Explore Outliers
Associated Constructors
Explore Outliers
Syntax: Explore Outliers( Y( columns ) )
Description: Identifies, explores, and manages outliers in univariate or multivariate data.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
Columns
By
Syntax: obj << By( column(s) )
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), By( _bycol ) );
Label
Syntax: obj << Label( column )
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
Validation
Syntax: obj << Validation( column )
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
Y
Syntax: obj << Y( column(s) )
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
Item Messages
K Nearest Neighbor Outliers
Syntax: obj << K Nearest Neighbor Outliers
Description: For each point, finds the distance to its kth nearest neighbor.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << k Nearest Neighbor Outliers( K( 5 ) );
Multivariate k Nearest Neighbor Outliers
Syntax: obj << Multivariate k Nearest Neighbor Outliers
JMP Version Added: 14
Quantile Range Outliers
Syntax: obj << Quantile Range Outliers
Description: Finds values more than a scale multiple of an interquantile range beyond the quantiles.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
Robust Fit Outliers
Syntax: obj << Robust Fit Outliers
Description: Finds values more than a multiple of the scale away from the center using robust estimates of the center and scale.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
Robust PCA Outliers
Syntax: obj << Robust PCA Outliers
Description: Robustly decomposes data into a low-rank matrix and a sparse matrix of residuals. Outliers are detected in the residuals. It can also impute missing values.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( 2 :: 10 ) );
obj << Robust PCA Outliers;
Shared Item Messages
Action
Syntax: obj << Action
Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.
dt = Open( "$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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );
Broadcast
Syntax: obj << Broadcast(message)
Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.
JMP Version Added: 18
dt = Open( "$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/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Copy Script;
Data Table Window
Syntax: obj << Data Table Window
Description: Move the data table window for this analysis to the front.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
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/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );
Platform with Filter
dt = Open( "$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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
t = obj << Get Datatable;
Show( N Rows( t ) );
Get Group Platform
Syntax: obj << Get Group Platform
Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().
dt = Open( "$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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
t = obj << Get Script;
Show( t );
Get Script With Data Table
Syntax: obj << Get Script With Data Table
Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
t = obj << Get Timing;
Show( t );
Get Web Support
Syntax: obj << Get Web Support
Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.
dt = Open( "$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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Redo Analysis;
Redo ByGroup Analysis
Syntax: obj << Redo ByGroup Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Relaunch Analysis;
Relaunch ByGroup
Syntax: obj << Relaunch ByGroup
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Report View( "Summary" );
Save ByGroup Script to Data Table
Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), By( _bycol ) );
obj[1] << Save ByGroup Script to Journal;
Save ByGroup Script to Script Window
Syntax: obj << Save ByGroup Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), 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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Save Script for All Objects;
Save Script for All Objects To Data Table
Syntax: obj << Save Script for All Objects To Data Table( <name> )
Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.
Example 1
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table( "My Script" );
Save Script to Data Table
Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Save Script to Journal;
Save Script to Report
Syntax: obj << Save Script to Report
Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
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( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Save Script to Script Window;
SendToByGroup
Syntax: SendToByGroup( {":Column == level"}, command );
Description: Sends platform commands or display customization commands to each level of a by-group.
dt = Open( "$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/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Transform Column
Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)
Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.
JMP Version Added: 16
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;
Window View
Syntax: obj = Explore Outliers(...Window View( "Visible"|"Invisible"|"Private" )...)
Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object
dt = Open( "$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" ) ) )
);
K Nearest Neighbor Outliers
Item Messages
Close
Syntax: obj << Close
JMP Version Added: 16
Exclude Selected Rows
Syntax: obj << Exclude Selected Rows
JMP Version Added: 16
Impute Missing
Syntax: obj << Impute Missing( state=0 )
Description: If there are missing values, Robust PCA is used to impute them before analyzing with K Nearest Neighbors. On by default.
JMP Version Added: 16
K
Syntax: obj << K( number=8 )
Description: The number of near-neighbor rows to find for each row in the table. "8" by default.
