DOE

Associated Constructors

DOE

Syntax: DOE

Columns

Factor

Syntax: obj << Factor( column(s) )

Response

Syntax: obj << Response( column(s) )

X

Syntax: obj << X( column(s) )

Y

Syntax: obj << Y( column(s) )

Item Messages

A-Optimality Parameter Weights

Syntax: obj << A-Optimality Parameter Weights

Description: Sets the weights to be used for creating an A-optimal design.

JMP Version Added: 14

DOE(
    Custom Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ), 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( 14 ),
    Optimality Criterion( "Make A-Optimal Design"n ),
    "A-Optimality Parameter Weights"n( [1 1 1 1 0.1 0.1 0.1] )}
);

ALT Factor Settings

Syntax: obj << ALT Factor Settings

Description: For the given factor number in an accelerated life test plan, allows specification of factor name, number of levels, factor transformation, usage conditions and test conditions.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

ALT Plan Setup

Syntax: obj << ALT Plan Setup( 1|2|3 )

Description: Specifies the initial choice of model for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Add Alias Term

Syntax: obj << Add Alias Term

Description: Adds an alias term to the list of alias terms. Specify the factor number and power for each effect in a list. Create interactions by separating effects with commas.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Add Alias Term( {1, 1}, {2, 1} );
d << Add Alias Term( {1, 2} );

Add Constraint

Syntax: obj << Add Constraint

Description: Adds linear constraints through a matrix. Each row represents a constraint. The last column is for values on the right side of the inequality constraints. In JSL, the inequality constraints must be less than or equal to the values on the right.

DOE(
    Custom Design,
    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} )
);

Add Factor

Syntax: obj << Add Factor( Continuous|Discrete Numeric|Blocking|Constant|Categorical|Mixture )

Description: Adds a factor of the specified type and optional arguments. If nothing is specified, this command adds a continuous factor.

d = DOE( Custom Design );
d << Add Factor( Continuous, -1, 1, "X1", 0 );
d << Add Factor( Discrete Numeric, {1, 2, 3}, "X2", 0 );
d << Add Factor( Categorical, {"L1", "L2"}, "X3", 0 );
d << Add Factor( Blocking, 8, "X4" );
d << Add Factor( Constant, 3, "X5" );

Add Functional Response

Syntax: obj << Add Functional Response

Description: Adds a functional response with the specified name, number of measurements per run, and values.

JMP Version Added: 15

DOE(
    Custom Design,
    Add Response( Maximize, "Y", ., ., . ),
    Add Functional Response( "Y", 5, {1, 2, 3, 4, 5} ),
    Set Random Seed( 46055034 ),
    Simulate Responses( 0 ),
    Save X Matrix( 0 )
);

Add Potential Term

Syntax: obj << Add Potential Term

Description: Adds an If Possible term to the list of model terms. Specify the factor number and power for each effect in a list. Create interactions by separating effects with commas.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Add Potential Term( {1, 1}, {2, 1} );
d << Add Potential Term( {1, 2} );

Add Response

Syntax: obj << Add Response( goal, name, lower limit, upper limit, importance, lower detection limit, upper detection limit )

Description: Adds a response with the specified goal, name, lower limit, upper limit, and importance.

Example 1

DOE( Custom Design, Add Response( Match Target, "Y", 10, 30, 1 ) );

Example 2

DOE( Custom Design, Add Response( Match Target, "Y", ., ., 1, 10, 30 ) );

Add Term

Syntax: obj << Add Term

Description: Adds a "Necessary" term to list of the model terms. Effects are specified by {factor number, power}. Interactions can be created by separating effects by commas.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Add Term( {1, 1}, {2, 1} );
d << Add Term( {1, 2} );

Additional Designs

Syntax: obj << Additional Designs

Description: Specify up to nine additional designs to be compared to the reference design.

JMP Version Added: 14

DOE(
    Custom Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Set Sample Size( 12 ),
    Make Design,
    Make Table
);
DOE( Custom Design, Add Factor, Add Factor, Add Factor, Make Design, Make Table );
DOE(
    Custom Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Set Sample Size( 4 ),
    Make Design,
    Make Table
);
DOE(
    Compare Designs,
    Reference Design( "Custom Design", X( :X1, :X2, :X3 ) ),
    Additional Designs(
        "Custom Design 2",
        X( :X1, :X2, :X3 ),
        "Custom Design 3",
        X( :X1, :X2, :X3 )
    )
);

Allow covariate rows to be repeated

Syntax: obj << Allow covariate rows to be repeated( state=0|1 )

Description: Specifies if covariate rows are allowed to be repeated in the design.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
DOE(
    Custom Design,
    Add Response( Maximize, "Y", ., ., . ),
    Add Factor( Covariate, :sex, 0 ),
    Add Factor( Covariate, :height, 0 ),
    Add Factor( Covariate, :weight, 0 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Enforce Use of Selected Covariate Rows( 1 ),
    Allow covariate rows to be repeated( 1 ),
    Select Covariate Rows( [1 2 3 4] ),
    Set Sample Size( 24 )
);

Augment Method

Syntax: obj << Augment Method( Replicate|Centerpoints|Fold Over|Add Axial|Augment )

Description: Specifies the type of augment method and its parameters.

