Multivariate Embedding

Associated Constructors

Multivariate Embedding

Syntax: Multivariate Embedding( Y( columns ) )

Description: Maps data from very high-dimensional spaces to a low-dimensional space, using the Uniform Manifold Approximation and Projection (UMAP) method or the t-Distributed Stochastic Neighbor Embedding (t-SNE) method. Many times, you want to map the data to either two or three dimensions so that the low-dimensional space can be easily visualized. Both methods try to preserve the local structure of the data, but UMAP is generally faster than t-SNE for large data sets.

JMP Version Added: 17

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
/* Parameters can be changed according to data features */
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Maximum Iterations( 1500 ),
    Perplexity( 15 ),
    Initial Principal Component Dimensions( 55 ),
    Random Seed( 2022 ),
    Output Dimensions( 3 )
);

Example 3

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
/* by group example */
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);

Columns

By

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

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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);

Y

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

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);

Item Messages

Batch Mode if N Greater Than

Syntax: Batch Mode if N greater than( number = 4096 )

Description: Specifies that multithreading is used for optimizing the embedding coordinates when the sample size is larger than the specified number. "4096" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Batch Mode if N greater than( 100 )
);

Convergence Criterion

Syntax: Convergence Criterion( number = 1e-8 )

Description: "1e-8" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Convergence Criterion( 1e-8 )
);

Distance Metric

Syntax: Distance Metric( "Euclidean" | "Angular" | "Hamming" | "Manhattan")

Description: Specifies the metric that is used to compute distances between nearest neighbors. The options for the distance metric are Euclidean (the default), Angular, Hamming, and Manhattan. This option is applicable only when ANNOY is specified as the Nearest Neighbor Method.

Eta

Syntax: Eta( number = 200 )

Description: Specifies the learning rate. "200" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Eta( 200 )
);

Gradient Descent Method

Syntax: Gradient Descent Method( "SGD" | "ADAM")

Description: Specifies the gradient descent method that is used for optimizing the embedding layout. You can choose between Stochastic Gradient Descent (SGD) or Adaptive Moment Estimation (ADAM). The default method is SGD. The ADAM option is available only in batch mode.

Inflate Iterations

Syntax: Inflate Iterations( number = 250 )

Description: Specifies the iteration after which the perplexities are no longer exaggerated. "250" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Inflate Iterations( 250 )
);

Initial Principal Component Dimensions

Syntax: Initial Principal Component Dimensions( number = 50 )

Description: Specifies the number of dimensions that should be retained in the initial PCA step. "50" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Initial Principal Component Dimensions( 50 )
);

Initial Scale

Syntax: Initial Scale( number = 0.0001 )

Description: Specifies the initial scale that is used for the derived components. ".0001" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Initial Scale( 0.001 )
);

Learning Rate

Syntax: Learning Rate( number = 1.0 )

Description: Specifies the value of the learning rate in the computations, which impacts how quickly the model adapts to the problem. "1.0" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Learning Rate( 1.0 )
);

Local Connectivity

Syntax: Local Connectivity( number = 1 )

Description: Specifies the number of nearest neighbors that are assumed to be connected at a local level. The default value is 1, which assumes that every point in the high-dimensional space has at least one other neighbor to which it is connected. "1" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Local Connectivity( 1 )
);

Maximum Iterations

Syntax: Maximum Iterations( number = 1000 )

Description: Specifies the maximum number of iterations that are used when calculating the embedding components. "1000" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Maximum Iterations( 1500 )
);

Method

Syntax: Method( "t-SNE" | "UMAP" )

Description: Specifies the dimension reduction method.