JMP Version Added: 16
Save NN Distances
Syntax: obj << Save NN Distances
Description: Saves new columns to the data table containing distances to the kth nearest neighbor.
JMP Version Added: 14
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( 2 :: 10 ) );
obj << k Nearest Neighbor Outliers( K( 4 ) );
obj << Save NN Distances;
Scatterplot Matrix
Syntax: obj << Scatterplot Matrix
Description: Opens a window containing a scatterplot matrix for all columns.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( 2 :: 10 ) );
obj << k Nearest Neighbor Outliers( K( 4 ) );
obj << Scatterplot Matrix;
Multivariate Robust Outliers
Item Messages
Close
Syntax: obj << Close
JMP Version Added: 16
Exclude Selected Rows
Syntax: obj << Exclude Selected Rows
JMP Version Added: 16
Quantile Range Outliers
Item Messages
Add Highest Nines to Missing Value Codes
Syntax: obj << Add Highest Nines to Missing Value Codes( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and finds the highest nines in each column. Creates a Missing Value Codes property for these values in each selected column.
dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Responses" ) ),
Quantile Range Outliers( Show only columns with outliers( 1 ) )
);
obj << Add Highest Nines to Missing Value Codes( :PS_RPNBR );
dt:PS_RPNBR << Get Column Properties;
//See Log for Missing Value Codes column property
Add to Missing Value Codes
Syntax: obj << Add to Missing Value Codes( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and adds a Missing Value Code property in those columns for outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
obj << Add to Missing Value Codes( :"Q-E"n, :"ZN-E"n );
Change Highest Nines to Missing
Syntax: obj << Change Highest Nines to Missing( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and finds the highest nines in these columns. Changes the highest nines to missing. Note that this changes the data table.
dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Responses" ) ),
Quantile Range Outliers( Show only columns with outliers( 1 ) )
);
obj << Change Highest Nines to Missing( :PS_RPNBR );
Change to Missing
Syntax: obj << Change to Missing( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, changes the values identified as outliers to missing values.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
Wait( 2 );
obj << Change to Missing( :"Q-E"n, :"ZN-E"n );
Close
Syntax: obj << Close
Description: Removes a section of the analysis and reopens the command outline.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
obj << Robust Fit Outliers;
Wait( 2 );
obj << Close;
Color Cells
Syntax: obj << Color Cells( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, colors the cells that correspond to outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
obj << Color Cells( :"Q-E"n, :"ZN-E"n );
Color Rows
Syntax: obj << Color Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, assigns the Color row state to the rows that correspond to outliers.
JMP Version Added: 16
Exclude Rows
Syntax: obj << Exclude Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, excludes the rows containing values identified as outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
obj << Exclude Rows( :"Q-E"n, :"ZN-E"n );
Formula Columns
Syntax: obj << Formula Columns( ALL or column1, column2, ... )
Description: Creates new formula columns from the selected columns by changing the outliers to missing.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
Wait( 2 );
obj << Formula Columns( Suffix( "Culled" ) );
Formula Script
Syntax: obj << Formula Script( ALL or column1, column2, ... )
Description: Creates a script to make new formula columns from the selected columns by changing the outliers to missing.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
Wait( 2 );
obj << Formula Script( Suffix( "Culled" ) );
Get Quantile Outliers
Syntax: obj << Get Quantile Outliers
Description: Returns a list containing a list of the columns that contain outliers and a list of vectors that contain outlier values in those columns.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
obj << Get Quantile Outliers;
Q
Syntax: obj << Q( number=3 )
Description: Sets the scale multiple, Q, for the interquantile distance. Values that fall more than Q times the interquantile distance beyond the tail quantiles are considered outliers. Use Rescan to apply the setting. "3" by default.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Q( 4 ) );
Rescan
Syntax: obj << Rescan
Description: Use after changing settings to recompute the criteria and rescan the data to obtain outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
obj << Tail Quantile( 0.2 );
obj << Rescan;
Restrict search to integers
Syntax: obj << Restrict search to integers( state=0|1 )
Description: Restricts outlier values to only integer values. This setting limits the search for outliers in order to find industry-specific missing value codes and error codes. Available for the Quantile Range Outliers and Robust Fit Outliers methods. Off by default.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Quantile Range Outliers( Restrict search to integers( 1 ) )
