Support Vector Machines

Associated Constructors

Support Vector Machines

Syntax: Support Vector Machines(Y( column ), X( columns ))

Description: Predicts a response based on the support vectors in the space of the X variables. One of the goals of the Support Vector Machines algorithm is to use training data to learn how to classify new data.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Columns

By

Syntax: obj = Support Vector Machines(...<By( column(s) )>...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);

Factor

Syntax: obj = Support Vector Machines(...Factor( column(s) )...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Freq

Syntax: obj = Support Vector Machines(...<Freq( column )>...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Quality Control/Failure3Freq.jmp" );
obj = Support Vector Machines(
    Y( :clean ),
    X(
        :contamination, :corrosion, :doping, :metallization, :miscellaneous, :oxide defect,
        :silicon defect
    ),
    Freq( :SampleSize )
);

Response

Syntax: obj = Support Vector Machines(...Response( column )...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Validation

Syntax: obj = Support Vector Machines(...<Validation( column )>...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = Support Vector Machines(
    Y( :Y Binary ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation )
);

X

Syntax: obj = Support Vector Machines(...X( column(s) )...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Y

Syntax: obj = Support Vector Machines(...Y( column )...)

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Item Messages

Cost

Syntax: obj << Cost( number )

Description: Sets the cost parameter for the SVM fit.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit(
        Kernel Function( "Radial Basis Function" ),
        Gamma( 0.25 ),
        Cost( 1 ),
        Validation Method( "None" )
    )
);

Cost Max

Syntax: obj << Cost Max( number )

Description: Sets the maximum Cost for a tuning design.

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Cost Min( .1 ),
    Cost Max( 4 ),
    Gamma Min( 0.01 ),
    Gamma Max( 0.4 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

Cost Min

Syntax: obj << Cost Min( number )

Description: Sets the minimum Cost for a tuning design.

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Cost Min( .1 ),
    Cost Max( 4 ),
    Gamma Min( 0.01 ),
    Gamma Max( 0.4 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

Fit

Syntax: obj << Fit

Description: Specifies and fits the kernel structure for the support vector machine to the data.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);

Gamma

Syntax: obj << Gamma( number )

Description: Sets the gamma parameter for the Radial Basis kernel.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit(
        Kernel Function( "Radial Basis Function" ),
        Gamma( 0.25 ),
        Cost( 1 ),
        Validation Method( "None" )
    )
);

Gamma Max

Syntax: obj << Gamma Max( number )

Description: Sets the maximum Gamma for a tuning design.

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Cost Min( .1 ),
    Cost Max( 4 ),
    Gamma Min( 0.01 ),
    Gamma Max( 0.4 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

Gamma Min

Syntax: obj << Gamma Min( number )

Description: Sets the minimum Gamma for a tuning design.

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Cost Min( .1 ),
    Cost Max( 4 ),
    Gamma Min( 0.01 ),
    Gamma Max( 0.4 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

Go

Syntax: obj << Go

Description: Starts solving the support vector machine.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Go;

Number of Runs

Syntax: obj << Number of Runs( number )

Description: Sets the number of runs for a tuning design.

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Cost Min( .1 ),
    Cost Max( 4 ),
    Gamma Min( 0.01 ),
    Gamma Max( 0.4 ),
    Number of Runs( 15 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

Set Random Seed

Syntax: Set Random Seed( number )

Description: Sets the random seed for the randomization process used for KFold and Holdback validation. This is useful if you want to reproduce an analysis.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit(
        Set Random Seed( 1234 ),
        Kernel Function( "Radial Basis Function" ),
        Gamma( 0.25 ),
        Cost( 1 ),
        Validation Method( "Holdback", 0.3333 )
    )
);

Tuning Design

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

JMP Version Added: 16

Random Reset( 1234 );
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Tuning Design( 1 ),
    Fit( Kernel Function( "Radial Basis Function" ), Validation Method( "None" ) )
);

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" ) );

Automatic Recalc

Syntax: obj << Automatic Recalc( state=0|1 )

Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Broadcast

Syntax: obj << Broadcast(message)

Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Quality Control/Diameter.jmp" );
objs = Control Chart Builder(
    Variables( Subgroup( :DAY ), Y( :DIAMETER ) ),
    By( :OPERATOR )
);
objs[1] << Broadcast( Save Summaries );

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 ByGroup Script

Syntax: obj << Copy ByGroup Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Copy ByGroup Script;

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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 ByGroup Script

Syntax: obj << Get ByGroup Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
t = obj[1] << Get ByGroup Script;
Show( t );

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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Group Platform

Syntax: obj << Get Group Platform

Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Y( :weight ), X( :height ), By( :sex ) );
group = biv[1] << Get Group Platform;
Wait( 1 );
group << Layout( "Arrange in Tabs" );

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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Redo Analysis;

Redo ByGroup Analysis

Syntax: obj << Redo ByGroup Analysis

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Redo ByGroup 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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Relaunch Analysis;

Relaunch ByGroup

Syntax: obj << Relaunch ByGroup

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Relaunch ByGroup;

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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Report View( "Summary" );

Save ByGroup Script to Data Table

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

Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Data Table;

Save ByGroup Script to Journal

Syntax: obj << Save ByGroup 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/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Journal;

Save ByGroup Script to Script Window

Syntax: obj << Save ByGroup 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/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Script Window;

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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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/Iris.jmp" );
obj = Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Transform Column

Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)

Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    Transform Column( "age^2", Format( "Fixed Dec", 5, 0 ), Formula( :age * :age ) ),
    Continuous Distribution( Column( :"age^2"n ) )
);

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;

Window View

Syntax: obj = Support Vector Machines(...Window View( "Visible"|"Invisible"|"Private" )...)

Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Window View( "Private" ), Y( :weight ), X( :height ), Fit Line );
eqn = Report( biv )["Linear Fit", Text Edit Box( 1 )] << Get Text;
biv << Close Window;
New Window( "Bivariate Equation",
    Outline Box( "Big Class Linear Fit", Text Box( eqn, <<Set Base Font( "Title" ) ) )
);

SVM Fit

Item Messages

Confusion Matrix

Syntax: obj << (fit[number] << Confusion Matrix( state=0|1 ))

Description: Shows or hides a crosstabulation matrix of actual and predicted responses. On by default.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Confusion Matrix( 0 ));

Contour Profiler

Syntax: obj << (fit[number] << Contour Profiler( state=0|1 ))

Description: Displays or hides the contour profiler.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (Fit[1] << Contour Profiler( 1 ));

Get Measures

Syntax: obj << (fit[number] << Get Measures)

Description: Returns summary measures of fit from the model.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (Fit[1] << Response Profile Plot( 0 ));
obj << (Fit[1] << Get Measures);

Get Prediction Formula

Syntax: obj << (fit[number] << Get Prediction Formula)

Description: Constructs a script to create a prediction formula column and returns it.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Get Prediction Formula);

Lift Curve

Syntax: obj << (fit[number] << Lift Curve( state=0|1 ))

Description: Shows or hides the Lift Curve plot. A lift curve plots the lift versus the portion of the observations and provides another view of the predictive ability of a model. If you used validation, a plot is shown for each of the training, validation, and test sets.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
Wait( 0 );
obj << (fit[1] << Lift Curve( 1 ));

Plot Actual by Predicted

Syntax: obj << (fit[number] << Plot Actual By Predicted( state=0|1 ))

Description: For the specified fit, shows or hides a plot for the training set with actual values on the Y axis and predicted values on the X axis. If you are using validation or test sets, plots are shown for these as well. On by default.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Support Vector Machines(
    Y( :Y ),
    X( :Age, :BMI, :Total Cholesterol, :Glucose ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Plot Actual By Predicted( 0 ));

Plot Residual by Predicted

Syntax: obj << (fit[number] << Plot Residual By Predicted( state=0|1 ))

