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腳本自動化：檢索預測變數並設定 sigma 水平

你好，

我接到一個項目，需要寫一個建模流程。所有預測變數和響應都是連續數值型的，並且響應分別進行擬合。在進行了一些初始資料準備之後…

1. 逐步 K 折驗證<--自動化
2. 步驟 1 的模型在 SLS 中運作<--自動化
3. SLS 預測分析器：
   1. 初始化模擬器<--自動化
   2. 將所有因素類型變更為隨機<--非自動化
      
      1. 將每列的所有 SD 變更為 3 sigma 值 <-- 非自動化

此流程將使用 for 迴圈。在每次迭代中，它將對單一響應變數進行一系列分析。 3.2 的腳本很容易取得，但將標準差值改為 3 西格瑪值卻不容易。使用 for 迴圈時，如何自動修改 3.2 的標準差值？有沒有更好的方法？我查看了“首選項”>“平台”>“擬合模型”>“FIR 最小二乘法”，但沒有找到將標準差值從 1 標準差更改為 3 標準差的選項。

最新更新至 JMP 17，如果有幫助的話。

數據探索與視覺化回复：腳本自動化：檢索預測變量和設置西格瑪水平

是這樣的嗎？

Names Default To Here(1);
dt = Open("$SAMPLE_DATA/Tiretread.jmp");
obj = Profiler(
 Y(
  :Pred Formula ABRASION, :Pred Formula MODULUS, :Pred Formula ELONG,
  :Pred Formula HARDNESS
 )
);

val = 3;

obj << Simulator(
 1,
 Factors(
  SILICA << Random(Normal(1.25, 0.3266)), SILANE << Random(Normal weighted(50, 6.532)),
  SULFUR << Fixed(2.25)
 ),
 Responses(
  Pred Formula ABRASION << Add Random Noise(1), Pred Formula MODULUS << Add Random Noise(2),
  Pred Formula ELONG << Add Random Noise(val), Pred Formula HARDNESS << Add Random Noise(val*2)
 )
);

根據腳本索引中的示例修改

 

回复：腳本自動化：檢索預測變量和設置西格瑪水平

你好@jthi ,

 

不完全的。 下面是我所擁有的代碼片段，稍後我會解釋更多。

// Iterating through responses  - JMP 17
 mymodel_sw = Fit Model( Y( response[i] ),
  Effects( Factorial to degree( eval( predict ) ), Response Surface( eval( predict ) ) ),
  Personality("Stepwise"),
  Run( "K-Fold Crossvalidation"n(5) ) 
 );
 
 // Runs modeling
 mymodel_sw << Go;
 
 // Finishes predictor selection before proceeding
 mymodel_sw << Finish;
 
 // Makes model
 mymodel_sls = mymodel_sw << Make Model;
 
 // Runs SLS model
 mymodel_sls << Run;
 
 // Finishes modeling before proceeding
 mymodel_sls << Finish;
 
 // Gets list of predictors
 param_SLS = mymodel_sls << Get Parameter Names;

一旦我 << 獲取參數名稱，param_SLS 錯誤與已刪除的對象引用。但是，我需要這個參數列表來構建 << Simulate to table 中的參數，我將通過創建一個自定義函數來處理這個問題。

 

這段代碼位於一個 for 循環中，循環遍歷響應列表，一次一個。這有意義嗎？

回复：腳本自動化：檢索預測變量和設置西格瑪水平

或者有沒有一種方法可以從探查器工具中檢索參數名稱，這樣我就不會得到所有的交互項並且必須對所有這些進行排序？ 我想要一個我可以使用的參數名稱的唯一列表。 我必須假設適合的參數數量因模型而異。

回复：腳本自動化：檢索預測變量和設置西格瑪水平

這是一個更深入的示例腳本。請注意，這不起作用！

Names default to here(1);

dt = open( "$SAMPLE_DATA/Body Fat.jmp" );

/////////////////////////// Custom function - Help!

mysim = function( {list, iteration, stddev, step},
 
 // New list placeholder
 unique_factors = {};
 
 // Adds only unique factors to new list, uses "*" as identifier
 for( i = 1, i <= N items( list ), i++,
  if( list[i] << !contains( "*" ) , insert into( unique_factors, list[i] )  
  )  
 );
 
 num_sim_factors = N Items( unique_factors );
 
 // Modifies each factor in the simulator to normal distribution with 3 standard deviations
 simfactor = {};
 
 for( i = 1, i <= num_sim_factors, i++,
  temp_1 = char( unique_factors[i] ) || " << Random( Normal( " ||  char( col mean( unique_factors[i] ) ) ||
     ", " || char( stddev * col std dev( unique_factors[i] ) ) || ") ),"
  insert into( sim_factor, temp_1 )
 );
 
 // Inputs simfactor into simulator
 mymodel_sls << Simulator( 1, eval( simfactor ) );
 
