Fit Y by X Group
Bivariate > Bivariate Curve
Associated Constructors
Curve
Syntax: obj << ( Curve[number] )
Description: Accesses an individual curve for routing further messages.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ), Fit Line );
obj << (curve[1] << Line of Fit( 1 ));
Wait( 1 );
obj << (curve[1] << Line of Fit( 0 ));
Item Messages
Confid Curves Fit
Syntax: obj << ( Curve[number] << Confid Curves Fit( state=0|1 ) ); obj << Fit Name( {Confid Curves Fit( state=0|1 )} )
Description: Shows or hides the confidence curves for the fitted line.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Fit( 1 )} );
Wait( 1 );
obj << Fit Polynomial( 3 );
obj << (curve[2] << Confid Curves Fit( 1 ));
Confid Curves Indiv
Syntax: obj << ( Curve[number] << Confid Curves Indiv( state=0|1 ) ); obj << Fit Name( {Confid Curves Indiv( state=0|1 )} )
Description: Shows or hides the confidence curves for an individual predicted value.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line( {Confid Curves Indiv( 1 )} ) );
Wait( 1 );
obj << Fit Polynomial( 3 );
obj << (curve[2] << Confid Curves Indiv( 1 ));
Confid Shaded Fit
Syntax: obj << ( Curve[number] << Confid Shaded Fit( state=0|1 ) ); obj << Fit Name( {Confid Shaded Fit( state=0|1 )} )
Description: Shades the area between the confidence curves and the fitted line.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Fit( 1 ), Confid Shaded Fit( 1 )} );
Wait( 1 );
obj << (curve[1] << Confid Shaded Fit( 0 ));
Confid Shaded Indiv
Syntax: obj << ( Curve[number] << Confid Shaded Indiv( state=0|1 ) ); obj << Fit Name( {Confid Shaded Indiv( state=0|1 )} )
Description: Shades the area between the confidence curves for an individual predicted value and the fitted line.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Indiv( 1 ), Confid Shaded Indiv( 1 )} );
Wait( 1 );
obj << (curve[1] << Confid Shaded Indiv( 0 ));
Indiv Confidence Limit Formula
Syntax: obj << ( Curve[number] << Indiv Confidence Limit Formula( <alpha> ) ); obj << Fit Name( {Indiv Confidence Limit Formula( <alpha> ) )
Description: Saves new formula columns to the original data table. There are columns for the lower and upper confidence limits for an individual prediction that are functions of the regressors. The default level for alpha is 0.05, which creates 95% confidence limits.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Indiv Confidence Limit Formula( .001 ));
Wait( 1 );
obj << Fit Line( {Indiv Confidence Limit Formula( 0.01 )} );
Line Color
Syntax: obj << ( Curve[number] << Line Color( "color" ) ); obj << Fit Name( {Line Color( "color" )} ) obj << Density Ellipse( {Line Color( "color" )} )
Description: Changes the line color of the fitted line, confidence curves, and shaded confidence regions.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Indiv, Line Color( "Medium Dark BlueGreen" )} );
Wait( 1 );
obj << (curve[1] << Line Color( "black" ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95, {Line Color( "Medium Dark BlueGreen" )} );
Wait( 1 );
obj << (curve[1] << Line Color( "black" ));
Line Style
Syntax: obj << ( Curve[number] << Line Style( "pen style" ) ); obj << Fit Name( {Line Styel( "pen style" )} ) obj << Density Ellipse( {Line Style( "pen style" )} )
Description: Changes the line style of the fitted line.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line( {Confid Curves Fit} ) );
Wait( 1 );
obj << (Curve[1] << Line Style( "DashDot" ));
obj << Fit Polynomial( 3, {Line Style( "Dense Dash" )} );
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Density Ellipse( 0.95 ) );
Wait( 1 );
obj << (Curve[1] << Line Style( "DashDot" ));
obj << Density Ellipse( 0.90, {Line Style( "Dense Dash" )} );
Line Width
Syntax: obj << ( Curve[number] << Line Width( number ) ); obj << Fit Name( {Line Width( number )} ) obj << Density Ellipse( {Line Width( number )} )
Description: Changes the line width of both the fitted line and any confidence curves.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Line Width( 3 )} );
Wait( 1 );
obj << (curve[1] << Line Width( 1 ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.99, {Line Width( 3 )} );
Wait( 1 );
obj << (curve[1] << Line Width( 1 ));
Line of Fit
Syntax: obj << ( Curve[number] << Line of Fit( state=0|1 ) ); obj << Fit Name( {Line of Fit( state=0|1 )} ) obj << Density Ellipse( {Line of Fit( state=0|1 )} )
Description: Shows or hides the line of fit. On by default.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line );
Wait( 1 );
obj << (Curve[1] << Line of Fit( 0 ));
obj << Fit Polynomial( 3, {Line of Fit( 0 )} );
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Density Ellipse( 0.95 ) );
Wait( 1 );
obj << (Curve[1] << Line of Fit( 0 ));
obj << Density Ellipse( 0.90, {Line of Fit( 0 )} );
Mean Confidence Limit Formula
Syntax: obj << ( Curve[number] << Mean Confidence Limit Formula( <alpha> ) ); obj << Fit Name( {Mean Confidence Limit Formula( <alpha> ) )
Description: Saves new formula columns to the original data table. There are columns for the lower and upper confidence limits for the mean response that are functions of the regressors. The default level for alpha is 0.05, which creates 95% confidence limits.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Mean Confidence Limit Formula( .01 ));
Wait( 1 );
obj << Fit Line( {Mean Confidence Limit Formula( 0.05 )} );
Plot Residuals
Syntax: obj << ( Curve[number] << Plot Residuals( state=0|1 ) ); obj << Fit Name( {Plot Residuals( state=0|1 )} )
Description: Shows or hides five diagnostic plots.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( 1, {Plot Residuals( 1 )} );
Wait( 1 );
obj << (curve[1] << Plot Residuals( 0 ));
Profiler
Syntax: obj << ( Curve[number] << Profiler( state=0|1 ) ); obj << Fit Name( {Profiler( state=0|1 )} )
Description: Shows or hides a prediction profiler for the selected outcome given the selected predictor and the specified model.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Polynomial( 3, {Profiler( 1 )} );
Wait( 1 );
obj << (Curve[1] << Profiler( 0 ));
Remove Fit
Syntax: obj << ( Curve[number] << Remove Fit )
Description: Removes the fitted curve.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( 1 );
obj << Fit Polynomial( 3 );
Wait( 1 );
obj << (Curve[2] << Remove Fit);
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95 );
obj << Density Ellipse( 0.90 );
Wait( 1 );
obj << (Curve[2] << Remove Fit);
Report
Syntax: obj << ( Curve[number] << Report( state=0|1 ) ); obj << Fit Name( {Report( state=0|1 )} ) obj << Density Ellipse( {Report( state=0|1 )} )
Description: Shows or hides the reports for summary of fit, lack of fit, ANOVA, and parameter estimates. On by default.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Report( 0 )} );
Wait( 1 );
obj << (Curve[1] << Report( 1 ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95, {Report( 0 )} );
Wait( 1 );
obj << (Curve[1] << Report( 1 ));
Save Predicteds
Syntax: obj << ( Curve[number] << Save Predicteds ); obj << Fit Name( {Save Predicteds} )
Description: Saves a new column to the original data table. The column contains the predicted values for the specified fitted curve.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Polynomial( 3, {Save Predicteds} );
Wait( 1 );
obj << Fit Line( 1 );
obj << (curve[2] << Save Predicteds);
Save Residuals
Syntax: obj << ( Curve[number] << Save Residuals ); obj << Fit Name( {Save Residuals} )
Description: Saves a new column to the original data table. The column contains the residual values for the specified fitted curve.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Save Residuals);
Wait( 1 );
obj << Fit Line( {Save Residuals} );
Save Studentized Residuals
Syntax: obj << ( Curve[number] << Save Studentized Residuals ); obj << Fit Name( {Save Studentized Residuals} )
Description: Saves a new column to the original data table. The column contains the Studentized residuals for the specified fitted curve.
JMP Version Added: 14
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Save Studentized Residuals);
Wait( 1 );
obj << Fit Line( {Save Studentized Residuals} );
Set Alpha Level
Syntax: obj << ( Curve[number] << Set Alpha Level( alpha ) ); obj << Fit Name( {Set Alpha Level( alpha )} )
Description: Changes the alpha level used for the confidence curves.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Fit( 1 ), Set Alpha Level( 0.001 )} );
obj << Fit Polynomial( 2, {Confid Curves Fit( 1 )} );
Wait( 1 );
obj << (curve[2] << Set Alpha Level( 0.01 ));
Set α Level
Syntax: obj << ( Curve[number] << Set Alpha Level( alpha ) ); obj << Fit Name( {Set Alpha Level( alpha )} )
Description: Changes the alpha level used for the confidence curves.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Fit( 1 ), Set Alpha Level( 0.001 )} );
obj << Fit Polynomial( 2, {Confid Curves Fit( 1 )} );
Wait( 1 );
obj << (curve[2] << Set Alpha Level( 0.01 ));
Bivariate > Bivariate Nonpar Density
Item Messages
5% Contours
Syntax: obj << ( Curve[number] << "5% Contours"n( state=0|1 ) ); obj << Nonpar Density( {"5% Contours"n( state=0|1 )} )
Description: Shows or hides the 5% contour lines. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {"5% Contours"n( 0 )} );
Wait( 1 );
obj << (curve[1] << "5% Contours"n( 1 ));
Color By Density Quantile
Syntax: obj << ( Curve[number] << Color By Density Quantile ); obj << Nonpar Density( {Color by Density Quantile} )
Description: Colors the points and rows according to the density.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Nonpar Density );
Wait( 1 );
obj << (curve[1] << Color By Density Quantile);
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Color By Density Quantile} );
Color Theme
Syntax: obj << ( Curve[number] << Color Theme( "theme"(state=0|1 ) ) ); obj << Nonpar Density( {Color Theme( "theme"( state=0|1 ) )} )
Description: Sets the color theme for the quantile density contour lines.
