Partition Platform

Associated Constructors

Partition

Syntax: Partition( Y( column ), X( columns ) )

Description: Constructs a decision tree by recursively partitioning the data according to a relationship between the predictor and response values. Both the response and predictors can be either continuous or categorical.

Example 1

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Partition(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 3 )
);

Example 2

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 2 );

Item Messages

Method

Syntax: Method( "Decision Tree" )

Description: Specifies the method used for partitioning the data. Decision Tree is the default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" )
);
obj << Split Best( 2 );

Decision Tree

Associated Constructors

Decision Tree

Syntax: Partition(Y( column ), X( columns ), Method( "Decision Tree" ))

Description: Recursively partition the data to predict a response. This is also called Classification and Regression Trees.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Columns

By

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );

Factor

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Freq

Syntax: obj << Freq( column )

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    Freq( _freqcol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    Freq( _freqcol )
);
obj << Split Best( 2 );

Response

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Validation

Syntax: obj << Validation( column )

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Weight

Syntax: obj << Weight( column )

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    Weight( _weightcol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    Weight( _weightcol )
);
obj << Split Best( 2 );

X

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Y

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );

Item Messages

Color Points

Syntax: obj << Color Points

Description: Colors the points according to their classification.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Color Points;

Column Contributions

Syntax: obj << Column Contributions( state=0|1 )

Description: Shows or hides a report with each input column and its corresponding contribution to the fit.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Column Contributions( 1 );

Decision Threshold

Syntax: obj << Decision Threshold( state = 0|1, Set Probability Threshold( number ) )

Description: Shows or hides the distribution of fitted probabilities and actual versus predicted tables for each model. You can change the probability threshold to explore how different thresholds affect the classification results.

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
obj << Split Best( 5 );
obj << Show Tree( 0 );
obj << Decision Threshold( 1 );

Get Average Absolute Error Test

Syntax: obj << Get Average Absolute Error Test

Description: Returns the Mean Abs Dev statistic for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );

Get Average Absolute Error Training

Syntax: obj << Get Average Absolute Error Training

Description: Returns the Mean Abs Dev statistic for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );

Get Average Absolute Error Validation

Syntax: obj << Get Average Absolute Error Validation

Description: Returns the Mean Abs Dev statistic for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );

Get Average Log Error Test

Syntax: obj << Get Average Log Error Test

Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
avg = obj << Get Average Log Error Test;
Show( avg );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
avg = obj << Get Average Log Error Test;
Show( avg );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Method( "Decision Tree" ),
    Go
);
avg = obj << Get Average Log Error Test;
Show( avg );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
avg = obj << Get Average Log Error Test;
Show( avg );

Get Average Log Error Training

Syntax: obj << Get Average Log Error Training

Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
avg = obj << Get Average Log Error Training;
Show( avg );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
avg = obj << Get Average Log Error Training;
Show( avg );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 3 )
);
avg = obj << Get Average Log Error Training;
Show( avg );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
avg = obj << Get Average Log Error Training;
Show( avg );

Get Average Log Error Validation

Syntax: obj << Get Average Log Error Validation

Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Method( "Decision Tree" ),
    Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
avg = obj << Get Average Log Error Validation;
Show( avg );

Get Confusion Matrix Test

Syntax: obj << Get Confusion Matrix Test

Description: Returns the confusion matrix for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Test;
Show( cm );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Test;
Show( cm );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Test;
Show( cm );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Test;
Show( cm );

Get Confusion Matrix Training

Syntax: obj << Get Confusion Matrix Training

Description: Returns the confusion matrix for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Training;
Show( cm );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Training;
Show( cm );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Training;
Show( cm );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Training;
Show( cm );

Get Confusion Matrix Validation

Syntax: obj << Get Confusion Matrix Validation

Description: Returns the confusion matrix for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );

Get Confusion Rates Test

Syntax: obj << Get Confusion Rates Test

Description: Returns the confusion rates for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Test;
Show( cr );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Test;
Show( cr );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Test;
Show( cr );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Test;
Show( cr );

