If you do not need the platform for interaction, then you might use the Minimal Report for Emphasis and the Invisible option in the Fit Model call. (Then remember to close it.
I suggest that you use the Scripting Index to explore the other messages for Fit Least Squares platform instead of << Prediction Formula. Here is one example:
The column formulas are actually JSL expressions. As such, they may be manipulated as data. That is to say, you can apply the anti-log transformation after these expressions are saved to result in an updated formula. So use the << Get Formula message with the new column to obtain the current expression. Insert it into the anti-log function expression as its argument, and then save the new expression with the << Set Formula message to replace the original formula.
Here is an example using the approach that I suggested:
Names Default to Here( 1 );
// open example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
// create a transformed response
dt << New Column( "Log weight", Numeric, Continuous,
Values( Log10( :weight << Get As Matrix ) )
);
// fit model for transformed response
fit = dt << Fit Model(
Y( :Log weight ),
Effects( :height ),
Emphasis( "Minimal Report" ),
Run
);
// save formulas for transformed response
fit << Mean Confidence Limit Formula( 0.05 ) << Indiv Confidence Limit Formula( 0.05 );
// back-transform prediction formulas
n = N Col( dt );
For( col = n, col > n - 3, col--, // start at end and work back
old formula = Column( dt, col ) << Get Formula;
new formula = Substitute(
Expr( Power( 10, yyy ) ),
Expr( yyy ), Name Expr( old formula )
);
Eval(
Substitute(
Expr( Column( dt, col ) << Set Formula( fff ) ),
Expr( fff ), Name Expr( new formula )
)
);
col name = Column( dt, col ) << Get Name;
Column( dt, col ) << Set Name( Substitute( col name, "Log ", "" ) );
);