Tracking Desirability with Importance Info Included

Bill_Worley

I have a customer that is using the Importance feature in Profiler to answer questions for different customers or just better understand what happens when the Importance is changed.  His ultimate goal is to get this information, Importance included, into a table format or report format where he can compare all of the changes he made to inputs along with the associated desirability values for each one.  I showed him how to use Remember settings, but that did not include the Importance information.  I then showed him how to save the desirability formula for each change and then make a profiler out of that which helped.  My question is, does anyone have an idea for a better way to do this?  The customer has as many as 8 responses and in some cases small changes in importance make big changes in the desirability creating the need to keep track of  the changes.

Thanks,

Bill

Jeff_Perkinson

Hmmm...it's there in the Community. I wonder if you're looking in email? Unfortunately attachments don't get emailed out.

Regardless, here's the JSL:

dt = Open( "$SAMPLE_DATA\Tiretread.jmp" );

/* DOE(
  Mixture Design,
  {Add Response( Maximize, "Y", ., ., . ),
  Change Factor Settings( 1, 0, 1, "X1" ),
  Change Factor Settings( 2, 0, 1, "X2" ),
  Change Factor Settings( 3, 0, 1, "X3" ),
  Add Factor( Mixture, 0, 1, "X4", 0 ), Set Random Seed( 17874509 ),
  Mixture Design Type( Simplex Centroid, 3 ), Make Table}
  );

  DOE(
  Mixture Design,
  {Add Response( Maximize, "Y", ., ., . ),
  Change Factor Settings( 1, 0, 1, "X1" ),
  Change Factor Settings( 2, 0, 1, "X2" ),
  Change Factor Settings( 3, 0, 1, "X3" ),
  Add Factor( Mixture, 0, 1, "X4", 0 ), Set Random Seed( 565809822 ),
  FFF Optimality Criterion( MaxPro ),
  Mixture Design Type( Space Filling, 40 ), Make Table}
  );
*/


design = [0 0 1 0,
0 0.333333333333333 0.333333333333333 0.333333333333333,
0 0.5 0.5 0,
0.5 0 0 0.5,
0.333333333333333 0.333333333333333 0 0.333333333333333,
0 0 0.5 0.5,
0.5 0 0.5 0,
0.5 0.5 0 0,
0 1 0 0,
0.333333333333333 0.333333333333333 0.333333333333333 0,
1 0 0 0,
0 0 0 1,
0 0.5 0 0.5,
0.25 0.25 0.25 0.25,
0.333333333333333 0 0.333333333333333 0.333333333333333,
0.129341793818258 0.113570435050926 0.555087961516463 0.201999809614354,
0.562771227793176 0.157814433214016 0.148687835965149 0.13072650302766,
0.0741087013672653 0.328445563019489 0.420469172634966 0.176976562978258,
0.480623308715391 0.0530165014015233 0.180649493928348 0.285710695954718,
0.0592396342732678 0.280842812986979 0.171235548321365 0.488682004418369,
0.000366353301893547 0.495624013200378 0.0487881475500917 0.455221485947623,
0.186685768025845 0.336115363970454 0.104004687477811 0.373194180525874,
0.260292056103974 0.0727162089053609 0.651530260887218 0.0154614741034504,
0.828415128580538 0.0374748875633555 0.124313455109702 0.00979652874637669,
0.321245157262151 0.270124081755294 0.210573039909886 0.198057721072667,
0.149328477429568 0.624182188294858 0.19282865878053 0.0336606754950328,
0.400971950069278 0.22411567842022 0.0549435869583736 0.319968784552114,
0.00268304266384922 0.0847781101674513 0.271270808584515 0.641268038584165,
0.297224499869083 0.472657546964829 0.0870077036572192 0.143110249508866,
0.0881917506854173 0.211142446831821 0.302453995000718 0.39821180748202,
0.0426574765117274 0.505817388672078 0.44454404537882 0.00698108943735945,
0.0805537590756301 0.00591609363364374 0.112870063470545 0.800660083820156,
0.439432835456406 0.310900282522556 0.244787971596327 0.00487891042468458,
0.520820964911533 0.382882149468456 0.00245415020214207 0.0938427354178472,
0.0073590034706533 0.216816500678779 0.62793672047688 0.147887775373685,
0.543606879371427 0.0189307223736204 0.0167801271059772 0.420682271148957,
0.0958968363289629 0.0269966822822809 0.811607582691281 0.0654988986974496,
0.0653522147552467 0.0589166390368986 0.508187418554444 0.367543727653387,
0.307540491520319 0.178868474516907 0.000601747904712645 0.512989286058037,
0.378790658667367 0.534262613627289 0.0374831704119869 0.0494635572933473,
0.228200855028275 0.0481391794661855 0.0367453073442054 0.686914658161323,
0.624374502842959 0.124350728257548 0.0327274704081449 0.218547298491325,
0.242824259696557 0.357928291213458 0.277732658268606 0.121514790821368,
0.169480938736345 0.277455908097761 0.464646104135357 0.0884170490305078,
0.0109667478288627 0.780648445118542 0.133523607390446 0.0748611996621465,
0.587701533853539 0.0046052501227614 0.36434354740649 0.0433496686171922,
0.342249736507884 0.189514724332875 0.410175962678701 0.0580595764805061,
0.278955401294394 0.136777609910744 0.247921149460588 0.336345839334252,
0.0206314076018773 0.400331788169298 0.317539930941122 0.261496873287677,
0.427350755031108 0.0947138720984061 0.309302485763209 0.168632887107241,
0.689046880465832 0.202415723010461 0.078277243942716 0.0302601525809645,
0.176528462279281 0.0326441506964762 0.338740735704598 0.452086651319619,
0.0347873984308442 0.0028365342403808 0.722506120384281 0.239869946944483,
0.100272714301178 0.599357672806663 0.0279130570217295 0.272456555870395,
0.0305670374453033 0.363716767830115 0.0121726476393494 0.593543547085199];