Example 1

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Augment );
d << Set Sample Size( 24 );
d << Make Design;

Example 2

dt = Open( "$SAMPLE_DATA/Design Experiment/2x3x4 Factorial.jmp" );
d = DOE( Augment Design, X( :X1, :X2, :X3 ), Y( :Y ) );
d << Augment Method( Replicate, 2 );

Example 3

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Centerpoints, 3 );

Example 4

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Fold Over, [1 2] );

Example 5

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Augment Method( Add Axial, 1, 2 );

Blocks

Syntax: obj << Blocks

Description: Specifies the block size for a balanced incomplete block design (BIBD).

JMP Version Added: 14

d = DOE( Balanced Incomplete Block Design, Treatments( 3, {"L1", "L2", "L3"} ) );
d << Blocks( 2 );
d << Make Design;

Center Points

Syntax: obj << Center Points

Description: Specifies the number of center points.

Example 1

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Make Model( Linear );
d << Center Points( 2 );

Example 2

DOE(
    Definitive Screening Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Show Blocking Options( 1, 2 ),
    Number of Extra Runs( 4 ),
    Center Points( 1 )
);

Change Anticipated Coefficients

Syntax: obj << Change Anticipated Coefficients

Description: Change the Anticipated Coefficients in Power Analysis.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.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] );

Change Factor Settings

Syntax: obj << Change Factor Settings

Description: Specifies the minimum, maximum, and name of the continuous or mixture factor that you included in the first argument. Most useful for platforms that initially have predefined factors.

Example 1

d = DOE( Response Surface Design );
d << Change Factor Settings( 1, 2, 3, "A" );
d << Change Factor Settings( 2, 0, 4 );

Example 2

d = DOE( Mixture Design );
d << Change Factor Settings( 1, 0.1, 0.4, "A" );
d << Change Factor Settings( 3, 0, 0.8, "C" );

Check Inscribe

Syntax: obj << Check Inscribe

Description: Rescales the design so that axial points are at the low and high ends of the range.

d = DOE( Response Surface Design, Make Design( 2 ) );
d << Set Axial Choice( 2 );
d << Check Inscribe;

Choice Design Table Output

Syntax: obj << Choice Design Table Output( "Separate"|"Combined" )

Description: Specifies how to create a data table for a choice design.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Add Term( {1, 1} ), Add Term( {2, 1} ),
    Set Prior Mean Choice( [0 0] ), Set Prior Variance Matrix( [1 0, 0 1] ),
    Set Number of Attributes( 2 ), Set Number of Profiles( 2 ),
    Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 ), Make Design,
    Choice Design Table Output( Combined )}
);

D Efficiency Weight

Syntax: obj << D Efficiency Weight

Description: Use this option to control the relative importance of D-efficiency and reduction of aliasing. Supply a number between zero and one.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    D Efficiency Weight( 0.5 ),
    Make Design
);

Design Search Time

Syntax: obj << Design Search Time( number )

Description: Specifies the number of seconds to search for a design.

DOE(
    Custom Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set Sample Size( 7 ), Design Search Time( 8 ), Make Design}
);

Disallowed Combinations

Syntax: obj << Disallowed Combinations

Description: Enables you to supply a script that returns true for any factor combinations that should be excluded from your design.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ),
    Number of Starts( 100 ),
    Disallowed Combinations( X1 > 0.5 & X2 == 2 ),
    Make Design
);

Discrete Numeric Powers Set to Necessary

Syntax: obj << Discrete Numeric Powers Set to Necessary( state=0|1 )

Description: Specifies if powers in discrete numeric factors should be necessary model terms.

DOE(
    Custom Design,
    Add Factor( Discrete Numeric, {1, 2, 3}, "X1", 0 ),
    Add Factor( Discrete Numeric, {1, 2, 3}, "X2", 0 ),
    Discrete Numeric Powers Set to Necessary( 1 ),
    Make Model( Linear )
);

Distribution Choice

Syntax: obj << Distribution Choice

Description: Specifies the distribution for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Enforce Use of Selected Covariate Rows

Syntax: obj << Enforce Use of Selected Covariate Rows( state=0|1 )

Description: Specifies if all selected covariate rows should be included in the design.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
DOE(
    Custom Design,
    Add Response( Maximize, "Y", ., ., . ),
    Add Factor( Covariate, :sex, 0 ),
    Add Factor( Covariate, :height, 0 ),
    Add Factor( Covariate, :weight, 0 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Enforce Use of Selected Covariate Rows( 1 ),
    Allow covariate rows to be repeated( 1 ),
    Select Covariate Rows( [1 2 3 4] ),
    Set Sample Size( 24 )
);

FFF Optimality Criterion

Syntax: obj << FFF Optimality Criterion( "MaxPro"|"Centroid" )

Description: Specifies the criterion used in the design. Recommended is the default value.