JMP Version Added: 14
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Nonpar Density );
obj << (curve[1] << Color Theme( "Jet"(1) ));
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Color Theme( "White to Black"(1) )} );
Contour Fill
Syntax: obj << ( Curve[number] << Contour Fill( state=0|1 ) ); obj << Nonpar Density( {Contour Fill( state=0|1 )} )
Description: Shows or hides the filled contours.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Nonpar Density( {Contour Lines( 0 )} ) );
obj << (curve[1] << Contour Fill( 1 ));
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Contour Fill( 1 )} );
Contour Lines
Syntax: obj << ( Curve[number] << Contour Lines( state=0|1 ) ); obj << Nonpar Density( {Contour Lines( state=0|1 )} )
Description: Shows or hides the contour lines. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Contour Lines( 0 )} );
Wait( 1 );
obj << (curve[1] << Contour Lines( 1 ));
Kernel Control
Syntax: obj << ( Curve[number] << Kernel Control( state=0|1 ) ); obj << Nonpar Density( {Kernel Control( state=0|1 )} )
Description: Shows or hides a slider to control the standard deviation for each variable. The standard deviation defines the range of X and Y values for determining the density of contour lines.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Kernel Control( 1 )} );
Wait( 1 );
obj << (curve[1] << Kernel Control( 0 ));
Mesh Plot
Syntax: obj << ( Curve[number] << Mesh Plot( state=0|1 ) ); obj << Nonpar Density( {Mesh Plot( state=0|1 )} )
Description: Shows or hides a three-dimensional plot of the density over a grid of the two analysis variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Mesh Plot( 1 )} );
Wait( 1 );
obj << (curve[1] << Mesh Plot( 0 ));
Modal Clustering
Syntax: obj << ( Curve[number] << Modal Clustering( state=0|1 ) ); obj << Nonpar Density( {Modal Clustering( state=0|1 )} )
Description: Shows or hides the results for a modal clustering of the data, which identifies cluster assignments based on the current contours. This option also saves the cluster numbers to a new column in the data table.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Modal Clustering( 1 )} );
Wait( 1 );
obj << (curve[1] << Modal Clustering( 0 ));
Remove Fit
Syntax: obj << ( Curve[number] << Remove Fit )
Description: Removes the nonparametric density.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density();
Wait( 1 );
obj << (curve[1] << Remove Fit);
Report
Syntax: obj << ( Curve[number] << Report( state=0|1 ) ); obj << Nonpar Density( {Report( state=0|1 )} )
Description: Shows or hides the Quantile Density Contours report. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Report( 0 )} );
Wait( 1 );
obj << (curve[1] << Report( 1 ));
Save Density Grid
Syntax: obj << ( Curve[number] << Save Density Grid ); obj << Nonpar Density( {Save Density Grid} )
Description: Saves columns to a new data table. The columns contain the density estimates and corresponding quantiles.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Save Density Grid} );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Nonpar Density );
obj << (curve[1] << Save Density Grid);
Save Density Quantile
Syntax: obj << ( Curve[number] << Save Density Quantile ); obj << Nonpar( {Save Density Quantile} )
Description: Saves a new column to the original data table. The column contains a value that represents the density quantile that contains each point.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Nonpar Density );
obj << (curve[1] << Save Density Quantile);
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Save Density Quantile} );
Select Points by Density
Syntax: obj << ( Curve[number] << Select Points by Density( lower probability, upper probability ) ); obj << Nonpar Density( {Select Points by Density( lower probability, upper probability )} )
Description: Selects points between the specified lower and upper probabilities.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Select Points by Density( 0.2, 0.5 )} );
Wait( 1 );
obj << (curve[1] << Select Points by Density( 0.8, 1 ));
Set Kernel
Syntax: obj << ( Curve[number] << Set Kernel( xStdDev, yStdDev )); obj << Nonpar Density( {Set Kernel( xStdDev, yStdDev )} )
Description: Sets the kernel standard deviations for both the X and Y values.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density( {Kernel Control( 1 ), Set Kernel( 8.537, 1.7333 )} );
Wait( 1 );
obj << (curve[1] << Set Kernel( 8, 1 ));
Bivariate > Bivariate Normal Ellipse
Item Messages
Confid Curves Fit
Syntax: obj << ( Curve[number] << Confid Curves Fit( state=0|1 ) ); obj << Fit Name( {Confid Curves Fit( state=0|1 )} )
Description: Shows or hides the confidence curves for the fitted line.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Fit( 1 )} );
Wait( 1 );
obj << Fit Polynomial( 3 );
obj << (curve[2] << Confid Curves Fit( 1 ));
Confid Curves Indiv
Syntax: obj << ( Curve[number] << Confid Curves Indiv( state=0|1 ) ); obj << Fit Name( {Confid Curves Indiv( state=0|1 )} )
Description: Shows or hides the confidence curves for an individual predicted value.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line( {Confid Curves Indiv( 1 )} ) );
Wait( 1 );
obj << Fit Polynomial( 3 );
obj << (curve[2] << Confid Curves Indiv( 1 ));
Indiv Confidence Limit Formula
Syntax: obj << ( Curve[number] << Indiv Confidence Limit Formula( <alpha> ) ); obj << Fit Name( {Indiv Confidence Limit Formula( <alpha> ) )
Description: Saves new formula columns to the original data table. There are columns for the lower and upper confidence limits for an individual prediction that are functions of the regressors. The default level for alpha is 0.05, which creates 95% confidence limits.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Indiv Confidence Limit Formula( .001 ));
Wait( 1 );
obj << Fit Line( {Indiv Confidence Limit Formula( 0.01 )} );
Line Color
Syntax: obj << ( Curve[number] << Line Color( "color" ) ); obj << Fit Name( {Line Color( "color" )} ) obj << Density Ellipse( {Line Color( "color" )} )
Description: Changes the line color of the fitted line, confidence curves, and shaded confidence regions.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Confid Curves Indiv, Line Color( "Medium Dark BlueGreen" )} );
Wait( 1 );
obj << (curve[1] << Line Color( "black" ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95, {Line Color( "Medium Dark BlueGreen" )} );
Wait( 1 );
obj << (curve[1] << Line Color( "black" ));
Line Style
Syntax: obj << ( Curve[number] << Line Style( "pen style" ) ); obj << Fit Name( {Line Styel( "pen style" )} ) obj << Density Ellipse( {Line Style( "pen style" )} )
Description: Changes the line style of the fitted line.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line( {Confid Curves Fit} ) );
Wait( 1 );
obj << (Curve[1] << Line Style( "DashDot" ));
obj << Fit Polynomial( 3, {Line Style( "Dense Dash" )} );
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Density Ellipse( 0.95 ) );
Wait( 1 );
obj << (Curve[1] << Line Style( "DashDot" ));
obj << Density Ellipse( 0.90, {Line Style( "Dense Dash" )} );
Line Width
Syntax: obj << ( Curve[number] << Line Width( number ) ); obj << Fit Name( {Line Width( number )} ) obj << Density Ellipse( {Line Width( number )} )
Description: Changes the line width of both the fitted line and any confidence curves.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Line Width( 3 )} );
Wait( 1 );
obj << (curve[1] << Line Width( 1 ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.99, {Line Width( 3 )} );
Wait( 1 );
obj << (curve[1] << Line Width( 1 ));
Line of Fit
Syntax: obj << ( Curve[number] << Line of Fit( state=0|1 ) ); obj << Fit Name( {Line of Fit( state=0|1 )} ) obj << Density Ellipse( {Line of Fit( state=0|1 )} )
Description: Shows or hides the line of fit. On by default.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Line );
Wait( 1 );
obj << (Curve[1] << Line of Fit( 0 ));
obj << Fit Polynomial( 3, {Line of Fit( 0 )} );
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Density Ellipse( 0.95 ) );
Wait( 1 );
obj << (Curve[1] << Line of Fit( 0 ));
obj << Density Ellipse( 0.90, {Line of Fit( 0 )} );
Mean Confidence Limit Formula
Syntax: obj << ( Curve[number] << Mean Confidence Limit Formula( <alpha> ) ); obj << Fit Name( {Mean Confidence Limit Formula( <alpha> ) )
Description: Saves new formula columns to the original data table. There are columns for the lower and upper confidence limits for the mean response that are functions of the regressors. The default level for alpha is 0.05, which creates 95% confidence limits.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Mean Confidence Limit Formula( .01 ));
Wait( 1 );
obj << Fit Line( {Mean Confidence Limit Formula( 0.05 )} );
Remove Fit
Syntax: obj << ( Curve[number] << Remove Fit )
Description: Removes the fitted curve.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( 1 );
obj << Fit Polynomial( 3 );
Wait( 1 );
obj << (Curve[2] << Remove Fit);
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95 );
obj << Density Ellipse( 0.90 );
Wait( 1 );
obj << (Curve[2] << Remove Fit);
Report
Syntax: obj << ( Curve[number] << Report( state=0|1 ) ); obj << Fit Name( {Report( state=0|1 )} ) obj << Density Ellipse( {Report( state=0|1 )} )
Description: Shows or hides the reports for summary of fit, lack of fit, ANOVA, and parameter estimates. On by default.
Curve Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( {Report( 0 )} );
Wait( 1 );
obj << (Curve[1] << Report( 1 ));
Normal Ellipse Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95, {Report( 0 )} );
Wait( 1 );
obj << (Curve[1] << Report( 1 ));
Save Predicteds
Syntax: obj << ( Curve[number] << Save Predicteds ); obj << Fit Name( {Save Predicteds} )
Description: Saves a new column to the original data table. The column contains the predicted values for the specified fitted curve.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Polynomial( 3, {Save Predicteds} );
Wait( 1 );
obj << Fit Line( 1 );
obj << (curve[2] << Save Predicteds);
Save Residuals
Syntax: obj << ( Curve[number] << Save Residuals ); obj << Fit Name( {Save Residuals} )
Description: Saves a new column to the original data table. The column contains the residual values for the specified fitted curve.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Save Residuals);
Wait( 1 );
obj << Fit Line( {Save Residuals} );
Save Studentized Residuals
Syntax: obj << ( Curve[number] << Save Studentized Residuals ); obj << Fit Name( {Save Studentized Residuals} )
Description: Saves a new column to the original data table. The column contains the Studentized residuals for the specified fitted curve.
JMP Version Added: 14
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Fit Polynomial( 3 ) );
obj << (Curve[1] << Save Studentized Residuals);
Wait( 1 );
obj << Fit Line( {Save Studentized Residuals} );
Select Points Inside
Syntax: obj << ( Curve[number] << Select Points Inside ); obj << Density Ellipse( {Select Points Inside} )
Description: Selects points inside the ellipse.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate(
Y( :Weight ),
X( :Height ),
Density Ellipse( 0.95, {Line Color( {213, 72, 87} )} ),
);
obj << (curve[1] << Select Points Inside);
Wait( 1 );
obj << Density Ellipse( 0.8, {Select Points Inside} );
Select Points Outside
Syntax: obj << ( Curve[number] << Select Points Outside ); obj << Density Ellipse( {Select Points Outside} )
Description: Selects points outside the ellipse.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate(
Y( :Weight ),
X( :Height ),
Density Ellipse( 0.8, {Line Color( {213, 72, 87} )} ),
);
obj << (curve[1] << Select Points Outside);
Wait( 1 );
obj << Density Ellipse( 0.95, {Select Points Outside} );
Shaded Contour
Syntax: obj << ( Curve[number] << Shaded Contour( state=0|1 ) ); obj << Density Ellipse( {Shaded Contour( state=0|1 )} )
Description: Shows or hides the shaded contour.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Bivariate( Y( :Weight ), X( :Height ), );
obj << Density Ellipse( 0.95, {Shaded Contour( 1 )} );
Wait( 1 );
obj << (Curve[1] << Shaded Contour( 0 ));
Bivariate
Associated Constructors
Bivariate
Syntax: Bivariate( Y( columns ), X( columns ) )
Description: Models a continuous response with respect to another continuous variable. Analysis methods include fitting lines, polynomials, splines, and bivariate densities.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
Columns
By
Syntax: obj = Bivariate(...<By( column(s) )>...)
Description: Performs a separate analysis for each level of the specified column.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
Freq
Syntax: obj = Bivariate(...<Freq( column )>...)
Description: Specifies a column whose values assign a frequency to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Freq( _freqcol ) );
Regressor
Syntax: obj = Bivariate(...Regressor( column(s) )...)
Description: Specifies the predictor variables. These variables must have a continuous modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
Response
Syntax: obj = Bivariate(...Response( column(s) )...)
Description: Specifies the continuous response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
Weight
Syntax: obj = Bivariate(...<Weight( column )>...)
Description: Specifies a column whose values assign a weight to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Bivariate( Y( :Weight ), X( :Height ), Weight( _weightcol ) );
X
Syntax: obj = Bivariate(...X( column(s) )...)
Description: Specifies the predictor variables. These variables must have a continuous modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
Y
Syntax: obj = Bivariate(...Y( column(s) )...)
