The following example performs multiple linear regression.
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,residuals,bstdev; 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,residuals,bstdev,true,w); // do basic regression stats // RegStat = (ResidualVar:0.037230395108; R2:0.99965713428; // AdjustedR2:0.99897140285; F:1457.7968725; SignifProb: 0.01851663347) }
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