Get Confusion Rates Training

Syntax: obj << Get Confusion Rates Training

Description: Returns the confusion rates for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Training;
Show( cr );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Training;
Show( cr );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Training;
Show( cr );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Training;
Show( cr );

Get Confusion Rates Validation

Syntax: obj << Get Confusion Rates Validation

Description: Returns the confusion rates for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Validation;
Show( cr );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
cr = obj << Get Confusion Rates Validation;
Show( cr );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Validation;
Show( cr );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
cr = obj << Get Confusion Rates Validation;
Show( cr );

Get Gen RSquare Test

Syntax: obj << Get Gen RSquare Test

Description: Returns the generalized RSquare for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :sex ),
    X( :marital status, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Test;
Show( r );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Test;
Show( r );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Test;
Show( r );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Test;
Show( r );

Get Gen RSquare Training

Syntax: obj << Get Gen RSquare Training

Description: Returns the generalized RSquare for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :sex ),
    X( :marital status, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Training;
Show( r );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Training;
Show( r );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Training;
Show( r );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Training;
Show( r );

Get Gen RSquare Validation

Syntax: obj << Get Gen RSquare Validation

Description: Returns the generalized RSquare for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :sex ),
    X( :marital status, :age, :country, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Validation;
Show( r );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Go
);
r = obj << Get Gen RSquare Validation;
Show( r );

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation( :Validation ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Validation;
Show( r );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
r = obj << Get Gen RSquare Validation;
Show( r );

Get MM SAS DATA Step

Syntax: obj << Get MM SAS DATA Step

Description: Creates SAS code that you can register in the SAS Model Manager and returns it to the Log window.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
code = obj << Get MM SAS Data Step;

Get MM Tolerant SAS DATA Step

Syntax: obj << Get MM Tolerant SAS DATA Step

Description: Creates SAS code for data that includes missing values that you can register in the SAS Model Manager and returns it to the Log window.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
code = obj << Get MM Tolerant SAS Data Step;

Get Measures

Syntax: obj << Get Measures

Description: Returns summary measures of fit from the model.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Get Measures;

Get Microseconds

Syntax: obj << Get Microseconds

Description: Returns the number of microseconds used to complete the analysis.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
time = obj << Get Microseconds;
Show( time );

Get Misclassification Rate Test

Syntax: obj << Get Misclassification Rate Test

Description: Returns the misclassification rate for the test set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
rate = obj << Get Misclassification Rate Test;
Show( rate );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
rate = obj << Get Misclassification Rate Test;
Show( rate );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Split Best( 2 )
);
rate = obj << Get Misclassification Rate Test;
Show( rate );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
    Training Set( .6 ),
    Validation Set( .2 ),
    Test Set( .2 ),
    New Column Name( "Valid1" ),
    Go
);
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Valid1 ),
    Split Best( 2 )
);
rate = obj << Get Misclassification Rate Test;
Show( rate );

Get Misclassification Rate Training

Syntax: obj << Get Misclassification Rate Training

Description: Returns the misclassification rate for the training set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
rate = obj << Get Misclassification Rate Training;
Show( rate );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
rate = obj << Get Misclassification Rate Training;
Show( rate );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Method( "Decision Tree" )
);
obj << Split Best( 2 );
rate = obj << Get Misclassification Rate Training;
Show( rate );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 )
);
rate = obj << Get Misclassification Rate Training;
Show( rate );

Get Misclassification Rate Validation

Syntax: obj << Get Misclassification Rate Validation

Description: Returns the misclassification rate for the validation set. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    validation( :Holdback1 ),
    Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    validation( :Holdback1 ),
    Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    validation( :Holdback1 ),
    Method( "Decision Tree" ),
    Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation( :Validation ),
    Split Best( 2 )
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );

Get Precision Recall Area Test

Syntax: obj << Get Precision Recall Area Test

Description: Returns the area under the precision-recall curve for the test set. The precision-recall curve must be displayed before the area is computed. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Method( "Decision Tree" ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );

Get Precision Recall Area Training

Syntax: obj << Get Precision Recall Area Training

Description: Returns the area under the precision-recall curve for the training set. The precision-recall curve must be displayed before the area is computed.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 2 )
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );

Get Precision Recall Area Validation

Syntax: obj << Get Precision Recall Area Validation

Description: Returns the area under the precision-recall curve for the validation set. The precision-recall curve must be displayed before the area is computed. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Method( "Decision Tree" ),
    Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );

Get Prediction Formula

Syntax: obj << Get Prediction Formula

Description: Constructs a script to create a prediction formula column and returns it.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Get Prediction Formula;

Get RMS Error Test

Syntax: obj << Get RMS Error Test

Description: Returns the square root of the mean square of the test errors. Available only when using a validation set.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
rms = obj << Get RMS Error Test;
Show( rms );

Get RMS Error Training

Syntax: obj << Get RMS Error Training

Description: Returns the square root of the mean square of the training errors.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
rms = obj << Get RMS Error Training;
Show( rms );

Get RMS Error Validation

Syntax: obj << Get RMS Error Validation

Description: Returns the square root of the mean square of the validation errors. Available only when using a validation set.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
rms = obj << Get RMS Error Validation;
Show( rms );

Get ROC Area Test

Syntax: obj << Get ROC Area Test

Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the test data. The ROC curve needs to be displayed before the area is computed. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation( :Validation 2 ),
    Method( "Decision Tree" ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );

Get ROC Area Training

Syntax: obj << Get ROC Area Training

Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the training data set. The ROC curve needs to be displayed before the area is computed.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Go
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 2 )
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );

Get ROC Area Validation

Syntax: obj << Get ROC Area Validation

Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the validation data set. The ROC curve needs to be displayed before the area is computed. Available only when using a validation set.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Validation Portion( 0.2 ),
    Method( "Decision Tree" ),
    Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );

Get RSquare Test

Syntax: obj << Get RSquare Test

Description: Returns the RSquare for the test set. Available only when using a validation set.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
r = obj << Get RSquare Test;
Show( r );

Get RSquare Training

Syntax: obj << Get RSquare Training

Description: Returns the RSquare for the training set.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
r = obj << Get RSquare Training;
Show( r );

Get RSquare Validation

Syntax: obj << Get RSquare Validation

Description: Returns the RSquare for the validation set. Available only when using a validation set.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
r = obj << Get RSquare Validation;
Show( r );

Get SAS DATA Step

Syntax: obj << Get SAS DATA Step

Description: Creates a SAS DATA step to score the data and returns it to the Log window.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
code = obj << Get SAS Data Step;

Get Seconds

Syntax: obj << Get Seconds

Description: Returns the number of seconds used to complete the analysis.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
time = obj << Get Seconds;
Show( time );

Get Tolerant Prediction Formula

Syntax: obj << Get Tolerant Prediction Formula

Description: Constructs a script to create a tolerant prediction formula column and returns it to the Log window.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Get Tolerant Prediction Formula;

Get Tolerant SAS DATA Step

Syntax: obj << Get Tolerant SAS DATA Step

Description: Creates a SAS DATA step to score data that includes missing values and returns it to the Log window. Missing values are randomly assigned to a tree branch.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
code = obj << Get Tolerant SAS Data Step;

Go

Syntax: obj << Go

Description: Begins iterating after K Fold Crossvalidation has been selected. If using JMP Pro, Go begins iterating after Validation column is specified.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << K Fold Crossvalidation( 5 );
obj << Go;

Informative Missing

Syntax: obj = Decision Tree(...Informative Missing( state=0|1 )...)

Description: For categorical variables, treats missing as a category. For continuous variables, treats missing as either low or high, whichever fits better. On by default.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Boosted Tree( Y( :height ), X( :age ), Informative Missing( 0 ), Go );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Bootstrap Forest( Y( :height ), X( :age ), Informative Missing( 0 ), Go );

Partition Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Partition( Y( :height ), X( :age ), Informative Missing( 0 ) );
obj << Split Best( 1 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt:Age[3] = .;
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Informative Missing( 0 ),
    Split Best( 3 )
);

Initial Splits

Syntax: obj = Partition(...Initial Splits( condition, {left condition}, {right condition} )...)

