Profiler

Associated Constructors

Profiler

Syntax: Profiler( Y( column1, <column2>, ..., <PredSE column1, PredSE column2>, ... ), <Expand> )

Description: Produces an interactive graph that enables you to explore how a predicted response changes as you change factor settings. For each factor, the profiler shows prediction traces that are based on saved predictions formulas and linear constraints and illustrate how the response changes with respect to that factor. The Expand argument corresponds to the Expand Intermediate Formulas option in the launch window.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);

Example 2

dt = Open( "$Sample_Data/Diabetes.jmp" );
colNum = N Items( dt << Get Column Names );
obj = dt << Fit Model(
    Validation( :Validation ),
    Y( :Y ),
    Effects( :Age, :Gender, :BMI, :BP, :Total Cholesterol ),
    Personality( "Standard Least Squares" ),
    Emphasis( "Effect Screening" ),
    Run()
);
obj << Save Columns( Prediction Formula( 1 ), StdErr Pred Formula( 1 ) );
obj << Close Window( 1 );
predCol = Column( dt, colNum + 1 );
stderrCol = Column( dt, colNum + 2 );
dt << Profiler(
    Y( predCol, stderrCol ),
    Profiler( 1, Confidence Intervals( 1 ), ),
    Use SE Formula( 1 )
);

Example 3

dt = Open( "$Sample_Data/Stochastic Optimization.jmp" );
dt << Profiler( Y( :Yield ), Profiler( 1, Desirability Functions( 1 ), ), Expand );

Columns

Noise Factors

Syntax: obj = Profiler(...<Noise Factors( column(s) )>...)

Description: Specifies noise factors, which must be columns that are ingredients to the formula columns. Noise factors are used to study robustness (or flatness) with respect to transmitted variation from these factors. The resulting profiler includes derivatives of the formulas with respect to the noise factors.

Contour Profiler Example

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Contour Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Noise Factors( :SILANE )
);

Custom Profiler Example

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Custom Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Noise Factors( :SILANE )
);

Mixture Profiler Example

dt = Open( "$SAMPLE_DATA/Plasticizer.jmp" );
obj = dt << Mixture Profiler( Y( :Pred Formula Y ), Noise Factors( :p1 ) );

Profiler Example

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Noise Factors( :SILANE )
);

Prediction Formula

Syntax: obj = Profiler(...Prediction Formula( column(s) )...)

Description: Specifies the response columns that contain formulas.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);

Y

Syntax: obj = Profiler(...Y( column(s) )...)

Description: Specifies the response columns that contain formulas.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);

Item Messages

Adapt Y Axis

Syntax: obj << Adapt Y Axis( state=0|1 )

Description: Rescales the vertical axis if the response is outside the axis range, so that the range of the response is included.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Maximize Desirability;
Wait( 1 );
obj << Adapt Y Axis;

Add Shapley graph scripts to data table

Syntax: obj << Add Shapley graph scripts to data table( state=0|1 )

Description: Adds JSL Graph Builder bar chart scripts of the Shapley values by rows for each response in the model.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Add Shapley graph scripts to data table( 1 ),
    Save Shapley Values
));

Animation

Syntax: obj << Animation( <Tour Type( "Sequential"|("Single Factor",factorname)|"Random"|"Data Sequential"|"Data Random" )>, <Speed(ticks)>, <Go>, <Stop> )

Description: Starts or stops the animation of the profiler. You can also specify how the animation cycles through factor combinations.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Animation( Tour Type( "Sequential" ), Go );
Wait( 3 );
obj << Animation( "Stop" );

Append Settings to Table

Syntax: obj << Append Settings to Table

Description: Saves the settings of the current profiler as a new row at the end of the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Append Settings to Table;

Arrange in Rows

Syntax: obj << Arrange in Rows( number )

Description: Specifies the number of plots that appear in a row.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
Wait( 2 );
obj << Arrange in Rows( 2 );

Broadcast Factor Settings

Syntax: obj << Broadcast Factor Settings

Description: Sends the factor settings for the current profiler to all other profilers. This option does not link the profilers.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 ),
    Term Value(
        SILICA( 1.75, Lock( 0 ), Show( 1 ) ),
        SILANE( 45.2, Lock( 0 ), Show( 1 ) ),
        SULFUR( 2.45, Lock( 0 ), Show( 1 ) )
    )
);
obj << Contour Profiler( 1 );
Wait( 1 );
obj << Broadcast Factor Settings;

Colorize

Syntax: obj << Colorize( matrix )

Description: Specifies a matrix of proportions between 0 for uncolored and 1 for dark red. The rows and columns of the matrix correspond to the Y and X variables in the Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Colorize( [.0 .4 .5, .1 .2 .3, .4 .5 .3, .5 .1 .1] );

Colorize Profiler

Syntax: subobj << Colorize Profiler

Description: Colors cells in the profiler by Total Effect importance indices using a red to white intensity scale.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Uniform Inputs( 1 );
Wait( 1 );
subobj = (Report( obj )["Variable Importance: Independent Uniform Inputs"] <<
get scriptable object);
subobj << Colorize Profiler;

Combinations

Syntax: obj << Combinations( "Mixed"|"Two-Way"|"Many-Way" )

Description: Specifies the types of interactions that are shown as overlaid interaction curves in the profiler.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Overlaid Interactions( 1 );
Wait( 1 );
obj << Combinations( "Many-Way" );

Compute Shapley values for all rows

Syntax: obj << Compute Shapley values for all rows( state=0|1 )

Description: Computes the Shapley values for all rows in the data table, excluded and not excluded.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
r = dt << Select Rows( [5, 7, 8, 10] );
r << Exclude;
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Compute Shapley values for all rows( 1 ),
    Save Shapley Values
));

Conditional Predictions

Syntax: obj << Conditional Predictions( state=0|1 )

Description: Includes random effects when formulating the predicted value and profiles. This option is available only in the Fit Mixed personality of the Fit Model platform when random effects are included in the model.

dt = Open( "$SAMPLE_DATA/Animals.jmp" );
obj1 = dt << Run Script( "Repeated Measures Model" );
obj1 << Profiler( Conditional Predictions( 1 ) );

Confidence Intervals

Syntax: obj << Confidence Intervals( state=0|1 )

Description: Shows or hides 95% confidence intervals for the simulated means on the curves of the profiler graph. Available only if a Std Error formula is specified in the launch window.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj1 = dt << Run Script( "RSM for 4 Responses" );
obj1 << Prediction Formula;
obj1 << StdErr Pred Formula;
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION 2, :Pred Formula MODULUS 2, :Pred SE ABRASION,
        :Pred SE MODULUS
    )
);
Wait( 1 );
obj << Confidence Intervals( 0 );

Contour Profiler

Syntax: obj << Contour Profiler( state=0|1 )

Description: Shows or hides the Contour Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Contour Profiler( 1 );

Converge Limit

Syntax: obj << Converge Limit( number )

