Discriminant

Associated Constructors

Discriminant

Syntax: Discriminant( Y( columns ), X( columns ) )

Description: Estimates the distance from each observation to each group's multivariate mean (centroid) using Mahalanobis distance. The observations are then classified into the group that they are closest to.

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

Columns

By

Syntax: obj = Discriminant(...<By( column(s) )>...)

Description: Performs a separate analysis for each level of the specified column.

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 << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    By( _bycol )
);

Categories

Syntax: obj = Discriminant(...Categories( column )...)

Description: Specifies the column that contains the categories or groups into which observations are to be classified.

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

Covariates

Syntax: obj = Discriminant(...Covariates( column(s) )...)

Description: Specifies the columns that contain continuous variables that are used to classify observations into categories.

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

Freq

Syntax: obj = Discriminant(...<Freq( column )>...)

Description: Specifies a column whose values assign a frequency to each row for the analysis.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Freq( _freqcol )
);

Validation

Syntax: obj = Discriminant(...<Validation( column )>...)

Description: Specifies a numeric column that defines the validation sets. This column should contain at most three distinct values.

dt = Open( "$SAMPLE_DATA/Liver Cancer.jmp" );
obj = dt << Discriminant(
    X( :Severity ),
    Validation( :Validation ),
    Y( :BMI, :Age, :Time ),
    Use Matrix Columns( 1 )
);

Weight

Syntax: obj = Discriminant(...<Weight( column )>...)

Description: Specifies a column whose values assign a weight to each row for the analysis.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Weight( _weightcol )
);

X

Syntax: obj = Discriminant(...X( column )...)

Description: Specifies the column that contains the categories or groups into which observations are to be classified.

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

Y

Syntax: obj = Discriminant(...Y( column(s) )...)

Description: Specifies the columns that contain continuous variables that are used to classify observations into categories.

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

Item Messages

Apply This Model

Syntax: obj << Apply This Model

Description: Applies the current variable selection to the model in the Stepwise Variable Selection and closes the dialog.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Step Forward;
Wait( 2 );
obj << Apply This Model;

Biplot Ray Position

Syntax: obj << Biplot Ray Position( [x position, y position, radius scaling] )

Description: Enables you to specify the position and radius scaling of the biplot rays in the Canonical Plot and in the Canonical 3D Plot.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Biplot Ray Position( [0, 1.7, 3.5] );

Canonical 3D Plot

Syntax: obj << Canonical 3D Plot( state=0|1 )

Description: Shows or hides a three-dimensional version of the Canonical Plot. Note: Only available when there are four or more groups.

dt = Open( "$SAMPLE_DATA/Cherts.jmp" );
obj = dt << Discriminant(
    X( :location name ),
    Y( :Al, :Mn, :Na, :Br, :Ce, :Co, :Cr, :Cs, :Eu, :Fe, :Hf, :La, :Sc, :Sm, :U )
);
obj << Canonical 3D Plot( 1 );
(obj << report)["Discriminant Scores"] << Close( 1 );

Canonical Plot

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

Description: Shows or hides the Canonical Plot. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Canonical Plot( 1 );

Color Points

Syntax: obj << Color Points

Description: Colors the points in the Canonical Plot and the Canonical 3D Plot based on the levels of the X variable. Color markers are added to the rows in the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
dt << Clear Row States;
Wait( 2 );
obj << Color Points;

Consider New Levels

Syntax: obj << Consider New Levels( fraction )

Description: Specifies that some points might not fit into any known group and should be considered to be from an unscored new group. Enter the prior probability of a new level.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Consider New Levels( 0.05 );

Cross Validate by Excluded Rows

Syntax: obj = Discriminant(...Cross Validate by Excluded Rows( state=0 )...)

Description: Specifies that the excluded rows form a validation set for which statistics of fit are calculated. "0" by default.

JMP Version Added: 14

Discriminant Method

Syntax: obj << Discriminant Method( Linear );obj << Discriminant Method( Quadratic );obj << Discriminant Method( Regularized, Regularization Lambda( fraction ), Regularization Gamma( fraction ) );obj << Discriminant Method( Wide Linear )

Description: Specifies the discriminant method.

The Regularized option requires additional arguments. The Regularization Lambda parameter ranges from 0 (quadratic discriminant analysis) to 1 (linear discriminant analysis). The Regularization Gamma parameter ranges from 0 (no shrinkage) to 1 (diagonals only).