Description: Describes the splits that are performed. The condition argument specifies the left side of the first split. The {left condition} and {right condition} arguments specify a split on the respective side and this format continues recursively for the desired number of splits. To specify a split on the right and not the left, assign the left argument as an empty list. To specify a split on the left and not the right, omit the right argument.

Example 1

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Initial Splits( :size == {"Large"} )
);

Example 2

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Initial Splits( :size == {"Large"}, {}, {:size == {"Medium"}, {:age >= 25}} )
);

Example 3

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Initial Splits( :size == {"Large"}, {:type == {"Family", "Sporty"}} )
);

K Fold Crossvalidation

Syntax: obj << K Fold Crossvalidation

Description: This is a deprecated feature.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Split Best( 2 )
);
obj << K Fold Crossvalidation( 5 );

Leaf Report

Syntax: obj << Leaf Report( state=0|1 )

Description: Shows or hides a report with the mean and count (continuous response) or the response rate and count (categorical response) of the leaf nodes.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Tree( 0 );
obj << Leaf Report( 1 );

Lift Curve

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

Description: Shows or hides the Lift Curve plot. A lift curve plots the lift versus the portion of the observations and provides another view of the predictive ability of a model. If you used validation, a plot is shown for each of the training, validation, and test sets.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 5 );
obj << Show Tree( 0 );
obj << Lift Curve( 1 );

Lock Columns

Syntax: obj << Lock Columns( state=0|1, columns )

Description: Locks out specified columns from being used for splits.

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Lock Columns( 1, :age, :size );
(obj << report)[CheckboxBox( 1 )] << Select;
Wait( .5 );
obj << Lock Columns( 0 );
Wait( .5 );
obj << Lock Columns( 1 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion )
);
obj << Lock Columns( 1, :Age, :Hair Color );
(obj << report)[CheckboxBox( 1 )] << Select;
Wait( .5 );
obj << Lock Columns( 0 );
Wait( .5 );
obj << Lock Columns( 1 );

Make SAS DATA Step

Syntax: obj << Make SAS DATA Step

Description: Creates a SAS DATA step to score the data and returns it to a script window.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Make SAS Data Step;

Make Tolerant SAS DATA Step

Syntax: obj << Make Tolerant SAS DATA Step

Description: Creates a SAS DATA step to score data that includes missing values and returns it to a script window. Missing values are randomly assigned to a tree branch.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Make Tolerant SAS Data Step;

Method

Syntax: Method( "Decision Tree" )

Description: Specifies the method used for partitioning the data. Decision Tree is the default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" )
);
obj << Split Best( 2 );

Minimum Size Split

Syntax: obj << Minimum Size Split( number )

Description: Sets the minimum group size when deciding whether to split a group.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Minimum Size Split( 15 );
obj << Split Best( 4 );

Missing Value Order

Syntax: Missing Value Order( Low(list of numeric columns),High(list of numeric columns))

Description: Specifies if missing values are treated as low or high.

JMP Version Added: 16

Multithreading

Syntax: Multithreading( state=0|1 )

Description: Divides up the calculations among the available threads on the machine. On by default.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Multithreading( 1 ),
    Go
);

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Multithreading( 1 ),
    Go
);

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Multithreading( 1 ),
    Split Best( 2 )
);

Ordinal Restricts Order

Syntax: obj = Decision Tree(...Ordinal Restricts Order( state=0|1 )...)

Description: For ordinal columns, considers only splits that preserve order. On by default.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Boosted Tree( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ), Go );

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bootstrap Forest( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ), Go );

Partition Example

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Partition( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ) );
obj << Split Best( 3 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Ordinal Restricts Order( 1 ),
    Split Best( 2 )
);

Plot Actual by Predicted

Syntax: obj << Plot Actual by Predicted( state=0|1 )

Description: Shows or hides a plot using the training data with the predicted values on the X axis and actual values on the Y axis.