Description: Specifies the continuous response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
Item Messages
Curve
Syntax: obj << ( Curve[number] << option )
Description: Array of handles to the fitted lines. This enables you to send Bivariate Curve messages to specific curves that have been fit.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( 1 );
obj << Fit Polynomial( 3 );
obj << (curve[2] << Confid Curves Fit( 1 ));
Density Ellipse
Syntax: obj << Density Ellipse( percent )
Description: Fits a bivariate normal contour. The contour contains the specified percent of total data points.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Density Ellipse( 0.95 );
Fit Cauchy
Syntax: obj << Fit Cauchy
Description: Fits a robust regression model where the parameters are estimated by maximum likelihood with a Cauchy link function.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Cauchy;
Fit Each Value
Syntax: obj << Fit Each Value
Description: Fits a line that goes through the mean Y value of each set of unique X values.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Each Value;
Fit Line
Syntax: obj << Fit Line
Description: Fits a least squares regression model to the data. The fit line is shown on the plot and a fit report is provided.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line;
Fit Mean
Syntax: obj << Fit Mean
Description: Fits the mean of the Y response variable. A flat line with a slope of zero is shown on the plot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Mean;
Fit Orthogonal
Syntax: obj << Fit Orthogonal( Univariate Variances|Equal Variances|Fit X to Y|Specified Variance Ratio(number) )
Description: Fits the specified orthogonal regression model. Orthogonal regression models are useful when both the X and Y variables are measured with error. The Specified Variance Ratio argument enables you to specify the ratio of the variance of the error in the X variable to the error in the Y variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Orthogonal( Fit X to Y );
Fit Passing Bablok
Syntax: obj << Fit Passing Bablok
Description: Fits a regression model using the Passing-Bablok procedure. This procedure is useful when both the X and Y variables are measured with error.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Passing Bablok;
Fit Polynomial
Syntax: obj << Fit Polynomial( degree of model )
Description: Fits a polynomial curve of the specified degree using least squares regression.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Polynomial( 3 );
Fit Robust
Syntax: obj << Fit Robust
Description: Fits a regression model using the Huber M-estimation method, which is robust with respect to outliers. The Huber loss function penalizes outliers and increases as a quadratic function for small errors and as a linear function for large errors.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Robust;
Fit Special
Syntax: obj << Fit Special( xTran( "Log"|"Square Root"|"Square"|"Reciprocal"|"Exponential" ), yTran( "Log"|"Square Root"|"Square"|"Reciprocal"|"Exponential" ), <Intercept( number )>, <Slope( number )>, <Degree( degree )>, Centered Polynomial> )
Description: Fits a regression model that contains transformations for the X and Y variables. You can also put constraints on the slope and intercept, as well as fit polynomial models using the degree argument.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Special( xTran( "Log" ) );
obj << Fit Special( xTran( "Square" ), yTran( "Reciprocal" ), Intercept( 0 ) );
Fit Spline
Syntax: obj << Fit Spline( lambda, <Standardized> )
Description: Fits a penalized least squares model to the data, where the smoothing parameter, lambda, determines the degree of smoothness for the model fit.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Spline( 341.1929, Standardized );
obj << Fit Spline( 341.1929 );
Fit Where
Syntax: obj << Fit Where( column == level, command )
Description: Fits a curve to a single level of a categorical variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :weight ), X( :height ) );
obj << Fit Where( :sex == "F", Fit Line( 1 ) );
Group By
Syntax: obj << Group By( column )
Description: Specifies a grouping variable. Once a grouping variable is specified, all analyses are performed separately for each level of the grouping variable.
dt = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj = dt << Bivariate( Y( :LogHist1 ), X( :LogHist0 ) );
obj << Group By( :drug );
obj << Fit Line;
Histogram Borders
Syntax: obj << Histogram Borders( state=0|1 )
Description: Shows or hides histograms on the horizontal and vertical axes of the scatterplot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Histogram Borders( 1 );
Kernel Smoother
Syntax: obj << Kernel Smoother( lambda = 0|1|2, weight = 1|2|3|4|5, alpha, robust passes = 0|1|2|3|4, delta proportion )
Description: Applies a local fit over repeated subsets of the data where the subset range is determined by alpha, the smoothness of the fit by lambda, and the weight by the weight function. Outliers are weighted downward as robustness increases. This method is also known as a LOESS smoother.
dt = Open( "$SAMPLE_DATA/Nonlinear Examples/Chemical Kinetics.jmp" );
obj = dt << Bivariate( Y( :"Velocity (y)"n ), X( :Concentration ) );
obj << Kernel Smoother( 1, 1, 0.84615, 0 );
Nonpar Density
Syntax: obj << Nonpar Density
Description: Fits nonparametric bivariate density contours and draws the corresponding contours on the graph. The contours are in 5% intervals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Nonpar Density;
Paired t test
Syntax: obj << Paired t test
Description: Runs a paired t-test, produces the report, and displays a 45-degree gray line on the scatterplot to represent where the two columns are equal.
This option has been promoted to the Matched Pairs platform. This option is also accessible from the Bivariate menu when you hold down the shift key.
dt = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj = dt << Bivariate( Y( :LogHist1 ), X( :LogHist0 ) );
obj << Paired t test;
Points Jittered
Syntax: obj << Points Jittered( "None"|"Auto"|"Random Uniform"|"Random Normal"|"Density Random"|"Packed"|"Grid"|"Hex Grid"|"Beeswarm"="Auto" )
Description: Specifies the spread of the data points. When selected, the data points are jittered to avoid overlapping markers. "Auto" by default.
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Oneway( Y( :Sepal length ), X( :Sepal width ) );
obj << Points Jittered( "Random Normal" );
Show Points
Syntax: obj << Show Points( state=0|1 )
Description: Shows or hides points on the graph. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Fit Line( 1 );
Wait( 1 );
obj << Show Points( 0 );
Summary Statistics
Syntax: obj << Summary Statistics( state=0|1 )
Description: Shows or hides summary statistics tables.
JMP Version Added: 14
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Summary Statistics( 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/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Copy ByGroup Script;
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Data Table Window;
Get By Levels
Syntax: obj << Get By Levels
Description: Returns an associative array mapping the by group columns to their values.
JMP Version Added: 18
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
t = obj[1] << Get ByGroup Script;
Show( t );
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Redo ByGroup Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Relaunch ByGroup;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Report View
Syntax: obj << Report View( "Full"|"Summary" )
Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Save ByGroup Script to Data Table;
Save ByGroup Script to Journal
Syntax: obj << Save ByGroup Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), 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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Save ByGroup Script to Script Window;
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bivariate( Y( :Weight ), X( :Height ), 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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );
Save Script to Journal
Syntax: obj << Save Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Save Script to Report;
Save Script to Script Window
Syntax: obj << Save Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
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 = Bivariate(...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( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Window View( "Private" ), Y( :weight ), X( :height ), Fit Line );
eqn = Report( biv )["Linear Fit", Text Edit Box( 1 )] << Get Text;
biv << Close Window;
New Window( "Bivariate Equation",
Outline Box( "Big Class Linear Fit", Text Box( eqn, <<Set Base Font( "Title" ) ) )
);
Contingency > Analysis of Means for Proportions
Item Messages
Point Options
Syntax: obj << Analysis of Means for Proportions( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the Analysis of Means for Proportions chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Point Options( "Show Only Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Point Options( "Show Connected Points" );
Set Alpha Level
Syntax: obj << Analysis of Means for Proportions( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << Analysis of Means for Proportions( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line for the Analysis of Means in Proportions chart. On by default.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << Analysis of Means for Proportions( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading in the Analysis of Means for Proportions chart. On by default.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << Analysis of Means for Proportions( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines in the Analysis of Means for Proportions chart. On by default.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << Analysis of Means for Proportions( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the response proportions and decision limits for each level of the X variable. The report also indicates whether a limit has been exceeded.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Switch Response Level for Proportion
Syntax: obj << Analysis of Means for Proportions( 1, Switch Response Level for Proportion( state=0|1 ) );scrobj << Switch Response Level for Proportion( state=0|1 )
Description: Changes the response category that is used in the analysis.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = Contingency( Y( :marital status ), X( :type ) );
obj << Analysis of Means for Proportions( 1, Switch Response Level for Proportion( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Proportions"] << Get Scriptable Object;
scrobj << Switch Response Level for Proportion( 0 );
Contingency > Contingency Equivalence Tests
Item Messages
Forest Plot
Syntax: obj << Equivalence Tests( ..., Forest Plot( state=0|1 ) );scobj << Forest Plot( state=0|1 )
Description: Shows or hides the Equivalence Tests Forest Plot. On by default.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( Count( 1 ), Total %( 0 ), Col %( 0 ), Row %( 1 ) ),
Equivalence Tests of Risk Difference(
0.1,
0.05,
"Equivalence",
Response Group( "Cancer" ),
Factor Group( "NonSmoker" )
)
);
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests for the Risk Difference"] << Get Scriptable Object);
scobj << Forest Plot( 0 );
Remove
Syntax: scobj << Remove
Description: Removes the Equivalence Tests report.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( Count( 1 ), Total %( 0 ), Col %( 0 ), Row %( 1 ) ),
Equivalence Tests of Risk Difference(
0.1,
0.05,
"Equivalence",
Response Group( "Cancer" ),
Factor Group( "NonSmoker" )
)
);
Wait( 1 );
scobj = (Report( obj )["Equivalence Tests for the Risk Difference"] << Get Scriptable Object);
Wait( 1 );
scobj << Remove;
Test Report
Syntax: obj << Equivalence Tests( ..., Test Report( state=0|1 ) );scobj << Test Report( state=0|1 )
Description: Shows or hides a report that summarizes the equivalence tests, superiority tests, or noninferiority tests for risk differences or risk ratios. On by default.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( Count( 1 ), Total %( 0 ), Col %( 0 ), Row %( 1 ) ),
Equivalence Tests of Risk Difference(
0.1,
0.05,
"Equivalence",
Response Group( "Cancer" ),
Factor Group( "NonSmoker" )
)
);
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests for the Risk Difference"] << Get Scriptable Object);
scobj << Test Report( 0 );
Contingency > Contingency Table
Item Messages
Cell Chi Square
Syntax: obj << Contingency Table( Cell Chi Square( state=0|1, <Format(...)> ) )
Description: Shows or hides the individual cell contribution to the chi-square statistic in the contingency table.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Cell Chi Square( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Cell Chi Square( 1, Format( "Fixed Dec", 8, 5 ) ) );
Col %
Syntax: obj << Contingency Table( Col %( state=0|1, <Format(...)> ) )
Description: Shows or hides each cells' percentage contribution to the column in the contingency table. On by default.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Col %( 0 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Col %( 1, Format( "Fixed Dec", 7, 1 ) ) );
Col Cum
Syntax: obj << Contingency Table( Col Cum( state=0|1, <Format(...)> ) )
Description: Shows or hides the cumulative column total in the contingency table.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Total %( 0 ), Col %( 0 ), Row %( 0 ), Col Cum( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table(
Total %( 0 ),
Col %( 0 ),
Row %( 0 ),
Col Cum( 1, Format( "Fixed Dec", 7, 1 ) )
);
Col Cum %
Syntax: obj << Contingency Table( Col Cum %( state=0|1, <Format(...)> ) )
Description: Shows or hides the cumulative column percentage in the contingency table.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Total %( 0 ), Col %( 0 ), Row %( 0 ), Col Cum %( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table(
Total %( 0 ),
Col %( 0 ),
Row %( 0 ),
Col Cum %( 1, Format( "Fixed Dec", 7, 1 ) )
);
Count
Syntax: obj << Contingency Table( Count( state=0|1, <Format(...)> ) )
Description: Shows or hides the cell count in the contingency table. On by default.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Count( 0 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Count( 1, Format( "Fixed Dec", 7, 1 ) ) );
Deviation
Syntax: obj << Contingency Table( Deviation( state=0|1, <Format(...)> ) )
Description: Shows or hides the individual cell deviation in the contingency table. The individual cell deviation is the actual cell count minus the expected cell count.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Deviation( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Deviation( 1, Format( "Fixed Dec", 7, 1 ) ) );
Expected
Syntax: obj << Contingency Table( Expected( state=0|1, <Format(...)> ) )
Description: Shows or hides the expected cell count in the contingency table. The expected cell count is the product of the corresponding row total and column total divided by the grand total.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Expected( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Expected( 1, Format( "Fixed Dec", 7, 1 ) ) );
Make Into Data Table
Syntax: obj << Contingency Table( Make Into Data Table )
Description: Creates a data table that contains the crosstabs data.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Contingency( Y( :Age ), X( :sex ), Contingency Table( Make into Data Table ) );
Row %
Syntax: obj << Contingency Table( Row %( state=0|1, <Format(...)> ) )
Description: Shows or hides each cells' percentage contribution to the row in the contingency table. On by default.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Row %( 0 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Row %( 1, Format( "Fixed Dec", 7, 1 ) ) );
Row Cum
Syntax: obj << Contingency Table( Row Cum( state=0|1, <Format(...)> ) )
Description: Shows or hides the cumulative row total in the contingency table.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Total %( 0 ), Col %( 0 ), Row %( 0 ), Row Cum( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table(
Total %( 0 ),
Col %( 0 ),
Row %( 0 ),
Row Cum( 1, Format( "Fixed Dec", 7, 1 ) )
);
Row Cum %
Syntax: obj << Contingency Table( Row Cum %( state=0|1, <Format(...)> ) )
Description: Shows or hides the cumulative row percentage in the contingency table.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Contingency Table( Total %( 0 ), Col %( 0 ), Row %( 0 ), Row Cum %( 1 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table(
Total %( 0 ),
Col %( 0 ),
Row %( 0 ),
Row Cum %( 1, Format( "Fixed Dec", 7, 1 ) )
);
Total %
Syntax: obj << Contingency Table( Total %( state=0|1, <Format(...)> ) )
Description: Shows or hides the cell total percentage in the contingency table. On by default.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Total %( 0 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( Total %( 1, Format( "Fixed Dec", 7, 1 ) ) );
Contingency > Correspondence Analysis
Item Messages
3D Correspondence Analysis
Syntax: obj << Correspondence Analysis( "3D Correspondence Analysis"( state=0|1 ) )
Description: Shows or hides a three-dimensional scatterplot.