Description: Specifies the criterion for convergence for the optimization algorithm. If the convergence criterion is less than this value for two consecutive iterations, the algorithm stops.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Converge limit( 0.0001 );
obj << Optimize;

Copy Settings Script

Syntax: obj << Copy Settings Script

Description: Copies the current factor settings to the clipboard. The settings can then be pasted into another profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Set to Data in Row( 4 );
obj << Copy Settings Script;
obj2 = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
Wait( 1 );
obj2 << Paste Settings Script;

Custom Profiler

Syntax: obj << Custom Profiler( state=0|1 )

Description: Shows or hides the Custom Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Custom Profiler( 1 );

Data Points

Syntax: obj << Data Points( state=0|1 )

Description: Shows or hides the individual data points in the Prediction Profiler plot. The data points fade according to how far away they are from the plane of each profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Data Points( 1 );

Default N Grid Points

Syntax: obj << Default N Grid Points( number )

Description: Sets the number of levels for each continuous factor.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Default N Grid Points( 5 );

Default N Levels

Syntax: obj << Default N Levels( number )

Dependent Resampled Inputs

Syntax: obj << Dependent Resampled Inputs( state=0|1 )

Description: Calculates the indices that are used in the Assess Variable Importance option by resampling the data table assuming that the inputs are dependent.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Dependent Resampled Inputs( 1 );

Design Space

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

Design Space Profiler

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

Description: Launches the Design Space Profiler, which helps map specification limits on the Y variables to specification limits on the X variables.

dt = Open( "$Sample_Data/Tiretread.jmp" );
dt:Pred Formula ABRASION << Set Property( "Spec Limits", {LSL( 120 ), Show Limits( 1 )} );
dt:Pred Formula MODULUS << Set Property( "Spec Limits", {LSL( 1200 ), Show Limits( 1 )} );
dt:Pred Formula ELONG << Set Property(
    "Spec Limits",
    {LSL( 350 ), USL( 500 ), Show Limits( 1 )}
);
dt:Pred Formula HARDNESS << Set Property(
    "Spec Limits",
    {LSL( 65 ), USL( 75 ), Show Limits( 1 )}
);
dt:Pred Formula ABRASION << Set Property(
    "Predicting",
    {:ABRASION, Creator( "Fit Least Squares" ), RMSE( 3 )}
);
dt:Pred Formula MODULUS << Set Property(
    "Predicting",
    {:MODULUS, Creator( "Fit Least Squares" ), RMSE( 100 )}
);
dt:Pred Formula ELONG << Set Property(
    "Predicting",
    {:ELONG, Creator( "Fit Least Squares" ), RMSE( 10 )}
);
dt:Pred Formula HARDNESS << Set Property(
    "Predicting",
    {:HARDNESS, Creator( "Fit Least Squares" ), RMSE( .6 )}
);
Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Profiler( 1, Desirability Functions( 0 ), Design Space Profiler( 1 ) )
);

Desirability Functions

Syntax: obj << Desirability Functions( state=0|1 )

Description: Shows or hides the desirability functions, which are useful when optimizing across several responses.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );

Edit Constraints

Syntax: obj << Edit Constraints

Description: Adds, changes, or deletes linear constraints.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Edit Constraints;

Extrapolation Control Option

Syntax: obj << Extrapolation Control Option( "Off"|"On"|"Warning On" )

Description: Specifies if extrapolation control is turned on or off, or if only extrapolation control warnings are turned on.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Fitness.jmp" );
obj = dt << Fit Model(
    Y( :Oxy ),
    Effects( :Age, :Weight, :Runtime, :RunPulse, :RstPulse, :MaxPulse ),
    Personality( "Standard Least Squares" ),
    Emphasis( "Minimal Report" ),
    Run
);
obj << Profiler( Extrapolation Control Option( "On" ) );

Extrapolation Details

Syntax: obj << Extrapolation Details( state=0|1 )

Description: Shows or hides the extrapolation control details that give the extrapolation metric of the current point and the extrapolation threshold.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Fitness.jmp" );
obj = dt << Fit Model(
    Y( :Oxy ),
    Effects( :Age, :Weight, :Runtime, :RunPulse, :RstPulse, :MaxPulse ),
    Personality( "Standard Least Squares" ),
    Emphasis( "Minimal Report" ),
    Run
);
obj << Profiler( Extrapolation Control Option( "On" ), Extrapolation Details( 1 ) );

Extrapolation Type Option

Syntax: obj << Extrapolation Type Option( "Regularized T2"|"K Nearest Neighbors" )

JMP Version Added: 18

Formulas for OPTMODEL

Syntax: obj << Formulas for OPTMODEL

Description: Saves the prediction formulas from the model in a new file as SAS statements for PROC OPTMODEL.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Formulas for OPTMODEL;

Get Constraints

Syntax: obj << Get Constraints

Description: Returns a list of factor constraints.

dt = Open( "$SAMPLE_DATA/Plasticizer.jmp" );
obj = dt << Profiler(
    Y( :Pred Formula Y ),
    Profiler( 1, Profile at Boundary( "Stop at Boundaries" ), )
);
obj << Get Constraints;

Get Desirability

Syntax: obj << Get Desirability

Description: Returns the current desirability settings.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
d = obj << Get Desirability;
Show( d );

Get Factor Settings

Syntax: obj << Get Factor Settings

Description: Returns the current factor settings as a list.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Get Factor Settings;

Get Factor Settings Script

Syntax: obj << Get Factor Settings Script

Description: Returns the current factor settings as an expression that can be used in a script.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Get Factor Settings Script;

Get Main Indices

Syntax: obj << Get Main Indices

Description: Saves the main indices from the Assess Variable Importance analysis to a new file as SAS statements for PROC OPTMODEL.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Resampled Inputs( 1 );
obj << Get Main Indices;

Get Simulator

Syntax: obj << Get Simulator

Description: Returns a reference to the Simulator.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ), SILANE << Fixed( 50 ),
        SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << Add Random Noise( 1 ),
        Pred Formula HARDNESS << Add Random Weighted Noise( 1 )
    )
);
obj2 = obj << Get Simulator;
obj2 << Simulation Experiment;

Get Total Indices

Syntax: obj << Get Total Indices

Description: Saves the total indices from the Assess Variable Importance analysis to a new file as SAS statements for PROC OPTMODEL.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Resampled Inputs( 1 );
obj << Get Total Indices;

Graph Spacing

Syntax: obj << Graph Spacing( number )

Description: Sets the amount of horizontal space between graph panels.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
Wait( 2 );
obj << Graph Spacing( 20 );

Hide Desirability Row

Syntax: obj << Hide Desirability Row( state=0|1 )

Description: Hides or unhides the row of desirability profiles.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Desirability Functions( 1 );
Wait( 1 );
obj << Hide Desirability Row( 1 );

Hide Y Variables

Syntax: obj << Hide Y Variables( Y columns )

Description: Specifies the response variables that you would like to show or hide in the profiler.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
Wait( 0.5 );
obj << Hide Y Variables( :Pred Formula MODULUS );

