Partial Least Squares

Associated Constructors

Partial Least Squares

Syntax: Partial Least Squares( Y( columns ), X( columns ) )

Description: Fits a model to one or more response variables using latent factors. This permits models to be fit when explanatory variables are highly correlated, or when there are more explanatory variables than there are observations.

Example 1

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

Example 2

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Go
);

Columns

By

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

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);

Factor

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

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

Freq

Syntax: obj << Freq( column )

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Freq( _freqcol ),
    Go
);

Response

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

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

Validation

Syntax: obj << Validation( column )

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

X

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

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

Y

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

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);

Item Messages

Centering

Syntax: obj = Partial Least Squares(...Centering( state=0|1)...)

Description: Centers all Y variables and model effects by subtracting the mean from each column. On by default.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Centering( 0 ),
    Validation Method( KFold( 7 ) ),
    Go
);

Fit

Syntax: obj << Fit( SVD( Fast|Classical ), Method( NIPALS|SIMPLS ), Number of Factors( number ) )

Description: Fits a partial least squares model with a specified method and number of factors.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Method( NIPALS ), Number of Factors( 7 ) ),
    Go
);

Go

Syntax: obj << Go

Description: Launches the partial least squares model fit.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    )
);
obj << Go;

Imputation Method

Syntax: obj = Partial Least Squares(...Imputation Method( "Mean"|"EM" )...)

Description: Specifies the imputation method. The Mean method replaces missing values with the mean of the nonmissing values in the same column. The EM method uses an iterative Expectation-Maximization (EM) approach to impute missing values.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Partial Least Squares(
    Y( :Y ),
    X( :OZONE, :CO, :SO2, :NO, :PM10, :Lead ),
    Impute Missing Data( 1 ),
    Imputation Method( "EM" ),
    Max Iterations( 2 ),
    Validation Method( None, Initial Number of Factors( 6 ) ),
    Fit( Method( NIPALS ), Number of Factors( 6 ) )
);

Impute Missing Data

Syntax: obj = Partial Least Squares(...Impute Missing Data( state=0|1 )...)

Description: Replaces missing data values in the responses and regressors with nonmissing values. Otherwise, rows with missing values are excluded from the analysis.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Partial Least Squares(
    Y( :Y ),
    X( :OZONE, :CO, :SO2, :NO, :PM10, :Lead ),
    Impute Missing Data( 1 ),
    Validation Method( None, Initial Number of Factors( 6 ) ),
    Go
);

Initial Number of Factors

Syntax: obj << Partial Least Squares( Validation Method(...Initial Number of Factors( number )...) )

Description: Specifies the initial number of factors for cross validation.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Validation Method( KFold( 7 ), Initial Number of Factors( 10 ) ), 

);
obj << Go;

Max Iterations

Syntax: obj = Partial Least Squares(...Max Iterations( number=1 )...)

Description: Specifies the maximum number of iterations to perform in the EM imputation loop. "1" by default.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Partial Least Squares(
    Y( :Y ),
    X( :OZONE, :CO, :SO2, :NO, :PM10, :Lead ),
    Impute Missing Data( 1 ),
    Imputation Method( "EM" ),
    Max Iterations( 2 ),
    Validation Method( None, Initial Number of Factors( 6 ) ),
    Fit( Method( NIPALS ), Number of Factors( 6 ) )
);

Method

Syntax: obj = Partial Least Squares(...Fit( Method( NIPALS|SIMPLS)... )

Description: Specifies the method used to fit the partial least squares model.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Method( NIPALS ), Number of Factors( 11 ) ),
    Go
);

Model Dialog

Syntax: obj << Model Dialog

Description: Opens the Fit Model launch window. You can fit a Partial Least Squares model from this launch window by selecting the Partial Least Squares personality.

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
obj << Model Dialog;

SVD

Syntax: obj << SVD( Fast|Classical )

Description: Sets the implementation of the SVD algorithm for computing the partial least squares model to Fast or Classical. The Fast option implements the Lanczos SVD routine and the Classical option implements the Golub-Kahan routine.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Validation Method( KFold( 7 ) ),
    Go
);
obj << Fit( SVD( Classical ), Method( SIMPLS ) );

Scaling

Syntax: obj = Partial Least Squares(...Scaling( state=0|1)...)

