ISmWhiteHeteroskedasticityTest.Fitted

Fore Syntax

Fitted: Array;

Fore.NET Syntax

Fitted: System.Array;

Description

The Fitted property returns a modeling series.

Comments

Values are available after method calculation.

Fore Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    test: SmWhiteHeteroskedasticityTest;
    d0: Double;
    i, res: Integer;
    y, x, y1, y2: Array[43] Of Double;
Begin
    test := New SmWhiteHeteroskedasticityTest.Create;

// Set explained series
 test.Explained.Value := y;
 // Define explanatory series
 test.Explanatories.Add.Value := x;
 test.Explanatories.Add.Value := y1;
 test.Explanatories.Add.Value := y2;
 // Set model constant parameters
 test.ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
 // Set sample period parameters
 test.ModelPeriod.FirstPoint := 1;
 test.ModelPeriod.LastPoint := 43;
 // Set missing data treatment method
 test.MissingData.Method := MissingDataMethod.SampleAverage;
 // determines whether regressor cross products are used
 test.CrossProduction := True;
 // Execute calculation and display results
 res := test.Execute;
 If res <> 0 Then
 Debug.WriteLine(test.Errors);
 Else
 Debug.Indent;
 Debug.WriteLine("Chi-square test");
 Debug.Unindent;
 d0 := test.ChiTest.Statistic;
 Debug.WriteLine("value: " + d0.ToString);
 d0 := test.ChiTest.Probability;
 Debug.WriteLine("probability: " + d0.ToString);
 Debug.Indent;
 Debug.WriteLine("Modeling series");
 Debug.Unindent;
 For i := 0 To test.Fitted.Length - 1 Do
 Debug.WriteLine(" " + (i + 1).ToString + ". " + test.Fitted[i].ToString);
 End For;
 End If;
End Sub UserProc;

After executing the example the console window displays chi-square test calculation results and a modeling series.

Fore.NET Example

The requirements and result of the Fore.NET example execution match with those in the Fore example.

Imports Prognoz.Platform.Interop.Stat;
…
Public Shared Sub Main(Params: StartParams);
Var
    test: SmWhiteHeteroskedasticityTest;
    d0: Double;
    i, res: Integer;
    y, x, y1, y2: Array[43] Of Double;
Begin
    test := New SmWhiteHeteroskedasticityTest.Create();