dt = Open( "$SAMPLE_DATA/Cars 1993.jmp" );
obj = Contingency(
Y( :Vehicle Category ),
X( :Manufacturer ),
Contingency Table( 0 ),
Tests( 0 )
);
Wait( 1 );
obj << Correspondence Analysis( "3D Correspondence Analysis"(1) );
Make Table
Syntax: obj << Correspondence Analysis( "Make Table" )
Description: Creates a data table that contains the correspondence analysis output.
dt = Open( "$SAMPLE_DATA/Cereal.jmp" );
obj = Contingency( Y( :Fiber Gr ), X( :Manufacturer ) );
obj << Correspondence Analysis( "Make Table" );
Save Value Order
Syntax: obj << Correspondence Analysis( "Save Value Order" )
Description: Saves a Value Ordering column property to both the X and Y variable columns in the data table. The column property specifies the order of the levels sorted by the first correspondence score coefficient.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Cereal.jmp" );
obj = Contingency( Y( :Fiber Gr ), X( :Manufacturer ) );
obj << Correspondence Analysis( "Save Value Order" );
Contingency
Associated Constructors
Contingency
Syntax: Contingency( Y( columns ), X( columns ) )
Description: Models a categorical response across a set of categorical groups. Analysis methods include chi-square tests and mosaic plots.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Columns
Block
Syntax: obj = Contingency(...<Block( column )>...)
Description: Specifies a blocking variable. This identifies a second factor and performs a Cochran-Mantel-Haenszel test.
dt = Open( "$SAMPLE_DATA/Car Poll.JMP" );
obj = dt << Contingency( Y( :marital status ), X( :type ), Block( :sex ) );
By
Syntax: obj = Contingency(...<By( column(s) )>...)
Description: Performs a separate analysis for each level of the specified column.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
Freq
Syntax: obj = Contingency(...<Freq( column )>...)
Description: Specifies a column whose values assign a frequency to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Contingency( Y( :Age ), X( :sex ), Freq( _freqcol ) );
Grouping Category
Syntax: obj = Contingency(...Grouping Category( column(s) )...)
Description: Specifies the predictor variables. These variables must have an ordinal or nominal modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Response Category
Syntax: obj = Contingency(...Response Category( column(s) )...)
Description: Specifies the categorical response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Weight
Syntax: obj = Contingency(...<Weight( column )>...)
Description: Specifies a column whose values assign a weight to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Contingency( Y( :Age ), X( :sex ), Weight( _weightcol ) );
X
Syntax: obj = Contingency(...X( column(s) )...)
Description: Specifies the predictor variables. These variables must have an ordinal or nominal modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Y
Syntax: obj = Contingency(...Y( column(s) )...)
Description: Specifies the categorical response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Item Messages
Agreement Statistic
Syntax: obj << Agreement Statistic( state=0|1 )
Description: Shows or hides a report that contains statistics that measure the agreement between levels. The report includes the Kappa statistic, as well as the standard error, confidence interval, and hypothesis test for the statistic. The report also includes Bowker's test of symmetry, which is also known as McNemar's test. This option is available only when the X and Y variables have the same levels.
dt = Open( "$SAMPLE_DATA/Prime Minister Ratings.jmp" );
obj = dt << Contingency(
Y( :Second Survey ),
X( :First Survey ),
Freq( :Count ),
Tests( 0 ),
Agreement Statistic( 1 )
);
Analysis of Means for Proportions
Syntax: obj << Analysis of Means for Proportions( state=0|1, <chart options> )
Description: Shows or hides an analysis of means for proportions (ANOMP) decision chart to compare group proportions. ANOMP is a multiple comparison procedure, which compares the response proportions for the levels of the X variable to the overall response proportion. This option is available only when the Y variable has exactly two levels.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency(
Y( :marital status ),
X( :type ),
Analysis of Means for Proportions( 1 )
);
Cochran Armitage Trend Test
Syntax: obj << Cochran Armitage Trend Test( state=0|1 )
Description: Shows or hides a test for trends in binomial proportions across levels of a single variable. This option is available only when one variable has exactly two levels and the other variable is ordinal.
dt = Open( "$SAMPLE_DATA/Car Poll.JMP" );
obj = dt << Contingency( Y( :size ), X( :sex ), Mosaic Plot( 0 ) );
obj << Cochran Armitage Trend Test( 1 );
Cochran Mantel Haenszel
Syntax: obj << Cochran Mantel Haenszel( column );obj << Cochran Mantel Haenszel( state=0|1 )
Description: Shows or hides a test that determines whether there is a relationship between two categorical variables after blocking across a third classification variable.
dt = Open( "$SAMPLE_DATA/Car Poll.JMP" );
obj = dt << Contingency( Y( :type ), X( :marital status ) );
obj << Cochran Mantel Haenszel( :country );
Wait( 2 );
obj << Cochran Mantel Haenszel( 0 );
Contingency Table
Syntax: obj << Contingency Table( state=0|1 )
Description: Shows or hides a two-way frequency table. The table contains a row for each level of the X variable and a column for each level of the Y variable. On by default.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Contingency Table( 0 );
Correspondence Analysis
Syntax: obj << Correspondence Analysis( state=0|1 );obj << Correspondence Analysis( correspondence chart options )
Description: Shows or hides a correspondence analysis, which identifies rows or columns of a frequency table that have similar patterns of counts. In the correspondence analysis plot, there is a point for each row and for each column of the contingency table.
dt = Open( "$SAMPLE_DATA/Cereal.jmp" );
obj = dt << Contingency( Y( :Fiber Gr ), X( :Manufacturer ) );
obj << Correspondence Analysis( 1 );
Equivalence Tests of Relative Risk
Syntax: obj << Equivalence Tests of Relative Risk( ratio, <alpha=.05>, <test type>, <Response Group( level )>, <Factor Group( level )> )
Description: Tests that the relative risks do not differ by more than a ratio determined to be practically equivalent. This is the reverse of the usual significance test. Alpha, test type, and the group levels are optional arguments. The test type argument is "Equivalence" by default, but it can also be used to specify superiority or noninferiority tests.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( Count( 1 ), Total %( 0 ), Col %( 0 ), Row %( 1 ) ),
Equivalence Tests of Relative Risk(
0.8,
0.05,
"Equivalence",
Response Group( "Cancer" ),
Factor Group( "NonSmoker" )
)
);
Equivalence Tests of Risk Difference
Syntax: obj << Equivalence Tests of Risk Difference( difference, <alpha=.05>, <test type>, <Response Group( level )>, <Factor Group( level )> )
Description: Tests that the risk differences do not differ by more than an amount (difference) determined to be practically equivalent. This is the reverse of the usual significance test. Alpha, test type, and the group levels are optional arguments. The test type argument is "Equivalence" by default, but it can also be used to specify superiority or noninferiority tests.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( Count( 1 ), Total %( 0 ), Col %( 0 ), Row %( 1 ) ),
Equivalence Tests of Risk Difference(
0.1,
0.05,
"Equivalence",
Response Group( "Cancer" ),
Factor Group( "NonSmoker" )
)
);
Exact Agreement Statistic
Syntax: obj << Exact Agreement Statistic( state=0|1 )
Description: Shows or hides the exact version of the agreement statistic Kappa.
dt = Open( "$SAMPLE_DATA/Prime Minister Ratings.jmp" );
obj = dt << Contingency(
Y( :Second Survey ),
X( :First Survey ),
Freq( :Count ),
Tests( 0 )
);
obj << Exact Agreement Statistic( 1 );
Exact Cochran Armitage Trend Test
Syntax: obj << Exact Cochran Armitage Trend Test( state=0|1 )
Description: Shows or hides the exact version of the Cochran-Armitage trend test.
dt = Open( "$SAMPLE_DATA/Prime Minister Ratings.jmp" );
obj = dt << Contingency(
Y( :Second Survey ),
X( :First Survey ),
Freq( :Count ),
Tests( 0 )
);
obj << Exact Cochran Armitage Trend Test( 1 );
Fisher's Exact Test
Syntax: obj << Fisher's Exact Test( state=0|1 )
Description: Shows or hides Fisher's exact test for testing the association between two categorical variables. This test does not depend on any large-sample distributional assumptions.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
obj << Fisher's Exact Test( 1 );
Horizontal Mosaic
Syntax: obj << Horizontal Mosaic( state=0|1 )
Description: Rotates the mosaic plot horizontally (1) or vertically (0).
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
Wait( 2 );
obj << Horizontal Mosaic( 1 );
Jonckheere Terpstra Test
Syntax: obj << Jonckheere Terpstra Test( state=0|1 )
Description: Shows or hides a report of the Jonckheere-Terpstra test, which is a nonparametric test for ordered differences among classes. It tests the null hypothesis that the distribution of the response variable does not differ among classes.
dt = Open( "$SAMPLE_DATA/Big Class.JMP" );
:height << Nominal( 1 );
obj = dt << Contingency(
Y( :height ),
X( :age ),
Contingency Table(
Count( 1 ),
Total %( 0 ),
Col %( 0 ),
Row %( 0 ),
Expected( 0 ),
Deviation( 0 ),
Cell Chi Square( 0 ),
Col Cum( 0 ),
Col Cum %( 0 ),
Row Cum( 0 ),
Row Cum %( 0 )
)
);
obj << Jonckheere Terpstra Test( 1 );
Measures of Association
Syntax: obj << Measures of Association( state=0|1 )
Description: Shows or hides a report that contains measures of the association between the variables in the contingency table.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Measures of Association( 1 )
);
Mosaic Plot
Syntax: obj << Mosaic Plot( state=0|1 )
Description: Shows or hides a graphical representation of the contingency table. On by default.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Mosaic Plot( 0 );
Odds Ratio
Syntax: obj << Odds Ratio( state=0|1 )
Description: Shows or hides a report of the odds ratio. This option is available only when the X and Y variables each have exactly two levels.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ), Odds Ratio( 1 ) );
Relative Risk
Syntax: obj << Relative Risk( state=0|1, <Y variable level, X variable level> ); obj << Relative Risk( state=0|1, <"All"> )
Description: Shows or hides the relative risk between the levels of the response. This option is available only when the X and Y variables each have exactly two levels.