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" )
);

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "UMAP" )
);

Minimum Distance

Syntax: Minimum Distance( number = 0.01 )

Description: Specifies the minimum standardized distance that points in the low-dimensional space can be from one another. "0.01" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Minimum Distance( 0.001 )
);

Missing Value Imputation

Syntax: Missing Value Imputation( state =0|1 )

Description: Specifies that missing values in the data are imputed using a multivariate singular value decomposition (SVD) technique. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Missing Value Imputation( 0 )
);

Nearest Neighbor Method

Syntax: Nearest Neighbor Method( "Default" | "VPTree (Exact)" | "ANNOY (Approximate)")

Description: Specifies the method that is used for finding nearest neighbors. You can choose between a vantage-point tree (VPTree) or the Approximate Nearest Neighbors method (ANNOY). The default option chooses the nearest neighbor method according to the sample size and the number of variables.

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Method( "UMAP" ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Nearest Neighbor Method( "VPTree (Exact)" )
);

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Method( "UMAP" ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Nearest Neighbor Method( "ANNOY (Approximate)" )
);

Negative Sample Rate

Syntax: Negative Sample Rate( number = 5 )

Description: Specifies the number of negative 1-simplex samples to use per positive 1-simplex sample in finding the low-dimensional representation of the data. The Negative Sample Rate value can range from 2 to 20. "5" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Negative Sample Rate( 5 )
);

Number of Epochs

Syntax: Number of Epochs( number = 500 )

Description: Specifies the number of training epochs to use when optimizing the low-dimensional representation. This is the number of times the algorithm works through the full training data. "500" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Epochs( 500 )
);

Number of Neighbors

Syntax: Number of Neighbors( number = 15 )

Description: Specifies the number of near neighbors that are found for each data point. The smaller the number of near neighbors specified, the more the UMAP algorithm concentrates on the local structure of the data. As the number of near neighbors increases, the UMAP algorithm captures more of the global structure of the data. "15" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Method( "UMAP" ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Neighbors( 20 )
);

Output Dimensions

Syntax: Output Dimensions( number = 2 )

Description: Specifies the number of components that are derived by the selected method. This number must be >=2. "2" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Output Dimensions( 3 )
);

Perplexity

Syntax: Perplexity( number = 30 )

Description: Specifies the value of the perplexity parameter, which is related to computing similarities of the samples. The value of the perplexity parameter should be between 5 and 50 and should not be greater than one-eighth of the sample size. The default value is the smaller of 30 or one-eighth of the sample size. "30" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Perplexity( 20 )
);

Random Seed

Syntax: Random Seed( number = 1234 )

Description: Specifies the random seed number that is used to obtain reproducible results. "123" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Random Seed( 1234 )
);

Save Embedding Component Values

Syntax: obj << Save Embedding Component Values

Description: Saves the derived embedding components as new columns in the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Random Seed( 1234 )
);
obj << Save Embedding Component Values;

Save PQ Matrices

Syntax: obj << Save PQ Matrices

Description: Shows the P and Q matrices, which refer to van der Maaten (2008). This option is valid only when Sparse mode for t-SNE is not selected.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Perplexity( 15 ),
    Sparse( 0 )
);
obj << Save PQ Matrices;

Sparse

Syntax: Sparse( state =0|1 )

Description: Specifies if sparse mode is used. Sparse mode allows for the computation for high-dimensional data sets. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Sparse( 1 )
);

Standardize

Syntax: Standardize( state =0|1 )

Description: Specifies if data is standardized internally prior to distance calculations. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Method( "t-SNE" ),
    Standardize( 1 )
);

a

Syntax: a( number = 0 )

Description: Specifies one of the parameters that control the embedding optimization algorithm. If this value is specified as 0 or a negative number, a is calculated in the algorithm by a nonlinear least squares procedure. "0" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    a( 0 )
);

b

Syntax: b( number = 0 )

Description: Specifies one of the parameters that control the embedding optimization algorithm. If this value is specified as 0 or a negative number, b is calculated in the algorithm by a nonlinear least squares procedure. "0" by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    b( 0 )
);

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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = dt << Multivariate Embedding(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
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 = Multivariate Embedding(...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" ) ) )
);