Example 1

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Optimality Criterion( "Make I-optimal Design" ),
    Make Design
);

Example 2

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Optimality Criterion( 2 ),
    Make Design
);

Find Subset

Syntax: obj << Find Subset

Description: Finds the D-optimal subset of an Extreme Vertices design.

d = DOE( Mixture Design, Add Factor( Mixture, 0.1, 1, "X4", 0 ) );
d << Mixture Design Type( Extreme Vertices, 3 );
d << Find Subset( 10 );

GOSSDDetails

Syntax: obj << GOSSDDetails

Description: Returns the current factor settings as a list.

JMP Version Added: 15

d = DOE( Group Orthogonal Supersaturated Design );
Show( d << GOSSDDetails );

GOSSDStructure

Syntax: obj << GOSSDStructure

Description: Specifies the structure of a GOSSD

JMP Version Added: 15

d = DOE( Group Orthogonal Supersaturated Design );
d << GOSSDStructure( 6, 8 );

Get Alias Matrix

Syntax: obj << Get Alias Matrix

Description: Returns the alias matrix from design evaluation.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Get Alias Matrix;

Get Design Diagnostics

Syntax: obj << Get Design Diagnostics

Description: Return D-Efficiency, G-Efficiency, A-Efficiency and Average Variance of Prediction.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Get Design Diagnostics;

Get Effect Power

Syntax: obj << Get Effect Power

Description: Return vector of powers for effect estimates.

dt = Open( "$SAMPLE_DATA/Design Experiment/2x3x4 Factorial.jmp" );
d = DOE( Evaluate Design, X( :X1, :X2, :X3 ), Y( :Y ) );
d << Get Effect Power;

Get Estimation Efficiencies

Syntax: obj << Get Estimation Efficiencies

Description: Returns a vector for the increased width of each parameter estimate compared to an ideal design.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Get Estimation Efficiencies;

Get MaxPro Values

Syntax: obj << Get MaxPro Values

Description: Returns the MaxPro values for a fast-flexible design, including any subdesigns based on levels of a categorical factor.

JMP Version Added: 14

d = DOE(
    Space Filling Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Categorical, {"L1", "L2", "L3", "L4"}, "X3", 0 ),
    FFF Optimality Criterion( MaxPro ), MaxPro Categorical Weight( 4 ),
    Space Filling Design Type( Fast Flexible Filling, 100 )}
);
d << Get MaxPro Values;

Get Number of Random Starts

Syntax: obj << Get Number of Random Starts

Description: Returns the number of random starts used in design generation.

JMP Version Added: 15

Get Power

Syntax: obj << Get Power

Description: Return vector of powers for parameter estimates.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Get Power;

Get Prediction Variances

Syntax: obj << Get Prediction Variances

Description: Returns the vector of prediction variances from the Fraction of Design Space Plot.

JMP Version Added: 14

d = DOE(
    Custom Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set Sample Size( 7 ), Design Search Time( 8 ), Set Number of FDS points( 20000 ),
    Make Design}
);
d << Get Prediction Variances;

Get X Matrix

Syntax: obj << Get X Matrix

Description: Returns the design matrix (also called the X matrix).

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Get X Matrix;

Group New Runs Into Separate Block

Syntax: obj << Group New Runs Into Separate Block

Description: Adds a blocking factor, which groups new runs into separate blocks when augmenting a design.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Augment Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Group New Runs Into Separate Block;

Load Constraints

Syntax: obj << Load Constraints

Description: Load a previously saved factor constraints table for use in this experiment.

dt = Open( "$SAMPLE_DATA/Design Experiment/Diamond Constraints.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
);

Load Design

Syntax: obj << Load Design

Description: Load Design

d = DOE( Custom Design );
d << Load Design();

Load Factors

Syntax: obj << Load Factors

Description: Load a previously saved factors table for use in this experiment.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Factors.jmp" );
DOE( Custom Design, Load Factors );

Load Responses

Syntax: obj << Load Responses

Description: Loads a previously saved data table of responses.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Response.jmp" );
DOE( Custom Design, Load Responses );

Local Design

Syntax: obj << Local Design( state=0|1 )

Description: Specifies if local design for the prior mean should be created.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( 2, {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Local Design( 0 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] )}
);

Make Design

Syntax: obj << Make Design

Description: Creates the design that you specified in the script.

d = DOE( Custom Design, Add factor, Add factor, Add factor );
d << Make Model( RSM );
d << Make Design;

Make Model

Syntax: obj << Make Model( Linear|Interactions|RSM )

Description: Adds terms to the list of model terms for the specified model.