Description: Scales all Y variables and model effects by dividing each column by its standard deviation. On by default.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Scaling( 0 ),
    Validation Method( KFold( 7 ) ),
    Go
);

Set Random Seed

Syntax: obj << Set Random Seed( number )

Description: Specifies the random seed for running a partial least squares model with cross validation.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Set Random Seed( 12345 ),
    Validation Method( KFold( 7 ) ),
    Go
);

Validation Method

Syntax: obj << Validation Method( KFold( number )|Holdback( fraction )|"Leave-One-Out"|None, Initial Number of Factors( number ) )

Description: Sets the method used for model validation.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Validation Method( KFold( 7 ), Initial Number of Factors( 15 ) ),
    Go
);

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" ) );

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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Wine Tasting.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    By( _bycol ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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/Wine Tasting.jmp" );
obj = dt << Partial Least Squares(
    Y( :Hedonic, :Goes with meat, :Goes with dessert ),
    X( :Price, :Sugar, :Alcohol, :Acidity ),
    Go
);
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 = Partial Least Squares(...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" ) ) )
);

Partial Least Squares Fit

Item Messages

Coefficient Plots

Syntax: obj << (Fit[number] << Coefficient Plots( state=0|1 ))

Description: Shows or hides plots of the model coefficients for each response across the X variables. There is a plot for the centered and scaled data and a plot for the original data.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Coefficient Plots( 1 ));

Correlation Loading Plot

Syntax: obj << (Fit[number] << Correlation Loading Plot( state=0|1 ))

Description: Shows or hides either a single scatterplot or a scatterplot matrix of the X and Y loadings overlaid on the same plot. The scatterplot matrix is shown if the specified number of factors is greater than 2.

Example 1

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Correlation Loading Plot( 2 ));

Example 2

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Correlation Loading Plot( 4 ));

Diagnostics Plots

Syntax: obj << (Fit[number] << Diagnostics Plots( state=0|1 ))

Description: Shows or hides diagnostic plots.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Diagnostics Plots( 1 ));

Distance Plots

Syntax: obj << (Fit[number] << Distance Plots( state=0|1 ))

Description: Shows or hides the distance plots. There is a plot of the distance from each observation to the X model, a plot of the distance from each observation to the Y model, and a scatterplot of distances to both the X and Y models.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Distance Plots( 1 ));

Fit Line

Syntax: obj << (Fit[number] << Fit Line( state=0|1 ))

Description: Shows or hides a fitted line through the points on the X-Y Scores Plots. On by default.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
Wait( 2 );
obj << (Fit[1] << Fit Line( 0 ));

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/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Get Measures);

Loading Plots

Syntax: obj << (Fit[number] << Loading Plots( state=0|1 ))

Description: Shows or hides plots of the X and Y loadings for each extracted factor. There are separate plots for the X and Y variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Loading Plots( 1 ));

Loading Scatterplot Matrices

Syntax: obj << (Fit[number] << Loading Scatterplot Matrices( state=0|1 ))

Description: Shows or hides scatterplot matrices of the X and Y loadings. There are separate scatterplot matrices for the X and Y variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Loading Scatterplot Matrices( 1 ));

Make Model Using VIP

Syntax: obj << (Fit[number] << Make Model Using VIP)

Description: Opens and populates a launch window with the appropriate responses entered as Ys and the variables whose VIPs exceed the specified threshold entered as Xs.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Make Model Using VIP);

Model Driven Multivariate Control Chart for Saved X Scores

Syntax: obj << (Fit[number] << Model Driven Multivariate Control Chart for Saved X Scores)

Description: Saves the formulas for each X Score and launches the Model Driven Multivariate Control Chart (MDMCC) launch window.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Model Driven Multivariate Control Chart for Saved X Scores);

Percent Variation Plots

Syntax: obj << (Fit[number] << Percent Variation Plots( state=0|1 ))

Description: Shows or hides plots of the percent of variation explained for X Effects and for Y Responses.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Percent variation plots( 1 ));

Profiler

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

Description: Shows or hides a profiler for each response.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Profiler( 1 ));

Profiler for Predicteds

Syntax: obj << (Fit[number] << Model Driven Multivariate Control Chart for Saved X Scores)

Description: Saves the formulas for each Y as a function of the X Score and launches the Profiler launch window.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Profiler for Predicteds);

Publish Prediction Formula

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

Description: Creates a prediction formula and publishes it as a formula column script in the Formula Depot platform.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Publish Prediction Formula);

Publish Score Formula

Syntax: obj << (Fit[number] << Publish Score Formula)

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

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Publish Score Formula);

