Explore Missing Values

Associated Constructors

Explore Missing Values

Syntax: Explore Missing Values( Y( columns ) )

Description: Find patterns of missing values and conduct imputation.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );

Columns

By

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

dt = Open( "$Sample_Data/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), By( _bycol ) );

Validation

Syntax: obj << Validation( column )

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );

Y

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

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );

Item Messages

ADI Loading Matrix

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

Description: Shows or hides a report that shows the columns that correspond to the factor loading for each component.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Set Random Seed( 123 ),
    Automated Data Imputation
);
obj << ADI Loading Matrix( 1 );

Automated Data Imputation

Syntax: obj << Automated Data Imputation

Description: Imputes missing values using a low-rank matrix approximation method. This method automatically selects the best dimension for the low-rank approximation based on the data.

JMP Version Added: 15

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Automated Data Imputation;

Close

Syntax: obj << Close

Description: Closes the Missing Columns report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :POP, :OZONE, :CO, :SO2, :NO, :PM10 ),
    Missing Value Report
);
Wait( 2 );
obj << Close;

Color Cells

Syntax: obj << Color Cells( ALL or column1, column2, ... )

Description: Colors the cells in the data table that contain missing values for the column(s) that you select in the Missing Columns report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Color cells( :OZONE );

Color Rows

Syntax: obj << Color Rows( ALL or column1, column2, ... )

Description: Colors the rows in the data table that contain missing values for the column(s) that you select in the Missing Columns report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Color rows( :OZONE );

Exclude Rows

Syntax: obj << Exclude Rows( ALL or column1, column2, ... )

Description: Applies the excluded row state for rows in the data table that contain missing values for the column(s) that you select in the Missing Columns report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Exclude rows( :OZONE );

Get U V Sigma ADI Matrices

Syntax: obj << Get U V Sigma ADI Matrices

Description: Returns the U, V, and Sigma matrices from the low-rank approximation in the ADI method.

JMP Version Added: 16

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Set Random Seed( 123 ),
    Automated Data Imputation
);
obj << Get U V Sigma ADI Matrices;

Maximum Dimension

Syntax: obj << Maximum Dimension( number )

Description: Sets the maximum dimension for Automated Data Imputation.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Maximum Dimension( 3 ),
    Automated Data Imputation
);

Maximum Iteration

Syntax: obj << Maximum Iteration( number=10 )

Description: Sets the maximum number of iterations for Automated Data Imputation. "10" by default.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Maximum Iteration( 8 ),
    Automated Data Imputation
);

Missing Value Clustering

Syntax: obj << Missing Value Clustering

Description: Provides a hierarchical clustering analysis of the missing data.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Missing Value Clustering;

Missing Value Report

Syntax: obj << Missing Value Report

Description: Opens the Missing Columns report, which lists the name of each column and the number of missing values in that column.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Missing Value Report;

Missing Value Snapshot

Syntax: obj << Missing Value Snapshot

Description: Shows a cell plot for the missing values. A black cell indicates a missing value.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Missing Value Snapshot;

Multivariate Normal Imputation

Syntax: obj << Multivariate Normal Imputation( Shrink Covariances( state=0|1 ) )

Description: Imputes missing values based on the multivariate normal distribution. To improve the estimation of the covariance matrix, use the shrinkage estimator option.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Multivariate Normal Imputation( Shrink Covariances( 1 ) );

Multivariate RPCA Imputation

Syntax: obj << Multivariate RPCA Imputation( Lambda( number ), Tolerance( number = 1e-7 ), MaxIt( number ) )

Description: Imputes missing values using robust principal components, which replaces missing values using a low-rank matrix factorization (SVD) that is robust to outliers. This method is useful for wide problems. The default value for lambda is 2/sqrt(max(n, p)), where n is the number of rows and p is the number of columns. If min(n, p) < 100, the default value for the maximum number of iterations (MaxIt) is 75. If 100<= min(n, p) < 1000, the default value for MaxIt is 100. If min(n,p) >= 1000, the default value for MaxIt is 200. If the algorithm does not converge after the specified maximum number of iterations, the solution at MaxIt is accepted and shown in the report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Multivariate RPCA Imputation;

Multivariate SVD Imputation

Syntax: obj << Multivariate SVD Imputation( Number of Singular Vectors( number ), Maximum Iterations( number ), Show Iteration Log( state=0|1 ) )

Description: Imputes missing values quickly for large problems using an iterated low-rank SVD matrix completion method.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Multivariate SVD Imputation(
    Number of Singular Vectors( 3 ),
    Maximum Iterations( 10 ),
    Show Iteration Log( 1 ),

);

Options for Saving Imputed Values

Syntax: obj << Options for Saving Imputed Values(1|2|3)

Description: Specifies the method by which to save the imputed values for the ADI method. Input 1 to specify the Create New Data Table option, 2 to specify the Save Scoring Formula to Current Data Table option, and 3 to specify the Impute Values in Place option.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Options for Saving Imputed Values( 1 ),
    Set Random Seed( 123 ),
    Automated Data Imputation
);

Select Rows

Syntax: obj << Select Rows( ALL or column1, column2, ... )

Description: Selects the rows in the data table that contain missing values for the column(s) that you select in the Missing Columns report.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Select rows( :OZONE );

Set Random Seed

Syntax: obj << Set Random Seed( number=0 )

Description: Sets the random seed for Automated Data Imputation. "0" by default.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Set Random Seed( 1234 ),
    Automated Data Imputation
);

Show only columns with missing

Syntax: obj << Show only columns with missing( state=0|1 )

Description: Removes columns from the list that do not have missing values.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :POP, :OZONE, :CO, :SO2, :NO, :PM10 ) );
obj << Show Only Columns With Missing( 1 );

Undo Imputation

Syntax: obj << Undo Imputation

Description: Replaces the most recent imputed data with missing values.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Multivariate Normal Imputation
);
Wait( 2 );
obj << Undo Imputation;

Validation Proportion

Syntax: obj << Validation Proportion( number=0.3 )

Description: Sets the proportion of rows to use as validation rows for Automated Data Imputation. "0.3" by default.

JMP Version Added: 14

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values(
    Y( :OZONE, :CO, :SO2, :NO, :PM10 ),
    Validation Proportion( 0.25 ),
    Automated Data Imputation
);

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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$Sample_Data/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$Sample_Data/Cities.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ), 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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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/Cities.jmp" );
obj = dt << Explore Missing Values( Y( :OZONE, :CO, :SO2, :NO, :PM10 ) );
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 = Explore Missing Values(...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" ) ) )
);