public static string Fit(Altaxo.Gui.Graph.Gdi.Viewing.IGraphController ctrl, int order, double fitCurveXmin, double fitCurveXmax, bool showFormulaOnGraph)
{
string error;
double[] xarr, yarr;
error = GetActivePlotPoints(ctrl, out xarr, out yarr);
int numberOfDataPoints = xarr.Length;
if (null != error)
return error;
string[] plotNames = GetActivePlotName(ctrl);
int numberOfParameter = order + 1;
double[] parameter = new double[numberOfParameter];
LinearFitBySvd fit =
new LinearFitBySvd(
xarr, yarr, null, numberOfDataPoints, order + 1, new FunctionBaseEvaluator(EvaluatePolynomialBase), 1E-5);
// Output of results
Current.Console.WriteLine("");
Current.Console.WriteLine("---- " + DateTime.Now.ToString() + " -----------------------");
Current.Console.WriteLine("Polynomial regression of order {0} of {1} over {2}", order, plotNames[1], plotNames[0]);
Current.Console.WriteLine(
"Name Value Error F-Value Prob>F");
for (int i = 0; i < fit.Parameter.Length; i++)
Current.Console.WriteLine("A{0,-3} {1,20} {2,20} {3,20} {4,20}",
i,
fit.Parameter[i],
fit.StandardErrorOfParameter(i),
fit.TofParameter(i),
1 - FDistribution.CDF(fit.TofParameter(i), numberOfParameter, numberOfDataPoints - 1)
);
Current.Console.WriteLine("R²: {0}, Adjusted R²: {1}",
fit.RSquared,
fit.AdjustedRSquared);
Current.Console.WriteLine("------------------------------------------------------------");
Current.Console.WriteLine("Source of Degrees of");
Current.Console.WriteLine("variation freedom Sum of Squares Mean Square F0 P value");
double regressionmeansquare = fit.RegressionCorrectedSumOfSquares / numberOfParameter;
double residualmeansquare = fit.ResidualSumOfSquares / (numberOfDataPoints - numberOfParameter - 1);
Current.Console.WriteLine("Regression {0,10} {1,20} {2,20} {3,20} {4,20}",
numberOfParameter,
fit.RegressionCorrectedSumOfSquares,
fit.RegressionCorrectedSumOfSquares / numberOfParameter,
regressionmeansquare / residualmeansquare,
1 - FDistribution.CDF(regressionmeansquare / residualmeansquare, numberOfParameter, numberOfDataPoints - 1)
);
Current.Console.WriteLine("Residual {0,10} {1,20} {2,20}",
numberOfDataPoints - 1 - numberOfParameter,
fit.ResidualSumOfSquares,
residualmeansquare
);
Current.Console.WriteLine("Total {0,10} {1,20}",
numberOfDataPoints - 1,
fit.TotalCorrectedSumOfSquares
);
Current.Console.WriteLine("------------------------------------------------------------");
// add the fit curve to the graph
IScalarFunctionDD plotfunction = new PolynomialFunction(fit.Parameter);
XYFunctionPlotItem fittedCurve = new XYFunctionPlotItem(new XYFunctionPlotData(plotfunction), new G2DPlotStyleCollection(LineScatterPlotStyleKind.Line, ctrl.Doc.GetPropertyContext()));
var xylayer = ctrl.ActiveLayer as XYPlotLayer;
if (null != xylayer)
xylayer.PlotItems.Add(fittedCurve);
return null;
}
/// <summary>
/// Fits data provided as xcolumn and ycolumn with a polynomial base. Here special measures are taken (scaling of the x-variable) in order
/// to keep the precision high.
