Model Screening

Associated Constructors

Model Screening

Syntax: Model Screening( Y( column ), X( columns ) )

Description: Fits many different predictive models, so that you can select the best.

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Columns

By

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

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    By( _bycol )
);

Factor

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

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Freq

Syntax: obj << Freq( column )

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Freq( _freqcol )
);

Response

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

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Validation

Syntax: obj << Validation( column )

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Weight

Syntax: obj << Weight( column )

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Weight( _weightcol )
);

X

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

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Y

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

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);

Item Messages

Add Quadratics

Syntax: obj = Model Screening(...Add Quadratics( state=0|1 )...)

Description: Adds effects for the squares of continuous variables to linear modeling fits.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :LTG, :BMI, :BP, :Glucose, :HDL ),
    Add Quadratics( 1 )
);

Add Two Way Interactions

Syntax: obj = Model Screening(...Add Two Way Interactions( state=0|1 )...)

Description: Adds all two-way interaction effects to linear modeling fits.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :LTG, :BMI, :BP, :Glucose, :HDL ),
    Add Two Way Interactions( 1 )
);

Additional Methods

Syntax: obj = Model Screening(...Additional Methods( state=0|1 )...)

Description: Calls several additional methods in the Generalized Regression platform in addition to Lasso: Forward Selection, Pruned Forward Selection, Elastic Net, and Ridge.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Additional Methods( 1 )
);

Boosted Tree

Syntax: obj = Model Screening(...Boosted Tree( state=0|1 )...)

Description: Builds a decision tree that is a sequence of smaller trees to predict a response. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Bootstrap Forest

Syntax: obj = Model Screening(...Bootstrap Forest( state=0|1 )...)

Description: Builds a collection of decision trees using random sampling and averages the results to predict a response. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 1 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Cardinality of Predictors

Syntax: obj << Cardinality of Predictors( state=0|1 )

Description: Shows or hides a report of the number of levels and how many parameters are used in the linear model fit for each categorical predictor.

JMP Version Added: 16

Open( "$Sample_Data/Equity.jmp" );
Model Screening(
    Y( :BAD ),
    Validation( :Validation ),
    X(
        :LOAN, :MORTDUE, :VALUE, :REASON, :JOB, :YOJ, :DEROG, :DELINQ, :CLAGE, :NINQ, :CLNO,
        :DEBTINC
    ),
    Neural( 0 ),
    Bootstrap Forest( 0 ),
    Generalized Regression( 0 ),
    Support Vector Machines( 0 ),
    Cardinality of Predictors( 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.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),
    Decision Threshold( 1 )
);

Decision Tree

Syntax: obj = Model Screening(...Decision Tree( state=0|1 )...)

Description: Builds a decision tree to predict a response. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 1 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Discriminant

Syntax: obj = Model Screening(...Discriminant( state=0|1 )...)

Description: Classifies categorical group membership based on continuous variables. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Iris.jmp" );
Make Validation Column( Validation Set( .3 ), Training Set( .7 ), Go );
obj = Model Screening(
    Y( :Species ),
    Validation( :Validation ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Discriminant( 1 ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 0 ),
    Generalized Regression( 0 )
);

Elapsed Time

Syntax: obj << Elapsed Time( state=0|1 )

Description: Shows or hides a report that contains the total elapsed time that was spent fitting each method.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Elapsed Time( 1 )
);

Fit Least Squares

Syntax: obj = Model Screening(...Fit Least Squares( state=0|1 )...)

Description: Fits a linear regression model for a continuous response. Techniques include regression, analysis of variance, analysis of covariance, mixed models, and analysis of designed experiments. The Emphasis option enables you to specify the report layout. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 1 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Fit Stepwise

Syntax: obj = Model Screening(...Fit Stepwise( state=0|1 )...)

Description: Fits stepwise regression models, which facilitate variable selection for standard least squares and ordinal logistic models, as well as nominal logistic models with a binary response. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 1 ),
    Generalized Regression( 0 )
);

Generalized Regression

Syntax: obj = Model Screening(...Generalized Regression( state=0|1 )...)

