K Nearest Neighbors

Associated Constructors

K Nearest Neighbors

Syntax: K Nearest Neighbors(Y( column ), X( columns ))

Description: Predicts a continuous or categorical response based on the responses of the k nearest neighbors in the space of the X variables.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);

Columns

By

Syntax: obj = K Nearest Neighbors(...<By( column(s) )>...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);

Factor

Syntax: obj = K Nearest Neighbors(...Factor( column(s) )...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);

Validation

Syntax: obj = K Nearest Neighbors(...<Validation( column )>...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);

X

Syntax: obj = K Nearest Neighbors(...X( column(s) )...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);

Y

Syntax: obj = K Nearest Neighbors(...<Y( column(s) )>...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);

Item Messages

Category Bias

Syntax: obj = K Nearest Neighbors(...Category Bias( number=0.5 )...)

Description: Specifies a tuning parameter that ensures that the fitted probabilities for categorical responses are always positive. "0.5" by default.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = K Nearest Neighbors(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    K( 10 ),
    Category Bias( 0.2 )
);

Get Measures

Syntax: obj << Get Measures

Description: Returns summary measures of fit from the model.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Get Measures;

K

Syntax: obj = K Nearest Neighbors(...K( number=10 )...)

Description: Sets the maximum number of nearest neighbors to analyze. "10" by default.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = K Nearest Neighbors(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    K( 8 )
);

Response

Syntax: obj << Response( "<Response Variable Name>", <Set K( number )>, <Mosaic Plot ( state=0|1 ) >, <Plot Actual by Predicted( state=0|1 )>, <Plot Residual by Predicted( state=0|1 )> )

Description: Specifies report options available for the model response. Available options depend on the response type.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 ),
    Response(
        "Y",
        Set K( 6 ),
        Plot Actual by Predicted( 1 ),
        Plot Residual by Predicted( 1 )
    )
);

Save Near Neighbor Distances

Syntax: obj << Save Near Neighbor Distances

Description: Saves the distance to the k'th nearest data point.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << Save Near Neighbor Distances;

Save Near Neighbor Rows

Syntax: obj << Save Near Neighbor Rows

Description: Saves the row numbers of the k nearest neighbors.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << Save Near Neighbor Rows;

Set Random Seed

Syntax: obj = K Nearest Neighbors(...Set Random Seed( number )...)

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

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = K Nearest Neighbors(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Set Random Seed( 123456 ),
    K( 8 )
);

Use Excluded Rows for Validation

Syntax: obj << Use Excluded Rows for Validation( state=0|1 )

Description: Uses the excluded rows in the data table to create a validation set. This option appears in the launch window only if you are using standard JMP and there are excluded rows.

JMP Version Added: 15

Validation Portion

Syntax: obj = K Nearest Neighbors(...Validation Portion( fraction=0 )...)

Description: Forms a validation set by randomly selecting rows with each row having probability p (fraction) of being selected. "0" by default.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = K Nearest Neighbors(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation Portion( 0.2 ),
    K( 10 )
);

Shared Item Messages

Action

Syntax: obj << Action

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

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

Apply Preset

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

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

JMP Version Added: 18

Anonymous preset

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

Search by name

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

Search within folder(s)

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

Automatic Recalc

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

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Broadcast

Syntax: obj << Broadcast(message)

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

JMP Version Added: 18

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

Column Switcher

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

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

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

Copy ByGroup Script

Syntax: obj << Copy ByGroup Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Copy ByGroup Script;

Copy Script

Syntax: obj << Copy Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Copy Script;

Data Table Window

Syntax: obj << Data Table Window

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Data Table Window;

Get By Levels

Syntax: obj << Get By Levels

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

JMP Version Added: 18

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

Get ByGroup Script

Syntax: obj << Get ByGroup Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
t = obj[1] << Get ByGroup Script;
Show( t );

Get Container

Syntax: obj << Get Container

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

General

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );

Platform with Filter

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

Get Data Table

Syntax: obj << Get Data Table

Description: Returns a reference to the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Group Platform

Syntax: obj << Get Group Platform

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

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

Get Script

Syntax: obj << Get Script

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
t = obj << Get Script;
Show( t );

Get Script With Data Table

Syntax: obj << Get Script With Data Table

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
t = obj << Get Timing;
Show( t );