Example 1
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( 0 )
);
obj << Relative Risk( 1, "Cancer", "Smoker" );
Example 2
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Contingency Table( 0 )
);
obj << Relative Risk( 1, "All" );
Set Alpha Level
Syntax: obj << Set Alpha Level( alpha=0.05 )
Description: Changes the alpha level used to compute the decision limits. "0.05" by default.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Set Alpha Level( 0.1 );
obj << Measures of Association( 1 );
Set α Level
Syntax: obj << Set α Level( alpha=0.05 )
Description: Changes the alpha level used to compute the decision limits. "0.05" by default.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
obj << Set Alpha Level( 0.1 );
obj << Measures of Association( 1 );
Tests
Syntax: obj << Tests( state=0|1 )
Description: Shows or hides tests that measure if the response level rates are the same across the levels of the X variable. These tests are analogous to the Analysis of Variance table for continuous data. On by default.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency( Y( :Lung Cancer ), X( :Smoker ), Freq( :Count ) );
Wait( 2 );
obj << Tests( 0 );
Two Sample Test for Proportions
Syntax: obj << Two Sample Test for Proportions( state=0|1 )
Description: Show or hides a two-sample test for proportions. This test compares the proportions of the Y variable between the two levels of the X variable. This option is available only when the X and Y variables each have exactly two levels.
dt = Open( "$SAMPLE_DATA/Lung Cancer.jmp" );
obj = dt << Contingency(
Y( :Lung Cancer ),
X( :Smoker ),
Freq( :Count ),
Two Sample Test for Proportions( 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/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Copy ByGroup Script;
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
obj << Data Table Window;
Get By Levels
Syntax: obj << Get By Levels
Description: Returns an associative array mapping the by group columns to their values.
JMP Version Added: 18
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
t = obj[1] << Get ByGroup Script;
Show( t );
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Redo ByGroup Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Relaunch ByGroup;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Report View
Syntax: obj << Report View( "Full"|"Summary" )
Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Save ByGroup Script to Data Table;
Save ByGroup Script to Journal
Syntax: obj << Save ByGroup Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), 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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Save ByGroup Script to Script Window;
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Contingency( Y( :Age ), X( :sex ), 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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );
Save Script to Journal
Syntax: obj << Save Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
obj << Save Script to Report;
Save Script to Script Window
Syntax: obj << Save Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Contingency( Y( :Age ), X( :sex ) );
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 = Contingency(...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( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Window View( "Private" ), Y( :weight ), X( :height ), Fit Line );
eqn = Report( biv )["Linear Fit", Text Edit Box( 1 )] << Get Text;
biv << Close Window;
New Window( "Bivariate Equation",
Outline Box( "Big Class Linear Fit", Text Box( eqn, <<Set Base Font( "Title" ) ) )
);
Logistic
Associated Constructors
Logistic
Syntax: Logistic( Y( columns ), X( columns ) )
Description: Models a categorical response with respect to a continuous variable. Analysis methods include logistic regression and ROC curves.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Columns
By
Syntax: obj = Logistic(...<By( column(s) )>...)
Description: Performs a separate analysis for each level of the specified column.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), By( _bycol ) );
Categorical Response
Syntax: obj = Logistic(...Categorical Response( column(s) )...)
Description: Specifies the categorical response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Continuous Regressor
Syntax: obj = Logistic(...Continuous Regressor( column(s) )...)
Description: Specifies the predictor variables. These variables must have a continuous modeling type.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Freq
Syntax: obj = Logistic(...<Freq( column )>...)
Description: Specifies a column whose values assign a frequency to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Weight
Syntax: obj = Logistic(...<Weight( column )>...)
Description: Specifies a column whose values assign a weight to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Logistic(
Y( :Response ),
X( :"ln(dose)"n ),
Freq( :Count ),
Weight( _weightcol )
);
X
Syntax: obj = Logistic(...X( column(s) )...)
Description: Specifies the predictor variables. These variables must have a continuous modeling type.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Y
Syntax: obj = Logistic(...Y( column(s) )...)
Description: Specifies the categorical response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Item Messages
Confidence Intervals
Syntax: obj << Confidence Intervals( <state=0|1> | <fraction> )
Description: Displays or hides confidence intervals in the parameter estimates table to the right of each effect.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Freq( :Count ), Y( :Response ), X( :"ln(dose)"n ) );
obj << Confidence Intervals( 0.01 );
Inverse Prediction
Syntax: obj << Inverse Prediction( Response( prob1, prob2, ... ), <Confidence Level( percent=0.95 )>, <Two sided|Lower One Sided|Upper One Sided> )
Description: Enables you to predict values of the predictor variable for one or more values of the response variable. By default, two-sided 95% confidence limits are computed for each inverse prediction.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Inverse Prediction( Response( 0.5, 0.9 ) );
Lift Curve
Syntax: obj << Lift Curve( state=0|1 )
Description: Shows or hides the Lift Curve plot. A lift curve plots the lift versus the portion of the observations and provides another view of the predictive ability of a model.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Lift Curve( 1 );
Line Color
Syntax: obj << Line Color( color )
Description: Enables you to select the color of the plot curves.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Line Color( "Magenta" );
Logistic Plot
Syntax: obj << Logistic Plot( state=0|1 )
Description: Shows or hides the logistic plot. On by default.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Logistic Plot( 0 );
Odds Ratios
Syntax: obj << Odds Ratios( state=0|1 )
Description: Adds or removes columns that contain odds ratios to the Parameter Estimates report.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Odds Ratios( 1 );
Precision Recall Curve
Syntax: obj << Precision Recall Curve( state=0|1 )
Description: Shows or hides the Precision-Recall Curve plot that contains a curve for each level of the response variable. A precision-recall curve plots the precision values against the recall values at a variety of thresholds.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic(
Y( :Response ),
X( :"ln(dose)"n ),
Freq( :Count ),
Target Level( "Cured" )
);
Wait( 1 );
obj << Precision Recall Curve( 1 );
ROC Curve
Syntax: obj << ROC Curve( state=0|1 )
Description: Shows or hides the Receiver Operating Characteristic (ROC) curve for each level of the response variable. The ROC curve is a plot of sensitivity versus (1 - specificity).
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic(
Y( :Response ),
X( :"ln(dose)"n ),
Freq( :Count ),
Target Level( "Cured" )
);
Wait( 1 );
obj << ROC Curve( 1 );
Save Probability Formula
Syntax: obj << Save Probability Formula
Description: Saves new columns to the data table. The new columns contain the formula for the probability that is predicted by the model.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Save Probability Formula;
Show Points
Syntax: obj << Show Points( state=0|1 )
Description: Shows or hides the points in the logistic plot. On by default.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Show Points( 0 );
Show Rate Curve
Syntax: obj << Show Rate Curve( state=0|1 )
Description: Shows or hides the rate curve in the logistic plot. The rate curve is useful only if you have several points for each value of the X variable.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
Wait( 1 );
obj << Show Rate Curve( 1 );
Target Level
Syntax: obj = Logistic(...Target Level( level )...)
Description: Specifies the level of the response whose probability you want to model.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic(
Y( :Response ),
X( :"ln(dose)"n ),
Freq( :Count ),
Target Level( "Cured" )
);
obj << ROC Curve( 1 );
Shared Item Messages
Action
Syntax: obj << Action
Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.
dt = Open( "$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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );
Broadcast
Syntax: obj << Broadcast(message)
Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.
JMP Version Added: 18
dt = Open( "$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/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Copy Script;
Data Table Window
Syntax: obj << Data Table Window
Description: Move the data table window for this analysis to the front.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
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/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );
Platform with Filter
dt = Open( "$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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
t = obj << Get Datatable;
Show( N Rows( t ) );
Get Group Platform
Syntax: obj << Get Group Platform
Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().
dt = Open( "$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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
t = obj << Get Script;
Show( t );
Get Script With Data Table
Syntax: obj << Get Script With Data Table
Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
t = obj << Get Timing;
Show( t );
Get Web Support
Syntax: obj << Get Web Support
Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.
dt = Open( "$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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Redo Analysis;
Redo ByGroup Analysis
Syntax: obj << Redo ByGroup Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Relaunch Analysis;
Relaunch ByGroup
Syntax: obj << Relaunch ByGroup
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Report View( "Summary" );
Save ByGroup Script to Data Table
Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), By( _bycol ) );
obj[1] << Save ByGroup Script to Journal;
Save ByGroup Script to Script Window
Syntax: obj << Save ByGroup Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), 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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Save Script for All Objects;
Save Script for All Objects To Data Table
Syntax: obj << Save Script for All Objects To Data Table( <name> )
Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.
Example 1
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table( "My Script" );
Save Script to Data Table
Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Save Script to Journal;
Save Script to Report
Syntax: obj << Save Script to Report
Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
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( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Save Script to Script Window;
SendToByGroup
Syntax: SendToByGroup( {":Column == level"}, command );
Description: Sends platform commands or display customization commands to each level of a by-group.
dt = Open( "$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/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Penicillin.jmp" );
obj = dt << Logistic( Y( :Response ), X( :"ln(dose)"n ), Freq( :Count ) );
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Transform Column
Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)
Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.
JMP Version Added: 16
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;
Window View
Syntax: obj = Logistic(...Window View( "Visible"|"Invisible"|"Private" )...)
Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object
dt = Open( "$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" ) ) )
);
Oneway > ANOM for Ranges
Item Messages
Point Options
Syntax: obj << ANOM for Ranges( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Point Options( "Show Connected Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Point Options( "Show Only Points" );
Set Alpha Level
Syntax: obj << ANOM for Ranges( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << ANOM for Ranges( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line (overall average range). On by default.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << ANOM for Ranges( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading for the Analysis of Means for Ranges chart. On by default.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << ANOM for Ranges( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines for the Analysis of Means for Ranges chart. On by default.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << ANOM for Ranges( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the group ranges and corresponding decision limits.
