The Explore Outliers platform provides options to identify, explore, and manage outliers in your univariate or multivariate data.
In the Probe.jmp sample data table, several columns contain outlier values of 9999. Many industries use nines as a missing value code. The following example selects VDP_PCOLL, VDP_PINNBASE, and VDP_PINPBASE, the columns with the highest number of 9s, and adds the Missing Value Codes column property to the columns. The script then rescans the columns and removes these columns from the report.
dt = Open( "$SAMPLE_DATA/Probe.jmp" );
obj = Explore Outliers(
Y(
:VDP_M1,
:VDP_M2,
:VDP_NBASE,
:VDP_NEMIT,
:VDP_NENBNI,
:VDP_NSINK,
:VDP_PBASE,
:VDP_PBL,
:VDP_PCOLL,
:VDP_PEMIT,
:VDP_PINNBASE,
:VDP_PINPBASE,
:VDP_PSINK,
:VDP_SICR
),
Quantile Range Outliers( 1 ),
Show only columns with outliers( 1 ),
);
obj << Add Highest Nines to Missing Value Codes( :VDP_PCOLL, :VDP_PINNBASE, :VDP_PINPBASE );
obj << Rescan;