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Discriminant Method(
    Regularized,
    Regularization Lambda( 0.2 ),
    Regularization Gamma( 0.6 )
);

Discriminant Scores

Syntax: obj << Discriminant Scores( state=0|1 )

Description: Shows or hides a table of the discriminant scores for each row. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Discriminant Scores( 1 );

Enter All

Syntax: obj << Enter All

Description: Adds all the variables to the model in the Stepwise Variable Selection.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Enter All;

Get Discrim Matrices

Syntax: obj << Get Discrim Matrices

Description: Returns a list that contains the discriminant matrices from the analysis. The list contains a named list for each of the following items: the Y names, the X names, the X values, and the Y means.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
z = obj << Get Discrim Matrices;
Show( z );

Get Measures

Syntax: obj << Get Measures

Description: Returns summary measures of fit from the model.

JMP Version Added: 16

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

Go

Syntax: obj << Go

Description: Enters covariates in forward steps until there is no further improvement in RSquare.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Go;

Make Scoring Script

Syntax: obj << Make Scoring Script

Description: Creates a script that constructs the formula columns saved by the Save Formulas option. You can save this script and use it, perhaps with other data tables, to create the formula columns that calculate membership probabilities and predict group membership.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Make Scoring Script;

Precision Recall Curve

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

Description: Shows or hides the Precision-Recall Curve plot that contains a curve for each level of the response variable. A precision-recall curve plots the precision values against the recall values at a variety of thresholds.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Precision Recall Curve( 1 );

Profiler

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

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

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Profiler;

Publish Probability Formulas

Syntax: obj << Publish Probability Formulas

Description: Creates probability formulas and saves them as formula column scripts in the Formula Depot platform. If a Formula Depot report is not open, this option creates a Formula Depot report.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Publish Probability Formulas;

ROC Curve

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

Description: Shows or hides the Receiver Operating Characteristic (ROC) curve for each level of the response variable. The ROC curve is a plot of sensitivity versus (1 - specificity).

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
Wait( 0 );
obj << ROC Curve( 1 );

Remove All

Syntax: obj << Remove All

Description: Removes all the variables in the model in the Stepwise Variable Selection.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Enter All;
Wait( 2 );
obj << Remove All;

Save Canonical Scores

Syntax: obj << Save Canonical Scores

Description: Saves columns to the data table that contain canonical score formulas for each observation.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Save Canonical Scores;

Save Discrim Matrices

Syntax: obj << Save Discrim Matrices

Description: Saves a script to the data table that contains a list of the discriminant matrices from the analysis. The list contains a named list for each of the following items: the Y names, the X names, the X values, and the Y means.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Save Discrim Matrices;

Save Formulas

Syntax: obj << Save Formulas

Description: Saves distance, probability, and predicted membership formulas to the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Save Formulas;

Save To New Data Table

Syntax: obj << Save To New Data Table

Description: Saves the group means and the biplot rays on the canonical variables, together with the canonical scores to a new data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Save To New Data Table;

Scatterplot Matrix

Syntax: obj << Scatterplot Matrix

Description: Opens a Scatterplot Matrix report that shows a matrix with a scatterplot for each pair of covariates. The option invokes the Scatterplot Matrix platform with shaded density ellipses for each group. The scatterplots include all observations in the data table, even if validation is used.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Scatterplot Matrix( 1 );

Score Data

Syntax: obj << Score Data( state=0|1 )

Select Misclassified Rows

Syntax: obj << Select Misclassified Rows

Description: Selects the misclassified rows in the data table and in report windows that display a listing by row.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Interesting Rows Only( 1 );
obj << Select Misclassified Rows;

Select Uncertain Rows

Syntax: obj << Select Uncertain Rows( fraction )

Description: Selects rows with uncertain classifications in the data table and in report windows that display a listing by row. An uncertain row is one whose probability of group membership for any group is neither close to 0 nor close to 1. The fraction argument represents the difference in probability from 0 or 1 to be defined uncertain.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Interesting Rows Only( 1 );
obj << Select Uncertain Rows( 0.2 );

Show Biplot Rays

Syntax: obj << Show Biplot Rays( state=0|1 )

Description: Shows or hides the biplot rays in the Canonical Plot and the Canonical 3D Plot. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Biplot Rays( 1 );

Show Canonical Details

Syntax: obj << Show Canonical Details( state=0|1 )

Description: Shows or hides the Canonical Details report.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Canonical Details( 1 );

Show Canonical Structure

Syntax: obj << Show Canonical Structure( state=0|1 )

Description: Shows or hides the Canonical Structures report.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Canonical Structure( 1 );

Show Canonical Structures

Syntax: obj << Show Canonical Structures( state=0|1 )

Show Classification Counts

Syntax: obj << Show Classification Counts( state=0|1 )