Description: Fits generalized linear models using penalized regression techniques, which help automate variable selection in a way that avoids overfitting. The penalized regression techniques include the lasso, the adaptive lasso, the elastic net, the adaptive elastic net, and ridge regression. The response distributions can accommodate continuous, categorical, count, and time-to-event response data. This is the recommended personality for most regression settings. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 1 )
);

Informative Missing

Syntax: obj = Model Screening(...Informative Missing( state=0|1 )...)

Description: Enables the informative missing option for all platforms.

JMP Version Added: 16

Open( "$Sample_Data/Equity.jmp" );
Model Screening(
    Y( :BAD ),
    Validation( :Validation ),
    X(
        :LOAN, :MORTDUE, :VALUE, :REASON, :JOB, :YOJ, :DEROG, :DELINQ, :CLAGE, :NINQ, :CLNO,
        :DEBTINC
    ),
    Neural( 0 ),
    Informative Missing( 1 )
);

K Fold Crossvalidation

Syntax: obj = Model Screening(...K Fold Crossvalidation( state=0|1 )...)

Description: Partitions the data randomly into K parts or folds. A model is fit to the data K times, each time with a different fold held out as a crossvalidation set.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    K Fold Crossvalidation( 1 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

K Nearest Neighbors

Syntax: obj = Model Screening(...K Nearest Neighbors( state=0|1 )...)

Description: Predicts a response based on the responses of the k nearest neighbors. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 1 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

K for K Fold

Syntax: obj = Model Screening(...K for K Fold( number=5 )...)

Description: Specifies the number of folds for K Fold Crossvalidation. The default is 5 and K must be greater than 1. "5" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    K Fold Crossvalidation( 1 ),
    K for K Fold( 6 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

K for Nested

Syntax: obj = Model Screening(...K for Nested( number=5 )...)

Description: Specifies the number of folds for Nested Crossvalidation. The default is 5 and K must be greater than 1. "5" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Nested Crossvalidation( 1 ),
    K for Nested( 3 ),
    L for Nested( 4 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

L for Nested

Syntax: obj = Model Screening(...L for Nested( number=4 )...)

Description: Specifies the number of inner folds for Nested Crossvalidation. The default is 4 and L must be greater than 1. "4" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Nested Crossvalidation( 1 ),
    K for Nested( 5 ),
    L for Nested( 4 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

Log Methods

Syntax: obj = Model Screening(...Log Methods( state=0|1 )...)

Logistic Regression

Syntax: obj = Model Screening(...Logistic Regression( state=0|1 )...)

Description: Fits a logistic regression model of nominal response categories for both continuous and categorical predictors. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 0 )
);

Model NParm Limit

Syntax: obj << Model NParm Limit( number=450 )

Description: Specifies the number of parameters above which the modeling platforms are not run. "450" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Add Two Way Interactions( 1 ),
    Add Quadratics( 1 ),
    Model NParm Limit( 40 ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Fit Least Squares( 1 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 0 ),
    Generalized Regression( 0 )
);

Naive Bayes

Syntax: obj = Model Screening(...Naive Bayes( state=0|1 )...)

Description: Predicts group membership for a categorical variable.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 1 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 0 ),
    Generalized Regression( 0 ), 

);

Nested Crossvalidation

Syntax: obj = Model Screening(...Nested Crossvalidation( state=0|1 )...)

Description: Partitions the data randomly into K equal parts and then further partitions all but one of those parts into L equal parts.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Nested Crossvalidation( 1 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

Neural

Syntax: obj = Model Screening(...Neural( state=0|1 )...)

Description: Predicts one or more response variables using a flexible function of the input variables. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Partial Least Squares

Syntax: obj = Model Screening(...Partial Least Squares( state=0|1 )...)

Description: Fits a model to one or more response variables using latent factors. This permits models to be fit when explanatory variables are highly correlated, or when there are more explanatory variables than there are observations.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 ),
    Partial Least Squares( 1 )
);

Plot Actual by Predicted

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

Description: Overlays Actual by Predicted points from several model fits.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 1 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 1 ),
    Generalized Regression( 1 ),
    Plot Actual by Predicted( 1 )
);

Precision Recall Curve

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

Description: Shows or hides overlaid precision-recall curves for all of the model fits. There are separate plots for the Training, Validation, and Test sets.

