Response Screening

Associated Constructors

Response Screening

Syntax: Response Screening( Y( columns ), X( columns ) )

Description: Automates the process of conducting tests for linear model effects across a large number of responses. Test results and summary statistics are presented in data tables and plots. The false discovery rate (FDR) guards against incorrect declarations of significance. A robust estimation method reduces the sensitivity of tests to outliers.

Example 1

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);

Example 2

dt = Open( "$Sample_Data/Probe.jmp" );
obj = dt << Response Screening( X( :Process ), Y( Eval( 8 :: 108 ) ) );

Columns

By

Syntax: obj = Response Screening(...<By( column(s) )>...)

Description: Performs a separate analysis for each level of the specified column.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    By( _bycol )
);

Freq

Syntax: obj = Response Screening(...<Freq( column )>...)

Description: Specifies a column whose values assign a frequency to each row for the analysis.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    Freq( _freqcol )
);

Grouping

Syntax: obj = Response Screening(...<Grouping( column(s) )>...)

Description: Specifies categorical columns as grouping variables. The rows assigned to each level of the specified column are analyzed separately.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Grouping( :Site )
);

Response

Syntax: obj = Response Screening(...Response( column(s) )...)

Description: Specifies the response variables that contain the measurements to be analyzed.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);

Subgroup

Syntax: obj = Response Screening(...<Subgroup( column(s) )>...)

Description: Specifies one or more subgroup variables. When a subgroup variable is defined, additional fits are performed for each category of the subgroup variable.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Subgroup( :Site )
);

Weight

Syntax: obj = Response Screening(...<Weight( column )>...)

Description: Specifies a column whose values assign a weight to each row for the analysis.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    Weight( _weightcol )
);

X

Syntax: obj = Response Screening(...X( column(s) )...)

Description: Specifies the predictor variables.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);

Y

Syntax: obj = Response Screening(...Y( column(s) )...)

Description: Specifies the response variables that contain the measurements to be analyzed.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);

Item Messages

Cauchy

Syntax: obj = Response Screening(...Cauchy( state=0|1 )...)

Description: Estimates parameters using maximum likelihood and a Cauchy link function. This estimation method assumes that the errors have a Cauchy distribution, which has fatter tails than the normal distribution. This method reduces the emphasis on outliers.

dt = Open( "$Sample_Data/Probe.jmp" );
dt << Response Screening( X( :Process ), Y( Eval( 8 :: 48 ) ), Cauchy( 1 ) );

Common X Scale

Syntax: obj = Response Screening(...Common X Scale( state=0|1 )...)

Description: Notifies the platform that all continuous X variables are on the same scale. This is necessary to compare the slopes of different X variables.

dt = Open( "$Sample_Data/Iris.jmp" );
dt << Response Screening(
    Y( :Sepal length, :Sepal width ),
    X( :Petal length, :Petal width ),
    Common X Scale
);

Common Y Scale

Syntax: obj = Response Screening(...Common Y Scale( state=0|1 )...)

Description: Notifies the platform that all continuous responses are on the same scale. This is necessary to compare the means differences or slopes.

dt = Open( "$Sample_Data/Iris.jmp" );
dt << Response Screening(
    X( :Species ),
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Common Y Scale
);

Comparisons

Syntax: obj = Response Screening(...Comparisons( "Each with Control"|"All Combinations" )...)

Description: Specifies the method for comparing means or rates. You can compare each level with a control group level or compare all possible level combinations.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Response Screening(
    X( :Age Group ),
    Y( :Single Status, :Gender, :I am working on my career ),
    Comparisons( "All combinations" ),
    Name( "2 by M Table" )(1)
);

Corr

Syntax: obj = Response Screening(...Corr( state=0|1 )...)

Description: Computes the Pearson product-moment correlation in terms of the indices that are defined by the value ordering.

dt = Open( "$Sample_Data/Consumer Preferences.jmp" );
dt << Response Screening(
    X( :Employee Tenure, :Position Tenure, :Age Group ),
    Y( :Job Satisfaction ),
    Corr( 1 )
);

Empirical Bayes Shrinkage

Syntax: obj = Response Screening(...Empirical Bayes Shrinkage( state=0|1 )...)

