早上好
我想为我的JSL实现一个具有“双因素”的FIT模型响应表面模型”，
但我还没有找到特定的JSL命令来做到这一点。
有人可以帮助我
这里我实现的代码.. 但对于FIT MODEL部分来说还不完整....
Clear Globals();
Clear Log();
Names Default To Here( 1 );
// code to choose among all opened tables which one will be selected for the analysis
OpenDT = List();
For( i = 1, i <= N Table(), i++,
Insert Into( OpenDT, Data Table( i ) )
);
dlgDT = Dialog(
"Please select Data Table for analysis",
VList( selectDT = List Box( OpenDT ) ),
HList( Button( "OK" ), Button( "Cancel" ) )
);
Show( dlgDT );
If( dlgDT["button"] == 1,
Remove From( dlgDT, N Items( dlgDT ) );
Eval List( dlgDT );
);
Current Data Table( selectDT[1] );
cd = Column Dialog(
Var = collist( " select Response >>", MinCol( 1 ), MaxCol( 1 ) ),
Hlist( "" ),
Ref = Collist( " Select Predictors >>", MinCol( 1 ) ),
Hlist( "" ),
Hlist( "" ),
mypareto = Check Box( " Show results's Pareto ", 1 ),
Hlist( "Number of recursive trees -> ", forest = EditNumber( 10 ) ),
Vlist(
mypareto = Check Box( " Show results's Pareto ", 1 ),
Hlist( "" ),
Hlist( "" ),
Hlist( "select top predictors ", myrank = EditNumber( 5 ) ),
),
);
//);
//declaration of global variables
Response = cd["Var"]; //(Response Variable) ok
Predictor = cd["Ref"]; //(Predictor Variable) ok
Fsize = cd["forest"]; // number of tree to run
pareto = cd["mypareto"];
rank = cd["myrank"];
risultato = New Table( "Tree Table",
Add Rows( 0 ),
New Column( "Y", Character, "Nominal" ),
New Column( "Term", Character, "Nominal", Set Property( "ID Column", 1 ) ),
New Column( "Number of Splits", Numeric, "Continuous" ),
New Column( "SS", Numeric, "Continuous" ),
New Column( "SS 2", Character, "Nominal" ),
New Column( "Portion", Numeric, "Continuous" ),
);
tree = Expr(
partitioning = Partition(
Y( Response( 1 ) ),
X( Eval( Predictor ) ),
Split History( 1 ),
Informative Missing( 1 ),
Validation Portion( 0.33 ),
Minimun Size Split( 5 ),
Column Contributions( 1 ),
Go
);
part = Report( partitioning )["Column Contributions"][Table Box( 1 )] <<
Make Combined Data Table;
part << select where( :Number of Splits == 0 );
part << delete rows;
partitioning << close window();
part << set name( "Parts" );
risultato = Data Table( risultato ) << Concatenate(
Data Table( part ),
Append to first table
);
Close( part, "no save" );
);
For( i = 1, i <= Fsize, i++,
Current Data Table( selectDT[1] );
tree;
);
If( Pareto == 1,
Current Data Table( risultato );
Pareto Plot(
Cause( :Term ),
);
);
//create Ranking Table
Summary_Rank = Data Table( risultato ) << Summary(
Group( :Term ),
output table name( "Bootstrap Ranking" ),
);
Data Table( Summary_Rank ) << Sort(
By( :N Rows ),
Order( Descending ),
replace Table,
);
x = N Rows( Summary_Rank );
Data Table( Summary_Rank ) << New Column( "Rank", numeric, , formula( Sequence( 1, x, 1 ) ) );
Data Table( Summary_Rank ) << select where( :Rank <= rank );
Column( 1 ) << set selected( 1 );
tempDt = Summary_Rank << Subset( columns( :Term ) );
Column( 1 ) << set selected( 1 );
predictor = Column( 1 ) << get values();
Close( tempDt, "no save" );
// declaration VarExpr variable
varExpr = {};
//------------------------------------------------FIT MODEL Two Factore Response Surface -------------------//
// parse word '& RS' into predictors variablee
For( i = 1, i <= N Items( predictor ), i++,
Insert Into( varExpr, Parse( ":Name(\!"" || predictor[i] || "\!")" || Char( "& RS" ) ) )
);
Current Data Table( selectDT[1] );
FIT_MODEL = Fit Model(
Y( Response( 1 ) ),
Effects(
(Eval( varExpr )),
),
Personality( "Standard Least Squares" ),
Emphasis( "Effect Leverage" ),
Run( Profiler( 1, Confidence Intervals( 1 ), Arrange in Rows( 6 ), ), Show VIF( 1 ) )
);
FIT_MODEL;
我认为您要求的是使用以下示例解决的：
predictor = {"X1","X2","X3","X4"};
varExpr = {};
// parse word '& RS' into predictors variablee
For( i = 1, i <= N Items( predictor ), i++,
Insert Into( varExpr, Parse( ":Name(\!"" || predictor[i] || "\!")" || Char( "& RS" ) ) )
);
For( i = 1, i <= N Items( predictor ), i++,
For( k = i, k <= N Items( predictor ), k++,
Insert Into( varExpr, Parse( ":Name(\!"" || predictor[i] || "\!")" || Char( " * " ) || ":Name(\!"" || predictor[k] || "\!")" ) )
))JMP最好的脚本编写者是JMP本身。正如Jim所说，以交互方式构建模型，然后使用红色三角形来保存脚本。它向您展示了它在JSL脚本中的显示方式：
适合模型（
Y（：Y），
影响（
：X1和RS，
：X2和RS，
：X3和RS，
：X4和RS，
：X1 *：X1，
：X1 *：X2，
：X2 *：X2，
：X1 *：X3，
：X2 *：X3，
：X3 *：X3，
：X1 *：X4，
：X2 *：X4，
：X3 *：X4，
：X4 *：X4
）
个性（“标准最小二乘法”）
）;
感谢yu Jim的回答，
似乎没有一种快速的方法来实现响应表面条件的影响:(如下例所示）
X1 RS
X2 RS
X3 RS
X4 RS
X1 * X1
X1 * X2
X1 X3 *
X1 * X4
X2 * X2
X2 * X3
X2 * X4
X3 * X3
X3 X4 *
X4 * X4
通过我的脚本我能够创建X1 RS，X2 RS，X3 RS，X4 RS，
但现在我需要通过单独的脚本创建乘法因子......
您可以在“拟合线性模型”一书中找到双因素响应曲面模型的一个很好的示例：
帮助==>书籍==>拟合线性模型
交互式生成模型后，从输出显示中的红色三角形中，您可以请求“保存脚本”。 它将为您提供运行分析所需的JSL。
