Model Comparison

Associated Constructors

Model Comparison

Syntax: Model Comparison( Predictors( columns ), Group( column ) )

Description: Compares performance across models using prediction formula columns.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();

Columns

Freq

Syntax: obj << Freq( column )

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    Freq( _freqcol )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    Freq( _freqcol )
);
obj = Model Comparison();

Group

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();

Predictors

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();

Weight

Syntax: obj << Weight( column )

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    Weight( _weightcol )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    Weight( _weightcol )
);
obj = Model Comparison();

Y

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();

Item Messages

AUC Comparison

Syntax: obj << AUC Comparison( state=0|1 )

Description: Shows or hides a comparison of the area under the ROC curve (AUC) from each model.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( AUC Comparison( 1 ) );

Confusion Matrix

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

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( Confusion Matrix( 1 ) );

Cum Gains Curve

Syntax: obj << Cum Gains Curve( state=0|1 )

Description: Shows or hides a plot of cumulative gains curves for each level of the response variable. A cumulative gains curve plots the proportion of a response level that is identified by the model against the proportion of all responses.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( Cum Gains Curve( 1 ) );

Decision Threshold

Syntax: obj << Decision Threshold( state = 0|1, Set Probability Threshold( number ) )

Description: Shows or hides the distribution of fitted probabilities and actual versus predicted tables for each model. You can change the probability threshold to explore how different thresholds affect the classification results.

JMP Version Added: 16

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( Decision Threshold( 1 ) );

Lift Curve

Syntax: obj << Lift Curve( state=0|1 )

Description: Shows or hides lift curves for each level of the response variable. The curves for the different models are overlaid in the plots.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( Lift Curve( 1 ) );

Model Averaging

Syntax: obj << Model Averaging

Description: Saves a new prediction column of the average of the predicted probabilities across models. This prediction column often results in a model with better prediction capability than the individual models.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
Model Comparison( Model Averaging );

Plot Actual by Predicted

Syntax: obj << Plot Actual by Predicted( state=0|1 )

Description: Shows or hides a plot with the actual response values on the vertical axis and the predicted values on the horizontal axis. In good fits, points are near the diagonal. You can see which points are far from the diagonal, look for patterns, and visualize the test.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
Model Comparison( Plot Actual by Predicted( 1 ) );

Plot Residual by Row

Syntax: obj << Plot Residual by Row( state=0|1 )

Description: Shows or hides a plot with the residuals on the vertical axis and the row number on the horizontal axis.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
Model Comparison( Plot Residual by Row( 1 ) );

Precision Recall Curve

Syntax: obj << Precision Recall Curve( state=0|1 )

Description: Shows or hides Precision-Recall Curve plots for each level of the response variable. The curves for the different models are overlaid in the plots.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( Precision Recall Curve( 1 ) );

Profiler

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

Description: Shows or hides the prediction profiler, which is used to graphically explore the prediction equation by slicing it one factor at a time. The prediction profiler contains features for optimization.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
Model Comparison( Profiler( 1 ) );

ROC Curve

Syntax: obj << ROC Curve( state=0|1 )

Description: Shows or hides Receiver Operating Characteristic (ROC) curves for each level of the response variable. The curves for the different models are overlaid in the plots.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :sex ),
    Effects( :height ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
dt << Fit Model(
    Y( :sex ),
    Effects( :age ),
    Target Level( "M" ),
    Personality( "Nominal Logistic" ),
    Run( Save Probability Formula, Close Window )
);
Model Comparison( ROC Curve( 1 ) );

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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Column Switcher

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

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

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

Copy Script

Syntax: obj << Copy Script

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
obj << Data Table Window;

Get By Levels

Syntax: obj << Get By Levels

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

JMP Version Added: 18

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

Get Container

Syntax: obj << Get Container

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

General

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Script

Syntax: obj << Get Script

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
obj << Redo Analysis;

Relaunch Analysis

Syntax: obj << Relaunch Analysis

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
obj << Relaunch Analysis;

Remove Column Switcher

Syntax: obj << Remove Column Switcher

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

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

Remove Local Data Filter

Syntax: obj << Remove Local Data Filter

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

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

Render Preset

Syntax: Render Preset( preset )

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

JMP Version Added: 18

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

Report

Syntax: obj << Report;Report( obj )

Description: Returns a reference to the report object.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
obj << Report View( "Summary" );

Save Script for All Objects

Syntax: obj << Save Script for All Objects

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

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    By( _bycol )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    By( _bycol )
);
obj = Model Comparison();
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    By( _bycol )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window ),
    By( _bycol )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
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/Big Class.jmp" );
dt << Fit Model(
    Y( :weight ),
    Effects( :height ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
dt << Fit Model(
    Y( :weight ),
    Effects( :age ),
    Personality( "Standard Least Squares" ),
    Run( Prediction Formula, Close Window )
);
obj = Model Comparison();
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

View Web XML

Syntax: obj << View Web XML

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

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