Independent Resampled Inputs

Syntax: obj << Independent Resampled Inputs( state=0|1 )

Description: Calculates the indices that are used in the Assess Variable Importance option by resampling the data table assuming that the inputs are independent.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Resampled Inputs( 1 );

Independent Uniform Inputs

Syntax: obj << Independent Uniform Inputs( state=0|1 )

Description: Calculates the indices that are used in the Assess Variable Importance option by resampling the data table assuming that the inputs have independent uniform distributions.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Uniform Inputs( 1 );

Interaction Profiler

Syntax: obj << Interaction Profiler( state=0|1 )

Description: Shows or hides an interaction profiler for each response.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Interaction Profiler( 1 );

Linearly Constrained Inputs

Syntax: obj << Linearly Constrained Inputs( state=0|1 )

Description: Calculates the indices that are used in the Assess Variable Importance option by resampling the data table over a uniform distribution defined by the linear constraints.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Script( "Constraint", {1 * :LDL + 1 * :HDL <= 250} );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Linearly Constrained Inputs( 1 );

Link Profilers

Syntax: obj << Link Profilers( state=0|1 )

Description: Links all the profilers in a single report together, so that a change in a factor in one profiler causes that factor to change to that value in all other profilers.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Prediction Profiler( 1 );
obj << Contour Profiler( 1 );
obj << Link Profilers( 1 );
Wait( 1 );
obj << Term Value( :Silica( 1.78 ), :Sulfur( 2.34 ) );

Load Constraints from Table

Syntax: obj << Load Constraints from Table

Description: Loads linear constraints from a data table.

JMP Version Added: 19

dtlc = New Table( "Linear Constraints",
    Add Rows( 2 ),
    New Column( "SILICA", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [1, 2] ) ),
    New Column( "SILANE", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [0, 0] ) ),
    New Column( "SULFUR", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [1, 1] ) ),
    New Column( "Comparison", Character, "Nominal", Set Values( {">=", "<="} ) ),
    New Column( "RHS", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [3, 6] ) )
);
dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Load Constraints from Table( dtlc );
obj << Profile at Boundary( "Stop at Boundaries" );

Log Iterations

Syntax: obj << Log Iterations( state=0|1 )

Description: Creates a new data table that contains iterations of the optimization algorithm.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Log Iterations( 1 );
obj << Optimize;

Max Cycles

Syntax: obj << Max Cycles( number )

Description: Specifies the maximum number of cycles within each trip in the optimization algorithm.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Max Cycles( 5 );
obj << Optimize;

MaxIter

Syntax: obj << MaxIter( number )

Description: Specifies the maximum number of iterations within each trip in the optimization algorithm.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << MaxIter( 10 );
obj << Optimize;

Maximize Desirability

Syntax: obj << Maximize Desirability

Description: Sets the current factor values to maximize the desirability functions.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
Wait( 2 );
obj << Maximize Desirability;

Maximize and Remember

Syntax: obj << Maximize and Remember

Description: Maximizes the desirability functions and remembers the associated settings.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Maximize and Remember;

Maximize for Each Grid Point

Syntax: obj << Maximize for Each Grid Point

Description: Maximizes the desirability functions for each grid point, holding one or more factors constant. This option requires locking at least one factor.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Term Value( SILANE( 60, Lock( 1 ) ) );
obj << Maximize For Each Grid Point;

Maximum Number of Curves

Syntax: obj << Maximum Number of Curves( number=500 )

Description: Specifies the maximum number of curves to show when the Overlaid Interactions option is selected. If the total possible number of curves is greater than the specified maximum number of curves, an arbitrary sample is drawn. "500" by default.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Overlaid Interactions( 1 );
Wait( 1 );
obj << Maximum Number of Curves( 100 );

Optimization Control Panel

Syntax: obj << Optimization Control Panel( state=0|1 )

Output Grid Table

Syntax: obj << Output Grid Table

Description: Creates a new data table that contains columns for the factors that contain grid values, columns for each of the responses with computed values at each grid point, and the desirability computation at each grid point.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Output Grid Table;

Output Random Table

Syntax: obj << Output Random Table( number of runs,<Add Random Noise> )

Description: Creates a new data table of random factor settings and predicted values over those factor settings for the specified number of runs. There is also an option to add random noise to the responses.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Output Random Table( 1000 );

Overlaid Interactions

Syntax: obj << Overlaid Interactions( state=0|1 )

Description: Shows or hides faded curves in the Prediction Profiler plots. The faded curves represent the profilers for different types of interactions among the ranges of the factors.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Overlaid Interactions( 1 );

Paste Settings Script

Syntax: obj << Paste Settings Script

Description: Pastes the profiler settings from the clipboard to a profiler in another report.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Set to Data in Row( 4 );
obj << Copy Settings Script;
obj2 = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
Wait( 1 );
obj2 << Paste Settings Script;

Predict for Another Table

Syntax: obj << Predict for Another Table( <data table> )

Description: Adds prediction columns to a specified data table, using the factors in that table. This option is available only for continuous responses.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
dt2 = dt << Subset(
    All rows,
    columns( :SILICA, :SILANE, :SULFUR ),
    Output Table( "Subset" )
);
obj << Predict For Another Table( dt2 );

Prediction Intervals

Syntax: obj << Prediction Intervals( state=0|1 )

Description: Shows or hides the 95% prediction intervals that include both the variation in estimating the model and the variation in the residual error.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << Fit Model(
    Y( :ELONG ),
    Effects( :SILICA, :SILANE, :SULFUR, :SILANE * :SILANE ),
    Personality( "Standard Least Squares" ),
    Emphasis( "Minimal Report" ),
    Run(
        Profiler(
            1,
            Confidence Intervals( 1 ),
            Prediction Intervals( 1 ),
            Desirability Functions( 0 )
        ),
        :ELONG << {Summary of Fit( 0 ), Analysis of Variance( 0 ), Parameter Estimates( 1 ),
        Effect Tests( 0 ), Effect Details( 0 ), Lack of Fit( 0 ),
        Plot Actual by Predicted( 0 ), Plot Regression( 0 ), Plot Residual by Predicted( 0 ),
        Effect Summary( 0 )}
    )
);

Prediction Profiler

Syntax: obj << Prediction Profiler( state=0|1 )

Description: Shows or hides the Prediction Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Prediction Profiler( 1 );

Profile at Boundary

Syntax: obj << Profile at Boundary( "Turn at Boundaries"|"Stop at Boundaries" )

Description: Identifies the boundary handling method for factors that have constraints. This option is available only for prediction models containing mixture variables, when there is a linear constraint, or when the Alter Linear Constraints option is specified.

dt = Open( "$SAMPLE_DATA/Design Experiment/Donev Mixture Data.jmp" );
obj1 = Fit Model(
    Y( :Damping ),
    Effects( :CuSO4 & RS & Mixture, :Na2S2O3 & RS & Mixture, :Glyoxal & RS & Mixture ),
    Personality( "Standard Least Squares" ),
    Run Model( 1 )
);
obj1 << Prediction Formula;
obj2 = Profiler( Y( :Pred Formula Damping ) );
Wait( 1 );
obj2 << Profile at Boundary( "Stop at Boundaries" );