Get Web Support

Syntax: obj << Get Web Support

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

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

Get Where Expr

Syntax: obj << Get Where Expr

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

JMP Version Added: 18

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

Ignore Platform Preferences

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

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

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

Local Data Filter

Syntax: obj << Local Data Filter

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

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

New JSL Preset

Syntax: New JSL Preset( preset )

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

JMP Version Added: 18

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

New Preset

Syntax: obj = New Preset()

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

JMP Version Added: 18

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

Paste Local Data Filter

Syntax: obj << Paste Local Data Filter

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

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

Redo Analysis

Syntax: obj << Redo Analysis

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Redo Analysis;

Redo ByGroup Analysis

Syntax: obj << Redo ByGroup Analysis

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Redo ByGroup Analysis;

Relaunch Analysis

Syntax: obj << Relaunch Analysis

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Relaunch Analysis;

Relaunch ByGroup

Syntax: obj << Relaunch ByGroup

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Relaunch ByGroup;

Remove Column Switcher

Syntax: obj << Remove Column Switcher

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

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

Remove Local Data Filter

Syntax: obj << Remove Local Data Filter

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

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

Render Preset

Syntax: Render Preset( preset )

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

JMP Version Added: 18

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

Report

Syntax: obj << Report;Report( obj )

Description: Returns a reference to the report object.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Report View

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

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Report View( "Summary" );

Save ByGroup Script to Data Table

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

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Data Table;

Save ByGroup Script to Journal

Syntax: obj << Save ByGroup Script to Journal

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Journal;

Save ByGroup Script to Script Window

Syntax: obj << Save ByGroup Script to Script Window

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Save ByGroup Script to Script Window;

Save Script for All Objects

Syntax: obj << Save Script for All Objects

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Save Script for All Objects;

Save Script for All Objects To Data Table

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

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

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table( "My Script" );

Save Script to Data Table

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

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );

Save Script to Journal

Syntax: obj << Save Script to Journal

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Save Script to Journal;

Save Script to Report

Syntax: obj << Save Script to Report

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Save Script to Report;

Save Script to Script Window

Syntax: obj << Save Script to Script Window

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Save Script to Script Window;

SendToByGroup

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

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

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

SendToEmbeddedScriptable

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

Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.

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

SendToReport

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

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

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

Sync to Data Table Changes

Syntax: obj << Sync to Data Table Changes

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

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

Title

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

Description: Sets the title of the platform.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
obj << Title( "My Platform" );

Top Report

Syntax: obj << Top Report

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Nearest Neighbors(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    K( 10 )
);
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 = K Nearest Neighbors(...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" ) ) )
);

KNN Fit

Item Messages

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

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Plot Actual by Predicted( 1 ));
obj << (Response[1] << Plot Residual by Predicted( 1 ));
preset = obj << (Response[1] << New Preset);
obj2 = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 5 )
);
Wait( 1 );
obj2 << (Response[1] << Apply Preset( preset ));

Get Best K

Syntax: obj << (Response[number] << Get Best K)

Description: Returns the value of the best K neighbors.

JMP Version Added: 15

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Get Best K);

Get Prediction Formula

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

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

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Get Prediction Formula( 9 ));

Mosaic Plot

Syntax: obj << Mosaic Plot( state=0|1 )

Description: Displays or hides a mosaic plot of the data. On by default.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Mosaic Plot( 0 ));

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/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Plot Actual by Predicted( 1 ));
obj << (Response[1] << Plot Residual by Predicted( 1 ));
preset = obj << (Response[1] << New Preset);

Plot Actual by Predicted

Syntax: obj << (Response[number] << 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.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Plot Actual by Predicted( 1 ));

Plot Residual by Predicted

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

Description: Plots the residuals on the Y axis and the predicted values on the X axis. Use the scatterplot to detect patterns in the fit or in the variation.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Plot Residual by Predicted( 1 ));

Publish Prediction Formula

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

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

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Publish Prediction Formula( 9 ));

Save Predicteds

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

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

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Save Predicteds);

Save Prediction Formula

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

Description: Saves the prediction formula in a new column in the data table.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 )
);
obj << (Response[1] << Save Prediction Formula( 9 ));

Set K

Syntax: obj << ( Response[number] << Set K( number ) )

Description: Changes the specified model to a different model in the solution path.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = K Nearest Neighbors(
    Y( :Y ),
    X( :Age, :Gender, :BMI ),
    Validation( :Validation ),
    K( 10 ),
    Plot Actual by Predicted( 1 )
);
Wait( 3 );
obj << (Response[1] << Set K( 6 ));