Regress.RegressTest Function

Regression tests.

Pascal

procedure RegressTest(const Y: TVec; const YCalc: TVec; const ATA: TMtx; out RegStat: TRegStats; const Residuals: TVec; const BStdDev: TVec; Constant: boolean = True; const Weights: TVec = nil); overload;

File

Regress

Parameters

Parameters	Description
Y	Dependant variables.
YCalc	Estimated (calculated) dependant variables.
ATA	Inverse matrix of normal equations i.e [A(T)*A]^-1.
RegStat	Returns regression statistics parameters.
Residuals	Returns residual errors.
BStdDev	Returns standard deviation.
Constant	If true then include intercept term b(0) in calculations. If false, set intercept term b(0) to 0.0.
Weights	Model weights (optional).

Description

Using regression results the routine calculates additional regression statistical parameters, together with model coefficients standard errors and model errors.

See Also

R2, PRESS

Example

Perform MLR and use results to calculate additional statistical parameters.

Uses Regress, MtxExpr;
procedure Example;
var y,b,w,yhat, resid, bstd: Vector;
  A, ATA : Matrix;
  RegStat : TRegStats;
begin
  A.SetIt(4,2,false,[1.0, 2.0,
                    -3.2, 2.5,
                     8.0, -0.5,
                     -2.2, 1.8]); // independent variables
  w.SetIt(false,[1,2,2,1]); // weights
  y.SetIt(false,[-3.0, 0.25, 8.0, 5.5]); // dependent variables
  MulLinRegress(y,A,b,w,true,yhat,ATA); //do regression
  // b=(19.093757944, -2.0141843616, -10.082487055)
  RegressTest(y,yhat,ATA,RegStat,resid, bstd, true,w); // do basic regression stats
  // RegStat = (ResidualVar:0.037230395108; R2:0.99965713428;
  // AdjustedR2:0.99897140285; F:1457.7968725; SignifProb: 0.01851663347)
end;

#include "MtxExpr.hpp"
#include "Regress.hpp"
void _fastcall Example()
{
    sMatrix A,ATA;
    sVector y,b,w,yhat,res,bse;
    TRegStats rs;

    // independent variables
    A.SetIt(4,2,false,OPENARRAY(double,(1.0, 2.0,
                      -3.2, 2.5,
                       8.0,   -0.5,
                       -2.2, 1.8)));
    w.SetIt(false,OPENARRAY(double,(1,2,2,1))); // weights
    y.SetIt(false,OPENARRAY(double,(-3.0, 0.25, 8.0, 5.5))); // dependent variables
    MulLinRegress(y,A,b,w,true,yhat,ATA); //do regression

    // b=(19.093757944, -2.0141843616, -10.082487055)
    RegressTest(y,yhat,ATA, rs,res,bse,true,w); // do basic regression stats

    // RegStat = (ResidualVar:0.037230395108; R2:0.99965713428;
    // AdjustedR2:0.99897140285; F:1457.7968725; SignifProb: 0.01851663347)
}

Regress, Functions, Example, See Also

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