Prop of Error Bars

Syntax: obj << Prop of Error Bars( state=0|1 )

Description: Shows or hides error bars on the profiler graph. This option is available only when the column contains a Sigma column property.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
:Pred Formula ABRASION << Set Property( Sigma, 5 );
:Pred Formula MODULUS << Set Property( Sigma, 100 );
obj = dt << Profiler( Y( :Pred Formula ABRASION, :Pred Formula MODULUS ) );
obj << Prop of Error Bars( 1 );

Remember Settings

Syntax: obj << Remember Settings

Description: Adds an outline node to the report with the values of the factor settings.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Remember Settings;

Remove Profiler

Syntax: scobj << Remove Profiler

Description: Removes the profiler from the platform report. This option is available only in a limited number of platforms.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Job Satisfaction.jmp" );
obj = dt << Run Script( "SEM: Path Analysis w / Latent" );
rpt = obj << Report();
rpt["Model Specification"] << Close( 1 );
obj << Prediction Profiler(
    1,
    Confidence Intervals( 1 ),
    Term Value( Leadership( 0, Lock( 0 ), Show( 1 ) ), Conflict( 0, Lock( 0 ), Show( 1 ) ) ),
    Y Terms( Conflict, Satisfaction )
);
scobj = rpt[Outline Box( "Prediction Profiler" )] << Get Scriptable Object();
scobj << Remove Profiler;

Reorder X Variables

Syntax: obj << Reorder X Variables( columns )

Description: Reorders the model main effects in the profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
Wait( 2 );
obj << Reorder X Variables( :SULFUR, :SILANE, :SILICA );

Reorder Y Variables

Syntax: obj << Reorder Y Variables( columns )

Description: Reorders the response variables.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
Wait( 2 );
obj << Reorder Y Variables(
    :Pred Formula HARDNESS, :Pred Formula MODULUS, :Pred Formula ELONG
);

Reorder factors by main effect importance

Syntax: subobj << Reorder factors by main effect importance

Description: Reorders the cells in the Prediction Profiler in accordance with the importance indices for the main effects.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Uniform Inputs( 1 );
Wait( 1 );
subobj = (Report( obj )["Variable Importance: Independent Uniform Inputs"] <<
get scriptable object);
subobj << Reorder factors by main effect importance;

Reorder factors by total importance

Syntax: subobj << Reorder factors by total importance

Description: Reorders the cells in the Prediction Profiler in accordance with the total importance indices for the factors.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
fit = Neural(
    Y( :Y ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
fit << Save Formulas;
obj = Profiler( Y( :Predicted Y ), Expand );
obj << Independent Uniform Inputs( 1 );
subobj = (Report( obj )["Variable Importance: Independent Uniform Inputs"] <<
get scriptable object);
subobj << Reorder factors by main effect importance;
Wait( 1 );
subobj << Reorder factors by total importance;

Reset

Syntax: obj << Reset

Description: Resets the desirability functions.

Reset Factor Grid

Syntax: obj << Reset Factor Grid

Reset Factors

Syntax: obj << Reset Factors

Description: Opens a window for changing the factor grid.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Reset Factor Grid;

Response Limits

Syntax: Pred Y << Response Limits( {Lower( value, fraction ), Middle( value, fraction ), Upper( value, fraction ), Goal( Minimize|Maximize|Target ), Importance( number )} )

Description: Sets the desirability function settings for an individual response as well as the associated desirability values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Pred Formula ABRASION << Response Limits(
        {Lower( 90, 0.9819 ), Middle( 145, 0.5 ), Upper( 200, 0.066 ), Goal( Minimize ),
        Importance( 1 )}
    )
);
obj << Desirability Functions( 1 );

Samples per Factor

Syntax: obj << Samples per Factor( number=6 )

Description: Specifies the number of sample values taken for each continuous factor for two-way interactions. This value is reduced for many-way interactions and is conditional on the maximum number of curves. "6" by default.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Overlaid Interactions( 1 );
Wait( 1 );
obj << Samples per Factor( 10 );

Save Bagged Predictions

Syntax: obj << Save Bagged Predictions( nsample, Random Seed(number), Fractional Weights(0|1), Save Prediction Formulas(0|1) )

Description: Uses bootstrap aggregating (bagging) to make predictions and saves the bagged prediction means and standard errors to the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Neural(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Crossvalidation( No Crossvalidation ),
    Go
);
obj << Profiler( Save Bagged Predictions( 10 ) );

Save Constraints to Script

Syntax: obj << Save Constraints to Script

Description: Saves existing linear constraints to a table script called Constraint.

JMP Version Added: 19

dtlc = New Table( "Linear Constraints",
    Add Rows( 2 ),
    New Column( "SILICA", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [1, 2] ) ),
    New Column( "SILANE", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [0, 0] ) ),
    New Column( "SULFUR", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [1, 1] ) ),
    New Column( "Comparison", Character, "Nominal", Set Values( {">=", "<="} ) ),
    New Column( "RHS", Numeric, "Continuous", Format( "Best", 12 ), Set Values( [3, 6] ) )
);
dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Load Constraints from Table( dtlc );
obj << Save Constraints to Script;

Save Constraints to Table

Syntax: obj << Save Constraints to Table

Description: Saves existing linear constraints to a new data table.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Script(
    "Constraint",
    {1 * :SILICA + 1 * :SULFUR >= 3, 2 * :SILICA + 1 * :SULFUR <= 6}
);
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Save Constraints to Table;

Save Desirabilities

Syntax: obj << Save Desirabilities

Description: Saves the three desirability function settings for each response, as well as the associated desirability values, as a Response Limits column property in the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Save Desirabilities;

Save Desirability Formula

Syntax: obj << Save Desirability Formula

Description: Saves a new formula column in the data table. The new column contains a formula for the combined desirability across the responses.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Save Desirability Formula;

Save Expanded Formulas

Syntax: obj << Save Expanded Formulas

Description: Saves a new formula column to the data table. The new column contains resolved formula references within the formulas used as Y variables to see the underlying variables. This is available only after the Expand Intermediate Formulas option is selected in the launch window or the Expand message is specified in the Profiler script.

dt = Open( "$SAMPLE_DATA/Nonlinear Examples/CES Production Function.jmp" );
obj = dt << Profiler( Y( :GP Fit, :NL Fit, :Difference ), Expand, Contour Profiler( 1 ) );
obj << Save Expanded Formulas;

Save Shapley Values

Syntax: obj << Save Shapley Values

Description: Calculates Shapley values for each row in the data table that is not excluded.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler( 1, Save Shapley Values ));

Sensitivity Indicator

Syntax: obj << Sensitivity Indicator( state=0|1 )

Description: Shows or hides a purple triangle that can help quickly spot sensitive cells in large profiles. The triangle's height and direction correspond to the value of the partial derivative of the profile function at its current value.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Sensitivity Indicator( 1 );

Set Desirabilities

Syntax: obj << Set Desirabilities

Description: Opens the Response Goal window where you can set specific desirability values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Set Desirabilities;

Set Script

Syntax: obj << Set Script( Function( {arguments}, <{locals}>, expr ) )

Description: Sets a script that is run each time a factor changes.

