Uplift
Associated Constructors
Uplift
Syntax: Uplift( Y( column ), X( columns ), Treatment( column ) )
Description: Fits a recursive partition tree that selects splits to maximize treatment differences. The models identify groups of individuals who are most likely to respond to a treatment.
Example 1
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Example 2
dt = Open( "$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( 3 )
);
Columns
By
Syntax: obj << By( column(s) )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
Factor
Syntax: obj << Factor( column(s) )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Freq
Syntax: obj << Freq( column )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
Freq( _freqcol )
);
Response
Syntax: obj << Response( column(s) )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Treatment
Syntax: obj << Treatment( column )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Validation
Syntax: obj << Validation( column )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Weight
Syntax: obj << Weight( column )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
Weight( _weightcol )
);
X
Syntax: obj << X( column(s) )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Y
Syntax: obj << Y( column(s) )
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
Item Messages
Color Points
Syntax: obj << Color Points
Description: Colors the points according to their classification.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Column Contributions( 1 );
Get Average Absolute Error Test
Syntax: obj << Get Average Absolute Error Test
Description: Returns the Mean Abs Dev statistic for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$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( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Validation;
Show( cr );
Get Difference Formula
Syntax: obj << Get Difference Formula
Description: Constructs a script to create a difference formula and returns it.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Get Difference Formula;
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 Measures
Syntax: obj << Get Measures
Description: Returns summary measures of fit from the model.
JMP Version Added: 16
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Get Measures;
Get Microseconds
Syntax: obj << Get Microseconds
Description: Returns the number of microseconds used to complete the analysis.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
rms = obj << Get RMS Error Test;
Show( rms );
Get RMS Error Training
Syntax: obj << Get RMS Error Training
Description: Returns the square root of the mean square of the training errors.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
rms = obj << Get RMS Error Validation;
Show( rms );
Get ROC Area Test
Syntax: obj << Get ROC Area Test
Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the test data. The ROC curve needs to be displayed before the area is computed. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
r = obj << Get RSquare Test;
Show( r );
Get RSquare Training
Syntax: obj << Get RSquare Training
Description: Returns the RSquare for the training set.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
r = obj << Get RSquare Validation;
Show( r );
Get Seconds
Syntax: obj << Get Seconds
Description: Returns the number of seconds used to complete the analysis.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Get Tolerant Prediction Formula;
Go
Syntax: obj << Go
Description: Begins iterating after K Fold Crossvalidation has been selected. If using JMP Pro, Go begins iterating after Validation column is specified.
dt = Open( "$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 = Uplift(...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 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Show Tree( 0 );
obj << Leaf Report( 1 );
Lock Columns
Syntax: obj << Lock Columns( state=0|1, columns )
Description: Locks out specified columns from being used for splits.
Partition Example
dt = Open( "$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( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion )
);
obj << Lock Columns( 1, :Age, :Hair Color );
(obj << report)[CheckboxBox( 1 )] << Select;
Wait( .5 );
obj << Lock Columns( 0 );
Wait( .5 );
obj << Lock Columns( 1 );
Minimum Size Split
Syntax: obj << Minimum Size Split( number )
Description: Sets the minimum group size when deciding whether to split a group.
dt = Open( "$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 );
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( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Multithreading( 1 ),
Split Best( 2 )
);
Ordinal Restricts Order
Syntax: obj = Uplift(...Ordinal Restricts Order( state=0|1 )...)
Description: For ordinal columns, considers only splits that preserve order. On by default.
Boosted Tree Example
dt = Open( "$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;
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Profiler( 1 );
Prune Worst
Syntax: obj << Prune Worst
Description: Removes the terminal split that has the least discrimination ability.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Prune Worst;
Wait( .5 );
obj << Prune Worst;
Publish Difference Formula
Syntax: obj << Publish Difference Formula
Description: Builds a difference formula and publishes it as a formula column script in Formula Depot.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Publish Difference Formula;
Publish Prediction Formula
Syntax: obj << Publish Prediction Formula
Description: Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Publish Tolerant Prediction Formula;
Save Difference
Syntax: obj << Save Difference
Description: Saves the predicted treatment difference.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Difference;
Save Difference Formula
Syntax: obj << Save Difference Formula
Description: Saves a formula column with the predicted treatment difference.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Difference Formula;
Save Leaf Label Formula
Syntax: obj << Save Leaf Label Formula
Description: Saves the leaf label formula in a new column in the data table.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Leaf Labels;
Save Leaf Number Formula
Syntax: obj << Save Leaf Number Formula
Description: Saves the leaf number formula in a new column in the data table.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Leaf Numbers;
Save Predicteds
Syntax: obj << Save Predicteds
Description: Saves the predicted values in a new column in the data table.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Prediction Formula;
Save Residuals
Syntax: obj << Save Residuals
Description: Saves the residuals in a new column in the data table.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Tolerant Prediction Formula;
Set Random Seed
Syntax: obj << Set Random Seed( number )
Description: Specifies a random seed to reproduce the results for future launches of the platform.