// Set values fornbsp;variables
 y[00] := 6209; x[00] := 4110; y1[00] := 3415; y2[00] := 2822;
 y[01] := 6385; x[01] := 4280; y1[01] := 3673; y2[01] := 3023;
 y[02] := 6752; x[02] := 4459; y1[02] := 4013; y2[02] := 3131;
 y[03] := 6837; x[03] := 4545; y1[03] := 4278; y2[03] := 3351;
 y[04] := 6495; x[04] := 4664; y1[04] := 4577; y2[04] := 3463;
 y[05] := 6907; x[05] := 4861; y1[05] := 5135; y2[05] := 3686;
 y[06] := 7349; x[06] := 5195; y1[06] := 5388; y2[06] := 3815;
 y[07] := 7213; x[07] := 5389; y1[07] := 5610; y2[07] := 3960;
 y[08] := 7061; x[08] := 5463; y1[08] := 5787; y2[08] := 4119;
 y[09] := 7180; x[09] := 5610; y1[09] := 6181; y2[09] := 4351;
 y[10] := 7132; x[10] := 5948; y1[10] := 6633; y2[10] := 4641;
 y[11] := 7137; x[11] := 6218; y1[11] := 6910; y2[11] := 5008;
 y[12] := 7473; x[12] := 6521; y1[12] := 7146; y2[12] := 5305;
 y[13] := 7722; x[13] := 6788; y1[13] := 7248; y2[13] := 5611;
 y[14] := 8088; x[14] := 7222; y1[14] := 7689; y2[14] := 5693;
 y[15] := 8516; x[15] := 7486; y1[15] := 8046; y2[15] := 5804;
 y[16] := 8941; x[16] := 7832; y1[16] := 8143; y2[16] := 6121;
 y[17] := 9064; x[17] := 8153; y1[17] := 8064; y2[17] := 6546;
 y[18] := 9380; x[18] := 8468; y1[18] := 8556; y2[18] := 6918;
 y[19] := 9746; x[19] := 9054; y1[19] := 9177; y2[19] := 7349;
 y[20] := 9907; x[20] := 9499; y1[20] := 9705; y2[20] := 7769;
 y[21] := 10333; x[21] := 9866; y1[21] := 9923; y2[21] := 7809;
 y[22] := 10863; x[22] := 10217; y1[22] := 10268; y2[22] := 7951;
 y[23] := 11693; x[23] := 10763; y1[23] := 10681; y2[23] := 8395;
 y[24] := 12242; x[24] := 10683; y1[24] := 10448; y2[24] := 8653;
    y[25] := 12227; x[25] := 10494; y1[25] := 10366; y2[25] := 8304;     y[26] := 12910; x[26] := 10938; y1[26] := 10958; y2[26] := 8809;     y[27] := 13049; x[27] := 11198; y1[27] := 11292; y2[27] := 9028;     y[28] := 13384; x[28] := 11546; y1[28] := 11726; y2[28] := 9314;     y[29] := 14036; x[29] := 11865; y1[29] := 12172; y2[29] := 9820;     y[30] := Double.Nan; x[30] := 11781; y1[30] := 12058; y2[30] := 10246;     y[31] := 14704; x[31] := 11681; y1[31] := 11804; y2[31] := 10153;     y[32] := 13802; x[32] := 11903; y1[32] := 11682; y2[32] := 10197;     y[33] := 14197; x[33] := 11900; y1[33] := 12001; y2[33] := 10294;     y[34] := 15010; x[34] := 11986; y1[34] := 12300; y2[34] := 10555;     y[35] := 15589; x[35] := 12206; y1[35] := 12535; y2[35] := 10808;     y[36] := 15932; x[36] := 12734; y1[36] := 13173; y2[36] := 11318;     y[37] := 16631; x[37] := 12990; y1[37] := 13482; y2[37] := 11683;     y[38] := 17394; x[38] := 13516; y1[38] := 13945; y2[38] := 12153;     y[39] := 17758; x[39] := 13866; y1[39] := 14278; y2[39] := 12464;     y[40] := 17308; x[40] := 14141; y1[40] := 14840; y2[40] := 12782;     y[41] := Double.Nan; x[41] := 14141; y1[41] := 15263; y2[41] := 13066;     y[42] := 16413; x[42] := 14237; y1[42] := 15357; y2[42] := 13113;     rtest := New SmRedundantVariablesTest.Create();     // Set explained and explanatory variables     rtest.Explained.Value := y;     rtest.Explanatories.Add().Value := x;     rtest.Explanatories.Add().Value := y1;     rtest.Explanatories.Add().Value := y2;     // Set the method of constant definition     rtest.ModelCoefficients.Intercept.Mode := InterceptMode.imAutoEstimate;     // Set calculation periods     rtest.ModelPeriod.FirstPoint := 1;     rtest.ModelPeriod.LastPoint := 43;     // Set missing data treatment method     rtest.MissingData.Method := MissingDataMethod.mdmLinTrend;     // Set moving average parameters     ARMA := rtest.ARMA;     OrderList[0] := 1;     ARMA.OrderMA := OrderList;     ARMA.CalcInitMode := ARMAInitType.armaitAuto;     // Set excessive regressors     redlist[0] := 0;     rtest.RedundantExplanatories := redlist;     // Perform calculation and display results     res := rtest.Execute();     If res <> 0 Then         System.Diagnostics.Debug.WriteLine(rtest.Errors);     Else         System.Diagnostics.Debug.WriteLine("=== Fisher test ===");         d0 := rtest.FTest.Statistics;         System.Diagnostics.Debug.WriteLine("value: " + d0.ToString());         d0 := rtest.FTest.Probability;         System.Diagnostics.Debug.WriteLine("probability: " + d0.ToString());         System.Diagnostics.Debug.WriteLine("== Smoothed series ==");         For i := 0 To rtest.Fitted.Length - 1 Do             System.Diagnostics.Debug.Write(i.ToString() + ", ");             System.Diagnostics.Debug.WriteLine(rtest.Fitted.GetValue(i));         End For;     End If; End Sub RedundantVar; y[26] := 12910; x[26] := 10938; y1[26] := 10958; y2[26] := 8809;
 y[27] := 13049; x[27] := 11198; y1[27] := 11292; y2[27] := 9028;
 y[28] := 13384; x[28] := 11546; y1[28] := 11726; y2[28] := 9314;
 y[29] := 14036; x[29] := 11865; y1[29] := 12172; y2[29] := 9820;
 y[30] := 14242; x[30] := 11781; y1[30] := 12058; y2[30] := 10246;
 y[31] := 14704; x[31] := 11681; y1[31] := 11804; y2[31] := 10153;
 y[32] := 13802; x[32] := 11903; y1[32] := 11682; y2[32] := 10197;
 y[33] := 14197; x[33] := 11900; y1[33] := 12001; y2[33] := 10294;
 y[34] := 15010; x[34] := 11986; y1[34] := 12300; y2[34] := 10555;
 y[35] := 15589; x[35] := 12206; y1[35] := 12535; y2[35] := 10808;
 y[36] := 15932; x[36] := 12734; y1[36] := 13173; y2[36] := 11318;
 y[37] := 16631; x[37] := 12990; y1[37] := 13482; y2[37] := 11683;
 y[38] := 17394; x[38] := 13516; y1[38] := 13945; y2[38] := 12153;
 y[39] := 17758; x[39] := 13866; y1[39] := 14278; y2[39] := 12464;
 y[40] := 17308; x[40] := 14141; y1[40] := 14840; y2[40] := 12782;
 y[41] := 16444; x[41] := 14141; y1[41] := 15263; y2[41] := 13066;
 y[42] := 16413; x[42] := 14237; y1[42] := 15357; y2[42] := 13113;