JMP Version Added: 16

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = dt << Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),

);
obj << Precision Recall Curve( 1 );

Predictor Properties

Syntax: obj << Predictor Properties( state=0|1 )

Description: Available if you hold down the shift button, for each platform called, shows information about supported interfaces.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Predictor Properties( 1 )
);

Profiler

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

Description: Shows or hides prediction profilers for each type of model fit. This option is available only for continuous responses.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 1 ),
    Profiler( 1 )
);

ROC Curve

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

Description: Shows or hides overlaid Receiver Operating Characteristic (ROC) curves for all of the model fits. There are separate plots for the Training, Validation, and Test sets.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),
    ROC Curve( 1 )
);

Remove Live Reports

Syntax: obj = Model Screening(...Remove Live Reports( state=0|1 )...)

Description: Removes the individual model platform reports from the Model Screening report window. You can use this option to free up memory for further work.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    K Fold Crossvalidation( 1 ),
    Remove Live Reports( 1 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

Repeated K Fold

Syntax: obj = Model Screening(...Repeated K Fold( number=0 )...)

Description: Specifies the number of times the K Fold Crossvalidation or Nested Crossvalidation process is repeated. "0" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Repeated K Fold( 2 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

SVM NRow Limit

Syntax: obj << SVM NRow Limit( number=10000 )

Description: Specifies the number of rows above which Support Vector Machines are not run. "10000" by default.

JMP Version Added: 16

Open( "$Sample_Data/Equity.jmp" );
Model Screening(
    Y( :BAD ),
    Validation( :Validation ),
    X(
        :LOAN, :MORTDUE, :VALUE, :REASON, :JOB, :YOJ, :DEROG, :DELINQ, :CLAGE, :NINQ, :CLNO,
        :DEBTINC
    ),
    Decision Tree( 1 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 1 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 ),
    SVM NRow Limit( 6000 )
);

Save Folded Prediction Formula

Syntax: obj << Save Folded Prediction Formula

Description: Saves new columns to the original data table. The new columns contain a leak-free prediction formula for K Fold Crossvalidation. For each row, the formula avoids using model fits that were trained using that row.

Save KFold Results Table

Syntax: obj << Save KFold Results Table

Description: Saves the information in the Summary Across the Folds report to a new data table.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    K Fold Crossvalidation( 1 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 1 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 1 ),
    Save KFold Results Table
);

Save Prediction Formulas

Syntax: obj << Save Prediction Formulas

Description: Saves the prediction formulas to the data table.

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Fit Least Squares( 1 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 0 ),
    Generalized Regression( 0 )
);
obj << Select Fit( "Training", "Best" );
obj << Save Prediction Formulas;

Save Results Table

Syntax: obj << Save Results Table

Description: Saves the information in the Validation report to a new data table. If there is a test set, the information in the Test report is also saved to a new data table.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Save Results Table
);

Select Fit

Syntax: <<Select Fit( Training | Validation | Test | Summary | Clear All, Clear | Dominant | Best(<number>), | Largest(name,<number>) | Smallest(name,<number>) | Where(expression) )

Description: Select fits in various reports based on specified criteria. This option is available only in JSL.

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
obj << Select Fit( Validation, Largest( "RSquare", 2 ) );

Set Probability Threshold

Syntax: obj << Set Probability Threshold( number=0.5 )

Description: Specifies the number of parameters above which the modeling platforms are not run. "0.5" by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 1 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),
    Decision Threshold( 1 ),
    Set Probability Threshold( .2 )
);

Set Random Seed

Syntax: obj = Model Screening(...Set Random Seed( number )...)

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

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Set Random Seed( 123454321 )
);

Show Methods in Log

Syntax: obj = Model Screening(...Show Methods in Log( state=0|1 )...)