Remove Fit

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

Description: Removes the model report from the main platform report.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
Wait( 3 );
obj << (Fit[1] << Remove Fit);

Save Distance

Syntax: obj << (Fit[number] << Save Distance)

Description: Saves new columns to the original data table. The new columns contain the Distance to X Model (DModX) and Distance to Y Model (DModY) values.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Distance);

Save Distance as X Score Formula

Syntax: obj << (Fit[number] << Save Distance as X Score Formula)

Description: Saves new formula columns to the original data table. The new columns contain the Distance to X Model (DModX) and Distance to Y Model (DModY) formulas that are functions of the X Score formulas.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Distance as X Score Formula);

Save Imputation

Syntax: obj << (Fit[number] << Save Imputation)

Description: Saves columns to a new data table. For each X and Y variable, there is a column that contains the original data column with missing values replaced by their imputed values.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Partial Least Squares(
    Y( :Y ),
    X( :OZONE, :CO, :SO2, :NO, :PM10, :Lead ),
    Impute Missing Data( 1 ),
    Imputation Method( "EM" ),
    Max Iterations( 2 ),
    Validation Method( None, Initial Number of Factors( 6 ) ),
    Fit( Method( NIPALS ), Number of Factors( 6 ) )
);
obj << (Fit[1] << Save Imputation);

Save Indiv Confidence Limit Formula

Syntax: obj << (Fit[number] << Save Indiv Confidence Limit Formula)

Description: Saves new formula columns to the original data table. For each Y variable, there are columns for the lower and upper confidence limits for an individual prediction that are functions of the X Score formulas. The default level for alpha is 0.05, which creates 95% confidence limits.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Indiv Confidence Limit Formula);

Save Loadings

Syntax: obj << (Fit[number] << Save Loadings)

Description: Saves columns to two new data tables. There is a data table that contains the loadings for the X variables and a data table that contains the loadings for the Y variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Loadings);

Save Mean Confidence Limit Formula

Syntax: obj << (Fit[number] << Save Mean Confidence Limit Formula( <alpha=0.05> ))

Description: Saves new formula columns to the original data table. For each Y variable, there are columns for the lower and upper confidence limits for the mean response that are functions of the X Score formulas. The default level for alpha is 0.05, which creates 95% confidence limits.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Mean Confidence Limit Formula);

Save Percent Variation Explained For X Effects

Syntax: obj << (Fit[number] << Save Percent Variation Explained For X Effects)

Description: Saves columns to a new data table. For each X variable, there is a column that contains the percent of variation explained across all extracted factors.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Percent Variation Explained For X Effects);

Save Percent Variation Explained For Y Responses

Syntax: obj << (Fit[number] << Save Percent Variation Explained For Y Responses)

Description: Saves columns to a new data table. For each Y variable, there is a column that contains the percent of variation explained across all extracted factors.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Percent Variation Explained For Y Responses);

Save Prediction As X Score Formula

Syntax: obj << (Fit[number] << Save Prediction as X Score Formula)

Description: Saves new formula columns to the original data table. For each Y variable, there is a column that contains a prediction formula that is a function of the X Score formulas.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Prediction as X Score Formula);

Save Prediction Formula

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

Description: Saves new formula columns to the original data table. For each Y variable, there is a column that contains a prediction formula that is a function of the X variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Prediction Formula);

Save Score Formula

Syntax: obj << (Fit[number] << Save Score Formula)

Description: Saves new formula columns to the original data table. For each extracted factor, there is a column that contains an X Score formula and a column that contains a Y Score formula. The X Score formulas are functions of the X variables and the Y Score formulas are functions of the X Score formulas.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Score Formula);

Save Scores

Syntax: obj << (Fit[number] << Save Scores)

Description: Saves new columns to the original data table. For each extracted factor, there is a column that contains the X scores and a column that contains the Y scores.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Scores);

Save Standard Errors of Prediction Formula

Syntax: obj << (Fit[number] << Save Standard Errors of Prediction Formula)

Description: Saves new formula columns to the original data table. For each Y variable, there is a column that contains the formula for the standard error of the predicted mean that is a function of the X variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Standard Errors of Prediction Formula);

Save Standardized Loadings

Syntax: obj << (Fit[number] << Save Standardized Loadings)

Description: Saves columns to two new data tables. There is a data table that contains the standardized loadings for the X variables and a data table that contains the standardized loadings for the Y variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Standardized Loadings);

