = d << report;
tcp7 = rpt[CellPlotBox( 1 )] << get journal;
tct7 = Substr( tcp7, Contains( tcp7, "colorTheme" ) );
cp7 = Parse( Substr( tct7, 1, Contains( tct7, ")" ) ) );
colstr = Arg( Extract Expr( cp7, colorTheme( Wild List() ) ) );
timg7 = Substr( tcp7, Contains( tcp7, "values" ) );
timg7 = Parse( Substr( timg7, 1, Contains( timg7, ")" ) ) );
imglst = Round( Substitute( Extract Expr( timg7, values( Wild List() ) ), Expr( values() ), {} ), 5 );
oraclelst1 = {1, 0.48148, 0.00191, 0.41622, 0.19783, 0.50918, 0.48148, 1, 0.00191, 0.19783, 0.41622, 0.50918, 0.00191, 0.00191, 1, 0.30579,
0.30061, 0.00375, 0.41622, 0.19783, 0.30579, 1, 0.35811, 0.21446, 0.19783, 0.41622, 0.30061, 0.35811, 1, 0.21446, 0.50918, 0.50918, 0.00375,
0.21446, 0.21446, 1};
oraclelst2 = {1, 0.48148, 0.00191, 0.41622, 0.19783, 0.50918, 0.48148, 1, 0.00191, 0.19783, 0.41622, 0.50918, 0.00191, 0.00191, 1, 0.30061,
0.30579, 0.00375, 0.41622, 0.19783, 0.30061, 1, 0.35811, 0.21446, 0.19783, 0.41622, 0.30579, 0.35811, 1, 0.21446, 0.50918, 0.50918, 0.00375,
0.21446, 0.21446, 1};
diff1 = Max( Abs( imglst - oraclelst1 ) );
diff2 = Max( Abs( imglst - oraclelst2 ) );
If( diff1 < diff2,
    oraclelst = oraclelst1,
    oraclelst = oraclelst2
);
d << Close Window;

Code Explanation:

1. Open data table;
2. Define height column as covariate.
3. Create custom DOE design.
4. Add categorical factors X1 and X2.
5. Set disallowed combinations for factors.
6. Add polynomial terms to model.
7. Set random seed for reproducibility.
8. Generate DOE design.
9. Extract report from design.
10. Compare generated values with oracle lists.

Example 4

Summary: Creates a design of experiments (DOE) with continuous and categorical covariates, generating a data table for further analysis.

Code:

dtcov = Open("data_table.jmp");
cov1 = Column( dtcov, "sex" );
cov2 = Column( dtcov, "height" );
cov3 = Column( dtcov, "weight" );
d = doe();
d << Add Factor( Continuous, -1, 1, "shoe", 0 );
d << Add Factor( Covariate, cov1 ) << Add Factor( Covariate, cov2 ) << Add Factor( Covariate, cov3 );
d << Make Design;
dt = d << Make Table;

Code Explanation:

1. Open data table;
2. Assign "sex" column to cov1.
3. Assign "height" column to cov2.
4. Assign "weight" column to cov3.
5. Create new DOE.
6. Add continuous factor "shoe".
7. Add covariates "sex", "height", "weight".
8. Generate DOE design.
9. Create data table from design.
10. Save data table.

Example 5

Summary: Creates a DOE design with continuous, mixture, and categorical factors, as well as covariate columns for multivariate correlations analysis.

Code:

dtcov = Open("data_table.jmp");
cov1 = Column( dtcov, "sex" );
cov2 = Column( dtcov, "height" );
cov3 = Column( dtcov, "weight" );
d = doe();
d << Add Factor( Continuous, -1, 1, "shoe", 0 );
d << Add Factor( Mixture, 0, 1, "mix1", 0 );
d << Add Factor( Mixture, 0, 1, "mix2", 0 );
d << Add Factor( Mixture, 0, 1, "mix3", 0 );
d << Add Factor( Categorical, {"L1", "L2"}, "cat1", 0 );
d << Add Factor( Categorical, {"L1", "L2", "L3"}, "cat2", 0 );
d << Add Factor( Covariate, cov1 ) << Add Factor( Covariate, cov2 ) << Add Factor( Covariate, cov3 );

Code Explanation:

1. Open data table;
2. Assign sex column to cov1.
3. Assign height column to cov2.
4. Assign weight column to cov3.
5. Create new DOE design.
6. Add continuous factor shoe.
7. Add mixture factor mix1.
8. Add mixture factor mix2.
9. Add mixture factor mix3.
10. Add categorical factor cat1.
11. Add categorical factor cat2.
12. Add covariate factors sex, height, weight.