ProfileCallbackLog = Function( {arg}, Show( arg ) );
dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Set Script( ProfileCallbackLog );
obj << Term Value( :Silica( 1 ) );

Set Threshold Criterion

Syntax: obj << Set Threshold Criterion( Extrapolation Control Criterion( "Num Model Terms / Num Observations " | "Maximum Leverage" ), <multiplier> )

Description: Can be used to specify the general extrapolation threshold multiplier. Alternatively, you can use this function to open a window that enables you to adjust the extrapolation threshold multiplier.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Neural(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Informative Missing( 0 ),
    Validation Method( "Holdback", 0.3333 ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Desirability Functions( 1 ),
    Extrapolation Details( 1 ),
    Extrapolation Control Option( "Warning On" ),
    Set Threshold Criterion( General Extrapolation Control Multiplier( 4 ) )
));

Set to Data in Row

Syntax: obj << Set to Data in Row( row number )

Description: Assigns the values of a data table row to the X variables in the profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
Wait( 2 );
obj << Set to Data in Row( 4 );

Shapley Background Data Choice

Syntax: obj << Shapley Background Data Choice( "Percent training data set"|"Number of rows of training data set" )

Description: Specifies the background data in the Shapley computations as either a percentage of the training data or a number of rows of the training data.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Shapley Background Data Choice( Number of rows of training data set ),
    Shapley Number of Rows of Training Data( 150 ),
    Save Shapley Values
));

Shapley Number of Permutations

Syntax: obj << Shapley Number of Permutations( number=10 )

Description: Sets the number of permutations to be used for calculating the Shapley values. "10" by default.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler( 1, Shapley Number of Permutations( 15 ), Save Shapley Values ));

Shapley Number of Rows of Training Data

Syntax: obj << Shapley Number of Rows of Training Data( number=100 )

Description: Sets the number of rows of the training data that was used to fit the model for use as the background data in the Shapley computations. "100" by default.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Shapley Background Data Choice( Number of rows of training data set ),
    Shapley Number of Rows of Training Data( 125 ),
    Save Shapley Values
));

Shapley Percent Training Data

Syntax: obj << Shapley Percent Training Data( number=100 )

Description: Sets the percentage of the training data that was used to fit the model for use as the background data in the Shapley computations. "100" by default.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler(
    1,
    Shapley Background Data Choice( Percent training data set ),
    Shapley Percent Training Data( 50 ),
    Save Shapley Values
));

Shapley Set Random Seed

Syntax: obj << Shapley Set Random Seed( number )

Description: Sets a random seed for calculating the Shapley values.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Body Fat.jmp" );
obj = dt << Neural(
    Y( :Percent Body Fat ),
    X(
        :"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n, :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n, :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n, :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n
    ),
    Validation Method( :Validation ),
    Set Random Seed( 123 ),
    Fit( NTanH( 3 ) )
);
obj << (Fit[1] << Profiler( 1, Shapley Set Random Seed( 12345 ), Save Shapley Values ));

Show Creator

Syntax: obj << Show Creator( state=0|1 )

Description: Shows or hides the name of the platform that created the formula in the response column. The platform name appears on the vertical axis. Available only if the response column contains a Creator named argument in the Predicting column property.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Drug.jmp" );
fm = dt << Fit Model(
    Y( :y ),
    Effects( :Drug, :x, :Drug * :x ),
    Personality( "Standard Least Squares" ),
    Emphasis( "Minimal Report" ),
    Run(
        :y << {Summary of Fit( 0 ), Analysis of Variance( 0 ), Parameter Estimates( 1 ),
        Effect Tests( 0 ), Effect Details( 0 ), Lack of Fit( 0 ), Scaled Estimates( 0 ),
        Plot Actual by Predicted( 0 ), Plot Regression( 0 ), Plot Residual by Predicted( 0 ),
        Plot Studentized Residuals( 0 ), Plot Effect Leverage( 0 ),
        Plot Residual by Normal Quantiles( 0 ), Box Cox Y Transformation( 0 )},
        Effect Summary( 0 )
    )
);

predForm = fm << Save Columns( "Prediction Formula" );

Profiler( Y( predForm ), Show Creator( 1 ) );

Show Formulas

Syntax: obj << Show Formulas

Description: Opens a script window that contains JSL for all formulas that are being profiled.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Show Formulas;

Simulator

Syntax: obj << Simulator( state=0|1 )

Description: Shows or hides the Simulator.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Simulator( 1 );

Spanning Range

Syntax: obj << Spanning Range( "Inner Axis Range"|"Full Axis Range"|"One Standard Deviation"|"Two Standard Deviations"|"Data Range" )

Description: Specifies how the sampling range of each continuous factor is determined. The sampling range for each factor defines the lowest and highest values for which the interaction curves are created.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Overlaid Interactions( 1 );
Wait( 1 );
obj << Spanning Range( "Two Standard Deviations" );

Surface Profiler

Syntax: obj << Surface Profiler( state=0|1 )

Description: Shows or hides the Surface Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Surface Profiler( 1 );

Term Value

Syntax: obj << Term Value( factor( current value, <Lock( 0|1 )>, <Min( number )>, <Max( number)> ) )

Description: Specifies settings for individual factors, including the current value, lock status, and range.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
Wait( 2 );
obj << Term Value( SILANE( 60, Lock( 1 ) ) );

Trips

Syntax: obj << Trips( number )

Description: Specifies the number of random starts in the optimization algorithm. Each trip restarts the algorithm at a different starting point.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Trips( 10 );
obj << Optimize;

Unthreaded

Syntax: obj << Unthreaded( state=0|1 )

Description: To suppress any multithreading in evaluating the profile traces, the contour grid, and the optimizer trips.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Desirability Functions( 1 );
obj << Unthreaded( 1 );
obj << Maximize Desirability;

Shared Item Messages

Action

Syntax: obj << Action

Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Apply Preset

Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )

Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.

JMP Version Added: 18

Anonymous preset

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
dt2 = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj2 = dt2 << Oneway( Y( :LogHist0 ), X( :drug ) );
Wait( 1 );
obj2 << Apply Preset( preset );

Search by name

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "Compare Distributions" );

Search within folder(s)

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "t-Tests", Folder( "Compare Means" ) );

Column Switcher

Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)

Description: Adds a control panel for changing the platform's variables

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);

Copy Script

Syntax: obj << Copy Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Copy Script;

Data Table Window

Syntax: obj << Data Table Window

Description: Move the data table window for this analysis to the front.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Data Table Window;

Get By Levels

Syntax: obj << Get By Levels

Description: Returns an associative array mapping the by group columns to their values.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;

Get Container

Syntax: obj << Get Container

Description: Returns a reference to the container box that holds the content for the object.

