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LR Model Fit calculate R2

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.. lots of folks understand what R2 means for a linear
   regression so calculate it alongside RMSE when we
   are performing a linear regression.
This commit is contained in:
Mark Liversedge
2018-08-25 07:47:23 +01:00
parent 9e5242a782
commit 2a6083071f
2 changed files with 20 additions and 5 deletions
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2
src/Charts/CPPlot.cpp
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@@ -703,7 +703,7 @@ CPPlot::plotModel()
// Pmax is often higher than the test values (they're for
// 3-20 mins typically so well short of pmax).
if (!showDelta && rideSeries == RideFile::watts && pdModel && pdModel->PMax() > ymax) {
if (pdModel->PMax() > ymax) setAxisScale(yLeft, 0, pdModel->PMax());
if (pdModel->PMax() > ymax) setAxisScale(yLeft, 0, pdModel->PMax() * 1.1f);
}
}

23
src/Metrics/PDModel.cpp
View File

@@ -176,11 +176,26 @@ PDModel::deriveCPParameters(int model)
// get vector of residuals
QVector<double> residuals(t.size());
double errtot=0;
double sse=0;
double meany=0;
for(int i=0; i<t.size(); i++){
double err = p[i] - y(t[i]/60.0f);
errtot += err; // for mean
residuals[i]=err;
double py = y(t[i]/60.0f); // predicted y
meany += p[i]; // calculatng mean divided at end
double err = p[i] - py; // error for this point
errtot += err; // for mean errors divided at end
sse += err*err; // sum of squared errors for r2 calc
residuals[i]=err; // vector of residuals
}
// lets use to calc R2
meany /= double(t.size());
double sv=0;
for(int i=0; i<t.size(); i++) {
sv += (p[i]-meany) * (p[i]-meany);
}
double R2=1-(sse/sv);
// carry on with RMSE
double mean = errtot/double(t.size());
errtot = 0;
@@ -190,7 +205,7 @@ PDModel::deriveCPParameters(int model)
// RMSE
double RMSE=sqrt(mean);
fitsummary = QString("RMSE %1 watts [LR] %2 points").arg(RMSE, 0, 'f', 2).arg(t.size());
fitsummary = QString("RMSE %1 watts R2=%3 [LR] %2 points").arg(RMSE, 0, 'f', 2).arg(t.size()).arg(R2, 0, 'f', 3);
} else if (fit == LeastSquares && this->nparms() > 0) {
// only try lmfit if the model supports that