ISummaryStatistics.MAE

Fore Syntax

MAE: Double;

Fore.NET Syntax

MAE: double;

Description

The MAE property returns mean absolute error.

Comments

The standard absolute error is the mean of absolute deviations of dependent variable source values from model values.

Fore Example

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

Sub UserProc;
Var
    LinearR: SmLinearRegress;
    can, fr: Array[9] Of Double;
    res, i: Integer;
    Con: IIntercept;
    ss: ISummaryStatistics;
Begin
    LinearR := New SmLinearRegress.Create;
    For i := 0 To 8 Do
        can[i] := 1230 + i * 302;
        fr[i] := 579.5 + i * 9.4;
    End For;
    // Set model parameters
    LinearR.Explained.Value := can;
    LinearR.Explanatories.Add.Value := fr;
    Con := LinearR.ModelCoefficients.Intercept;
    con.Mode := InterceptMode.ManualEstimate;
    con.Estimate := 35.7;
    // Calculate
    res := LinearR.Execute;
    ss := LinearR.SummaryStatistics;
    Debug.Write("Mean absolute error: ");
    Debug.WriteLine(ss.MAE);
    Debug.Write("Maximum absolute residual value: ");
    Debug.WriteLine(ss.MaxAE);
    Debug.Write("McFadden determination coefficient: ");
    Debug.WriteLine(ss.McFaddenRsquared);
    Debug.Write("Mean of dependent variable: ");
    Debug.WriteLine(ss.MD);
    Debug.Write("Mean error: ");
    Debug.WriteLine(ss.ME);
    Debug.Write("Mean squared error: ");
    Debug.WriteLine(ss.MSE);
    Debug.Write("Root mean squared error: ");
    Debug.WriteLine(ss.SqMSE);
    Debug.Write("Number of iterations, within which the decision was found: ");
    Debug.WriteLine(ss.NumOfIter);
End Sub UserProc;

After executing the example the console window displays calculated summary statistics.

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
    LinearR: SmLinearRegress;
    can, fr: Array[9] Of Double;
    res, i: Integer;
    Con: IIntercept;
    ss: ISummaryStatistics;
Begin
    LinearR := New SmLinearRegress.Create();
    For i := 0 To 8 Do
        can[i] := 1230 + i * 302;
        fr[i] := 579.5 + i * 9.4;
    End For;
    // Set model parameters
    LinearR.Explained.Value := can;
    LinearR.Explanatories.Add().Value := fr;
    Con := LinearR.ModelCoefficients.Intercept;
    con.Mode := InterceptMode.imManualEstimate;
    con.Estimate := 35.7;
    // Calculate
    res := LinearR.Execute();
    ss := LinearR.SummaryStatistics;
    System.Diagnostics.Debug.Write("Mean absolute error: ");
    System.Diagnostics.Debug.WriteLine(ss.MAE);
    System.Diagnostics.Debug.Write("Maximum absolute residual value: ");
    System.Diagnostics.Debug.WriteLine(ss.MaxAE);
    System.Diagnostics.Debug.Write("McFadden determination coefficient: ");
    System.Diagnostics.Debug.WriteLine(ss.McFaddenRsquared);
    System.Diagnostics.Debug.Write("Mean of dependent variable: ");
    System.Diagnostics.Debug.WriteLine(ss.MD);
    System.Diagnostics.Debug.Write("Mean error: ");
    System.Diagnostics.Debug.WriteLine(ss.ME);
    System.Diagnostics.Debug.Write("Mean squared error: ");
    System.Diagnostics.Debug.WriteLine(ss.MSE);
    System.Diagnostics.Debug.Write("Root mean squared error: ");
    System.Diagnostics.Debug.WriteLine(ss.SqMSE);
    System.Diagnostics.Debug.Write("Number of iterations, within which the solution was found: ");
    System.Diagnostics.Debug.WriteLine(ss.NumOfIter);
End Sub;