Example 6

Summary: Creates a custom design of experiments (DOE) with continuous factors, covariates, and constraints to generate new data.

Code:

dt = Open("data_table.jmp");
cov = Column( dt, "sex" );
d = DOE(
    Custom Design,
    {Add Response( "Maximize", "Y", ., ., . ), Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Number of Starts( 1000 )}
);
d << Add Factor( Covariate, cov );
d << Add Constraint( [1 1 1.5] );
dtnew = d << Make Table;         

d << Close Window;

Code Explanation:

1. Open data table;
2. Assign "sex" column to cov.
3. Create custom DOE design.
4. Add response variable "Y".
5. Add continuous factor "X1".
6. Add continuous factor "X2".
7. Set number of starts to 1000.
8. Add "sex" as covariate.
9. Add constraint [1 1 1.5].
10. Generate new data table from design.

DOE using New Script

Summary: Runs the design and execution of an experiment using Easy DOE, capturing log output and collapsing whitespace.

Code:

dt = Open("data_table.jmp");
dt << New Script( "Design Columns", {{"Interfer", "Wall", "Depth", "Adhesive", "Time", "Temperature", "Humidity"}} );
d = DOE( Easy DOE );
lc = Collapse Whitespace( Log Capture( d << Load Design( dt ) ) );
d << close window;

Code Explanation:

1. Open data table.
2. Create new script for design columns.
3. Define design factors.
4. Run Easy DOE.
5. Capture log output.
6. Collapse whitespace in log.
7. Load design into table.
8. Close DOE window.

DOE using Select Where

Summary: Creates a custom design for a DOE experiment, utilizing factors and covariates to optimize response variables.

Code:

dtCov = Open("data_table.jmp");
dtCov << Select Where( :age == 12 ) << Delete Rows();
dtCov << Clear Select;
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Rating", ., ., . ), Add Factor( Categorical, {"Action", "Comedy", "Drama", "Horror"}, "Genre", 0 ),
    Add Factor( Continuous, 90, 180, "Length", 0 ), Add Factor( Continuous, 0, 100, "RT Score", 0 ),
    Add Factor( Categorical, {"Candy", "Popcorn", "None"}, "Snack", 0 ), Add Factor( Categorical, {"Yes", "None"}, "Soda", 0 ),
    Add Factor( Covariate, age, 0 ), Add Factor( Covariate, sex, 0 ), Set Random Seed( 1679279337 ), Number of Starts( 2526 ),
    Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ),
    Add Term( {6, 1} ), Add Term( {7, 1} ), Add Potential Term( {1, 1}, {2, 1} ), Add Potential Term( {1, 1}, {3, 1} ),
    Add Potential Term( {1, 1}, {4, 1} ), Add Potential Term( {1, 1}, {5, 1} ), Add Potential Term( {1, 1}, {6, 1} ),
    Add Potential Term( {1, 1}, {7, 1} ), Add Potential Term( {2, 1}, {3, 1} ), Add Potential Term( {2, 1}, {4, 1} ),
    Add Potential Term( {2, 1}, {5, 1} ), Add Potential Term( {2, 1}, {6, 1} ), Add Potential Term( {2, 1}, {7, 1} ),
    Add Potential Term( {3, 1}, {4, 1} ), Add Potential Term( {3, 1}, {5, 1} ), Add Potential Term( {3, 1}, {6, 1} ),
    Add Potential Term( {3, 1}, {7, 1} ), Add Potential Term( {4, 1}, {5, 1} ), Add Potential Term( {4, 1}, {6, 1} ),
    Add Potential Term( {4, 1}, {7, 1} ), Add Potential Term( {5, 1}, {6, 1} ), Add Potential Term( {5, 1}, {7, 1} ),
    Add Potential Term( {6, 1}, {7, 1} ), Enforce Use of Selected Covariate Rows( 1 ), Select Covariate Rows(
        [1 2 3 5 6 7 8 9 10 14 15 16 17 18 23 24 25 28 29 32]
    ), Set Sample Size( 24 ), Simulate Responses( 1 ), Save X Matrix( 0 ), Make Design, Set Run Order( Keep the Same )}
);
dt = d << Make Table;
dt << Sort( By( :Covariate Row Index ), Order( Ascending ), Replace Table );