Description: Shrinks residual variance estimates toward an estimated prior mode, borrowing strength across all estimates. This is useful when screening many continuous Y variables on a common scale.

dt = Open( "$Sample_Data/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Eval( 8 :: 88 ) ),
    Common Y Scale,
    Empirical Bayes Shrinkage( 1 )
);

Fit Selected Items

Syntax: obj << Fit Selected Items

Description: Adds Fit Y by X reports to the Response Screening report. The added reports correspond to selected points in the plots or selected rows in the Result Table.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Response Screening( X( :Process ), Y( Column Group( "Responses" ) ) );
obj << Select Where( FDR Logworth > 200 );
obj << Fit Selected Items;

Force X Categorical

Syntax: obj = Response Screening(...Force X Categorical( state=0|1 )...)

Description: Ignores the modeling type and treats all X columns as categorical.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( X( :height, :sex ), Y( :age, :weight ), Force X Categorical( 1 ) );

Force X Continuous

Syntax: obj = Response Screening(...Force X Continuous( state=0|1 )...)

Description: Ignores the modeling type and treats all X columns as continuous.

dt = Open( "$Sample_Data/Consumer Preferences.jmp" );
dt << Response Screening(
    X( :Age Group, :Job Satisfaction ),
    Y( :Gender, :Single Status ),
    Force X Continuous( 1 )
);

Force Y Categorical

Syntax: obj = Response Screening(...Force Y Categorical( state=0|1 )...)

Description: Ignores the modeling type and treats all Y columns as categorical.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( Y( :height, :sex ), X( :age, :weight ), Force Y Categorical( 1 ) );

Force Y Continuous

Syntax: obj = Response Screening(...Force Y Continuous( state=0|1 )...)

Description: Ignores the modeling type and treats all Y columns as continuous.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( Y( :age ), X( :height, :weight ), Force Y Continuous( 1 ) );

Get Crosstab RTF

Syntax: obj << Get Crosstab RTF( state=0|1 )

Description: Get an RTF source for a crosstab table.

JMP Version Added: 19

Get Crosstab Script

Syntax: obj << Get Crosstab Script( state=0|1 )

Description: Get a JSL display script for a crosstab table.

JMP Version Added: 19

Get PValues

Syntax: obj << Get PValues

Description: Returns a reference to the PValues table.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save Outlier Indicator
);
pvals = obj << Get PValues;
Show( pvals );

Kappa

Syntax: obj = Response Screening(...Kappa( state=0|1 )...)

Description: Adds a new column called Kappa to the Result Table. Kappa is a measure of agreement between Y and X.

dt = Open( "$Sample_Data/Mail Messages.jmp" );
dt << Response Screening( X( :From ), Y( :To ), Kappa( 1 ) );

Kruskal Wallis Test

Syntax: obj = Response Screening(...Kruskal Wallis Test( state=0|1 )...)

Description: Computes the Kruskal-Wallis test, a nonparametric (Wilcoxon) rank-based test for continuous Y by categorical X.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( X( :sex ), Y( :height, :weight ), Kruskal Wallis Test( 1 ) );

Max Comparison Levels

Syntax: obj = Response Screening(...Max Comparison Levels( number=100 )...)

Description: Specifies the number of levels that are supported in comparisons. "100" by default.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Wafer Number ),
    Y( Column Group( "Responses" ) ),
    Max Comparison Levels( 24 )
);

Max Logworth

Syntax: obj = Response Screening(...Max Logworth( number )...)

Description: Controls the scale of plots involving logworth values. Logworth values that exceed the specified value are plotted as the specified value to prevent extreme scales in logworth plots.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Max Logworth( 1000 )
);

Missing is Category

Syntax: obj = Response Screening(...Missing is Category( state=0|1 )...)

Description: Treats the missing values of a categorical variable as a separate category.

dt = Open( "$Sample_Data/Big Class.jmp" );
Row() = 1;
:age = .;
Row() = 8;
:age = .;
dt << Response Screening( X( :age ), Y( :sex ), Missing is Category( 1 ) );

Negative Binomial Y

Syntax: obj = Response Screening(...Negative Binomial Y( state=0|1 )...)

Description: Fits each Y response as a count having a Negative Binomial distribution.

dt = Open( "$Sample_Data/Quality Control/Failure2.jmp" );
dt << Response Screening(
    X( :clean ),
    Grouping( :failure ),
    Y( :N ),
    Negative Binomial Y( 1 )
);

No Report

Syntax: obj = Response Screening(...No Report( state=0|1 )...)

Description: Suppresses the report window. Use this option to run Save commands to obtain results without the report window appearing.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save PValues,
    No Report( 1 )
);

PValues Table on Launch

Syntax: obj = Response Screening(...PValues Table on Launch( state=0|1 )...)

