Hello JMP community,
I currently am trying to write a script that opens an existing data table which Contains an "X1" column and a "Y1" column with 10 rows. The script then predicts Y1 in a new column, makes a residual column (Predicted Y1 - Y1), and then makes a predicted residual column. From there, I want the script to add a certain amount of "made up" X1/Y1 values to the existing data table, in hopes of getting the process to automate. The following code is what I believe is responsible for adding the data points to the data table. The problem is that the following script doesn't add an accurate list of X1 values (the X1 values is simply every integer between 1-10, and when I run the following code I usually only get integers 1 and 10).
d = DOE( Augment Design, X( :X1 ), Y( :Y1 )/*, {Make Design, Make Table}*/ );
d << Make Design;
dt3 = d << Make Table;
dt2 << concatenate( dt3, Append to first table( 1 ) );
Here is my entire script if it helps any:
// 1. Creates X1 column, and corresponding Y1
Close All( Data Tables, No Save );
sourceDT = Open( "SH2.jmp" );
dt2 = sourceDT << Subset( Columns(), Rows(), Output Table Name( "Fixed.jmp" ) );
// 2. Creates predicted Ystat column, saves formulas into same data table (first iteration)
PredictedY1 = dt2 << Neural(
Y( :Y1 ),
X( :X1 ),
Informative Missing( 0 ),
Validation Method( "Holdback", 0.3333 ),
Fit( NTanH( 3 ) )
);
PredictedY1 << Save Profile Formulas;
// 3. Adds residual column
dt2 << New Column( "Residual 1", Formula( Abs( :PredictedY1 - :Y1 ) ) );
// 4. Creates and predicts residual column
PredictedResidual1 = dt2 << Neural(
Y( :Residual 1 ),
X( :X1 ),
Informative Missing( 0 ),
Validation Method( "Holdback", 0.3333 ),
Fit( NTanH( 3 ) )
);
PredictedResidual1 << Save Profile Formulas;
// Iteration 2
d = DOE( Augment Design, X( :X1 ), Y( :Y1 )/*, {Make Design, Make Table}*/ );
d << Make Design;
dt3 = d << Make Table;
dt2 << concatenate( dt3, Append to first table( 1 ) );
PredictedY2 = dt2 << Neural(
Y( :Y1 ),
X( :X1 ),
Informative Missing( 0 ),
Validation Method( "Holdback", 0.3333 ),
Fit( NTanH( 3 ) )
);
PredictedY2 << Save Profile Formulas;
// 3. Adds residual column
dt2 << New Column( "Residual 2", Formula( Abs( :PredictedY1 2 - :Y1 ) ) );
// 4. Creates and predicts residual column
PredictedResidual2 = dt2 << Neural(
Y( :Residual 2 ),
X( :X1 ),
Informative Missing( 0 ),
Validation Method( "Holdback", 0.3333 ),
Fit( NTanH( 3 ) )
);
PredictedResidual2 << Save Profile Formulas;
Column( dt2, "X1" ) << Suppress Formula Eval();
Column( dt2, "Y1" ) << Suppress Formula Eval();
dt2 << Select where( :Residual 1 <= 3 );
dt2 << delete rows;
Thanks,
Kyle
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