Example 1

d = DOE( Custom Design, Add Factor, Add Factor, Add Factor );
d << Make Model( RSM );

Example 2

d = DOE( Custom Design, Add Factor, Add Factor, Add Factor );
d << Make Model( Interactions );

Make Strip Plot Design

Syntax: obj << Make Strip Plot Design

Description: Specifies a strip plot design when hard-to-change factors vary independently from very hard-to-change factors.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 2 ),
    Add Factor( Continuous, -1, 1, "X2", 1 ),
    Add Factor( Continuous, -1, 1, "X3", 0 )
);
d << Set N Whole Plots( 4 );
d << Make Strip Plot Design;

Make Table

Syntax: obj << Make Table

Description: Creates a data table from the current design.

d = DOE( Custom Design, Add factor, Add factor, Add factor );
d << Make Design;
d << Make Table;

Make Test Plan

Syntax: obj << Make Test Plan

Description: Creates the test plan for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Monitoring at Intervals", {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] ),
    Make Design, Make Test Plan}
);

MaxPro Categorical Weight

Syntax: obj << MaxPro Categorical Weight

Description: Specifies MaxPro weight. Values larger than 1 increase the separation of points that have the same categorical level.

JMP Version Added: 14

DOE(
    Space Filling Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Categorical, {"L1", "L2", "L3", "L4"}, "X3", 0 ),
    FFF Optimality Criterion( MaxPro ), MaxPro Categorical Weight( 4 ),
    Space Filling Design Type( Fast Flexible Filling, 100 )}
);

Mixture Design Type

Syntax: obj << Mixture Design Type( Simplex Centroid|Simplex Lattice|ABCD|Extreme Vertices|Space Filling )

Description: Specifies the type of mixture design. The default parameters are used unless you specify the parameter as the second argument.

Example 1

d = doe( Mixture Design );
d << Mixture Design Type( Simplex Centroid, 2 );

Example 2

d = doe( Mixture Design );
d << Mixture Design Type( Simplex Lattice, 4 );

Example 3

d = doe( Mixture Design );
d << Mixture Design Type( ABCD );

Example 4

d = doe( Mixture Design );
d << Change Factor Settings( 1, .05, .25 );
d << Mixture Design Type( Extreme Vertices, 3 );

Example 5

d = doe( Mixture Design );
d << Mixture Design Type( Space Filling, 25 );

Mixture Sum

Syntax: obj << Mixture Sum

Description: Use this option when you want to express the sum of all the ingredients to be other than 1. The mixture total is the sum of all the ingredient amounts.

DOE(
    Custom Design,
    Mixture Sum( 50 ),
    Add Factor( Mixture, 10, 25, "X1", 0 ),
    Add Factor( Mixture, 0, 15, "X2", 0 ),
    Add Factor( Mixture, 25, 40, "X3", 0 ),
    Make Design
);

Nesting Structure

Syntax: obj << Nesting Structure

Description: Specifies the nesting structure of the design. Use a bracketed list to indicate nesting (first element is nesting factor, second element is bracketed list of nested factors or structures). Use horizontal concatenation ('||') to indicate crossed factors or structures.

DOE(
    MSA Design,
    Add Factor( Categorical, {"L1", "L2"}, "X1", MSA( 4, 1, 1 ) ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", MSA( 4, 1, 1 ) ),
    Add Factor( Categorical, {"L1", "L2"}, "X3", MSA( 4, 1, 1 ) ),
    Nesting Structure( {"X1", {"X2"}} || "X3" )
);

Number of Column Starts

Syntax: obj << Number of Column Starts

Description: Specifies the number of times that random columns are optimized for each factor of a main effects screening design.

DOE(
    Screening Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Screening Type( 1 ),
    Number of Column Starts( 100 ),
    Set Sample Size( 12 ),
    Make Design
);

Number of Extra Runs

Syntax: obj << Number of Extra Runs

Description: Specifies the number of extra runs to include in a definitive screening design.

DOE(
    Definitive Screening Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Show Blocking Options( 1, 2 ),
    Number of Extra Runs( 4 )
);

Number of Starts

Syntax: obj << Number of Starts

Description: Specifies the number of times the design is regenerated to optimize the overall design.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Number of Starts( 1000 ),
    Make Design
);

Optimality Criterion

Syntax: obj << Optimality Criterion( "Recommended"|"Make D-Optimal Design"|"Make I-Optimal Design"|"Make A-Optimal Design"|"Make Alias Optimal Design" )

Description: Specifies the criterion used in the design. Recommended is the default value.

Example 1

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Optimality Criterion( "Make I-optimal Design" ),
    Make Design
);

Example 2

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Optimality Criterion( 2 ),
    Make Design
);

Order Column

Syntax: obj << Order Column

Description: Requests an order column when the data table is created.