Description: Creates a data table for the p-values and individual model fit statistics. "0" by default.

JMP Version Added: 16

dt = Open( "$Sample_Data/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Eval( 8 :: 88 ) ),
    Robust,
    PValues Table on Launch( 1 )
);

Paired X and Y

Syntax: obj = Response Screening(...Paired X and Y( state=0|1 )...)

Description: Performs tests only for Y columns paired with X columns according to their order in the launch window. For example, Y1 is paired with X1 and Y2 is paired with X2.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( X( :age, :sex ), Y( :height, :weight ), Paired X and Y( 1 ) );

Poisson Y

Syntax: obj = Response Screening(...Poisson Y( state=0|1 )...)

Description: Fits each Y response as a count having a Poisson distribution.

dt = Open( "$Sample_Data/Quality Control/Failure2.jmp" );
dt << Response Screening( X( :clean ), Grouping( :failure ), Y( :N ), Poisson Y( 1 ) );

Practical Difference Portion

Syntax: obj << Practical Difference Portion( number=0.10 )

Description: Specifies the fraction of the specification range that represents a difference that you consider practically meaningful. "0.10" by default.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Eval( 8 :: 48 ) ),
    Practical Difference Portion( .2 ),
    Save Compare Means
);

Practical Differences and Equivalences

Syntax: obj << Practical Differences and Equivalences( Practical Portion(fraction) | Specific Difference(number) )

Description: Given a difference to detect, tests if the actual difference is significantly greater than or significantly less than that difference to detect in absolute value.

Quartiles per Group

Syntax: obj = Response Screening(...Quartiles per Group( state=0|1 )...)

Description: Computes the quartiles and range for each group for continuous Y by categorical X.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening( X( :sex ), Y( :height, :weight ), Quartiles per Group( 1 ) );

Ratio Adjustment

Syntax: obj = Response Screening(...Ratio Adjustment( "No Adjustment"|"Add 0.5 when any zero"|"Add 0.5 always" )...)

Description: Provides options to add 0.5 to cell counts when calculating risk ratios, odds ratios, and risk differences. This adjustment avoids problems that arise from dividing by zero.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Response Screening(
    X( :Age Group ),
    Y( :Single Status, :Gender, :I am working on my career ),
    Ratio Adjustment( "Add 0.5 Always" ),
    Name( "2 by M Table" )(1)
);

Robust

Syntax: obj = Response Screening(...Robust( state=0|1 )...)

Description: Fits regression and ANOVA models using the Huber M-estimation method, which is resistant to outliers.

dt = Open( "$Sample_Data/Probe.jmp" );
dt << Response Screening( X( :Process ), Y( Eval( 8 :: 88 ) ), Robust( 1 ) );

Save 2 by M

Syntax: obj << Name( "Save 2 by M table" )

Description: Saves the information in the 2 by M Results report, as well as other test statistics, to a new data table.

Example 1

dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Response Screening(
    X( :Age Group ),
    Y( :Single Status, :Gender, :I am working on my career )
);
obj << Name( "2 by M Table" )(1);
obj << Name( "Save 2 by M Table" );

Example 2

dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Response Screening(
    X( :Age Group ),
    Y( :Single Status, :Gender, :I am working on my career )
);
obj << "2 by M Table"n( 1 );
obj << "Save 2 by M Table"n;

Save Compare Means

Syntax: obj << Save Compare Means

Description: Creates a data table that contains the results from testing all pairwise comparisons across the levels of the categorical variable.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save Compare Means
);

Save Means

Syntax: obj << Save Means

Description: Creates a data table that contains the counts, means, and standard deviations for each level of the categorical variable.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening( X( :Process ), Y( Column Group( "Responses" ) ), Save Means );

Save Means Differences

Syntax: obj << Save Means Differences

Description: Creates a data table that contains the results from testing all pairwise comparisons across the levels of the categorical variable.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save Means Differences
);

Save Outlier Indicator

Syntax: obj << Save Outlier Indicator

Description: Saves a group of indicator columns to the original data table to indicate outliers.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save Outlier Indicator
);

Save PValues

Syntax: obj << Save PValues

Description: Creates a data table that contains the information in the Result Table.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening( X( :Process ), Y( Column Group( "Responses" ) ), Save PValues );

Save Std Residuals

Syntax: obj << Save Std Residuals

Description: For each fit, adds a column to the original data table that contains the residuals divided by their estimated standard deviation.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Column Group( "Responses" ) ),
    Save Std Residuals
);