Boosted Tree Example
dt = Open( "$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/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << ShowGraph( 0 );
Wait( .5 );
obj << ShowGraph( 1 );
Show Points
Syntax: obj << Show Points( state=0|1 )
Description: Shows the points (1 or on) or shows color panels (0 or off) in the partition graph. On by default.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << ShowPoints( 0 );
Wait( .5 );
obj << ShowPoints( 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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Show Split Candidates( 1 );
(obj << Report)["Candidates"] << Close( 0 ) << select;
Show Split Stats
Syntax: obj << Show Split Stats( state=0|1 )
Description: Shows or hides the count and the split statistics. The statistics shown include the G² or the mean and standard deviation. On by default.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << ShowTree( 1 );
Small Tree View
Syntax: obj << Small Tree View( state=0|1 )
Description: Shows or hides a smaller version of the partition tree to the right of the Partition Graph.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Small Tree View( 1 );
Sort Split Candidates
Syntax: obj << Sort Split Candidates( state=0|1 )
Description: Sorts the candidates by significance.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
(obj << Report)["Candidates"] << Close( 0 ) << select;
Wait( 1 );
obj << Sort Split Candidates;
Split Best
Syntax: obj << Split Best( <number of splits> )
Description: Splits the tree at the optimal split point.
Partition Example
dt = Open( "$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( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion )
);
obj << Split Best( 2 );
obj << Show Tree( 0 );
obj << Split History;
Uplift Graph
Syntax: obj << Uplift Graph( state=0|1 )
Description: Shows uplift across sorted terminal leaves.
dt = Open( "$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 << Uplift Graph;
Use Excluded Rows for Validation
Syntax: obj = Uplift(...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 = Uplift(...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/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Copy ByGroup Script;
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Copy Script;
Data Table Window
Syntax: obj << Data Table Window
Description: Move the data table window for this analysis to the front.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
t = obj[1] << Get ByGroup Script;
Show( t );
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );
Platform with Filter
dt = Open( "$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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
t = obj << Get Datatable;
Show( N Rows( t ) );
Get Group Platform
Syntax: obj << Get Group Platform
Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().
dt = Open( "$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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
t = obj << Get Script;
Show( t );
Get Script With Data Table
Syntax: obj << Get Script With Data Table
Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
t = obj << Get Timing;
Show( t );
Get Web Support
Syntax: obj << Get Web Support
Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.
dt = Open( "$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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Redo Analysis;
Redo ByGroup Analysis
Syntax: obj << Redo ByGroup Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Redo ByGroup Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Relaunch Analysis;
Relaunch ByGroup
Syntax: obj << Relaunch ByGroup
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Relaunch ByGroup;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
:marital status,
{:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;
Remove Local Data Filter
Syntax: obj << Remove Local Data Filter
Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
Nominal Distribution( Column( :country ) ),
Local Data Filter(
Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
Mode( Show( 1 ), Include( 1 ) )
)
);
Wait( 2 );
dist << remove local data filter;
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Render Preset( Expr( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) ) );
Report
Syntax: obj << Report;Report( obj )
Description: Returns a reference to the report object.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Report View( "Summary" );
Save ByGroup Script to Data Table
Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Save ByGroup Script to Data Table;
Save ByGroup Script to Journal
Syntax: obj << Save ByGroup Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Save ByGroup Script to Journal;
Save ByGroup Script to Script Window
Syntax: obj << Save ByGroup Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Save ByGroup Script to Script Window;
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Script for All Objects;
Save Script for All Objects To Data Table
Syntax: obj << Save Script for All Objects To Data Table( <name> )
Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.
Example 1
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 ),
By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table( "My Script" );
Save Script to Data Table
Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Script to Journal;
Save Script to Report
Syntax: obj << Save Script to Report
Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
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( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Save Script to Script Window;
SendToByGroup
Syntax: SendToByGroup( {":Column == level"}, command );
Description: Sends platform commands or display customization commands to each level of a by-group.
dt = Open( "$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/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$Sample_Data/Hair Care Product.jmp" );
obj = Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 3 )
);
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Transform Column
Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)
Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.
JMP Version Added: 16
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;
Window View
Syntax: obj = Uplift(...Window View( "Visible"|"Invisible"|"Private" )...)
Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object
dt = Open( "$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" ) ) )
);