/// </summary>
/// <param name="order">The order of the fit (1:linear, 2:quadratic, etc.)</param>
/// <param name="xValues">The array of x-values. The values of the array are destroyed (altered) during the evaluation!</param>
/// <param name="yValues">The array of y-values.</param>
/// <param name="errorValues">The column of errorValues. If null, errorValues are set to 1 for each element.</param>
/// <param name="count">Number of values to use (array[0] ... array[count-1].</param>
/// <returns>The fit.</returns>
public static LinearFitBySvd FitPolymomialDestructive(int order, double[] xValues, double[] yValues, double[] errorValues, int count)
{
if (!(xValues != null))
{
throw new ArgumentNullException(nameof(xValues));
}
if (!(yValues != null))
{
throw new ArgumentNullException(nameof(yValues));
}
if (!(count > 0))
{
throw new ArgumentOutOfRangeException(nameof(count), "must be >0");
}
if (!(count <= xValues.Length))
{
throw new ArgumentOutOfRangeException(nameof(count), "exceeds capacity of array " + nameof(xValues));
}
if (!(count <= yValues.Length))
{
throw new ArgumentOutOfRangeException(nameof(count), "exceeds capacity of array " + nameof(yValues));
}
if (null != errorValues && !(count <= errorValues.Length))
{
throw new ArgumentOutOfRangeException(nameof(count), "exceeds capacity of array " + nameof(errorValues));
}
double[] xarr = xValues;
double[] yarr = yValues;
double[] earr = errorValues;
if (null == earr)
{
earr = new double[count];
VectorMath.FillWith(earr, 1);
}
int numberOfDataPoints = count;
// we scale the x-values in order to keep the Condition number reasonable
var xmin = Altaxo.Calc.LinearAlgebra.VectorMath.Min(xarr, 0, numberOfDataPoints);
var xmax = Altaxo.Calc.LinearAlgebra.VectorMath.Max(xarr, 0, numberOfDataPoints);
double xscale = Math.Max(-xmin, xmax);
double xinvscale = 1 / xscale;
if (0 == xscale)
{
xscale = xinvscale = 1;
}
for (int i = 0; i < numberOfDataPoints; ++i)
{
xarr[i] *= xinvscale;
}
var fit =
new LinearFitBySvd(
xarr, yarr, earr, numberOfDataPoints, order + 1, new FunctionBaseEvaluator(GetPolynomialFunctionBase(order)), 1E-15);
// rescale parameter of fit in order to account for rescaled x variable
for (int i = 0; i <= order; ++i)
{
fit._parameter[i] *= RMath.Pow(xinvscale, i);
for (int j = 0; j <= order; ++j)
{
fit._covarianceMatrix[i][j] *= RMath.Pow(xinvscale, i + j);
}
}
return(fit);
}
/// <summary>
/// Fits data provided as xcolumn and ycolumn with a polynomial base.
/// </summary>
/// <param name="order">The order of the fit (1:linear, 2:quadratic, etc.)</param>
/// <param name="xcolumn">The column of x-values.</param>
/// <param name="ycolumn">The column of y-values.</param>
/// <returns>The fit.</returns>
public static LinearFitBySvd Fit(int order, Altaxo.Data.DataColumn xcolumn, Altaxo.Data.DataColumn ycolumn)
{
if (!(xcolumn is Altaxo.Data.INumericColumn))
throw new ArgumentException("The x-column must be numeric", "xcolumn");
if (!(ycolumn is Altaxo.Data.INumericColumn))
throw new ArgumentException("The y-column must be numeric", "ycolumn");
int firstIndex = 0;
int count = Math.Min(xcolumn.Count, ycolumn.Count);
double[] xarr = new double[count];
double[] yarr = new double[count];
double[] earr = new double[count];
Altaxo.Data.INumericColumn xcol = (Altaxo.Data.INumericColumn)xcolumn;
Altaxo.Data.INumericColumn ycol = (Altaxo.Data.INumericColumn)ycolumn;
int numberOfDataPoints = 0;
int endIndex = firstIndex + count;
for (int i = firstIndex; i < endIndex; i++)
{
double x = xcol[i];
double y = ycol[i];
if (double.IsNaN(x) || double.IsNaN(y))
continue;
xarr[numberOfDataPoints] = x;
yarr[numberOfDataPoints] = y;
earr[numberOfDataPoints] = 1;
numberOfDataPoints++;
}
LinearFitBySvd fit =
new LinearFitBySvd(
xarr, yarr, earr, numberOfDataPoints, order + 1, new FunctionBaseEvaluator(EvaluatePolynomialBase), 1E-5);
return fit;
}