// Set explained series
 test.Explained.Value := y;
 // Define explanatory series
 test.Explanatories.Add().Value := x;
 test.Explanatories.Add().Value := y1;
 test.Explanatories.Add().Value := y2;
 // Set model constant parameters
 test.ModelCoefficients.Intercept.Mode := InterceptMode.imAutoEstimate;
 // Set sample period parameters
 test.ModelPeriod.FirstPoint := 1;
 test.ModelPeriod.LastPoint := 43;
 // Set missing data treatment method
 test.MissingData.Method := MissingDataMethod.mdmSampleAverage;
 // determines whether regressor cross products are used
 test.CrossProduction := True;
 // Execute calculation and display results
 res := test.Execute();
 If res <> 0 Then
 System.Diagnostics.Debug.WriteLine(test.Errors);
 Else
 System.Diagnostics.Debug.Indent();
 System.Diagnostics.Debug.WriteLine("Chi-square test");
 System.Diagnostics.Debug.Unindent();
 d0 := test.ChiTest.Statistic;
 System.Diagnostics.Debug.WriteLine("value: " + d0.ToString());
 d0 := test.ChiTest.Probability;
 System.Diagnostics.Debug.WriteLine("probability: " + d0.ToString());
 System.Diagnostics.Debug.Indent();
 System.Diagnostics.Debug.WriteLine("Modeling series");
 System.Diagnostics.Debug.Unindent();
 For i := 0 To test.Fitted.Length - 1 Do
 System.Diagnostics.Debug.WriteLine(" " + (i + 1).ToString() + ". " + test.Fitted.GetValue(i).ToString());
 End For;
 End If;
End Sub;