Split

Example 1

Summary: Split data table into separate tables based on the Drug Type and Measurement columns, grouping by the Subject column.

Code:

// Open data table
dt = Open("$Sample_Data/Drug Measurements.jmp");
// Split
Data Table( "Drug Measurements" ) <<
Split(
    Split By( :Drug Type ),
    Split( :Measurement ),
    Group( :Subject )
);

Example 2

Summary: Prepare an initial uplift report by generating an uplift analysis using the Hair Care Product data table.

Code:

// Open data table
dt = Open("$Sample_Data/Hair Care Product.jmp");
// Initial Uplift Report
Uplift(
    Y( :Purchase ),
    X(
        :Gender, :Age, :Hair Color,
        :U.S. Region, :Residence
    ),
    Validation( :Validation ),
    Minimum Size Split( 63 ),
    Treatment( :Promotion ),
    Split History( 1 ),
    Informative Missing( 1 )
);

Example 3

Summary: Analyze uplift effects using the Uplift function, with Purchase as the outcome and Gender, Age, Hair Color, U.S. Region, and Residence as predictors, incorporating a validation column, specifying a minimum split size of 63, considering Promotion as the treatment, utilizing split history, handling informative missing values, generating an uplift graph, and limiting the number of splits to 4.

Code:

// Open data table
dt = Open("$Sample_Data/Hair Care Product.jmp");
// Uplift
Uplift(
    Y( :Purchase ),
    X(
        :Gender, :Age, :Hair Color,
        :U.S. Region, :Residence
    ),
    Validation( :Validation ),
    Minimum Size Split( 63 ),
    Treatment( :Promotion ),
    Split History( 1 ),
    Informative Missing( 1 ),
    Uplift Graph( 1 ),
    Split Best( 4 )
);