Description: Shows or hides the confusion matrices, showing actual by predicted counts, in the Score Summaries report. By default, the Score Summaries report shows a confusion matrix for each level of the categorical X.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Classification Counts( 1 );

Show Distances to Each Group

Syntax: obj << Show Distances to Each Group( state=0|1 )

Description: Shows or hides a report that contains each observation's squared Mahalanobis distance to each group mean.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Distances to each group( 1 );

Show Group Means

Syntax: obj << Show Group Means( state=0|1 )

Description: Shows or hides the Group Means report that provides the mean of each covariate. Means for each level of the X variable and overall means appear.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Group Means( 1 );

Show Interesting Rows Only

Syntax: obj << Show Interesting Rows Only( state=0|1 )

Description: In the Discriminant Scores report, shows only rows that are misclassified and those with predicted probability between 0.05 and 0.95.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Interesting Rows Only( 1 );

Show Means CL Ellipses

Syntax: obj << Show Means CL Ellipses( state=0|1 )

Description: Shows or hides 95% confidence ellipses for the mean of each group on the Canonical Plot and the Canonical 3D Plot, assuming normality. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Means CL Ellipses( 1 );

Show Normal 50% Contours

Syntax: obj << Show Normal 50% Contours( state=0|1 )

Description: Shows or hides the normal ellipse region estimated to contain 50% of the population for each group in the Canonical Plot and the Canonical 3D Plot. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Normal 50% Contours( 1 );

Show Points

Syntax: obj << Show Points( state=0|1 )

Description: Shows or hides the points in the Canonical Plot and the Canonical 3D Plot. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Points( 1 );

Show Probabilities to Each Group

Syntax: obj << Show Probabilities to Each Group( state=0|1 )

Description: Shows or hides a report that contains the probability that an observation belongs to each of the groups defined by the categorical X.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Probabilities to each group( 1 );

Show Within Covariances

Syntax: obj << Show Within Covariances( state=0|1 )

Description: Shows or hides reports related to the covariance matrices. The reports that appear depend on the specified discriminant method. Not available for the Wide Linear discriminant method.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Show Within Covariances( 1 );

Shrink Covariances

Syntax: obj = Discriminant(...Shrink Covariances( state=0|1 )...)

Description: Shrinks the off-diagonal elements of the pooled within-group covariance matrix and the within-group covariance matrices. This can improve stability and reduce the variance of prediction.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Shrink Covariances( 1 )
);

Specify Priors

Syntax: obj << Specify Priors( Equal Probabilities | Proportional to Occurrence | [matrix of priors] )

Description: Sets the prior probabilities for each level of the X variable.

dt = Open( "$SAMPLE_DATA/Cherts.jmp" );
obj = dt << Discriminant(
    X( :location name ),
    Y( :Al, :Mn, :Na, :Br, :Ce, :Co, :Cr, :Cs, :Eu, :Fe, :Hf, :La, :Sc, :Sm, :U )
);
obj << Specify Priors( Proportional to Occurrence );

Step Backward

Syntax: obj << Step Backward

Description: Moves one step backward in the Stepwise Variable Selection by removing one variable from the model.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Enter All;
Wait( 2 );
obj << Step Backward;

Step Forward

Syntax: obj << Step Forward

Description: Moves one step forward in the Stepwise Variable Selection by adding one variable to the model.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );
obj << Step Forward;

Stepwise Variable Selection

Syntax: obj = Discriminant(...Stepwise Variable Selection( state=0|1 )...)

Description: Shows or hides the Column Selection control panel. This control panel contains options that enable you to perform stepwise variable selection using covariance analysis and p-values. This option is not available for the Wide Linear method.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width )
);
obj << Stepwise Variable Selection( 1 );

Uncentered Canonical

Syntax: obj = Discriminant(...Uncentered Canonical( state=0|1 )...)

Description: Suppresses centering of canonical scores for compatibility with older versions of JMP.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Uncentered Canonical( 1 )
);

Use Matrix Columns

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

Description: Specifies that matrix columns be used in calculations. Matrix columns can cut down the overhead in calculating scoring predictions in formula columns.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Use Matrix Columns( 1 )
);

Use Pseudoinverses

Syntax: obj = Discriminant(...Use Pseudoinverses( state=0|1 )...)

Description: Uses Moore-Penrose pseudoinverses in the analysis when the covariance matrix is singular. The resulting scores involve all covariates. If left unchecked, the analysis drops covariates that are linear combinations of covariates that precede them in the list of Y, Covariates. On by default.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Discriminant(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Use Pseudoinverses( 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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 << Discriminant(
    X( :Species ),
    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 = Discriminant(...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" ) ) )
);