最大化期望函数后提取输出我正在按组对数据集运行回归模型 (JMP15) - 即从同一张数据表中生成 100 多个回归模型。然后我最大化所有这些组的可取性。我想提取对应于 100 多个模型中每个模型的“最大化可取性”的变量值（下图中带圆圈的值）。关于如何做到这一点有什么建议吗（JSL 或使用 GUI 都可以）？
我可以用一种繁琐的方式来完成它（Ctrl-Prediction Profiler -> Factor Settings -> Remember Settings 将每个案例的设置保存为“记住的设置”，然后我可以使用 Ctrl-Make Combined Data Table 来提取它。然而，这需要我根据每个组点击“Enter”100 多次！）。
基础数据分析与建模回复：最大化期望函数后提取输出以下是执行所需操作的示例。该示例创建一个示例表。然后运行拟合模型。您必须最大化可取性，然后运行其余代码以获取新的输出表
Names Default To Here( 1 );
dt =
// Open Data Table: Tiretread.jmp
// → Data Table( "Tiretread" )
Open( "$SAMPLE_DATA/Tiretread.jmp" );
dtSub = dt << subset( selected columns( 0 ), selected rows( 0 ), output table( "Subset" ) );
For Each Row(
:Abrasion = :Abrasion + :Abrasion*random normal(0,1);
:MODULUS = :MODULUS + :MODULUS*random normal(0,1);
:ELONG = :ELONG + :ELONG * random normal(0,1);
:HARDNESS = :HARDNESS + :HARDNESS* random normal(0,1);
);
dt << Concatenate(
dtSub,
Output Table( "Concat of Tiretread, Subset" ),
Append to first table,
Create source column
);
Close( dtSub, nosave );
// Run the Model
fm = Fit Model(
Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
By( :Source Table ),
Effects( :SILICA, :SILANE, :SULFUR ),
Personality( "Standard Least Squares" ),
Emphasis( "Effect Leverage" ),
Run(
:ABRASION << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:MODULUS << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:ELONG << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:HARDNESS << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
Profiler(
1,
Confidence Intervals( 1 ),
Desirability Functions( 1 )
)
)
);
// Stop the JSL
stop();
// Set the Maximize Desirability and the run the code below
// Create an output table
dtMax = New Table( "Desirability",
New Column( "By Group", character ),
New Column( "Silica" ),
New Column( "Silane" ),
New Column( "Sulfer" )
);
// Populate the table getting the Desirability values from the chart
// In this example, the 2 by groups are actually just a replicate of the same
// data, so the output values will be the same.
For Each( {profile}, fm,
dtMax << add rows( 1 );
:By Group[N Rows( dtMax )] = Report( profile )[Outline Box( 1 )] << get title;
:Silica[N Rows( dtMax )] = Report( profile )["Prediction Profiler"][Number Edit Box( 1 )]
<< get;
:Silane[N Rows( dtMax )] = Report( profile )["Prediction Profiler"][Number Edit Box( 2 )]
<< get;
:Sulfer[N Rows( dtMax )] = Report( profile )["Prediction Profiler"][Number Edit Box( 3 )]
<< get;
);
回复：最大化可取性函数后提取输出你好，吉姆，
非常感谢您的代码。但是它在最后一节（在完成拟合最小二乘法并创建空表“Desirability”之后）给出了错误，如下所示：
我正在运行 JMP15。
回复：最大化可取性函数后提取输出此外，我该如何将与最大化需求相对应的优化响应（即耐磨性、模量、伸长率、硬度）保存到同一张表中？
谢谢。
回复：最大化可取性函数后提取输出直到 JMP 16 才引入 For Each() 函数。因此，代码需要更改为 For() 循环方法。
For( i=1,i<=nitems(fm), i++,
以下是修改后的 JSL，使用 For() 函数，并读取响应变量的优化值。我在 JMP 15 中测试了代码
Names Default To Here( 1 );
dt =
// Open Data Table: Tiretread.jmp
// → Data Table( "Tiretread" )
Open( "$SAMPLE_DATA/Tiretread.jmp" );
dtSub = dt << subset( selected columns( 0 ), selected rows( 0 ), output table( "Subset" ) );
For Each Row(
:Abrasion = :Abrasion + :Abrasion*random normal(0,1);
:MODULUS = :MODULUS + :MODULUS*random normal(0,1);
:ELONG = :ELONG + :ELONG * random normal(0,1);
:HARDNESS = :HARDNESS + :HARDNESS* random normal(0,1);
);
dt << Concatenate(
dtSub,
Output Table( "Concat of Tiretread, Subset" ),
Append to first table,
Create source column
);
Close( dtSub, nosave );
// Run the Model
fm = Fit Model(
Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
By( :Source Table ),
Effects( :SILICA, :SILANE, :SULFUR ),
Personality( "Standard Least Squares" ),
Emphasis( "Effect Leverage" ),
Run(
:ABRASION << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:MODULUS << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:ELONG << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
:HARDNESS << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
Box Cox Y Transformation( 0 )},
Profiler(
1,
Confidence Intervals( 1 ),
Desirability Functions( 1 )
)
)
);
// Stop the JSL
stop();
// Set the Maximize Desirability and the run the code below
// Create an output table
dtMax = New Table( "Desirability",
New Column( "By Group", character ),
New Column( "Silica" ),
New Column( "Silane" ),
New Column( "Sulfer" ),
New Column( "Optimized Abrasion"),
New Column( "Optimized Modulus"),
New Column( "Optimized Elong"),
New Column( "Optimized Hardness")
);
// Populate the table getting the Desirability values from the chart
// In this example, the 2 by groups are actually just a replicate of the same
// data, so the output values will be the same.
For( i=1,i<=nitems(fm), i++,
dtMax << add rows( 1 );
:By Group[N Rows( dtMax )] = Report( fm[i] )[Outline Box( 1 )] << get title;
:Silica[N Rows( dtMax )] = Report( fm[i] )["Prediction Profiler"][Number Edit Box( 1 )]
<< get;
:Silane[N Rows( dtMax )] = Report( fm[i] )["Prediction Profiler"][Number Edit Box( 2 )]
<< get;
:Sulfer[N Rows( dtMax )] = Report( fm[i] )["Prediction Profiler"][Number Edit Box( 3 )]
<< get;
:Optimized Abrasion[N Rows( dtMax )] = Num(Report( fm[i] )["Prediction Profiler",TextBox(2)]
<<GET text=""></GET>
<P>现在开始您的工作。您需要研究提供的 JSL 并了解它在做什么。在不了解其功能的情况下剪切和粘贴代码并不是一个好习惯。请花时间了解所使用的函数，以及显示树输出以及如何解释和使用输出显示的值。当您遇到理解问题时，请回到讨论社区寻求帮助。</P>
<P> </P>
