ISummaryStatistics.R2

Fore Syntax

R2: Double;

Fore.NET Syntax

R2: double;

Description

The R2 property returns determination coefficient.

Comments

This coefficient determines the variation percentage of a dependent variable caused by changed exogenous variables.

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;
    // Run calculation
    res := LinearR.Execute;
    ss := LinearR.SummaryStatistics;
    Debug.Write("Determination coefficient: ");
    Debug.WriteLine(ss.R2);
    Debug.Write("Determination coefficient (uncentered): ");
    Debug.WriteLine(ss.R2_2);
    Debug.Write("Schwarz information criterion: ");
    Debug.WriteLine(ss.SC);
    Debug.Write("Standard deviation of dependent variable: ");
    Debug.WriteLine(ss.SD);
    Debug.Write("Standard error: ");
    Debug.WriteLine(ss.SE);
    Debug.Write("Standard deviation of residuals: ");
    Debug.WriteLine(ss.SEE);
    Debug.Write("Sum of squared residuals of source data deviations from model data: ");
    Debug.WriteLine(ss.SSR);
    Debug.Write("Residual variance: ");
    Debug.WriteLine(ss.VE);
End Sub UserProc;

After executing the example the console window displays summary statistics:

Determination coefficient value.
Adjusted determination coefficient (uncentered).
Schwarz information criterion.
Standard deviation of dependent variable.
Standard error.
Standard deviation of residuals.
Sum of squared residuals of source data deviations from model data.
Residual variance.

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;
    // Run calculation
    res := LinearR.Execute();
    ss := LinearR.SummaryStatistics;
    System.Diagnostics.Debug.Write("Determination coefficient: ");
    System.Diagnostics.Debug.WriteLine(ss.R2);
    System.Diagnostics.Debug.Write("Determination coefficient (uncentered): ");
    System.Diagnostics.Debug.WriteLine(ss.R2_2);
    System.Diagnostics.Debug.Write("Schwarz information criterion: ");
    System.Diagnostics.Debug.WriteLine(ss.SC);
    System.Diagnostics.Debug.Write("Standard deviation of dependent variable: ");
    System.Diagnostics.Debug.WriteLine(ss.SD);
    System.Diagnostics.Debug.Write("Standard error: ");
    System.Diagnostics.Debug.WriteLine(ss.SE);
    System.Diagnostics.Debug.Write("Standard deviation of residuals: ");
    System.Diagnostics.Debug.WriteLine(ss.SEE);
    System.Diagnostics.Debug.Write("Sum of squared residuals of source data deviations from model data: ");
    System.Diagnostics.Debug.WriteLine(ss.SSR);
    System.Diagnostics.Debug.Write("Error variance: ");
    System.Diagnostics.Debug.WriteLine(ss.VE);
End Sub;