dt = Open( "$SAMPLE_DATA/Michelson.jmp" );
obj = dt << Oneway( Y( :Velocity ), X( :Trial# ) );
obj << ANOM for Ranges( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Ranges"] << Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Oneway > ANOM for Variances with Levene(ADM)
Item Messages
Point Options
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Point Options( "Show Only Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Point Options( "Show Connected Points" );
Set Alpha Level
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line (overall mean ADM). On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading for the ANOMV-Levene (ADM) chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines for the ANOMV-Levene (ADM) chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the group mean ADMs and the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances-Levene(ADM)"] <<
Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Oneway > ANOM for Variances
Item Messages
Graph in Variance Scale
Syntax: obj << ANOM for Variances( 1, Graph in Variance Scale( state=0|1 ) );scrobj << Graph in Variance Scale( state=0|1 )
Description: Specifies the scale of the vertical axis. You can choose between standard deviations and variances.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Graph in Variance Scale( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Graph in Variance Scale( 0 );
Point Options
Syntax: obj << ANOM for Variances( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Point Options( "Show Only Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Point Options( "Show Connected Points" );
Set Alpha Level
Syntax: obj << ANOM for Variances( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << ANOM for Variances( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line (RMSE or MSE depending on Y-scale). On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << ANOM for Variances( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading for the ANOMV chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << ANOM for Variances( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines for the ANOMV chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << ANOM for Variances( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the group standard deviations (or variances) and the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means for Variances"] << Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Oneway > ANOM with Transformed Ranks
Item Messages
Point Options
Syntax: obj << ANOM with Transformed Ranks( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Point Options( "Show Only Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Point Options( "Show Connected Points" );
Set Alpha Level
Syntax: obj << ANOM with Transformed Ranks( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << ANOM with Transformed Ranks( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line (overall mean). On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << ANOM with Transformed Ranks( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading for the ANOM-TR chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << ANOM with Transformed Ranks( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines for the ANOM-TR chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << ANOM with Transformed Ranks( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the group mean transformed ranks and the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means-Transformed Ranks"] << Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Oneway > ANOM
Item Messages
Point Options
Syntax: obj << ANOM( 1, Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" ) );scrobj << Point Options( "Show Needles"|"Show Connected Points"|"Show Only Points" )
Description: Specifies the drawing style of the points in the chart. You can choose between vertical needles, connected points, and points only. By default, the chart is drawn with needles that connect the points to the horizontal line that is drawn at the average.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Point Options( "Show Only Points" ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Point Options( "Show Connected Points" );
Set Alpha Level
Syntax: obj << ANOM( 1, Set Alpha Level( alpha ) );scrobj << Set Alpha Level( alpha )
Description: Changes the alpha level used to compute the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Set Alpha Level( 0.1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Set Alpha Level( 0.05 );
Show Center Line
Syntax: obj << ANOM( 1, Show Center Line( state=0|1 ) );scrobj << Show Center Line( state=0|1 )
Description: Shows or hides the center line (overall mean) on the ANOM chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Show Center Line( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Show Center Line( 1 );
Show Decision Limit Shading
Syntax: obj << ANOM( 1, Show Decision Limit Shading( state=0|1 ) );scrobj << Show Decision Limit Shading( state=0|1 )
Description: Shows or hides the decision limit shading for the ANOM chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Show Decision Limit Shading( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Show Decision Limit Shading( 1 );
Show Decision Limits
Syntax: obj << ANOM( 1, Show Decision Limits( state=0|1 ) );scrobj << Show Decision Limits( state=0|1 )
Description: Shows or hides the decision limit lines for the ANOM chart. On by default.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Show Decision Limits( 0 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Show Decision Limits( 1 );
Show Summary Report
Syntax: obj << ANOM( 1, Show Summary Report( state=0|1 ) );scrobj << Show Summary Report( state=0|1 )
Description: Shows or hides a report that contains the group means and the decision limits.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1, Show Summary Report( 1 ) );
Wait( 2 );
scrobj = Report( obj )["Analysis of Means"] << Get Scriptable Object;
scrobj << Show Summary Report( 0 );
Oneway > Oneway Equivalence Tests
Item Messages
Forest Plot
Syntax: obj << Equivalence Tests( ..., Forest Plot( state=0|1 ) );scobj << Forest Plot( state=0|1 )
Description: Shows or hides the Equivalence Tests Forest Plot. On by default.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests( 4, 0.05, "Pooled Variance", "Equivalence", Forest Plot( 1 ) );
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests with Pooled Variance"] << Get Scriptable Object);
scobj << Forest Plot( 0 );
Pairwise Comparisons
Syntax: obj << Equivalence Tests( ..., Equivalence Tests Pairwise Comparisons( state=0|1 ) );scobj << Equivalence Tests Pairwise Comparisons( state=0|1 )
Description: Shows or hides the Equivalence Tests Pairwise Comparisons report for all pairwise comparisons.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests(
4,
0.05,
"Pooled Variance",
"Equivalence",
Equivalence Tests Pairwise Comparisons( 1 )
);
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests with Pooled Variance"] << Get Scriptable Object);
scobj << Pairwise Comparisons( 0 );
Remove
Syntax: scobj << Remove
Description: Removes the Equivalence Tests report.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests(
4,
0.05,
"Pooled Variance",
"Equivalence",
Equivalence Tests Pairwise Comparisons( 1 )
);
Wait( 1 );
scobj = (Report( obj )["Equivalence Tests with Pooled Variance"] << Get Scriptable Object);
Wait( 1 );
scobj << Remove;
Scatterplot
Syntax: obj << Equivalence Tests( ..., Scatterplot( state=0|1 ) );scobj << Scatterplot( state=0|1 )
Description: Shows or hides the Equivalence Tests Scatterplot. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests( 4, 0.05, "Pooled Variance", "Equivalence", Scatterplot( 1 ) );
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests with Pooled Variance"] << Get Scriptable Object);
scobj << Scatterplot( 0 );
Test Report
Syntax: obj << Equivalence Tests( ..., Test Report( state=0|1 ) );scobj << Test Report( state=0|1 )
Description: Shows or hides a report that summarizes the equivalence tests, superiority tests, or noninferiority tests for means or standard deviations. On by default.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests( 4, 0.05, "Pooled Variance", "Equivalence", Test Report( 1 ) );
Wait( 2 );
scobj = (Report( obj )["Equivalence Tests with Pooled Variance"] << Get Scriptable Object);
scobj << Test Report( 0 );
Oneway > Oneway Means Comparisons
Item Messages
Confidence Quantile
Syntax: obj << Each Pair( 1, Confidence Quantile( state=0|1 ) );obj << All Pairs( 1, Confidence Quantile( state=0|1 ) );obj << With Best( 1, Confidence Quantile( state=0|1 ) );obj << With Control( 1, Confidence Quantile( state=0|1 ) );obj << Each Pair Stepwise( 1, Confidence Quantile( state=0|1 ) )
Description: Shows or hides the critical value(s) and alpha level used for the means comparison.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, Confidence Quantile( 1 ) );
Connecting Letters Report
Syntax: obj << Each Pair( 1, Connecting Letters Report( state=0|1 ) );obj << All Pairs( 1, Connecting Letters Report( state=0|1 ) );obj << Each Pair Stepwise( 1, Connecting Letters Report( state=0|1 ) )
Description: Shows or hides the traditional letter-coded report where means that do not share a letter are significantly different.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, Connecting Letters Report( 1 ) );
Detailed Comparisons Report
Syntax: obj << Each Pair( 1, Detailed Comparisons Report( state=0|1 ) )
Description: Shows or hides a detailed report for each comparison. Each section shows the difference between the levels, standard error and confidence intervals, t-ratios, p-values, and degrees of freedom.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, Detailed Comparisons Report( 1 ) );
Difference Matrix
Syntax: obj << Each Pair( 1, Difference Matrix( state=0|1 ) );obj << All Pairs( 1, Difference Matrix( state=0|1 ) );obj << With Best( 1, Difference Matrix( state=0|1 ) );obj << With Control( 1, Difference Matrix( state=0|1 ) );obj << Each Pair Stepwise( 1, Difference Matrix( state=0|1 ) )
Description: Shows or hides a table of all differences of means.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, Difference Matrix( 1 ) );
Dunnett's Lower
Syntax: obj << Dunnett's Lower( state=0|1 )
Description: Shows or hides a Dunnett's lower one-tailed t test, which tests whether means are smaller than the mean of a control group.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Control( 1, {15}, Dunnett's Lower( 1 ) );
Dunnett's Upper
Syntax: obj << Dunnett's Upper( state=0|1 )
Description: Shows or hides a Dunnett's upper one-tailed t test, which tests whether means are larger than the mean of a control group.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Control( 1, {15}, Dunnett's Upper( 1 ) );
LSD Threshold Matrix
Syntax: obj << Each Pair( 1, LSD Threshold Matrix( state=0|1 ) );obj << All Pairs( 1, LSD Threshold Matrix( state=0|1 ) );obj << With Best( 1, LSD Threshold Matrix( state=0|1 ) );obj << With Control( 1, LSD Threshold Matrix( state=0|1 ) )
Description: Shows or hides a matrix of pairwise differences of means minus the least significant difference for those means. A positive value indicates a pair of means that are significantly different.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, LSD Threshold Matrix( 1 ) );
Ordered Differences Report
Syntax: obj << Each Pair( 1, Ordered Differences Report( state=0|1 ) );obj << All Pairs( 1, Ordered Differences Report( state=0|1 ) )
Description: Shows or hides all pairwise positive-side differences, standard error of the difference, confidence intervals, p-values, and a plot of the magnitude of the difference with overlaid confidence intervals.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1, Ordered Differences Report( 1 ) );
Ordered Ratio Report
Syntax: obj << Each Pair( 1, Ordered Differences Report( state=0|1 ) );obj << Ratio Comparison for Pooled Variance( 1, Ordered Differences Report( state=0|1 ) )
Description: Shows or hides all pairwise positive-side differences, standard error of the difference, confidence intervals, p-values, and a plot of the magnitude of the difference with overlaid confidence intervals.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Ratio Comparison for Pooled Variance( 1, Ordered Differences Report( 1 ) );
Ratio Matrix
Syntax: obj << Ratios with Pooled Variance( 1, Ratio Matrix( state=0|1 ) );obj << Ratio Comparison for Pooled Variance( 1, Ratio Matrix( state=0|1 ) )
Description: Shows or hides a table of all differences of means.
Each Pair is equivalent to Student's t. All Pairs is equivalent to Tukey HSD. With Best is equivalent to Hsu MCB. With Control is equivalent to Dunnett's. Each Pair Stepwise is equivalent to Newman-Keuls.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Oneway( Y( :Height ), X( :Age ) );
obj << Ratios with Pooled Variance( 1, Ratio Matrix( 1 ) );
Oneway > Post Hoc Analysis for Friedman's Test
Item Messages
Nemenyi Test
Syntax: obj << Nemenyi Test( state=0|1 )
Description: Shows or hides a report of the Nemenyi test. The Nemenyi test is a pairwise post-hoc test for multiple comparisons of mean rank sums for unreplicated blocked data. This test is usually conducted post-hoc after significant results of the Friedman test.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Snapdragon.jmp" );
obj = dt << Oneway( Y( :Y ), X( :Soil ), Block( :Block ) );
obj << Friedman Rank Test( 1, Nemenyi Test( 1 ) );
Oneway
Associated Constructors
Oneway
Syntax: Oneway( Y( columns ), X( columns ) )
Description: Models a continuous response across a set of categorical groups. Analysis methods include ANOVA, means comparisons, analysis of means, and quantile plots.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Columns
Block
Syntax: obj << Block( column )
Description: Specifies a blocking variable. When this column is specified, the values of the response variable are centered by the block variable.
dt = Open( "$SAMPLE_DATA/Snapdragon.jmp" );
obj = dt << Oneway( Y( :Y ), X( :Soil ), Block( :Block ) );
obj << Friedman Rank Test( 1 );
By
Syntax: obj << By( column(s) )
Description: Performs a separate analysis for each level of the specified column.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
Freq
Syntax: obj << Freq( column )
Description: Specifies a column whose values assign a frequency to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Oneway( Y( :Height ), X( :Age ), Freq( _freqcol ) );
Grouping
Syntax: obj << Grouping( column(s) )
Description: Specifies the predictor variables. These variables must have an ordinal or nominal modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Response
Syntax: obj << Response( column(s) )
Description: Specifies the continuous response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Weight
Syntax: obj << Weight( column )
Description: Specifies a column whose values assign a weight to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Oneway( Y( :Height ), X( :Age ), Weight( _weightcol ) );
X
Syntax: obj << X( column(s) )
Description: Specifies the predictor variables. These variables must have an ordinal or nominal modeling type.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Y
Syntax: obj << Y( column(s) )
Description: Specifies the continuous response variable or variables that you want to analyze.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Item Messages
ANOM
Syntax: obj << ANOM( state=0|1, <chart options> )
Description: Compares each group mean to the overall mean.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM( 1 );
ANOM for Ranges
Syntax: obj << ANOM for Ranges( state=0|1, <chart options> )
Description: Test for unequal variance by comparing group ranges to the overall average range.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Ranges( 1 );
ANOM for Variances
Syntax: obj << ANOM for Variances( state=0|1, <chart options> )
Description: Test for unequal variance by comparing group standard deviations to the root mean squared error.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM for Variances( 1 );
ANOM for Variances with Levene(ADM)
Syntax: obj << "ANOM for Variances with Levene(ADM)"n( state=0|1, <chart options> )
Description: Test for unequal variance by comparing the group means of the absolute deviations from the median (ADM) to the overall mean ADM.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << "ANOM for Variances with Levene(ADM)"n( 1 );
ANOM with Transformed Ranks
Syntax: obj << ANOM with Transformed Ranks( state=0|1, <chart options> )
Description: Compares each group mean transformed rank to the overall mean transformed rank.