/*factors = SILICA, SILANE, SULFUR in order*/
factorsettings = J( N Row( design ), 3, . );

pf = Profiler( Y( :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG, :Pred Formula HARDNESS ) );

For( i = 1, i <= N Row( design ), i++,
  pf << Profiler(
  1,
  Desirability Functions( 1 ),
  Pred Formula ABRASION << Response Limits(
  {Lower( 90, 0.066 ), Middle( 145, 0.5 ), Upper( 200, 0.9819 ), Goal( "Maximize" ), Importance( design[i, 1] )}
  ),
  Pred Formula MODULUS << Response Limits(
  {Lower( 500, 0.066 ), Middle( 1375, 0.5 ), Upper( 2250, 0.9819 ), Goal( "Maximize" ), Importance( design[i, 2] )}
  ),
  Pred Formula ELONG << Response Limits(
  {Lower( 200, 0.066 ), Middle( 425, 0.5 ), Upper( 650, 0.9819 ), Goal( "Maximize" ), Importance( design[i, 3] )}
  ),
  Pred Formula HARDNESS << Response Limits(
  {Lower( 60, 0.066 ), Middle( 70, 0.5 ), Upper( 80, 0.9819 ), Goal( "Maximize" ), Importance( design[i, 4] )}
  ),
  Term Value( SILICA( 1.25, Lock( 0 ), Show( 1 ) ), SILANE( 50, Lock( 0 ), Show( 1 ) ), SULFUR( 2.25, Lock( 0 ), Show( 1 ) ) ),
  Maximize Desirability
  );
  Wait( .1 );
  pf<<remember settings;
  f = pf << get factor settings;
  factorsettings[i, 1] = Arg( f[1], 2 );
  factorsettings[i, 2] = Arg( f[2], 2 );
  factorsettings[i, 3] = Arg( f[3], 2 );


);
desirabilities=report(pf)["Remembered Settings"][NumberColBox(8)]<<get as matrix;

result = design || factorsettings || desirabilities;

resultdt = As Table( result );


Column( resultdt, 1 ) << set name( "ABRASION Importance" );
Column( resultdt, 1 ) << Add Column Properties(
  Set Property( "Mixture", {0, 1, 1, L PseudoComponent Coding} ),
  Set Property( "Design Role", DesignRole( Mixture ) ),
  Set Property( "Factor Changes", Easy )
);
Column( resultdt, 2 ) << set name( "MODULUS Importance" );
Column( resultdt, 2 ) << Add Column Properties(
  Set Property( "Mixture", {0, 1, 1, L PseudoComponent Coding} ),
  Set Property( "Design Role", DesignRole( Mixture ) ),
  Set Property( "Factor Changes", Easy )
);
Column( resultdt, 3 ) << set name( "ELONG Importance" );
Column( resultdt, 3 ) << Add Column Properties(
  Set Property( "Mixture", {0, 1, 1, L PseudoComponent Coding} ),
  Set Property( "Design Role", DesignRole( Mixture ) ),
  Set Property( "Factor Changes", Easy )
);
Column( resultdt, 4 ) << set name( "HARDNESS Importance" );
Column( resultdt, 4 ) << Add Column Properties(
  Set Property( "Mixture", {0, 1, 1, L PseudoComponent Coding} ),
  Set Property( "Design Role", DesignRole( Mixture ) ),
  Set Property( "Factor Changes", Easy )
);
Column( resultdt, 5 ) << set name( "SILICA" );

Column( resultdt, 6 ) << set name( "SILANE" );

Column( resultdt, 7 ) << set name( "SULFUR" );

Column( resultdt, 8) << set name("Desirability");

resultdt << Fit Group(
  Gaussian Process(
  Y( :SILICA ),
  X( :ABRASION Importance, :MODULUS Importance, :ELONG Importance, :HARDNESS Importance ),
  Set Correlation Function( "Cubic" )
  ),
  Gaussian Process(
  Y( :SILANE ),
  X( :ABRASION Importance, :MODULUS Importance, :ELONG Importance, :HARDNESS Importance ),
  Set Correlation Function( "Cubic" )
  ),
  Gaussian Process(
  Y( :SULFUR ),
  X( :ABRASION Importance, :MODULUS Importance, :ELONG Importance, :HARDNESS Importance ),
  Set Correlation Function( "Cubic" )
  ),
  <<{Profiler(
  1,
  Confidence Intervals( 1 ),
  Term Value(
  ABRASION Importance( 0, Lock( 0 ), Show( 1 ) ),
  MODULUS Importance( 0, Lock( 0 ), Show( 1 ) ),
  ELONG Importance( 0, Lock( 0 ), Show( 1 ) ),
  HARDNESS Importance( 0, Lock( 0 ), Show( 1 ) )
  )
  )}
);