Thanks @danschikore for the expalantion.
When I posted my question I didn't think about opening one side of the filter.
The idea was:
if I want the data filter to automatically remove bad data points (<0) or some other well-defined data (missing data, indicator entry), such data has to be in the current data set - otherwise the data filter will automatically adapt.
In this case I want to remove all rows with names "reference" and missing names:
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
Data Table( "Big Class" ):name << Set Modeling Type( "Multiple Response" );
Graph Builder(
Summary Statistic( "Median" ),
Graph Spacing( 4 ),
Variables( X( :height ), Y( :weight ), Overlay( :sex ) ),
Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
Local Data Filter(
Add Filter(
columns( :name ),
Match None( Where( :name == "reference","" ) ),
Display( :name, Find( Set Text( "" ) ) )
)
)
);
but if there is no such row in the current data set, the data filter will just be ignored - and the user will lose this part when he saves the current version of the graph or Dashboard. Just run the code and save the resulting script.
Next time the user opens the Graph, rows with missing name and "reference" data will be displayed by default and the user has to exclude them manually.
This issue will get more severe as soon as the option not gets available for more modeling types:
data filters, new option: "not"