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Split Best( 3 )
);
obj << Plot Actual By Predicted;

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. If you used validation, a plot is shown for each of the training, validation, and test sets.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 5 );
obj << Show Tree( 0 );
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.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Profiler( 1 );

Prune Worst

Syntax: obj << Prune Worst

Description: Removes the terminal split that has the least discrimination ability.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Prune Worst;
Wait( .5 );
obj << Prune Worst;

Publish Prediction Formula

Syntax: obj << Publish Prediction Formula

Description: Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Publish Prediction Formula;

Publish Tolerant Prediction Formula

Syntax: obj << Publish Tolerant Prediction Formula

Description: Builds a prediction formula that predicts even when there are missing values and publishes it as a formula column script in Formula Depot.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Publish Tolerant Prediction Formula;

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). If you used validation, a plot is shown for each of the training, validation, and test sets.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 5 );
obj << Show Tree( 0 );
obj << ROC Curve( 1 );

Save Leaf Label Formula

Syntax: obj << Save Leaf Label Formula

Description: Saves the leaf label formula in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Leaf Label Formula;

Save Leaf Labels

Syntax: obj << Save Leaf Labels

Description: Saves the leaf labels in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Leaf Labels;

Save Leaf Number Formula

Syntax: obj << Save Leaf Number Formula

Description: Saves the leaf number formula in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Leaf Number Formula;

Save Leaf Numbers

Syntax: obj << Save Leaf Numbers

Description: Saves the leaf numbers in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Leaf Numbers;

Save Predicteds

Syntax: obj << Save Predicteds

Description: Saves the predicted values in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Predicteds;

Save Prediction Formula

Syntax: obj << Save Prediction Formula

Description: Saves the prediction formula in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Prediction Formula;

Save Residuals

Syntax: obj << Save Residuals

Description: Saves the residuals in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Residuals;

Save Tolerant Prediction Formula

Syntax: obj << Save Tolerant Prediction Formula

Description: Save a formula that predicts even when there are missing values in a new column in the data table.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Save Tolerant Prediction Formula;

Set Random Seed

Syntax: obj << Set Random Seed( number )

Description: Specifies a random seed to reproduce the results for future launches of the platform.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Set Random Seed( 1234 ),
    Go
);

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Set Random Seed( 1234 ),
    Go
);

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
    Y( :Y Binary ),
    X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Set Random Seed( 1234 ),
    Split Best( 2 )
);

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Set Random Seed( 1234 ),
    Split Best( 2 )
);

Show Fit Details

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

Description: Shows or hides a report with the definition of all the measures, the misclassification rates, and the confusion matrices.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Tree( 0 );
obj << Show Fit Details( 1 );

Show Graph

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

Description: Shows or hides the partition graph. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << ShowGraph( 0 );
Wait( .5 );
obj << ShowGraph( 1 );

Show Points

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

Description: Shows the points (1 or on) or shows color panels (0 or off) in the partition graph. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << ShowPoints( 0 );
Wait( .5 );
obj << ShowPoints( 1 );

Show Split Bar

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

Description: Shows or hides the colored bars that indicate the split proportions in each leaf. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Split Bar( 0 );
Wait( .5 );
obj << Show Split Bar( 1 );

Show Split Candidates

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

Description: Shows or hides the Candidates report in the terminal splits. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Split Candidates( 1 );
(obj << Report)["Candidates"] << Close( 0 ) << select;

Show Split Count

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

Description: Shows or hides the response counts for each level in each tree node.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Split Count( 0 );
Wait( .5 );
obj << Show Split Count( 1 );

Show Split Prob

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

Description: Shows or hides the response rates for each level in each tree node.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Split Prob( 0 );
Wait( .5 );
obj << Show Split Prob( 1 );

Show Split Stats

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

Description: Shows or hides the count and the split statistics. The statistics shown include the G² or the mean and standard deviation. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Show Split Stats( 0 );
Wait( .5 );
obj << Show Split Stats( 1 );