JMP Version Added: 14

d = DOE( Balanced Incomplete Block Design );
d << Treatments( 3, {"L1", "L2", "L3"} );
d << Make Design;
d << OrderColumn( 1 );

Prior Parameter Variance

Syntax: obj << Prior Parameter Variance

Description: Use this option to control the weight used for If Possible terms in a model. Higher values mean more prior information and smaller variance. The variances are the reciprocals of the entered values.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Potential Term( {1, 1} ),
    Add Potential Term( {2, 1} ),
    Add Potential Term( {1, 1}, {2, 1} ),
    Prior Parameter Variance( [0, 1, 2, 6] ),
    Make Design
);

Prior Specification Choice

Syntax: obj << Prior Specification Choice

Description: Sets the option for specifying prior parameters, where 1 indicates Specify Intercept and 2 indicates Specify Quantile.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Prior Specification Choice( 1 ), Set Prior Mean ALT( [-40 1.5 2] ),
    Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Reference Design

Syntax: obj << Reference Design

Description: Specify the reference design for design comparison.

JMP Version Added: 14

DOE(
    Custom Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Set Sample Size( 12 ),
    Make Design,
    Make Table
);
DOE( Custom Design, Add Factor, Add Factor, Add Factor, Make Design, Make Table );
DOE(
    Custom Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Set Sample Size( 4 ),
    Make Design,
    Make Table
);
DOE(
    Compare Designs,
    Reference Design( "Custom Design", X( :X1, :X2, :X3 ) ),
    Additional Designs(
        "Custom Design 2",
        X( :X1, :X2, :X3 ),
        "Custom Design 3",
        X( :X1, :X2, :X3 )
    )
);

Remove Alias Term

Syntax: obj << Remove Alias Term

Description: Removes a term from the list of alias terms. Specify the factor number and power for each effect in a list. Create interactions by separating effects with commas.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Remove Alias Term( {1, 1}, {3, 1} );

Remove All Alias Terms

Syntax: obj << Remove All Alias Terms

Description: Removes all Alias Terms from the list of alias terms

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Make Model( Linear );
d << Remove All Alias Terms;

Remove Term

Syntax: obj << Remove Term

Description: Removes a term from the list of model terms. Specify the factor number and power for each effect in a list. Create interactions by separating effects with commas.

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

Replicates

Syntax: obj << Replicates

Description: Specifies the number of replicate runs. For MSA Designs, a second argument specifies the replicate structure: 0=Completely Randomized, 1=Batch Repeat, 2=Fast Repeat.

Example 1

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Make Model( Linear );
d << Replicates( 2 );

Example 2

d = DOE(
    MSA Design,
    {Add Response( None, "Y", ., ., . ), Add Factor(
        Categorical,
        {"L1", "L2"},
        "X1",
        MSA( 4, 1 )
    ), Add Factor( Categorical, {"L1", "L2"}, "X2", MSA( 4, 1 ) ),
    Add Factor( Categorical, {"L1", "L2"}, "X3", MSA( 4, 1 ) ), Set Random Seed( 3983347 ),
    Replicates( 2, 0 ), Simulate Responses( 0 )}
);

Report

Syntax: obj << Report

Description: Returns a reference to the report object.

d = DOE( Custom Design );
r = d << report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Save Constraints

Syntax: obj << Save Constraints

Description: Save the factor constraints of the current experiment to a JMP table for use in another experiment

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
);

Save Factors

Syntax: obj << Save Factors

Description: Save the factors you just created to a JMP table so you can use these factors for another experiment.

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
);

Save Responses

Syntax: obj << Save Responses

Description: Saves the responses that you created as a JMP data table. You can load these responses in other experiments.

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 Responses
);

Save Script to Data Table

Syntax: obj << Save Script to Data Table

Description: Create a Script that will reproduce this design.

Save Script to Script Window

Syntax: obj << Save Script to Script Window

Description: Create a Script that will reproduce this design.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Make Design,
    Save Script to Script Window
);

Save X Matrix

Syntax: obj << Save X Matrix( state=0|1 )

Description: Saves the design matrix (also called the X matrix) as a table property in the JMP data table that contains the design.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Save X Matrix,
    Make Design,
    Make Table
);

Screening Type

Syntax: obj << Screening Type

Description: Specifies a main effects screening design, which is orthogonal or near orthogonal.

d = DOE(
    Screening Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 )
);
d << Screening Type( 1 );
d << Set Sample Size( 12 );
d << Make Design;

Select Covariate Rows

Syntax: obj << Select Covariate Rows

Description: Specifies the rows from the covariate table to be selected in DOE.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
DOE(
    Custom Design,
    Add Response( Maximize, "Y", ., ., . ),
    Add Factor( Covariate, :sex, 0 ),
    Add Factor( Covariate, :height, 0 ),
    Add Factor( Covariate, :weight, 0 ),
    Add Term( {1, 0} ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Term( {3, 1} ),
    Enforce Use of Selected Covariate Rows( 1 ),
    Allow covariate rows to be repeated( 1 ),
    Select Covariate Rows( [1 2 3 4] ),
    Set Sample Size( 24 )
);

Set ALT Probability of Interest

Syntax: obj << Set ALT Probability of Interest

Description: Sets the probability of interest for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set ALT Time Range

Syntax: obj << Set ALT Time Range

Description: Sets the time range of interest for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Failure Probability Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Average Cluster Size

Syntax: obj << Set Average Cluster Size

Description: Controls number of random points for clustering a Fast Flexible Filling Design.