General

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );

Platform with Filter

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
gb = Graph Builder(
    Show Control Panel( 0 ),
    Variables( X( :height ), Y( :weight ) ),
    Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
    Local Data Filter(
        Add Filter(
            columns( :age, :sex, :height ),
            Where( :age == {12, 13, 14} ),
            Where( :sex == "F" ),
            Where( :height >= 55 ),
            Display( :age, N Items( 6 ) )
        )
    )
);
New Window( "platform boxes",
    H List Box(
        Outline Box( "Report(platform)", Report( gb ) << Get Picture ),
        Outline Box( "platform << Get Container", (gb << Get Container) << Get Picture )
    )
);

Get Data Table

Syntax: obj << Get Data Table

Description: Returns a reference to the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Script

Syntax: obj << Get Script

Description: Creates a script (JSL) to produce this analysis and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
t = obj << Get Script;
Show( t );

Get Script With Data Table

Syntax: obj << Get Script With Data Table

Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
t = obj << Get Timing;
Show( t );

Get Web Support

Syntax: obj << Get Web Support

Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
s = obj << Get Web Support();
Show( s );

Get Where Expr

Syntax: obj << Get Where Expr

Description: Returns the Where expression for the data subset, if the platform was launched with By() or Where(). Otherwise, returns Empty()

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv2 = dt << Bivariate( X( :height ), Y( :weight ), Where( :age < 14 & :height > 60 ) );
Show( biv[1] << Get Where Expr, biv2 << Get Where Expr );

Ignore Platform Preferences

Syntax: Ignore Platform Preferences( state=0|1 )

Description: Ignores the current settings of the platform's preferences. The message is ignored when sent to the platform after creation.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Ignore Platform Preferences( 1 ),
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Local Data Filter

Syntax: obj << Local Data Filter

Description: To filter data to specific groups or ranges, but local to this platform

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);

New JSL Preset

Syntax: New JSL Preset( preset )

Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
preset = obj << New JSL Preset( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) );
Wait( 1 );
obj << Apply Preset( preset );

New Preset

Syntax: obj = New Preset()

Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();

Paste Local Data Filter

Syntax: obj << Paste Local Data Filter

Description: Apply the local data filter from the clipboard to the current report.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
filter = dist << Local Data Filter(
    Add Filter( columns( :Region ), Where( :Region == "MW" ) )
);
filter << Copy Local Data Filter;
dist2 = Distribution( Continuous Distribution( Column( :Lead ) ) );
Wait( 1 );
dist2 << Paste Local Data Filter;

Redo Analysis

Syntax: obj << Redo Analysis

Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Redo Analysis;

Relaunch Analysis

Syntax: obj << Relaunch Analysis

Description: Opens the platform launch window and recalls the settings that were used to create the report.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Relaunch Analysis;

Remove Column Switcher

Syntax: obj << Remove Column Switcher

Description: Removes the most recent Column Switcher that has been added to the platform.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;

Remove Local Data Filter

Syntax: obj << Remove Local Data Filter

Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);
Wait( 2 );
dist << remove local data filter;

Render Preset

Syntax: Render Preset( preset )

Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Render Preset( Expr( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) ) );

Report

Syntax: obj << Report;Report( obj )

Description: Returns a reference to the report object.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Report View

Syntax: obj << Report View( "Full"|"Summary" )

Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Report View( "Summary" );

Save Script for All Objects

Syntax: obj << Save Script for All Objects

Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Save Script for All Objects;

Save Script for All Objects To Data Table

Syntax: obj << Save Script for All Objects To Data Table( <name> )

Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table( "My Script" );

Save Script to Data Table

Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );

Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );

Save Script to Journal

Syntax: obj << Save Script to Journal

Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Save Script to Journal;

Save Script to Report

Syntax: obj << Save Script to Report

Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Save Script to Report;

Save Script to Script Window

Syntax: obj << Save Script to Script Window

Description: Create a JSL script to produce this analysis, and append it to the current Script text window.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Save Script to Script Window;

SendToByGroup

Syntax: SendToByGroup( {":Column == level"}, command );

Description: Sends platform commands or display customization commands to each level of a by-group.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    By( :Sex ),
    SendToByGroup(
        {:sex == "F"},
        Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) )
    ),
    SendToByGroup( {:sex == "M"}, Continuous Distribution( Column( :weight ) ) )
);

SendToEmbeddedScriptable

Syntax: SendToEmbeddedScriptable( Dispatch( "Outline name", "Element name", command );

Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.

dt = Open( "$SAMPLE_DATA/Reliability/Fan.jmp" );
dt << Life Distribution(
    Y( :Time ),
    Censor( :Censor ),
    Censor Code( 1 ),
    <<Fit Weibull,
    SendToEmbeddedScriptable(
        Dispatch(
            {"Statistics", "Parametric Estimate - Weibull", "Profilers", "Density Profiler"},
            {1, Confidence Intervals( 0 ), Term Value( Time( 6000, Lock( 0 ), Show( 1 ) ) )}
        )
    )
);

SendToReport

Syntax: SendToReport( Dispatch( "Outline name", "Element name", Element type, command );

Description: Send To Report is used in tandem with the Dispatch command to customize the appearance of a report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :age ) ),
    Continuous Distribution( Column( :weight ) ),
    SendToReport( Dispatch( "age", "Distrib Nom Hist", FrameBox, {Frame Size( 178, 318 )} ) )
);

Sync to Data Table Changes

Syntax: obj << Sync to Data Table Changes

Description: Sync with the exclude and data changes that have been made.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
Wait( 1 );
dt << Delete Rows( dt << Get Rows Where( :Region == "W" ) );
dist << Sync To Data Table Changes;

Title

Syntax: obj << Title( "new title" )

Description: Sets the title of the platform.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
obj << Title( "My Platform" );

Top Report

Syntax: obj << Top Report

Description: Returns a reference to the root node in the report.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Desirability Functions( 1 )
);
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

View Web XML

Syntax: obj << View Web XML

Description: Returns the XML code that is used to create the interactive HTML report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;

Design Space Profiler

Associated Constructors

Design Space Profiler

Syntax: Design Space Profiler

Description: Launches the Design Space Profiler, which helps map specification limits on the Y variables to specification limits on the X variables.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );

Item Messages

Connect Hide Mode

Syntax: obj << Connect Hide Mode( state=0|1 )

Description: For the connected table, instead of selecting points that fall within the limits, this option hides points that fall outside of the limits.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Connect Hide Mode( 1 );
dt2 = obj2 << Make and Connect Random Table( 10000, Add Random Noise );
dt2 << Run Script( (dt2 << Get Table Script Names)[1] );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );

Connect to Table

Syntax: obj << Connect to Table( data table )

Description: Connects the Design Space Profiler report to the specified data table. The rows that contain factors that are within the current lower and upper limits are selected in the connected table.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
dt2 = obj << Output Random Table( 10000, Add Random Noise );
dt2 << Run Script( (dt2 << Get Table Script Names)[1] );
obj2 << Connect to Table( dt2 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );

Get Midpoints from Profiler

Syntax: obj << Get Midpoints from Profiler( fraction )

Description: Obtains the current factor settings from the Prediction Profiler and sets the midpoints for each factor in the Design Space Profiler to those values. Limits are constructed around each midpoint value using a specified fraction of the factor range.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Get Midpoints from Profiler( 0.5 );

Lock

Syntax: obj << Lock( Lock(colume name(lock_value),...) )

Description: Locks the continuous factor at the specified value. This lock is temporary.