dt = Open( "$SAMPLE_DATA/Drug.jmp" );
obj = dt << Oneway( Y( :y ), X( :Drug ) );
obj << ANOM with Transformed Ranks( 1 );
All Graphs
Syntax: obj << All Graphs( state=0|1 )
Description: Shows or hides the Oneway plot. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Wait( 2 );
obj << All Graphs( 0 );
All Pairs
Syntax: obj << All Pairs( state=0|1 ); obj << Tukey HSD( state=0|1 ); obj << "All Pairs, Tukey HSD"n( state=0|1 )
Description: Computes the Tukey honestly significant difference (HSD) test; this test protects the overall error rate. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << All Pairs( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Tukey HSD( 1 );
Box Plots
Syntax: obj << Box Plots( state=0|1 )
Description: Shows or hides outlier box plots for each group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Box Plots( 1 );
CDF Plot
Syntax: obj << CDF Plot( state=0|1 )
Description: Shows or hides the cumulative distribution function for all of the groups in the Oneway report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << CDF Plot( 1 );
Cauchy Fit
Syntax: obj << Cauchy Fit( state=0|1 )
Description: Assumes that the errors have a Cauchy distribution. The Cauchy fit is a robust method that can handle extreme outliers.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Cauchy Fit( 1 );
Compare Densities
Syntax: obj << Compare Densities( state=0|1 )
Description: Shows or hides a plot of overlaid probability density functions for each group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Compare Densities( 1 );
Comparison Circles
Syntax: obj << Comparison Circles( state=0|1 )
Description: Shows or hides comparison circles. This option is available only when a multiple comparison report is open. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << All Pairs( 1 );
Wait( 2 );
obj << Comparison Circles( 0 );
Composition of Densities
Syntax: obj << Composition of Densities( state=0|1 )
Description: Shows or hides a plot of the summed densities, weighted by the count of each group. Across the range of the X variable, the Composition of Densities plot shows how each group contributes to the total density.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Composition of Densities( 1 );
Connect Means
Syntax: obj << Connect Means( state=0|1 )
Description: Shows or hides straight lines that connect the group means.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Connect Means( 1 );
Dunn All Pairs for Joint Ranks
Syntax: obj << Dunn All Pairs for Joint Ranks( state=0|1 )
Description: Shows or hides Dunn's test for all pairs by joint ranking. This test uses the Bonferroni adjustment, but it might not protect overall error rate.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Dunn All Pairs for Joint Ranks( 1 );
Dunn With Control for Joint Ranks
Syntax: obj << Dunn With Control for Joint Ranks( state = 0|1, {control level} )
Description: Shows or hides Dunn's test with a control group by joint ranking. This test uses the Bonferroni adjustment, but it might not protect overall error rate.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Dunn With Control for Joint Ranks( 1, {12} );
Dunnett's
Syntax: obj << With Control( state=0|1, {control ID} ); obj << "Dunnett's"n( state=0|1, {control ID} ); obj << "With Control, Dunnett's"n( state=0|1, {control ID} )
Description: Computes Dunnett's test; this tests whether means are different from the mean of a control group. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Control( 1, {15} );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Dunnett's"n( 1, {15} );
Each Pair
Syntax: obj << Each Pair( state=0|1 ); obj << "Student's t"n( state=0|1 ); obj << "Each Pair, Student's t"n( state=0|1 )
Description: Computes individual pairwise comparisons using Student's t tests with no adjustments made for multiple tests. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Student's t"n( 1 );
Each Pair Stepwise
Syntax: obj << Each Pair Stepwise( state=0|1 ); obj << "Newman-Keuls"n( state=0|1 ); obj << "Each Pair Stepwise, Newman-Keuls"n( state=0|1 )
Description: Computes the Newman-Keuls test; this tests whether there are differences between the means using the Studentized range test in a stepwise procedure. Also known as the Student-Newman-Keuls method, this test is less conservative and more powerful than a Tukey HSD test. See the Oneway Means Comparisons messages for further display options.
JMP Version Added: 14
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair Stepwise( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Newman-Keuls"n( 1 );
Equivalence Tests
Syntax: obj << Equivalence Tests( difference, <alpha=.05>, <"Pooled Variance"|"Unequal Variances">, <test type> )
Description: Tests that the means do not differ by more than an amount (difference) determined to be practically equivalent. This is the reverse of the usual significance test. Alpha, variance assumption, and test type are optional arguments. By default, the "Pooled Variance" assumption is used. The test type argument is "Equivalence" by default, but it can also be used to specify superiority or noninferiority tests.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests( 4, 0.1, "Unequal Variances" );
Equivalence Tests of Std Dev
Syntax: obj << Equivalence Tests of Std Dev( ratio, <alpha=.05>, <test type> )
Description: Tests that the standard deviations do not differ by more than a ratio determined to be practically equivalent. This is the reverse of the usual significance test. Alpha and test type are optional arguments. The test type argument is "Equivalence" by default, but it can also be used to specify superiority or noninferiority tests.
JMP Version Added: 17
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Equivalence Tests of Std Dev( 0.8, 0.05, "Equivalence" );
Friedman Rank Test
Syntax: obj << Friedman Rank Test( state=0|1 )
Description: Shows or hides a test based on Friedman Rank scores. The Friedman Rank scores are the ranks of the data within each level of the blocking variable. The parametric version of this test is a repeated measures ANOVA. This option is available only when a Block variable with an equal number of observations within each block is specified in the platform launch.
JMP Version Added: 14
dt = Open( "$SAMPLE_DATA/Snapdragon.jmp" );
obj = dt << Oneway( Y( :Y ), X( :Soil ), Block( :Block ) );
obj << Friedman Rank Test( 1 );
Games-Howell
Syntax: obj << "Games-Howell"n( state=0|1 );
Description: Shows or hides a Games-Howell multiple comparison of all pairs of means report. This test can be applied in settings when the individual group variances cannot be assumed to be equal.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Games-Howell"n( 1 );
Grand Mean
Syntax: obj << Grand Mean( state=0|1 )
Description: Shows or hides the overall mean of the Y variable. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ), Grand Mean( 0 ) );
Wait( 2 );
obj << Grand Mean( 1 );
Histograms
Syntax: obj << Histograms( state=0|1 )
Description: Shows or hides side-by-side histograms to the right of the original plot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Histograms( 1 );
Hsu MCB
Syntax: obj << With Best( state=0|1 ); obj << Hsu MCB( state=0|1 ); obj << "With Best, Hsu MCB"n( state=0|1 )
Description: Computes the Hsu multiple comparison with best (MCB) test; this tests whether means are less than the unknown maximum. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Best( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Hsu MCB( 1 );
Jonckheere Terpstra Test
Syntax: obj << Jonckheere Terpstra Test( state=0|1 )
Description: Shows or hides a report of the Jonckheere-Terpstra test, which is a nonparametric test for ordered differences among classes. It tests the null hypothesis that the distribution of the response variable does not differ among classes.
dt = Open( "$SAMPLE_DATA/Big Class.JMP" );
obj = dt << Oneway( Y( :Height ), X( :age ) );
obj << Jonckheere Terpstra Test( 1 );
Kolmogorov Smirnov Exact Test
Syntax: obj << Kolmogorov Smirnov Exact Test( state=0|1 )
Description: Shows or hides the Kolmogorov-Smirnov exact test, which is based on the empirical distribution function. This test determines whether the distribution of the response is the same across the groups. This option is available only when the X variable has exactly two levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Kolmogorov Smirnov Exact Test( 1 );
Kolmogorov Smirnov Test
Syntax: obj << Kolmogorov Smirnov Test( state=0|1 )
Description: Shows or hides a test based on the empirical distribution function, which tests whether the distribution of the response is the same across the groups. This option is available only when the X variable has exactly two levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Kolmogorov Smirnov Test( 1 );
Legend
Syntax: obj << Legend( state=0|1 )
Description: Shows or hides a legend for the normal quantile, cumulative distribution function (CDF), and density plots. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ), Plot Quantile by Actual( 1 ), Legend( 0 ) );
Wait( 2 );
obj << Legend( 1 );
Line of Fit
Syntax: obj << Line of Fit( state=0|1 )
Description: Shows or hides a reference line fit to the data for each level of the X variable on each open quantile plot. This option is available only when a quantile plot is open. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Plot Quantile by Actual( 1 );
Wait( 2 );
obj << Line of Fit( 0 );
Matching Column
Syntax: obj << Matching Column( column )
Description: Shows or hides a matched fit line and a corresponding fit line on the Oneway plot based on a specified matching variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Weight ), X( :Age, :sex ) );
Wait( 2 );
obj[1] << Matching Column( :sex );
obj[2] << Matching Column( :Age );
Matching Dotted Lines
Syntax: obj << Matching Dotted Lines( state=0|1 )
Description: Shows or hides dotted lines that connect the means through missing levels of the matching variable. The values used in place of the missing cell means are obtained using a two-way ANOVA model. This option is available only when the Matching Column option is selected and values of the matching variable are all missing for a level of the X variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
:sex[6 :: 8] = "";
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Matching Column( :sex );
Wait( 2 );
obj << Matching Dotted Lines( 1 );
Matching Lines
Syntax: obj << Matching Lines( state=0|1 )
Description: Shows or hides lines that connect the means of each level of the matching variable. This option is available only when the Matching Column option is selected.
dt = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj = dt << Oneway( Y( :LogHist0 ), X( :drug ) );
obj << Matching Column( :LogHist1 );
Wait( 2 );
obj << Matching Lines( 0 );
Mean CI Lines
Syntax: obj << Mean CI Lines( state=0|1 )
Description: Shows or hides lines at the upper and lower 95% confidence levels for each group. The 95% confidence levels are calculated using the pooled standard deviation.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Mean CI Lines( 1 );
Mean Diamonds
Syntax: obj << Mean Diamonds( state=0|1 )
Description: Shows or hides mean diamonds on the Oneway plot. Each mean diamond spans a 95% confidence interval for the corresponding group mean, with a horizontal line at the mean. The 95% confidence intervals are calculated using the pooled standard deviation.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Mean Diamonds( 1 );
Mean Error Bars
Syntax: obj << Mean Error Bars( state=0|1 )
Description: Shows or hides the mean of each group with error bars that are one standard error above and below the mean.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Mean Error Bars( 1 );
Mean Lines
Syntax: obj << Mean Lines( state=0|1 )
Description: Shows or hides a line at the mean of each group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Mean Lines( 1 );
Mean of Means
Syntax: obj << Mean of Means( state=0|1 )
Description: Shows or hides the mean of the group means.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Mean of Means( 1 );
Means and Std Dev
Syntax: obj << Means and Std Dev( state=0|1 )
Description: Shows or hides mean lines, error bars, and standard deviation lines on the Oneway plot and shows or hides a summary statistics table. The standard errors for the means use individual group standard deviations.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Means and Std Dev( 1 );
Means/Anova
Syntax: obj << Means( state=0|1 ); obj << "Means/Anova"n( state=0|1)
Description: Shows or hides mean diamonds on the Oneway plot and shows or hides an ANOVA report. This option is available only when the X variable has more than two levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Means( 1 );
Means/Anova/Pooled t
Syntax: obj << Means( state=0|1 ); obj << "Means/Anova/Pooled t"n( state=0|1)
Description: Shows or hides mean diamonds on the Oneway plot and shows or hides an ANOVA report. The ANOVA report includes a pooled t test report that assumes that the two groups have equal variances. This option is available only when the X variable has exactly two levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Means( 1 );
Median Exact Test
Syntax: obj << Median Exact Test( state=0|1 )
Description: Shows or hides an analysis of median scores.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Median Exact Test( 1 );
Median Test
Syntax: obj << Median Test( state=0|1 )
Description: Shows or hides a test based on median rank scores. The median rank scores are either 1 or 0, depending on whether a rank is above or below the median rank. The median test is the most powerful rank test for errors with double-exponential distributions.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Median Test( 1 );
Newman-Keuls
Syntax: obj << Each Pair Stepwise( state=0|1 ); obj << "Newman-Keuls"n( state=0|1 ); obj << "Each Pair Stepwise, Newman-Keuls"n( state=0|1 )
Description: Computes the Newman-Keuls test; this tests whether there are differences between the means using the Studentized range test in a stepwise procedure. Also known as the Student-Newman-Keuls method, this test is less conservative and more powerful than a Tukey HSD test. See the Oneway Means Comparisons messages for further display options.