Code Explanation:

1. Open data table;
2. Delete rows where age is 12.
3. Clear row selection.
4. Start DOE with Custom Design.
5. Add response "Rating" to maximize.
6. Add categorical factor "Genre" with levels.
7. Add continuous factor "Length" from 90 to 180.
8. Add continuous factor "RT Score" from 0 to 100.
9. Add categorical factor "Snack" with levels.
10. Add categorical factor "Soda" with levels.
11. Add covariates "age" and "sex".
12. Set random seed for reproducibility.
13. Define number of starts for optimization.
14. Add main effects for all factors.
15. Add interaction terms between factors.
16. Enforce use of selected covariate rows.
17. Select specific covariate rows.
18. Set sample size to 24.
19. Simulate responses for design.
20. Do not save X matrix.
21. Create design table.
22. Sort table by covariate row index.

DOE using Log Capture

Summary: Creates a design experiment with specified factors and response variable, utilizing the Augment Design method to generate a design.

Code:

dt = Open("data_table.jmp");
mylog = Log Capture(
    d = DOE(
        Augment Design,
        X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
        Y( :Percent Reacted ),
        {Augment Method( Augment ), Set Random Seed( 1225039653 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ),
        Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ), Add Term( {1, 2} ), Add Term( {1, 1}, {2, 1} ), Add Term( {2, 2} ),
        Add Term( {1, 1}, {3, 1} ), Add Term( {2, 1}, {3, 1} ), Add Term( {3, 2} ), Add Term( {1, 1}, {4, 1} ), Add Term( {2, 1}, {4, 1} ),
        Add Term( {3, 1}, {4, 1} ), Add Term( {4, 2} ), Add Term( {1, 1}, {5, 1} ), Add Term( {2, 1}, {5, 1} ), Add Term( {3, 1}, {5, 1} ),
        Add Term( {4, 1}, {5, 1} ), Add Term( {5, 2} ), Set Sample Size( 21 ), Disallowed Combinations( Feed > 0 ),
        D Efficiency Weight( 0 )}
    );
    d << Make Design;
);
d << Close Window();
new_message = Regex( mylog, "Invalid disallowed combinations script. See Log for details." );

Code Explanation:

1. Open data table.
2. Start log capture.
3. Create design experiment.
4. Define factors.
5. Define response variable.
6. Specify augment design method.
7. Set random seed.
8. Add polynomial terms.
9. Set sample size.
10. Define disallowed combinations.
11. Disable D-efficiency weighting.
12. Generate design.
13. Close design window.
14. Check log for errors.

DOE using N Row

Summary: Creates a custom DOE design with mixture sum constraint and optimality criterion, utilizing the DOE function to generate a model matrix.

Code:

dtc = Open("data_table.jmp");
mixsum = 1;
n = N Row( dtc );
dt = Current Data Table();
acol = Column( "age" );
col = Column( "height" );
scol = Column( "sex" );
wcol = Column( "weight" );
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Mixture, 0, 1, "X1", 0 ), Add Factor( Mixture, 0, 1, "X2", 0 ),
    Add Factor( Mixture, 0, 1, "X3", 0 ), Set Random Seed( 15322911 ), Simulate Responses( 0 ), Save X Matrix( 1 )}
);
d << add factor( covariate, acol );
d << mixture sum( mixsum );
d << optimality criterion( 1 );
d << make model;
d << make design;
dt = d << make table;
dt << run script( "Model Matrix" );
dt << New Column( "Mixture Sum", formula( Round( dt:X1 + dt:X2 + dt:X3, 8 ) ) );
c = 1;
Try( invxtx = Inverse( X` * X ), c = 2 );
d << optimality criterion( 2 );
d << make design;

Code Explanation:

1. Open data table.
2. Define mixture sum.
3. Count rows in data table.
4. Set current data table.
5. Assign columns to variables.
6. Create custom DOE design.
7. Add covariate factor.
8. Set mixture sum constraint.
9. Set optimality criterion.
10. Generate model and design.

DOE using Select Rows

Summary: Creates a custom Design of Experiments (DOE) design with specific row selection and covariate handling, utilizing JMP's DOE platform.