Select Columns

Syntax: obj << Select Columns( condition )

Description: Selects columns in the original data table that correspond to selected rows in the Result Table.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Response Screening( X( :Process ), Y( Column Group( "Responses" ) ) );
obj << Select Where( FDR Logworth > 200 );
obj << Select Columns;

Select Where

Syntax: obj << Select Where

Description: Selects items in the report table that correspond to a particular condition.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Response Screening( X( :Process ), Y( Column Group( "Responses" ) ) );
obj << Select Where( FDR Logworth > 200 );

Show Crosstab Report

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

Description: Experimental Hidden Feature: Show the details for each X and Y combination in a crosstab cell

JMP Version Added: 19

Show Means Differences

Syntax: obj << Show Means Differences

Description: Shows the Logworth by Difference plot and the Means Differences report in the Response Screening report window. This option assumes that the Y variables are on a common scale.

Open( "$Sample_Data/Life Sciences/Genotypes Pedigree.jmp" );
Response Screening(
    Y( Column Group( "Markers" ) ),
    X( :Sex, :Disease Status ),
    Common Y Scale( 1 ),
    Show Means Differences( 1 ),
    SendToReport(
        Dispatch( {}, "", TabListBox, {Set Selected( 4 )} ),
        Dispatch( {}, "", TabListBox( 2 ), {Set Selected( 2 )} )
    )
);

Show Plots

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

Description: Shows or hides the plots in the report window. On by default.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Response Screening( X( :Process ), Y( Column Group( "Responses" ) ) );
obj << Show Result Tables( 0 );

Show Report Tables

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

Description: Shows or hides the result tables in the report window. On by default.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Response Screening( X( :Process ), Y( Column Group( "Responses" ) ) );
obj << Show Result Tables( 0 );

Show Slopes

Syntax: obj << Show Slopes

Description: Shows the Logworth by Slope plot in the Response Screening report window. This option assumes that the Y variables are on a common scale and the X variables are on a common scale.

Open( "$Sample_Data/Life Sciences/Genotypes Pedigree.jmp" );
Response Screening(
    Y( :Trait1, :Trait2, :Trait3, :Trait4 ),
    X( Column Group( "Markers" ) ),
    Show Slopes( 1 ),
    SendToReport( Dispatch( {}, "", TabListBox, {Set Selected( 4 )} ) )
);

Specific Difference to Detect

Syntax: obj << Specific Difference to Detect( number )

Description: Specifies a difference to detect rather than a portion of a specification range or sigma. This option assumes that all Y variables are on the same scale.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    X( :Process ),
    Y( Eval( 8 :: 48 ) ),
    Practical Difference Portion( .2 ),
    Save Compare Means
);

Subgroup Twoway

Syntax: obj = Response Screening(...Subgroup Twoway( state=0|1 )...)

Description: Fits all of the two-way subgroup combinations. This option is available only when at least one Subgroup variable is defined.

dt = Open( "$Sample_Data/Big Class.jmp" );
dt << Response Screening(
    X( :height ),
    Y( :weight ),
    Subgroup( :age, :sex ),
    Subgroup Twoway( 1 )
);

Tabbed Report Layout

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

Description: On by default.

JMP Version Added: 17

Unthreaded

Syntax: obj = Response Screening(...Unthreaded( state=0|1 )...)

Description: Suppresses multithreading.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    Unthreaded( 1 )
);

Volcano Plots Use FDR Axis

Syntax: obj = Response Screening(...Volcano Plots Use FDR Axis( state=0 )...)

Description: Use FDR-adjusted Logworth instead of unadjusted Logworth on vertical axis for volcano plots. "0" by default.

JMP Version Added: 18

Open( "$Sample_Data/Life Sciences/Genotypes Pedigree.jmp" );
Response Screening(
    Y( :Trait1, :Trait2, :Trait3, :Trait4 ),
    X( Column Group( "Markers" ) ),
    Common Y Scale( 1 ),
    Common X Scale( 1 ),
    Volcano Plots Use FDR Axis( 1 ),
    SendToReport( Dispatch( {}, "", TabListBox, {Set Selected( 4 )} ) )
);

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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    By( _bycol )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Probe.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process ),
    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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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/Probe.jmp" );
obj = dt << Response Screening(
    Y( :DELL_RPNBR, :DELL_RPPBR, :DELW_M1, :DELW_M2, :DELW_NBASE ),
    X( :Process )
);
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 = Response 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" ) ) )
);