Show Tree

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

Description: Shows or hides the tree structure with the partition information. On by default.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << ShowTree( 1 );

Small Tree View

Syntax: obj << Small Tree View( state=0|1 )

Description: Shows or hides a smaller version of the partition tree to the right of the Partition Graph.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
obj << Small Tree View( 1 );

Sort Split Candidates

Syntax: obj << Sort Split Candidates( state=0|1 )

Description: Sorts the candidates by significance.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
(obj << Report)["Candidates"] << Close( 0 ) << select;
Wait( 1 );
obj << Sort Split Candidates;

Specify Profit Matrix

Syntax: obj << Specify Profit Matrix

Description: Enables you to specify profits or costs associated with correct or incorrect classification decisions.

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Split Best( 3 ),
    Specify Profit Matrix( [0 -1, -1 0, . .], "Married", "Single", "Undecided" ),
    Show Fit Details( 1 )
);

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Split Best( 2 ),
    Specify Profit Matrix( [0 -1, -1 0, . .], "Yes", "No", "Undecided" ),
    Show Fit Details( 1 )
);

Split Best

Syntax: obj << Split Best( <number of splits> )

Description: Splits the tree at the optimal split point.

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best;
Wait( .5 );
obj << Split Best( 2 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion )
);
obj << Split Best;
Wait( 1 );
obj << Split Best( 2 );

Split History

Syntax: obj << Split History( state=0|1 )

Description: Shows or hides a graph showing each split on the X axis and the corresponding R² value for the model on the Y axis.

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 5 );
obj << Show Tree( 0 );
obj << Split History;

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion )
);
obj << Split Best( 2 );
obj << Show Tree( 0 );
obj << Split History;

Tree 3D

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

Description: Shows or hides a 3D plot of the tree structure.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition( Y( :country ), X( :sex, :marital status, :age, :type, :size ) );
obj << Split Best( 14 );
obj << Show Tree( 0 );
obj << Tree 3D( 1 );

Use Excluded Rows for Validation

Syntax: obj = Decision Tree(...Use Excluded Rows for Validation( state=0|1 )...)

Description: Uses the excluded rows in the data table to create a validation set. This option appears in the launch window only if you are using standard JMP and there are excluded rows.

JMP Version Added: 15

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Boosted Tree(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Use Excluded Rows for Validation( 1 ),
    Go
);

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Bootstrap Forest(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Use Excluded Rows for Validation( 1 ),
    Go
);

Partition Example

dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Partition(
    Y( :Y ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Use Excluded Rows for Validation( 1 )
);
obj << Split Best( 5 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Use Excluded Rows for Validation( 1 ),
    Split Best( 2 )
);

Validation Portion

Syntax: obj = Decision Tree(...Validation Portion( fraction=0 )...)

Description: Forms a validation set by randomly selecting rows with each row having probability p (fraction) of being selected. "0" by default.

Boosted Tree Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Boosted Tree(
    Y( :marital status ),
    X( :sex, :country, :age, :type, :size ),
    Validation Portion( 0.2 ),
    Go
);

Bootstrap Forest Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Bootstrap Forest(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation Portion( 0.2 ),
    Go
);

Partition Example

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Validation Portion( 0.2 )
);
obj << Split Best( 2 );

Uplift Example

dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
    Y( :Purchase ),
    X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
    Treatment( :Promotion ),
    Validation Portion( 0.2 ),
    Go
);

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" ) );

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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
dt << Make Validation Column(
    Training Set( .5 ),
    Validation Set( .3 ),
    Test Set( .2 ),
    By( _bycol ),
    Go
);
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation ),
    By( _bycol )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .5 ), Validation Set( .3 ), Test Set( .2 ), Go );
obj = dt << Partition(
    Y( :country ),
    X( :sex, :marital status, :age, :type, :size ),
    Method( "Decision Tree" ),
    Validation( :Validation )
);
obj << Split Best( 2 );
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 = Decision Tree(...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" ) ) )
);