JMP Version Added: 14
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair Stepwise( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Newman-Keuls"n( 1 );
Normal Quantile Label
Syntax: obj << Normal Quantile Label( state=0|1 )
Description: Shows or hides the normal quantile scale on each open quantile plot. This option is available only when a quantile plot is open. On by default.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Plot Quantile by Actual( 1 );
Wait( 2 );
obj << Normal Quantile Label( 0 );
Plot Actual by Quantile
Syntax: obj << Plot Actual by Quantile( state=0|1 )
Description: Shows or hides a quantile plot to the right of the Oneway Analysis plot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Plot Actual by Quantile( 1 );
Plot Quantile by Actual
Syntax: obj << Plot Quantile by Actual( state=0|1 )
Description: Shows or hides a quantile plot with the Y variable on the horizontal axis and cumulative probabilities on the vertical axis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Plot Quantile by Actual( 1 );
Points
Syntax: obj << Points( state=0|1 )
Description: Shows or hides data points on the Oneway plot. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
Wait( 2 );
obj << Points( 0 );
Points Jittered
Syntax: obj << Points Jittered( "None"|"Auto"|"Random Uniform"|"Random Normal"|"Density Random"|"Packed"|"Grid"|"Hex Grid"|"Beeswarm"="Auto" )
Description: Specifies the spread of the data points. When selected, the data points are jittered to avoid overlapping markers. "Auto" by default.
dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << Oneway( Y( :Sepal length ), X( :Species ) );
obj << Points Jittered( "Binned" );
Points Spread
Syntax: obj << Points Spread( state=0|1 )
Description: Specifies the spread of the data points. When selected, the data points are spread across the width of the interval.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Points Spread( 1 );
Pooled Variance
Syntax: obj << Ratios with Pooled Variance( state=0|1 );
Description: Shows or hides a ratio comparison of each pairs of means report. Under the assumption of equal variances, the pooled confidence interval for the mean ratio is the Fieller confidence interval.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Ratios with Pooled Variance( 1 );
Power
Syntax: obj << Power( Alpha( from, <to>, <by> ), Sigma( from, <to>, <by> ), Delta( from, <to>, <by> ), Number( from, <to>, <by> ), Solve for Power|Solve for Least Significant Number|Solve for Least Significant Value|Adjusted Power and Confidence Interval, Power Plot, Done )
Description: Reports statistical power calculations. The arguments allow ranges to be specified for the alpha, sigma, delta, and total sample size (Number). The fifth argument specifies the results of the report. The sixth argument requests a Power Plot, and the Done argument dismisses the Power Dialog.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ), All Graphs( 0 ) );
obj << Power(
Alpha( 0.05 ),
Sigma( 3.382, 3.73 ),
Delta( 2.79679 ),
Number( 10, 90, 5 ),
Solve for Power,
Power Plot,
Done
);
Proportion of Densities
Syntax: obj << Proportion of Densities( state=0|1 )
Description: Shows or hides a plot of the contribution to the density made by each level of the X variable. The contribution is shown as a proportion of the total density across the range of the X variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Proportion of Densities( 1 );
Quantiles
Syntax: obj << Quantiles( state=0|1 )
Description: Shows or hides box plots on the Oneway plot and shows or hides a quantile report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Quantiles( 1 );
Robust Fit
Syntax: obj << Robust Fit( state=0|1 )
Description: Produces Huber estimates that are equivalent to least squares residuals for small residuals, and equivalent to least absolute values for large residuals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Robust Fit( 1 );
Robust Means Lines
Syntax: obj << Robust Means Lines( state=0|1 )
Description: Shows or hides a line at the robust mean of each group. This option is available only when a Robust option is selected.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Robust Fit( 1 );
obj << Robust Means Lines( 1 );
Save Normal Quantiles
Syntax: obj << Save Normal Quantiles
Description: Saves normal quantile values for each level of the X variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Normal Quantiles;
Save Predicted
Syntax: obj << Save Predicted
Description: Saves the predicted mean of the Y variable for each level of the X variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Predicted;
Save Residuals
Syntax: obj << Save Residuals
Description: Saves values computed as the Y variable minus the mean of the Y variable within each level of the X variable.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Residuals;
Save Standardized
Syntax: obj << Save Standardized
Description: Saves standardized values of the Y variable for each level of the X variable. The standardized value is the centered response divided by the standard deviation within each level.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Standardized;
Select Group
Syntax: obj << Select Group( X value )
Description: Selects a group so that its circle is highlighted.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Weight ), X( :Age ), Each Pair );
Wait( 2 );
obj << Select Group( 14 );
Set Alpha Level
Syntax: obj << Set Alpha Level( alpha=0.05 )
Description: Changes the alpha level used for confidence limits, means diamonds, and confidence level values in reports. "0.05" by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Means( 1 );
Wait( 2 );
obj << Set Alpha Level( 0.01 );
Set α Level
Syntax: obj << Set α Level( alpha=0.05 )
Description: Changes the alpha level used for confidence limits, means diamonds, and confidence level values in reports. "0.05" by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Means( 1 );
Wait( 2 );
obj << Set Alpha Level( 0.01 );
Standard Deviations
Syntax: obj << Standard Deviations
Description: Launches a window with options for equivalence, superiority, or noninferiority tests for standard deviations. Specify the critical ratio.
Std Dev Lines
Syntax: obj << Std Dev Lines( state=0|1 )
Description: Shows or hides lines that are one standard deviation above and below the mean of each group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Std Dev Lines( 1 );
Steel With Control
Syntax: obj << Steel With Control( state = 0|1, {control level} )
Description: Shows or hides Steel's test, which protects the overall error rate for comparing all other groups with a control group. This is the nonparametric version of Dunnett's method.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Steel With Control( 1, {12} );
Steel-Dwass All Pairs
Syntax: obj << "Steel-Dwass All Pairs"n( state=0|1 )
Description: Shows or hides Steel-Dwass's test, which protects the overall error rate. This is the nonparametric version of Tukey's method.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Steel-Dwass All Pairs"n( 1 );
Student's t
Syntax: obj << Each Pair( state=0|1 ); obj << "Student's t"n( state=0|1 ); obj << "Each Pair, Student's t"n( state=0|1 )
Description: Computes individual pairwise comparisons using Student's t tests with no adjustments made for multiple tests. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Each Pair( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Student's t"n( 1 );
Tukey HSD
Syntax: obj << All Pairs( state=0|1 ); obj << Tukey HSD( state=0|1 ); obj << "All Pairs, Tukey HSD"n( state=0|1 )
Description: Computes the Tukey honestly significant difference (HSD) test; this test protects the overall error rate. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << All Pairs( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Tukey HSD( 1 );
Unequal Variances
Syntax: obj << Unequal Variances( state=0|1 )
Description: Shows or hides four tests for equality of group variances. This option also produces the Welch test, which is an ANOVA test for comparing means when the variances within groups are not equal.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Unequal Variances( 1 );
Unpooled Variance
Syntax: obj << Ratios with Unpooled Variance( state=0|1 );
Description: Shows or hides a ratio comparison of each pairs of means report. Under the assumption of unequal variances, the unpooled Satterthwaite-based confidence interval for the mean ratio is computed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Ratios with Unequal Variance( 1 );
Van Der Waerden Exact Test
Syntax: obj << Van Der Waerden Exact Test( state=0|1 )
Description: Shows or hides an analysis of Van der Waerden or normal scores.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Van Der Waerden Exact Test( 1 );
Wilcoxon Each Pair
Syntax: obj << Wilcoxon Each Pair( state=0|1 )
Description: Shows or hides Wilcoxon's test for all possible individual comparisons with no adjustment for multiple tests. This is the nonparametric version of the Each Pair, Student's t method.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Wilcoxon Each Pair( 1 );
Wilcoxon Exact Test
Syntax: obj << Wilcoxon Exact Test( state=0|1 )
Description: Shows or hides an analysis of Wilcoxon scores using exact methods for each pair of levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << Wilcoxon Exact Test( 1 );
Wilcoxon Test
Syntax: obj << Wilcoxon Test( state=0|1 )
Description: Shows or hides a test based on Wilcoxon rank scores. The Wilcoxon rank scores are the simple ranks of the data. The Wilcoxon test is the most powerful rank test for errors with logistic distributions. If the X variable has exactly two levels, the Wilcoxon test is equivalent to the Mann-Whitney test. If the X variable has more than two levels, the Kruskal-Wallis test is performed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Wilcoxon Test( 1 );
With Best
Syntax: obj << With Best( state=0|1 ); obj << Hsu MCB( state=0|1 ); obj << "With Best, Hsu MCB"n( state=0|1 )
Description: Computes the Hsu multiple comparison with best (MCB) test; this tests whether means are less than the unknown maximum. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Best( 1 );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Hsu MCB( 1 );
With Control
Syntax: obj << With Control( state=0|1, {control ID} ); obj << "Dunnett's"n( state=0|1, {control ID} ); obj << "With Control, Dunnett's"n( state=0|1, {control ID} )
Description: Computes Dunnett's test; this tests whether means are different from the mean of a control group. See the Oneway Means Comparisons messages for further display options.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << With Control( 1, {15} );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << "Dunnett's"n( 1, {15} );
X Axis Proportional
Syntax: obj << X Axis Proportional( state=0|1 )
Description: Specifies the spacing on the horizontal axis. When selected, the spacing is proportional to the number of observations at each level. This option is not available when the Matching Column option is selected. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ), X Axis Proportional( 0 ) );
Wait( 2 );
obj << X Axis Proportional( 1 );
t Test
Syntax: obj << t Test( state=0|1 )
Description: Shows or hides a t test report assuming that the variances are not equal. This option is available only when the X variable has exactly two levels.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :sex ) );
obj << t Test( 1 );
van der Waerden Test
Syntax: obj << van der Waerden Test( state=0|1 )
Description: Shows or hides a test based on Van der Waerden rank scores. The Van der Waerden rank scores are the ranks of the data divided by one plus a score value. The score value is the number of observations transformed to a normal score by applying the inverse of the normal distribution function. The Van der Waerden test is the most powerful rank test for errors with normal distributions.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << van der Waerden Test( 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/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Copy ByGroup Script;
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Data Table Window;
Get By Levels
Syntax: obj << Get By Levels
Description: Returns an associative array mapping the by group columns to their values.
JMP Version Added: 18
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
t = obj[1] << Get ByGroup Script;
Show( t );
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Redo ByGroup Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Relaunch ByGroup;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Report View
Syntax: obj << Report View( "Full"|"Summary" )
Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Save ByGroup Script to Data Table;
Save ByGroup Script to Journal
Syntax: obj << Save ByGroup Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), 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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Save ByGroup Script to Script Window;
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Oneway( Y( :Height ), X( :Age ), 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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );
Save Script to Journal
Syntax: obj << Save Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Save Script to Report;
Save Script to Script Window
Syntax: obj << Save Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
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 = Oneway(...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" ) ) )
);