DOE(
    Space Filling Design,
    Change Factor Settings( 1, -1, 1, "X1" ),
    Change Factor Settings( 2, -1, 1, "X2" ),
    Set Average Cluster Size( 100 ),
    Space Filling Design Type( Fast Flexible Filling, 50 )
);

Set Axial Choice

Syntax: obj << Set Axial Choice( 1|2|3|4 )

Description: Specifies the axial value settings. Use 1 for Rotatable, 2 for Orthogonal, 3 for On Face, and 4 User Specified.

d = DOE( Response Surface Design, Make Design( 2 ) );
d << Set Axial Choice( 2 );

Set Axial Value

Syntax: obj << Set Axial Value

Description: Specifies the User Specified axial value.

d = DOE( Response Surface Design, Make Design( 2 ) );
d << Set Axial Value( 2 );

Set Candidate Runs

Syntax: obj << Set Candidate Runs

Description: Sets the candidate runs for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Monitoring at Intervals", {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] )}
);

Set Delta For Power

Syntax: obj << Set Delta For Power

Description: Specifies the values of the anticipated coefficients in Power Analysis. Anticipated coefficients will be one half of the specified value.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set Delta For Power( 3 ),
    Make Design
);

Set Expected Number of Respondents

Syntax: obj << Set Expected Number of Respondents

Description: Sets the expected number of respondents per survey.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Generators

Syntax: obj << Set Generators

Description: Specifies the generators to be used in a screening design.

DOE(
    Screening Design,
    {Add Factor, Add Factor, Add Factor, Make Design( 1 ), Set Generators( [1, 1, 0] )}
);

Set Inspection Times

Syntax: obj << Set Inspection Times

Description: Sets the inspection times for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Monitoring at Intervals", {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] )}
);

Set Length of Test

Syntax: obj << Set Length of Test

Description: Sets the length of test for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Level Values

Syntax: obj << Set Level Values

Description: Sets the level values for the accelerating factor(s) in an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Monitoring at Intervals", {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] )}
);

Set Monitoring Choice

Syntax: obj << Set Monitoring Choice

Description: Specifies the type of monitoring for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set N Subplots

Syntax: obj << Set N Subplots

Description: Specifies the number of subplots when there are both hard-to-change and very hard-to-change factors.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 2 ),
    Add Factor( Continuous, -1, 1, "X2", 1 ),
    Add Factor( Continuous, -1, 1, "X3", 0 )
);
d << Set N Whole Plots( 4 );
d << Set N Subplots( 8 );

Set N Whole Plots

Syntax: obj << Set N Whole Plots

Description: Specifies the number of whole plots when there are hard-to-change or very hard-to-change factors.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 1 ),
    Add Factor( Continuous, -1, 1, "X2", 0 )
);
d << Set N Whole Plots( 6 );

Set Number of Attributes

Syntax: obj << Set Number of Attributes

Description: Sets the number of attributes that can change within a choice set.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Number of Choice Sets

Syntax: obj << Set Number of Choice Sets

Description: Sets the number of choice sets per survey.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Number of FDS points

Syntax: obj << Set Number of FDS points

Description: Sets the number of points used to generate the Fraction of Design Space Plot.

JMP Version Added: 14

DOE(
    Custom Design,
    {Add Factor( Continuous, -1, 1, "X1", 0 ), Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set Sample Size( 7 ), Design Search Time( 8 ), Set Number of FDS points( 20000 ),
    Make Design}
);

Set Number of Profiles

Syntax: obj << Set Number of Profiles

Description: Sets the number of profiles per choice set.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Number of Surveys

Syntax: obj << Set Number of Surveys

Description: Sets the number of surveys for a choice design.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Number of Units

Syntax: obj << Set Number of Units

Description: Sets the number of units under test for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Prior Correlation ALT

Syntax: obj << Set Prior Correlation ALT

Description: Sets the prior correlations for an accelerated life test plan.