);
Save Quantile Outlier Limits
Syntax: obj << Save Quantile Outlier Limits
Description: Opens a new data table containing the Quantile Range Outliers report information and a column of outlier values.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers;
obj << Save Quantile Outlier Limits;
Select Rows
Syntax: obj << Select Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and selects the rows that have outlying values in any of those columns.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.3 ) );
obj << Select Rows( :"Q-E"n, :"ZN-E"n );
Show only columns with outliers
Syntax: obj << Show only columns with outliers( state=0|1 )
Description: Limits the list of columns in the report to those that contain outliers. Available for the Quantile Range Outliers and Robust Fit Outliers methods. Off by default.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Quantile Range Outliers( Show only columns with outliers( 1 ) )
);
Tail Quantile
Syntax: obj << Tail Quantile( number=.10 )
Description: Sets the quantile value for each tail. The quantiles are used in computing the interquantile distance. Use Rescan to apply the setting. ".10" by default.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Quantile Range Outliers( Tail Quantile( 0.2 ) );
Robust Fit Outliers
Item Messages
Add to Missing Value Codes
Syntax: obj << Add to Missing Value Codes( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and adds a Missing Value Code property in those columns for outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
obj << Add to Missing Value Codes( :"Q-E"n, :"ZN-E"n );
Cauchy
Syntax: obj << Cauchy( state=0|1 )
Description: Uses a Cauchy distribution to estimate the robust center and scale of the values. The robust center and scale are used to determine outliers.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << Cauchy( 1 );
obj << Rescan;
Change to Missing
Syntax: obj << Change to Missing( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, changes the values identified as outliers to missing values.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
Wait( 2 );
obj << Change to Missing( :"Q-E"n, :"ZN-E"n );
Close
Syntax: obj << Close
Description: Removes a section of the analysis and reopens the command outline.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
Wait( 2 );
obj << Close;
Color Cells
Syntax: obj << Color Cells( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, colors the cells that correspond to outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
obj << Color Cells( :"Q-E"n, :"ZN-E"n );
Color Rows
Syntax: obj << Color Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, assigns the Color row state to the rows that correspond to outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
dt << Clear Row States;
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
obj << Color Rows( :"Q-E"n, :"ZN-E"n );
Exclude Rows
Syntax: obj << Exclude Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments. In selected columns, excludes the rows containing values identified as outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
obj << Exclude Rows( :"Q-E"n, :"ZN-E"n );
Formula Columns
Syntax: obj << Formula Columns( ALL or column1, column2, ... )
Description: Creates new formula columns from the selected columns by changing the outliers to missing.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
Wait( 2 );
obj << Formula Columns( Suffix( "Culled" ) );
Formula Script
Syntax: obj << Formula Script( ALL or column1, column2, ... )
Description: Creates a script to make new formula columns from the selected columns by changing the outliers to missing.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
Wait( 2 );
obj << Formula Script( Suffix( "Culled" ) );
Huber
Syntax: obj << Huber( state=0|1 )
Description: Uses Huber estimation to estimate the robust center and scale of the values. The robust center and scale are used to determine outliers.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << Huber( 1 );
obj << Rescan;
K Sigma
Syntax: obj << K Sigma( number=4 )
Description: Sets the K Sigma value where outliers are defined to be K times the robust scale values away from the robust center. "4" by default.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << K Sigma( 3 );
obj << Rescan;
Quartile
Syntax: obj << Quartile( state=0|1 )
Description: Uses the median to estimate the robust center and the interquartile range divided by 1.349 to estimate the robust scale. The robust center and scale are used to determine outliers.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << Quartile( 1 );
obj << Rescan;
Rescan
Syntax: obj << Rescan
Description: Use after changing settings to recompute the criteria and rescan the data to obtain outliers.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << K Sigma( 2.5 );
obj << Rescan;
Save Robust Outlier Limits
Syntax: obj << Save Robust Outlier Limits
Description: Opens a new data table that contains information from the Robust Estimates and Outliers report.