JMP Version Added: 19

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Lock( Ethanol( 5 ) );

Make and Connect Random Table

Syntax: obj << Make and Connect Random Table( number, <Add Random Noise( state=0|1 )>, <Embed Factor Space Scatterplots>, <Embed Response Space Scatterplots> )

Description: Creates a new data table that contains uniformly distributed factor settings and their corresponding simulated responses. There are options to specify how the responses are simulated and whether to embed response and factor scatterplots in the report. The selection of rows in the data table is connected to the profilers in the report.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
dt2 = obj2 << Make and Connect Random Table(
    10000,
    Add Random Noise( 1 ),
    Embed Factor Space Scatterplots
);
Wait( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );

Move Corner Inward

Syntax: obj << Move Corner Inward

Move Corner Outward

Syntax: obj << Move Corner Outward

Move Inward

Syntax: obj << Move Inward( <number=1> )

Description: Finds the specification limit with the steepest upward path and moves that specification limit inward. Use the optional number argument to specify how many times this process is performed.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Move Inward( 4 );
Wait( 2 );
obj2 << Move Outward;

Move Outward

Syntax: obj << Move Outward( <number=1> )

Description: Finds the specification limit with the least steep downward path and moves that specification limit outward. Use the optional number argument to specify how many times this process is performed.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );
obj2 << Move Outward( 2 );
Wait( 2 );
obj2 << Move Outward;

Reset Factor Space

Syntax: obj << Reset Factor Space( factor1( lower, upper ), factor2( lower, upper ), ... )

Description: Changes the factor space to narrow, widen, or shift the range of one or more factors. If the intervals of the limits are too narrow, this can result in a small limits volume and create inaccurate simulation-based estimates.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
Wait( 1 );
obj2 << Reset Factor Space(
    Butanol( -0.275, 11 ),
    Ethanol( -0.25, 10.25 ),
    Methanol( -0.25, 10.25 ),
    Propanol( -0.25, 10.25 ),
    Time( 0.95, 3 )
);

Save Simulation Table

Syntax: obj << Save Simulation Table( state=0|1 )

Save X Spec Limits

Syntax: obj << Save X Spec Limits

Description: Saves the current X specification limits as column properties.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );
obj2 << Save X Spec Limits;

Send Limits to Profiler as Constraints

Syntax: obj << Send Limits to Profiler as Constraints

Description: Sends the current X limits to the Profiler as bound constraints.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );
obj2 << Send Limits to Profiler as Constraints;

Send Limits to Simulator

Syntax: obj << Send Limits to Simulator( "Uniform" | "Normal with limits at 2 sigma" | "Normal with limits at 3 sigma" | "Normal weighted with limits at 2 sigma" | "Normal weighted with limits at 3 sigma" )

Description: Sends the current X limits to the Simulator as parameters to a specified distribution. Also sends the Error StdDev values for each response as the standard deviation for added random noise.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );
obj2 << Send Limits to Simulator( "Normal with Limits at 3 Sigma" );

Send Midpoints to Profiler

Syntax: obj << Send Midpoints to Profiler

Description: Sends the midpoints for the current X limits to the Profiler.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );
obj2 << Send Midpoints to Profiler;

Set Limits

Syntax: obj << Set Limits( Set Limits(colume name(lower limit,upper limit),...) )

Description: Sets the factor limits using a script.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Set Limits( Ethanol( 1.9, 10 ), Methanol( 4.5, 10 ), Time( 1.2, 2 ) );

Show Corners

Syntax: obj << Show Corners( state=0|1 )

Description: Shows or hides the Corners report. This report contains a table of the in-specification probabilities at the extremes of the factor space. The probabilities are calculated using a normal distribution centered at the predicted values and cut at the specification limits.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Show Corners;

Show Current Profiler Values

Syntax: obj << Show Current Profiler Values( state=0|1 )

Description: Shows the current value from the profiler as a vertical gray sparse-dotted line.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Show Current Profiler Values( 1 );

Show Impact Ratios

Syntax: obj << Show Impact Ratios( state=0|1 )

Description: Shows or hides the impact ratios. These ratios show how sensitive changes in each factor, from midpoint to each limit, affect how far the predictions are from their specification limits.

JMP Version Added: 19

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 30 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.8 )} )
);
obj = Profiler( Y( :Pred Formula Yield ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Show Impact Ratios;

Show Portion for Each Response

Syntax: obj << Show Portion for Each Response( state=0|1 )

Description: Adds a column that contains the in-specification portion for each response at the current X limits.

Open( "$Sample_Data/Design Experiment/Extraction Data.jmp" );
New Column( "Pred Formula Yield",
    Numeric,
    Continuous,
    Formula(
        42.69 + -0.347 * :Butanol - 6.650 * :Ethanol - 2.286 * :Methanol - 0.326 * :Propanol
         - 10.380 * :Time + 0.415 * :Methanol ^ 2 + 0.0467 * :Butanol * :Methanol + 0.111 *
        :Ethanol * :Propanol + 4.313 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {LSL( 26 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 12321 ), Std Dev( 2.1 )} )
);
New Column( "Pred Formula Impurities",
    Numeric,
    Continuous,
    Formula(
        0.3 + -0.08 * :Ethanol + 0.06 * :Propanol + 0.12 * :Time + 0.06 * :Ethanol * :Time
    ),
    Set Property( "Spec Limits", {USL( 1 ), Show Limits( 1 )} ),
    Set Property( "Predicting", {:Yield, Creator( "DEMO" ), ID( 24642 ), Std Dev( 0.2 )} )
);
obj = Profiler( Y( :Pred Formula Yield, :Pred Formula Impurities ) );
obj2 = obj << Design Space Profiler( 1 );
obj2 << Show Portion for Each Response( 1 );
obj2 << Set Limits( Methanol( 5, 10 ), Propanol( 0, 5 ) );

Simulator

Associated Constructors

Simulator

Syntax: obj << Simulator( state=0|1, <Factors( column << Random( )|Fixed( constant )| Expression( )| Multivariate( ) )>, <Responses( column << No Noise| Add Random Noise| Add Random Weighted Noise| Add Multivariate Noise ) )>

Description: Launches the Simulator.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    )
);

Item Messages

Automatic Histogram Update

Syntax: simuobj << Automatic Historgram Update( state=0|1 )

