ISlARMA.UseAnalyticDeriv

Syntax

UseAnalyticDeriv: Boolean;

Description

The UseAnalyticDeriv property determines whether analytical derivatives are used in solution search.

Comments

Available values:

True. Default value. Analytical derivatives are used in search solution.
False. Numeric derivatives are used to search solution.

Example

To execute the example, add a link to the Stat system assembly.

Sub UserProc;
Var
    lr: ISmLinearRegress;
    W: Array[12] Of Double;
    X: array[20] Of Double;
    ARMA: ISlARMA;
    AR, MA: Array[1] Of Integer;
    res, i: Integer;
    ModelCoefficients: IModelCoefficients;
Begin
    lr := New SmLinearRegress.Create;
    // explained series values
    w[0] := 2; w[4] := -1.9; w[8] := -0.7;
    w[1] := 0.8; w[5] := Double.Nan; w[9] := Double.Nan;
    w[2] := -0.3; w[6] := 3.2; w[10] := 4.3;
    w[3] := -0.3; w[7] := 1.6; w[11] := 1.1;
    lr.Explained.Value := w;
    // Explanatory series values
    x[0] := Double.Nan; x[10] := 11;
    x[1] := 2; x[11] := 12;
    x[2] := 3; x[12] := 13;
    x[3] := 4; x[13] := Double.Nan;
    x[4] := 5; x[14] := 15;
    x[5] := 6; x[15] := 16;
    x[6] := Double.Nan; x[16] := 17;
    x[7] := 8; x[17] := Double.Nan;
    x[8] := 9; x[18] := 19;
    x[9] := 10; x[19] := 20;
    lr.Explanatories.Clear;
    lr.Explanatories.Add.Value := X;
    lr.Explanatories.Item(0).Name := "X";
    // Constant is used in model
    ModelCoefficients := lr.ModelCoefficients;
    ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
    // Sample period
    lr.ModelPeriod.FirstPoint := 1;
    lr.ModelPeriod.LastPoint := 12;
    lr.Forecast.LastPoint := 19;
    // Method of missing data treatment
    lr.MissingData.Method := MissingDataMethod.AnyValue;
    ARMA := lr.ARMA;
    // autoregression order
    AR[0] := 2;
    ARMA.OrderAR := AR;
    // moving average order
    MA[0] := 1;
    ARMA.OrderMA := MA;
    // method of initial approximations detection
    ARMA.CalcInitMode := ARMAInitType.Auto;
    // optimization method
    ARMA.EstimationMethod := ARMAEstimationMethodType.LevenbergMarquardt;
    // number of iterations and accuracy for optimization method
    ARMA.MaxIteration := 50;
    ARMA.Tolerance := 0.1;
    // Use analytical derivatives in solution search
    ARMA.UseAnalyticDeriv := True;
    // calculate model
    res := lr.Execute;
    If (res = 0) Then
        Debug.WriteLine(" ===Estimates of model coefficients=== ");
        Debug.WriteLine("Constnat: " + ModelCoefficients.Intercept.Estimate.ToString);
        For i:=0 To ModelCoefficients.Coefficients.Estimate.Length-1 Do
            Debug.WriteLine(lr.Explanatories.Item(i).Name +": " + ModelCoefficients.Coefficients.Estimate[i].ToString);
        End For;
        For i:=0 To lr.ARMA.CoefficientsAR.Estimate.Length-1 Do
            Debug.WriteLine("AR("+Ar[i].ToString +"): " + lr.ARMA.CoefficientsAR.Estimate[i].ToString);
        End For;
        For i:=0 To lr.ARMA.CoefficientsMA.Estimate.Length-1 Do
            Debug.WriteLine("MA("+Ma[i].ToString +"): " + lr.ARMA.CoefficientsMA.Estimate[i].ToString);
        End For;
        Debug.WriteLine(" ===Descriptive statistics=== ");
        Debug.WriteLine("Determination coefficient: " + lr.SummaryStatistics.R2.ToString);
        Debug.WriteLine("Sum of residual squares: " + lr.SummaryStatistics.SSR.ToString);
        Debug.WriteLine("Standard error: " + lr.SummaryStatistics.SE.ToString);
        Else
            Debug.WriteLine(lr.Errors);
    End If;
End Sub UserProc;

After executing the example the console window displays estimates of model coefficients and descriptive statistics.