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers;
obj << Save Robust Outlier Limits;
Select Rows
Syntax: obj << Select Rows( ALL or column1, column2, ... )
Description: Selects the columns listed as arguments and selects the rows that have outlying values in any of those columns.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers( Y( Column Group( "Sensor Measurements" ) ) );
obj << Robust Fit Outliers( K Sigma( 2 ) );
obj << Select Rows( :"Q-E"n, :"ZN-E"n );
Robust PCA Outliers
Item Messages
Center
Syntax: obj << Center( state=1 )
Description: Specifies whether to center the data by the median before the analysis. On by default.
JMP Version Added: 16
Close
Syntax: obj << Close
Description: Removes the RPCA analysis from the platform report.
JMP Version Added: 16
Lambda
Syntax: obj << Lambda( number )
Description: Robust PCA tuning with lower values making it more sensitive to declaring outliers. Default Lambda=2/sqrt(max(nRow,nCol))
JMP Version Added: 16
MaxIt
Syntax: obj << MaxIt( number )
Description: The maximum number of SVD iterations allowed before failing to converge.
JMP Version Added: 16
Outlier Threshold
Syntax: obj << Outlier Threshold( number=2 )
Description: Specifies that any scaled residual larger in absolute value than this threshold is shown as it is shown in the outlier report. "2" by default.
JMP Version Added: 16
Randomized SVD Dim
Syntax: obj << Randomized SVD Dim( state=0|1 )
Description: Specifies the number of dimensions in the randomized SVD to which to reduce the wide problem.
JMP Version Added: 17
Save Cleaned
Syntax: obj << Save Cleaned( Trim(<threshold>),Impute(<threshold>),Make Missing(<threshold>),Color Impute(0|1)--if none specified it will prompt with dialog )
Description: Creates a new set of columns that contain missing values imputed and outliers modified. Trim(arg) finds scaled residuals greater than arg and changes the scaled residuals in the corresponding cells to the signed arg. Impute(arg) finds scaled residuals greater than arg and changes the scaled residuals in the corresponding cells to the low-rank approximation. Make Missing(value) finds any scaled residual greater than arg and changes scaled residuals in the corresponding cells to missing.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Robust PCA Outliers
);
obj << Save Cleaned( Trim( 25 ), Impute( 50 ), Make Missing( 100 ) );
Save Large Outliers
Syntax: obj << Save Large Outliers
Description: Creates a new data table that contains the outliers in the report.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Robust PCA Outliers
);
obj << Save Large Outliers;
Save Low Rank Approx
Syntax: obj << Save Low Rank Approx
Description: Creates a new set of columns that contain the low-rank approximation, which is obtained from the singular value decomposition.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Robust PCA Outliers
);
obj << Save Low Rank Approx;
Save Residuals
Syntax: obj << Save Residuals
Description: Creates a new set of columns that contain the residuals, which are the observations minus the low-rank approximation.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Robust PCA Outliers
);
obj << Save Residuals;
Save Scaled Residuals
Syntax: obj << Save Scaled Residuals
Description: Creates a new set of columns that contain the scaled residuals, which are the scaled observations minus the low-rank approximation.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Water Treatment.jmp" );
obj = dt << Explore Outliers(
Y( Column Group( "Sensor Measurements" ) ),
Robust PCA Outliers
);
obj << Save Scaled Residuals;
Scale
Syntax: obj << Scale( state=1 )
Description: Specifies whether to scale the data by an interquantile range analog to the standard deviation before the analysis. On by default.
JMP Version Added: 16
Tolerance
Syntax: obj << Tolerance( number )
Description: Specifies the convergence criterion, which determines when to stop the algorithm. The default convergence criterion values are set based on the number of columns specified in the launch.
JMP Version Added: 16
Use Randomized SVD
Syntax: obj << Use Randomized SVD( state=0|1 )
Description: Reduces the dimensionality using the randomized SVD. This approach can speed up computations for very wide problems.
JMP Version Added: 17