Description: Updates the histogram with new simulated values when the distributions of the factors change.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    ),
    Simulate
);
simobj = obj << Get Simulator;
simobj << Automatic Histogram Update( 1 );
Wait( 1 );
obj << Term Value( SILANE( 60, Lock( 1 ) ) );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    ),
    Automatic Histogram Update( 1 ),
    Simulate
);
Wait( 1 );
obj << Term Value( SILANE( 60, Lock( 1 ) ) );

Defect Parametric Profile

Syntax: simobj << Defect Parametric Profile( state=0|1 )

Description: Graphs the mean defect rate by distributional parameters. This option is available only after the Defect Profiler is selected.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
:Pred Formula ABRASION << Set Property( "Spec Limits", {LSL( 110 )} );
:Pred Formula MODULUS << Set Property( "Spec Limits", {LSL( 750 ), USL( 1700 )} );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Simulator(
        1,
        Factors(
            SILICA << Random( Normal( 1.25, 0.3266 ) ),
            SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
        ),
        Responses(
            Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
            Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
        ),
        Defect Profiler( 1 ),
        Simulate
    )
);

simobj = obj << Get Simulator;
simobj << Defect Parametric Profile( 1 );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
:Pred Formula ABRASION << Set Property( "Spec Limits", {LSL( 110 )} );
:Pred Formula MODULUS << Set Property( "Spec Limits", {LSL( 750 ), USL( 1700 )} );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Simulator(
        1,
        Factors(
            SILICA << Random( Normal( 1.25, 0.3266 ) ),
            SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
        ),
        Responses(
            Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
            Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
        ),
        Defect Profiler( 1 ),
        Defect Parametric Profile( 1 ),
        Simulate
    )
);

Defect Profiler

Syntax: simobj << Defect Profiler( state=0|1 )

Description: Shows the defect rate as an isolated function of each factor. This option is available only if specification limits are defined.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
:Pred Formula ABRASION << Set Property( "Spec Limits", {LSL( 110 )} );
:Pred Formula MODULUS << Set Property( "Spec Limits", {LSL( 750 ), USL( 1700 )} );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Simulator(
        1,
        Factors(
            SILICA << Random( Normal( 1.25, 0.3266 ) ),
            SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
        ),
        Responses(
            Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
            Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
        ),
        Simulate
    )
);
simobj = obj << Get Simulator;
simobj << Defect Profiler( 1 );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
:Pred Formula ABRASION << Set Property( "Spec Limits", {LSL( 110 )} );
:Pred Formula MODULUS << Set Property( "Spec Limits", {LSL( 750 ), USL( 1700 )} );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    ),
    Simulator(
        1,
        Factors(
            SILICA << Random( Normal( 1.25, 0.3266 ) ),
            SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
        ),
        Responses(
            Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
            Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
        ),
        Defect Profiler( 1 ),
        Simulate
    )
);

N Runs

Syntax: obj << Simulator( N Runs(number=1000) )

Description: Sets the number of runs for the simulation. "10000" by default.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    ),

);
obj << Simulator( N Runs( 2500 ), Simulate );

Resimulate

Syntax: simobj << Resimulate

Description: Reruns the simulation. This option is useful after any changes to the distributions of the factors have been made.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Simulate
);
Wait( 1 );
obj << Term Value( SILANE( 60, Lock( 1 ) ) );
simobj = obj << Get Simulator;
simobj << Resimulate;

Set Random Seed

Syntax: obj << Simulator( Set Random Seed( number ) )

Description: Sets the random seed to a specific value assuring that all subsequent runs using the same seed are reproducible.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    ),

);
obj << Simulator( Set Random Seed( 1234 ), Simulate );

Simulate to table

Syntax: simobj << Simulate To Table(N Runs(n),factorName<<Sequence Location(low,high,nSteps),factorName2<<Sequence Spread(low,high,nSteps),factorName3<<Not Sequenced)

Description: Creates a table of simulation results, sequenced across different means or spreads.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    )
);
simobj = obj << Get Simulator;
simobj << Simulate to table(
    N Runs( 20 ),
    SILICA << Sequence Location( .5, 2, 4 ),
    SILANE << Sequence Location( 35, 65, 4 ),
    SULFUR << Sequence Location( 1.5, 3, 4 )
);

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    )
);
obj << Simulator(
    Simulate to table(
        N Runs( 20 ),
        SILICA << Sequence Location( .5, 2, 4 ),
        SILANE << Sequence Location( 35, 65, 4 ),
        SULFUR << Sequence Location( 1.5, 3, 4 )
    )
);

Simulation Experiment

Syntax: simobj << Simulation Experiment( NRun(number of experimental runs=128), Portion(factor space portion=1),NSim(number of simulations per experimental run=10000),<Run>,<Selected Factors(factor1,..)> )

Description: Runs a designed simulation experiment based on the locations of the factor distributions within the model.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    )
);
simobj = obj << Get Simulator;
simobj << Simulation Experiment( NRun( 100 ), Portion( 0.6 ) );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    )
);
obj << Simulator( Simulation Experiment( NRun( 128 ), NSim( 20000 ), Portion( 1.0 ), Run ) );

X Correlations

Syntax: obj << Simulator( X Correlations( state=0|1, {factor1, factor2, ..., factorN}, [NxN correlations] ) )

Description: Sets the correlations on the X factors when the simulation setting for the factors is set to Multivariate.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Multivariate( 1.2, 0.3266 ), SILANE << Multivariate( 50, 6.532 ),
        SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << No Noise, Pred Formula MODULUS << No Noise,
        Pred Formula ELONG << No Noise, Pred Formula HARDNESS << No Noise
    ),
    Automatic Histogram Update( 1 ),
    X Correlations( 1, {SILICA, SILANE, SULFUR}, [1 0.3 0, 0.3 1 0, 0 0 1] ),
    Simulate
);

Y Correlations

Syntax: obj << Simulator( Y Correlations( state=0|1, {response1, response2, ..., responseN}, [NxN correlations] ) )

Description: Sets the correlations on the Y responses when multivariate noise is added to the responses.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = Profiler(
    Y(
        :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
        :Pred Formula HARDNESS
    )
);
obj << Simulator(
    1,
    Factors(
        SILICA << Random( Normal( 1.25, 0.3266 ) ),
        SILANE << Random( Normal weighted( 50, 6.532 ) ), SULFUR << Fixed( 2.25 )
    ),
    Responses(
        Pred Formula ABRASION << Add Multivariate Noise( 1 ),
        Pred Formula MODULUS << No Noise, Pred Formula ELONG << Add Multivariate Noise( 1 ),
        Pred Formula HARDNESS << No Noise
    ),
    Y Correlations(
        1,
        {Pred Formula ABRASION, Pred Formula MODULUS, Pred Formula ELONG,
        Pred Formula HARDNESS},
        [1 0.15 0.27 0, 0.15 1 0 0, 0.27 0 1 0, 0 0 0 1]
    ),
    Simulate
);