 // Creates the arguments for Simulate to table
 simtable_pt1 = "N Runs( " || char( iteration ) || "),"
 simtable_pt2 = {};
 
  // Creates an argument for each factor
  for( i = 1, i <= num_sim_factors, i++,
   temp_2 = char( unique_factor[i] ) || " << Sequence Location( " || char ( Col Minimum( unique_factor[i] ) ) || ", "
    chart ( Col Maximum( unique_factor[i] ) ) || ", " || char( step ) || " ),";
   insert into( simtable_pt2, temp_2 )
  );
 
 // Combines strings into a single argument for use for Simulate to Table
 simarg = simtable_pt1 || simtable_pt2;
 
 // Simuates data to table for further analysis
 mymodel_sls << Get simulator;
 mymodel_sls << Simulate to table( simarg );
);

/////////////////////////// Cols to use - user adjustable

predict = {:"Neck circumference (cm)"n, :"Chest circumference (cm)"n,
 :"Abdomen circumference (cm)"n, :"Hip circumference (cm)"n,
 :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
 :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
 :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n};

response = {:"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n};

///////////////////////////  User input

num_response = N Items( response );
num_predict = N Items( predict );

// How many simulations to run?
sim_no = 1000;

// How many standard deviations for each predictor in the simulation?
stddev = 3;

// Steps in simulation
steps = 4;

///////////////////////////  Changing col attribute

// In a later step, Simulate, the mean should be 0 due to this
for( i = 1, i <= num_response, i++,
 response[i] << Set Property( "Coding", { col minimum( response[i] ), col maximum( response[i] ) } );
);

///////////////////////////  Modeling

/* Fit Model - Stepwise, w/ K-fold validation --> SLS --> Simulate */
for( i = 1, i <= num_response, i++,
 
 // Names models for calling later
 mymodel_sw = "FM_SW_" || char( i );

 // Iterating through responses  - JMP 17
 mymodel_sw = Fit Model( Y( response[i] ),
  Effects( Factorial to degree( eval( predict ) ), Response Surface( eval( predict ) ) ),
  Personality("Stepwise"),
  Run( "K-Fold Crossvalidation"n(5) ) 
 );
 
 // Runs modeling
 mymodel_sw << Go;
 
 // Finishes predictor selection before proceeding
 mymodel_sw << Finish;
 
 // Makes model
 mymodel_sls = mymodel_sw << Make Model;
 
 // Runs SLS model
 mymodel_sls << Run;
 
 // Finishes modeling before proceeding
 mymodel_sls << Finish;
 
 ////////////////// NOT WORKING BELOW
 
 // Gets list of predictors <--- This is first step
 param_sls1 = mymodel_sls << Get Effect Names;
 param_sls2 = mymodel_sls << Get Parameter Names;
 show(param_sls1, param_sls2);
 
 // Calls on custom function <--- This is second step
 mysim( param_sls1, sim_no, stddev, steps );
 
 // Names Simulate to table dataset by response variable in for loop
 Current data table() << Set name( char( response[i] ) || "_report" ); 
 
);

幫助！ 這有多個部分不起作用！這是有效的 - 註釋掉自定義函數和下面的任何內容（靠近腳本底部），其中狀態不在下面。 我可以通過SLS建模的生成，但是一旦涉及到修改模擬參數和運行模擬，這兩者都是自定義的（請參閱自定義功能），我就束手無策了。

回复：腳本自動化：檢索預測變量和設置西格瑪水平

該功能有很多錯別字，可能會導致問題。 我只是修復了一些東西，不知道它是否做了它應該做的

Names Default To Here(1);

dt = Open("$SAMPLE_DATA/Body Fat.jmp");

mysim = function({input_list, iteration, stddev, step},

 unique_factors = Filter Each({item}, input_list, !Contains(item, "*"));
 
 num_sim_factors = N Items(unique_factors);
 simfactor = {};
 
 simfactor = Transform Each({uniq_factor}, unique_factors,
  Eval Insert("^uniq_factor^ << Random(Normal(^col mean(column(dt, uniq_factor))^, ^stddev * col std dev(column(dt, uniq_factor))^");
 );
 
 // Inputs simfactor into simulator
 mymodel_sls << Simulator( 1, eval(simfactor));
 
 // Creates the arguments for Simulate to table
 simtable_pt1 = "N Runs( " || char(iteration) || "),";
 simtable_pt2 = {};
 
 // Creates an argument for each factor
 simtable_pt2 = Transform Each({uniq_factor}, unique_factors,
  Eval Insert("^uniq_factor^ << Sequence Location(^Col Minimum(column(dt, uniq_factor))^, ^Col Maximum(column(dt, uniq_factor))^, ^step^)");
 );
 
 // Combines strings into a single argument for use for Simulate to Table
 simarg = simtable_pt1 || Concat Items(simtable_pt2, ",");
 
