Screening

Example 1

Summary: Screens a data table for regression analysis by selecting Y and X variables, utilizing the Screening platform in JMP.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :OCV ),
    X( :A1, :A2, :A3, :A4, :C1, :C2 )
);

Code Explanation:

1. Open data table.
2. Set Y variable.
3. Set X variables.
4. Run screening analysis.

Example 2

Summary: Screens a data table for regression analysis by selecting predictor variables and performing a screening analysis.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :Taste ),
    X(
        :Cocoa, :Sugar, :Flour, :Butter,
        :Milk, :Eggs
    )
);

Code Explanation:

1. Open table.
2. Run screening analysis.
3. Set response variable.
4. Select predictor variables.
5. Perform analysis.

Example 3

Summary: Screens a data table using the Screening platform in JMP, selecting response and factor variables for further analysis.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening( Y( :Y ), X( :X1, :X2, :X3 ) );

Code Explanation:

1. Open table.
2. Perform screening analysis.
3. Set response variable.
4. Set factor variables.

Example 4

Summary: Screens a data table for regression analysis by selecting the most relevant predictor variables and response variable, utilizing the Screening platform in JMP.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :Percent Reacted ),
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    )
);

Code Explanation:

1. Open data table.
2. Run screening analysis.
3. Specify response variable.
4. List predictor variables.

Example 5

Summary: Screens a data table for regression analysis using the Screening platform in JMP, defining response and factor variables before performing the screening analysis.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :Number Popped, :Total Kernels ),
    X( :Brand, :Time, :Power )
);

Code Explanation:

1. Open data table.
2. Define response variables.
3. Define factor variables.
4. Perform screening analysis.

Example 6

Summary: Screens a data table for regression analysis, selecting the Odor variable as the response and including temp, gl ratio, and ht as factors.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :Odor ),
    X( :temp, :gl ratio, :ht )
);

Code Explanation:

1. Open data table.
2. Launch Screening platform.
3. Set Odor as response variable.
4. Include temp, gl ratio, ht as factors.
5. Perform screening analysis.

Example 7

Summary: Screens a data table using the Screening platform in JMP, selecting variables for analysis and specifying the response variable.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :Y ),
    X(
        :X1, :X2, :X3, :X4, :X5, :X6, :X7,
        :X8, :X9, :X10, :X11, :X12, :X13,
        :X14, :X15, :X16, :X17, :X18
    )
);

Code Explanation:

1. Open table.
2. Run screening analysis.
3. Specify response variable.
4. List predictor variables.
5. Execute analysis.

Example 8

Summary: Screens a data table for regression analysis by selecting the Log Life (×100) response variable and multiple predictor variables, including Initial Structure, Bead Size, Pressure Treatment, Heat Treatment, Cooling Rate, Polish, Final Treatment, ε1, ε2, ε3, and ε4.

Code:

// Screening
// Open data table
dt = Open("data_table.jmp");
// Screening
Screening(
    Y( :"Log Life (√ó100)"n ),
    X(
        :Initial Structure, :Bead Size,
        :Pressure Treatment,
        :Heat Treatment, :Cooling Rate,
        :Polish, :Final Treatment, :ε1,
        :ε2, :ε3, :ε4
    )
);

Code Explanation:

1. Open table.
2. Launch screening analysis.
3. Set response variable.
4. Add predictor variables.

Example 9

Summary: Runs a screening analysis to visualize the relationship between response variable Percent Reacted and predictor variables Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration.

Code:

dt1 = Open("data_table.jmp");
dt1 << Screening(
    Y( :Percent Reacted ),
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    SendToReport( Dispatch( {}, "Half Normal Plot", OutlineBox, {Close( 1 )} ) )
);

Code Explanation:

1. Open table.
2. Launch screening analysis.
3. Set response variable.
4. Add predictor variables.
5. Close half normal plot.

Example 10

Summary: Runs a screening analysis to identify the most relevant factors in predicting Percent Reacted, using Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration as independent variables.

Code:

dt under test = Open("data_table.jmp");
obj = Screening( Y( :Percent Reacted ), X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ) );

Code Explanation:

1. Open data table.
2. Define dependent variable.
3. Define independent variables.
4. Perform screening analysis.

Example 11

Summary: Runs a screening analysis to visualize the relationship between response variable Percent Reacted and predictor variables Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration.

Code:

dt under test = Open("data_table.jmp");
obj = Screening(
    Y( :Percent Reacted ),
    X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ),
    SendToReport( Dispatch( {}, "Half Normal Plot", OutlineBox, {Close( 1 )} ) )
);

Code Explanation:

1. Open data table;
2. Assign dataset to variable.
3. Launch Screening platform.
4. Set response variable.
5. Add predictor variables.
6. Customize report settings.
7. Close Half Normal Plot.

Example 12

Summary: Runs a screening analysis to identify relevant predictor variables for the Weight response variable in the data table, utilizing the Screening platform and Log Capture button click.

Code:

dt = Open("data_table.jmp");
obj = Screening( Y( :Weight ), X( :Country, :Type, :Turning Circle ) );
Log Capture( (obj << report)[Button Box( 2 )] << click );

Code Explanation:

1. Open data table.
2. Perform screening analysis.
3. Set response variable.
4. Define predictor variables.
5. Log capture button click.

Screening using Expr

Summary: Runs the fitting process for a screening model by defining an expression with Screening and executing it on a data table.

Code:

dt = Open("data_table.jmp");
fitTLSExpr = Expr(
    Screening( Y( :Percent Reacted ), X( :Feed Rate, :Catalyst, :Stir Rate, :Temperature, :Concentration ) )
);
fitTLSExpr;

Code Explanation:

1. Open data table.
2. Define fitting expression.
3. Execute fitting expression.