Save Standardized Scores

Syntax: obj << (Fit[number] << Save Standardized Scores)

Description: Saves new columns to the original data table. The new columns contain the X and Y standardized scores for each extracted factor.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Standardized Scores);

Save T Square

Syntax: obj << (Fit[number] << Save T Square)

Description: Saves a new formula column to the original data table. The new column contains the T squared formula as a function of the X variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save T Square);

Save T Square as X Score Formula

Syntax: obj << (Fit[number] << Save T Square as X Score Formula)

Description: Saves a new formula column to the original data table. The new column contains the T squared formula as a function of the X Score formulas.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save T Square as X Score Formula);

Save Validation

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

Description: Saves a new column to the original data table. The new column contains numbers that indicate how each observation was used in validation.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Validation);

Save X Predicted Values

Syntax: obj << (Fit[number] << Save X Predicted Values)

Description: Saves new columns to the original data table. For each X variable, there is a column that contains the predicted X values.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save X Predicted Values);

Save X Prediction as X Score Formula

Syntax: obj << (Fit[number] << Save X Prediction as X Score Formula)

Description: Saves new formula columns to the original data table. For each X variable, there is a column that contains a prediction formula that is a function of the X Score formulas.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save X Prediction as X Score Formula);

Save X Residuals

Syntax: obj << (Fit[number] << Save X Residuals)

Description: Saves new columns to the original data table. For each X variable, there is a column that contains the X residual values.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save X Residuals);

Save X Score Formula

Syntax: obj << Save X Score Formula

Save X Weights

Syntax: obj << (Fit[number] << Save X Weights)

Description: Saves columns to a new data table. For each extracted factor, there is a column that contains the weights for the X variables.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save X Weights);

Save Y Predicted Values

Syntax: obj << (Fit[number] << Save Y Predicted Values)

Description: Saves new columns to the original data table. For each Y variable, there is a column that contains the predicted Y values.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Y Predicted Values);

Save Y Residuals

Syntax: obj << (Fit[number] << Save Y Residuals)

Description: Saves new columns to the original data table. For each Y variable, there is a column that contains the Y residual values.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Save Y Residuals);

Score Scatterplot Matrices

Syntax: obj << (Fit[number] << Score Scatterplot Matrices( state=0|1 ))

Description: Shows or hides a scatterplot matrix of the X scores and a scatterplot matrix of the Y scores.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Score Scatterplot Matrices( 1 ));

Set VIP Threshold

Syntax: obj << (Fit[number] << Set VIP Threshold( number=0.8 ))

Description: Sets the threshold level for the Variable Importance Plot, Variance Importance Table, and the VIP vs Coefficients Plots. "0.8" by default.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Variable Importance Plot( 1 ));
Wait( 3 );
obj << (Fit[1] << Set VIP Threshold( 0.5 ));

Show Confidence Band

Syntax: obj << (Fit[number] << Show Confidence Band( state=0|1 ))

Description: Shows or hides 95% confidence bands for the fitted lines on the X-Y Scores Plots.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Show Confidence Band( 1 ));

Spectral Profiler

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

Description: Shows or hides a single profiler where all of the response variables appear in the first cell of the plot.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Spectral Profiler( 1 ));

T Square Plot

Syntax: obj << (Fit[number] << T Square Plot( state=0|1 ))

Description: Shows or hides a plot of T square statistics for each observation, along with a control limit.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << T Square Plot( 1 ));

VIP vs Coefficients Plots

Syntax: obj << (Fit[number] << VIP vs Coefficients Plots( state=0|1 ))

Description: Shows or hides a plot of the VIP statistics against the model coefficients.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << VIP vs Coefficients Plots( 1 ));

Variable Importance Plot

Syntax: obj << (Fit[number] << Variable Importance Plot( state=0|1 ))

Description: Shows or hides a plot that summarizes the contribution each variable makes to the model.

dt = Open( "$SAMPLE_DATA/Baltic.jmp" );
obj = dt << Partial Least Squares(
    Y( :ls, :ha, :dt ),
    X(
        :v1, :v2, :v3, :v4, :v5, :v6, :v7, :v8, :v9, :v10, :v11, :v12, :v13, :v14, :v15, :v16,
        :v17, :v18, :v19, :v20, :v21, :v22, :v23, :v24, :v25, :v26, :v27
    ),
    Fit( Number of Factors( 5 ) )
);
obj << (Fit[1] << Variable Importance Plot( 1 ));