JMP Version Added: 16

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Prior Mean ALT

Syntax: obj << Set Prior Mean ALT

Description: Sets the prior mean for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Prior Mean Choice

Syntax: obj << Set Prior Mean Choice

Description: Sets the Prior Mean for a choice design.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set Prior Quantile ALT

Syntax: obj << Set Prior Quantile ALT

Description: Sets the information for specifying the prior intercept based on a quantile.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Prior Specification Choice( 2 ), Set Prior Quantile ALT( {[1.5 2], 0.065, 2642, 45} ),
    Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Prior Std Error ALT

Syntax: obj << Set Prior Std Error ALT

Description: Sets the prior standard error for an accelerated life test plan.

JMP Version Added: 16

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Number of Units( 150 )}
);

Set Prior Variance ALT

Syntax: obj << Set Prior Variance ALT

Description: Sets the prior variance for an accelerated life test plan.

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( "Continuous Monitoring" ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Variance ALT( [0.1 0 0, 0 0.1 0, 0 0 0.1] ),
    Use Prior Uncertainty( 1 ), Set ALT Time Range( 10000, 20000 ),
    Set ALT Probability of Interest( 0.1 ), Set Length of Test( 1000 ),
    Set Number of Units( 150 )}
);

Set Prior Variance Matrix

Syntax: obj << Set Prior Variance Matrix

Description: Sets the Prior Variance Matrix for a choice design.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 )}
);

Set RMSE

Syntax: obj << Set RMSE

Description: Specifies the anticipated root mean square error (RMSE) in Power Analysis.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set RMSE( 1.5 );

Set Random Seed

Syntax: obj << Set Random Seed

Description: Useful for teaching. Setting the random seed to a specific value assures that all class members get the same design.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set Random Seed( 34067086 ),
    Make Design
);

Set Run Order

Syntax: obj << Set Run Order

Description: Specifies how the run order should be set when making a data table from a design.

d = DOE( Custom Design, Add factor, Add factor, Add factor );
d << Make Design;
d << Set Run Order( Sort Left to Right );
d << Make Table;

Set Runs Per Random Block

Syntax: obj << Set Runs Per Random Block

Description: Specifies the size of random blocks in the design.

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Make Model( Linear )
);
d << Set Runs Per Random Block( 4 );

Set Sample Size

Syntax: obj << Set Sample Size

Description: Specifies the sample size before the design is created. If the specified number is less than the minimum value shown in the designer, the sample size is set to the minimum value.

d = DOE( Custom Design, Add factor, Add factor, Add factor );
d << Make Model( Linear );
d << Set Sample Size( 12 );

Set Significance Level

Syntax: obj << Set Significance Level

Description: Change the significance level in Power Analysis.

dt = Open( "$SAMPLE_DATA/Design Experiment/Bounce Data.jmp" );
d = DOE( Evaluate Design, X( :Silica, :Sulfur, :Silane ), Y( :Stretch ) );
d << Set Significance Level( 0.10 );

Set Strength

Syntax: obj << Set Strength

Description: Sets the strength for Covering Arrays

d = DOE(
    Covering Array,
    Add factor( Categorical ),
    Add factor( Categorical ),
    Add factor( Categorical )
);
d << Set Strength( 3 );
d << Make Table;

Show Blocking Options

Syntax: obj << Show Blocking Options

Description: Specifies the blocking choice and number of blocks for a definitive screening design. Specifying a value of 0 indicates no blocks.

Example 1

DOE(
    Definitive Screening Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Show Blocking Options( 0, 0 ),
    Number of Extra Runs( 4 )
);

Example 2

DOE(
    Definitive Screening Design,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Add Factor,
    Show Blocking Options( 1, 2 ),
    Number of Extra Runs( 4 )
);

Simulate Responses

Syntax: obj << Simulate Responses( state=0|1 )

Description: Add data for the responses to the JMP design table. For use in teaching DOE.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Make Design,
    Simulate Responses,
    Make Table
);

Solve for Power

Syntax: obj << Solve for Power

Description: Sets the anticipated coefficients in Power Analysis so that the power is near the specified value.

JMP Version Added: 16

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Make Design,
    Solve for Power( 0.8 )
);

Space Filling Design Type

Syntax: obj << Space Filling Design Type( Sphere Packing|Latin Hypercube|Uniform|Minimum Potential|Maximum Entropy|IMSE Optimal|Fast Flexible Filling )

Description: Specifies the type of space filling design and the number of runs.

Example 1

d = DOE( Space Filling Design );
d << Space Filling Design Type( Sphere Packing, 30 );

Example 2

d = DOE( Space Filling Design );
d << Space Filling Design Type( Latin Hypercube, 100 );

Example 3

d = DOE( Space Filling Design );
d << Space Filling Design Type( Uniform, 20 );

Example 4

d = DOE( Space Filling Design );
d << Space Filling Design Type( Fast Flexible Filling, 100 );

Example 5

d = DOE( Space Filling Design, Space Filling Design Type( IMSE Optimal, 20 ) );
d << Theta( [2, 3] );
d << Make Design;

Sphere Radius

Syntax: obj << Sphere Radius

Description: Specifies a spherical design region and enables you to set the radius of the region.