Description: Writes out a progress message to the log each time a fitting platform is called.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Log Methods( 1 )
);

Show Profit

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

Description: Shows or hides the expected profit for each model using the specified Profit Matrix for the response levels.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
Column( "Y Binary" ) << Set Property(
    "Profit Matrix", {[1 - 1, -0.3333333 1, . .], {"Low", "High", "Undecided"}}
);
obj = Model Screening(
    Y( :Y Binary ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),
    Show Profit( 1 )
);

Specify Profit Matrix

Syntax: obj << Specify Profit Matrix

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

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Model Screening(
    Y( :marital status ),
    X( :sex, :age, :country, :type, :size ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Naive Bayes( 0 ),
    Neural( 1 ),
    Support Vector Machines( 0 ),
    Fit Stepwise( 0 ),
    Logistic Regression( 1 ),
    Generalized Regression( 1 ),
    Specify Profit Matrix( [0 -1, -0.6 0, . .], "Married", "Single", "Undecided" ),
    Show Profit( 1 )
);

Support Vector Machines

Syntax: obj = Model Screening(...Support Vector Machines( state=0|1 )...)

Description: Predicts a response based on the support vectors in the space of the X variables. On by default.

JMP Version Added: 16

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 1 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 )
);

Time Limit Each

Syntax: obj = Model Screening(...Time Limit Each( number )...)

Description: Specifies a time limit in seconds for each fit. For platforms that support early stopping, the best estimates up to that point are provided.

JMP Version Added: 16

Open( "$Sample_Data/Equity.jmp" );
Model Screening(
    Y( :BAD ),
    Validation( :Validation ),
    X(
        :LOAN, :MORTDUE, :VALUE, :REASON, :JOB, :YOJ, :DEROG, :DELINQ, :CLAGE, :NINQ, :CLNO,
        :DEBTINC
    ),
    Time Limit Each( 1 )
);

Use Two Way Splits for K Fold

Syntax: obj = Model Screening(...Use Two Way Splits for K Fold( state=0|1 )...)

Description: Uses only training and validation splits instead of training, validation, and test splits.

JMP Version Added: 19

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    K Fold Crossvalidation( 1 ),
    Use Two Way Splits for K Fold( 1 ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Neural( 0 ),
    Support Vector Machines( 0 )
);

XGBoost

Syntax: obj = Model Screening(...XGBoost( state=0|1 )...)

Description: Invokes XGBoost for gradient boosting if you have the XGBoost add-in. This option appears only if the add-in is installed.

Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    Decision Tree( 0 ),
    Bootstrap Forest( 0 ),
    Boosted Tree( 0 ),
    K Nearest Neighbors( 0 ),
    Neural( 0 ),
    Support Vector Machines( 0 ),
    Fit Least Squares( 0 ),
    Fit Stepwise( 0 ),
    Generalized Regression( 0 ),
    XGBoost( 1 )
);

Shared Item Messages

Action

Syntax: obj << Action

Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Apply Preset

Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )

Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.

JMP Version Added: 18

Anonymous preset

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
dt2 = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj2 = dt2 << Oneway( Y( :LogHist0 ), X( :drug ) );
Wait( 1 );
obj2 << Apply Preset( preset );

Search by name

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "Compare Distributions" );

Search within folder(s)

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "t-Tests", Folder( "Compare Means" ) );

Automatic Recalc

Syntax: obj << Automatic Recalc( state=0|1 )

Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Broadcast

Syntax: obj << Broadcast(message)

Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Quality Control/Diameter.jmp" );
objs = Control Chart Builder(
    Variables( Subgroup( :DAY ), Y( :DIAMETER ) ),
    By( :OPERATOR )
);
objs[1] << Broadcast( Save Summaries );

Column Switcher

Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)

Description: Adds a control panel for changing the platform's variables

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);

Copy ByGroup Script

Syntax: obj << Copy ByGroup Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

dt = Open( "$Sample_Data/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$Sample_Data/Diabetes.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
    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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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/Diabetes.jmp" );
obj = Model Screening(
    Y( :Y ),
    Validation( :Validation ),
    X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose )
);
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 = Model Screening(...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" ) ) )
);