Code:

dt = Open("data_table.jmp");
dt << Select Rows( [1, 5, 10, 15] );
d = DOE(
    Custom Design,
    {Add Response( Maximize, "Y", ., ., . ), Add Factor( Covariate, height, 0 ), Add Factor( Covariate, weight, 0 ),
    Set Random Seed( 1748952220 ), Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Alias Term( {1, 1}, {2, 1} ),
    Select Covariate Rows( [](1, 0) ), Set Sample Size( 40 ), Simulate Responses( 0 ), Save X Matrix( 0 )}
);
d << Enforce Use of Selected Covariate Rows( 1 );
d << Enforce Use of Selected Covariate Rows( 0 );
d << Allow covariate Rows to be repeated( 1 );
d << Allow covariate Rows to be repeated( 0 );
d << close window;

Code Explanation:

1. Open data table;
2. Select specific rows.
3. Create custom DOE design.
4. Define response variable.
5. Add height covariate.
6. Add weight covariate.
7. Set random seed.
8. Include intercept term.
9. Include height term.
10. Include weight term.

DOE using Expr

Example 1

Summary: Runs the design of experiments (DOE) analysis for a chemical reaction process, specifying factors and response variables, and setting augment method, random seed, and sample size.

Code:

dt = Open("data_table.jmp");
augmentExpr = Expr(
    DOE(
        Augment Design,
        X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
        Y( :Percent Reacted ),
        {Augment Method( Augment ), Set Random Seed( 21606762 ), Number of Starts( 1 ), Add Term( {1, 0} ), Add Term( {1, 1} ),
        Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ), Add Term( {1, 1}, {2, 1} ),
        Add Term( {1, 1}, {3, 1} ), Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ), Add Alias Term( {2, 1}, {3, 1} ),
        Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ), Add Alias Term( {3, 1}, {4, 1} ),
        Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} ), Set Sample Size( 16 ), Optimality Criterion( 3 ), Make Design,
        Save X Matrix( 0 ), Simulate Responses( 0 )}
    )
);
augmentExpr;

Code Explanation:

1. Open data table.
2. Define augment expression.
3. Start DOE analysis.
4. Specify factors.
5. Specify response.
6. Set augment method.
7. Set random seed.
8. Set number of starts.
9. Add polynomial terms.
10. Add alias terms.
11. Set sample size.
12. Set optimality criterion.
13. Generate design.
14. Do not save X matrix.
15. Do not simulate responses.
16. Execute augment expression.

Example 2

Summary: Evaluates a designed experiment with specified factors and terms, generating an expression that can be used for further analysis.

Code:

dt = Open("data_table.jmp");
evaluateExpr = Expr(
    DOE(
        Evaluate Design,
        X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
        {Add Term( {1, 0} ), Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ), Add Term( {5, 1} ),
        Add Term( {1, 1}, {2, 1} ), Add Term( {1, 1}, {3, 1} ), Add Alias Term( {1, 1}, {4, 1} ), Add Alias Term( {1, 1}, {5, 1} ),
        Add Alias Term( {2, 1}, {3, 1} ), Add Alias Term( {2, 1}, {4, 1} ), Add Alias Term( {2, 1}, {5, 1} ),
        Add Alias Term( {3, 1}, {4, 1} ), Add Alias Term( {3, 1}, {5, 1} ), Add Alias Term( {4, 1}, {5, 1} )}
    )
);
evaluateExpr;

Code Explanation:

1. Open data table.
2. Define evaluate expression.
3. Start DOE evaluation.
4. Set factors: Feed Rate, Catalyst, Stir Rate, Temperature, Concentration.
5. Add main effects terms.
6. Add interaction terms.
7. Add alias terms for interactions.
8. Execute evaluate expression.

Example 3

Summary: Creates a MaxDiff design expression for data analysis, setting parameters such as number of profiles and choice sets.

Code:

dt = Open("data_table.jmp");
maxDiffExpr = Expr(
    DOE(
        MaxDiff Design,
        X( :Flavor ),
        {Set Number of Profiles( 4 ), Set Number of Choice Sets( 15 ), Make Design, Simulate Responses( 0 )}
    )
);
maxDiffExpr;

Code Explanation:

1. Open data table.
2. Define MaxDiff design expression.
3. Set number of profiles to 4.
4. Set number of choice sets to 15.
5. Create design without simulation.
6. Execute MaxDiff design expression.