 // Simuates data to table for further analysis
 mymodel_sls << Get simulator;
 mymodel_sls << Simulate to table(simarg);
);

predict = {:"Neck circumference (cm)"n, :"Chest circumference (cm)"n,
 :"Abdomen circumference (cm)"n, :"Hip circumference (cm)"n,
 :"Thigh circumference (cm)"n, :"Knee circumference (cm)"n,
 :"Ankle circumference (cm)"n, :"Biceps (extended) circumference (cm)"n,
 :"Forearm circumference (cm)"n, :"Wrist circumference (cm)"n};

response = {:"Age (years)"n, :"Weight (lbs)"n, :"Height (inches)"n};

num_response = N Items(response);
num_predict = N Items(predict);

sim_no = 1000;
stddev = 3;
steps = 4;

For(i = 1, i <= num_response, i++,
 response[i] << Set Property("Coding", {Col Minimum(response[i]), Col Maximum(response[i])})
);

For(i = 1, i <= num_response, i++, 
 mymodel_sw = "FM_SW_" || Char(i);

 mymodel_sw = Fit Model(
  Y(response[i]),
  Effects(Factorial to degree(Eval(predict)), Response Surface(Eval(predict))),
  Personality("Stepwise"),
  Run("K-Fold Crossvalidation"n(5))
 );
 
 mymodel_sw << Go;
 mymodel_sw << Finish;
 
 mymodel_sls = mymodel_sw << Make Model;
 mymodel_sls = mymodel_sls << Run;
 mymodel_sls << Finish;
 
 
 param_sls1 = mymodel_sls << Get Effect Names;
 param_sls2 = mymodel_sls << Get Parameter Names;
 Show(param_sls1, param_sls2);
 
 mysim(param_sls1, sim_no, stddev, steps);
 
 dt << Set name(Char(response[i]) || "_report");
);

回复：腳本自動化：檢索預測變量和設置西格瑪水平

@jthi - 我將不得不深入研究一下你的建議。我見過 Filter Each、Transform Each 和 Eval Insert，但我沒有使用它們的經驗，也不知道它們是如何工作的。

 

另一個問題要問你。在 Eval Insert 中，你有胡蘿蔔 ^ 標記，這是我以前從未見過的。是否有一些文檔可以讓我更多地了解它們的功能？我認為從您的使用方式來看已經很清楚了，但是我很感興趣並且想確保我正確地解釋了您的用法。

 

simfactor = Transform Each({uniq_factor}, unique_factors,
  Eval Insert("^uniq_factor^ << Random(Normal(^col mean(column(dt, uniq_factor))^, ^stddev * col std dev(column(dt, uniq_factor))^");
 );

回复：腳本自動化：檢索預測變量和設置西格瑪水平

@jthi ,

 

你走在正確的軌道上！ 其他表達式操作對於創建參數以插入到它們各自的函數中是必要的。在mysim自定義函數中，參數insert into修改模擬的分佈，生成參數insert into Simulate to Table已經有詳細說明。這是一個非常令人興奮的學習機會，讓我對這些類型的高級表達式大開眼界。

如果有人想了解更多關於表達式操作的信息，這裡有一個文檔鏈接： https://www.jmp.com/support/help/en/17.0/#page/jmp/advanced-expressions-macros-and-lists.shtml#

 

感謝那些提供幫助的人！

 

Names default to here(1);

dt = open( "$SAMPLE_DATA/Body Fat.jmp" );

mysim = function( {list, iteration, stddev, step}, 
 
 // filter list of effects down to main effects.  These are the effects whose name matches a column name
 // take advantage of the speed of associative arrays to get the intersection set.
 list << Intersect(Associative Array(dt<<GET column="" names=""> SLS --> Simulate */
for( k = 1, k <= num_response, k++,
 
 // Names models for calling later
 mymodel_sw = "FM_SW_" || char( k );

 // Iterating through responses  - JMP 17
 mymodel_sw = Fit Model( Y( response[k] ),
  Effects( Factorial to degree( eval( predict ) ), Response Surface( eval( predict ) ) ),
  Personality("Stepwise"),
  Run( "K-Fold Crossvalidation"n(5) ) 
 );
 
 // Runs modeling
 mymodel_sw << Go;
 
 // Finishes predictor selection before proceeding
 mymodel_sw << Finish;
 
 // Makes model
 mymodel_sls = mymodel_sw << Make Model;
 
 // Runs SLS model
 mymodel_sls = mymodel_sls << Run; // this creates another object.  assign your variable to that object
 mymodel_sls << Profiler(1); // make sure profiler is on.
 
 // Gets associative array of effect names.
 // use this in the function to easily filter down to the main effect names
 param_sls1 = Associative Array(mymodel_sls << Get Effect Names); 
  
 // Calls on custom function <--- This is second step
 resultDT = mysim( param_sls1, sim_no, stddev, steps );  // assign variable to reference returned table
 
 // Names Simulate to table dataset by response variable in for loop
 resultDT << Set name( char( response[k] ) || "_report" );  
);</GET>

 

SS