Description: For the specified fit, shows or hides a plot for the training set with residual values on the Y axis and predicted values on the X axis. If you are using validation or test sets, plots are shown for these as well.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Support Vector Machines(
    Y( :Y ),
    X( :Age, :BMI, :Total Cholesterol, :Glucose ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Plot Residual By Predicted( 1 ));

Precision Recall Curve

Syntax: obj << (fit[number] << Precision Recall Curve( state=0|1 ))

Description: Shows or hides the Precision-Recall Curve plot that contains a curve for each level of the response variable. A precision-recall curve plots the precision values against the recall values at a variety of thresholds. If you used validation, a plot is shown for each of the training, validation, and test sets.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
Wait( 0 );
obj << (fit[1] << Precision Recall Curve( 1 ));

Profiler

Syntax: obj << (fit[number] << Profiler( state=0|1 ))

Description: Shows a Prediction Profiler plot for the specified fit.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Profiler( 1 ));

Publish Prediction Formula

Syntax: obj << (fit[number] << Publish Prediction Formula)

Description: Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Publish Prediction Formula);

Publish Probability Formula

Syntax: obj << (fit[number] << Publish Probability Formula)

Description: Saves the probability of each response level as a separate column in the data table.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Support Vector Machines(
    Y( :Y Binary ),
    X( :Age, :BMI, :Total Cholesterol, :Glucose ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Publish Probability Formula);

ROC Curve

Syntax: obj << (fit[number] << ROC Curve( state=0|1 ))

Description: Shows or hides the Receiver Operating Characteristic (ROC) curve for each level of the response variable. The ROC curve is a plot of sensitivity versus (1 - specificity). If you used validation, a plot is shown for each of the training, validation, and test sets.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
Wait( 0 );
obj << (fit[1] << ROC Curve( 1 ));

Remove Fit

Syntax: obj << (fit[number] << Remove Fit)

Description: Removes the entire model report.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) ),
    Fit( Kernel Function( "Linear" ), Cost( 1 ), Validation Method( "None" ) )
);
Wait( 2 );
obj << (Fit[1] << Remove Fit);

Response Profile Plot

Syntax: obj << (fit[number] << Response Profile Plot( state=0|1 ))

Description: Displays or hides the Response Profile plot. On by default.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (Fit[1] << Response Profile Plot( 0 ));

Save Predicteds

Syntax: obj << (fit[number] << Save Predicteds)

Description: Saves the predicted values in a new column in the data table.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Save Predicteds);

Save Prediction Formula

Syntax: obj << (fit[number] << Save Prediction Formula)

Description: Creates new columns in the data table that contain the prediction formulas.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Save Prediction Formula);

Save Probabilities

Syntax: obj << (fit[number] << Save Probabilities)

Description: Saves the probability of each response level as a separate column in the data table.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Save Probabilities);

Save Probability Formula

Syntax: obj << (fit[number] << Save Probability Formula)

Description: Saves the probability of each response level as a separate column in the data table.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Support Vector Machines(
    Y( :Y Binary ),
    X( :Age, :BMI, :Total Cholesterol, :Glucose ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (fit[1] << Save Probability Formula);

Save Validation

Syntax: obj << (fit[number] << Save Validation)

Description: Creates a new column in the data table that identifies which rows were used in the training, validation and test data sets.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit(
        Kernel Function( "Radial Basis Function" ),
        Gamma( 0.25 ),
        Cost( 1 ),
        Validation Method( "Holdback", 0.3333 ), 

    )
);
obj << (Fit[1] << Save Validation);

Support Vector Coefficients

Syntax: obj << (fit[number] << Support Vector Coefficients( state=0|1 ))

Description: Displays or hides the table of support vector coefficients.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (Fit[1] << Support Vector Coefficients( 1 ));

Surface Profiler

Syntax: obj << (fit[number] << Surface Profiler( state=0|1 ))

Description: Displays or hides the surface profiler.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Support Vector Machines(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit( Kernel Function( "Radial Basis Function" ), Gamma( 0.25 ), Cost( 1 ) )
);
obj << (Fit[1] << Surface Profiler( 1 ));