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Sphere Radius( 1 ),
    Make Design
);

Split Plot Variance Ratio

Syntax: obj << Split Plot Variance Ratio( Whole Plot Ratio | [Whole Plot Ratio, Subplot Ratio] )

Description: For hard-to-change factors, specify the ratio of the whole-plot error variance to the run-to-run error. For hard-to-change and very hard-to-change factors, specify the ratio of the whole plot and subplot error to the run-to-run error.

Example 1

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 1 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Set N Whole Plots( 4 ),
    Split Plot Variance Ratio( 2 ),
    Make Design
);

Example 2

d = DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 2 ),
    Add Factor( Continuous, -1, 1, "X2", 1 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Set N Whole Plots( 4 )
);
d << Split Plot Variance Ratio( [3, 2] );
d << Make Design;

Suppress Cotter Designs

Syntax: obj << Suppress Cotter Designs( state=0|1 )

Description: Shows or hides Cotter designs in the list of screening designs. This option is selected by default, which means that Cotters designs are initially not in the screening design list. On by default.

DOE(
    Screening Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Factor( Continuous, -1, 1, "X3", 0 ),
    Suppress Cotter Designs,
    Make Design( 5 )
);

Table of Correlations

Syntax: obj << Table of Correlations

Description: Create a data table with the Table of Correlations from Design Diagnostics.

JMP Version Added: 15

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Make Design,
    Table of Correlations
);

Theta

Syntax: obj << Theta

Description: Specifies the Covariance Parameter Vector for Space Filling designs.

d = DOE( Space Filling Design, Space Filling Design Type( IMSE Optimal, 20 ) );
d << Theta( [2, 3] );

Treatments

Syntax: obj << Treatments

Description: Specifies the number of treatments for a balanced incomplete block design (BIBD).

JMP Version Added: 14

d = DOE( Balanced Incomplete Block Design );
d << Treatments( 3, {"L1", "L2", "L3"} );
d << Make Design;

Use Bayesian information

Syntax: obj << Use Bayesian information( state=0|1 )

Description: Uses prior information in the Bayesian setting for the design diagnostics.

JMP Version Added: 15

DOE(
    Custom Design,
    Add Factor( Continuous, -1, 1, "X1", 0 ),
    Add Factor( Continuous, -1, 1, "X2", 0 ),
    Add Term( {1, 1} ),
    Add Term( {2, 1} ),
    Add Potential Term( {1, 1}, {2, 1} ),
    Number of Starts( 10 ),
    Make Design,
    Use Bayesian Information( 1 )
);

Use Blue to Red color theme for color map

Syntax: obj << Use Blue to Red color theme for color map( state=0|1 )

Description: Uses blue to red color theme for color map on correlations.

JMP Version Added: 15

Use Prior Uncertainty

Syntax: obj << Use Prior Uncertainty( state=0|1 )

Description: Specifies if the prior uncertainty should be used to construct the optimal design.

JMP Version Added: 16

DOE(
    Accelerated Life Test Plan,
    {ALT Plan Setup( 1 ), Set Monitoring Choice( 2, {5, 200, 200} ),
    ALT Optimality Criterion( "Make Quantile Estimate Optimal" ),
    ALT Factor Settings( 1, {"X1", 3, 1, 20, 30, 90, 110} ),
    Set Level Values( 1, [90 100 110] ), Distribution Choice( LogNormal ),
    Set Prior Mean ALT( [-40 1.5 2] ), Set Prior Std Error ALT( [10, 0.2, 0.5] ),
    Set Prior Correlation ALT( [1 -0.99 0, -0.99 1 0, 0 0 1] ), Use Prior Uncertainty( 1 ),
    Set ALT Time Range( 10000, 20000 ), Set ALT Probability of Interest( 0.1 ),
    Set Length of Test( 1000 ), Set Inspection Times( [200 400 600 800 1000] ),
    Set Number of Units( 150 ), Set Candidate Runs( [90 0 150, 100 0 150, 110 0 150] )}
);

Utility Neutral Design

Syntax: obj << Utility Neutral Design( state=0|1 )

Description: Specifies if Utility Neutral Choice Design should be created.

DOE(
    Choice Design,
    {Add Factor( Categorical, {"L1", "L2"}, "X1", 0 ),
    Add Factor( Categorical, {"L1", "L2"}, "X2", 0 ), Set Random Seed( 1245253625 ),
    Add Term( {1, 1} ), Add Term( {2, 1} ), Set Prior Mean Choice( [0 0] ),
    Set Prior Variance Matrix( [1 0, 0 1] ), Set Number of Attributes( 2 ),
    Set Number of Profiles( 2 ), Set Number of Choice Sets( 8 ), Set Number of Surveys( 1 ),
    Set Expected Number of Respondents( 1 ), Utility Neutral Design( 1 )}
);