public static string OutputFitResults(LinearFitBySvd fit, string[] paramNames)
{
// Output of results
Current.Console.WriteLine("");
Current.Console.WriteLine("---- " + DateTime.Now.ToString() + " -----------------------");
Current.Console.WriteLine("Multivariate regression of order {0}", fit.NumberOfParameter);
Current.Console.WriteLine("{0,-15} {1,20} {2,20} {3,20} {4,20}",
"Name", "Value", "Error", "F-Value", "Prob>F");
for (int i = 0; i < fit.Parameter.Length; i++)
{
Current.Console.WriteLine("{0,-15} {1,20} {2,20} {3,20} {4,20}",
paramNames == null ? string.Format("A{0}", i) : paramNames[i],
fit.Parameter[i],
fit.StandardErrorOfParameter(i),
fit.TofParameter(i),
1 - FDistribution.CDF(fit.TofParameter(i), fit.NumberOfParameter, fit.NumberOfData - 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 / fit.NumberOfParameter;
double residualmeansquare = fit.ResidualSumOfSquares / (fit.NumberOfData - fit.NumberOfParameter - 1);
Current.Console.WriteLine("Regression {0,10} {1,20} {2,20} {3,20} {4,20}",
fit.NumberOfParameter,
fit.RegressionCorrectedSumOfSquares,
fit.RegressionCorrectedSumOfSquares / fit.NumberOfParameter,
regressionmeansquare / residualmeansquare,
1 - FDistribution.CDF(regressionmeansquare / residualmeansquare, fit.NumberOfParameter, fit.NumberOfData - 1)
);
Current.Console.WriteLine("Residual {0,10} {1,20} {2,20}",
fit.NumberOfData - 1 - fit.NumberOfParameter,
fit.ResidualSumOfSquares,
residualmeansquare
);
Current.Console.WriteLine("Total {0,10} {1,20}",
fit.NumberOfData - 1,
fit.TotalCorrectedSumOfSquares
);
Current.Console.WriteLine("------------------------------------------------------------");
return(null);
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, out string[] paramNames)
{
DataColumnCollection table = parameters.Table;
IAscendingIntegerCollection selectedCols = parameters.SelectedDataColumns;
var selectedColsWODependent = new AscendingIntegerCollection(selectedCols);
selectedColsWODependent.RemoveAt(parameters.DependentColumnIndexIntoSelection);
IAscendingIntegerCollection validRows = DataTableWrapper.GetCollectionOfValidNumericRows(parameters.Table, selectedCols);
parameters.SelectedDataRows = validRows;
IROMatrix <double> xbase;
if (parameters.IncludeIntercept)
{
xbase = DataTableWrapper.ToROColumnMatrixWithIntercept(parameters.Table, selectedColsWODependent, validRows);
}
else
{
xbase = DataTableWrapper.ToROColumnMatrix(parameters.Table, selectedColsWODependent, validRows);
}
paramNames = new string[xbase.ColumnCount];
if (parameters.IncludeIntercept)
{
paramNames[0] = "Intercept";
for (int i = 0; i < selectedColsWODependent.Count; i++)
{
paramNames[i + 1] = table[selectedColsWODependent[i]].Name;
}
}
else
{
for (int i = 0; i < selectedColsWODependent.Count; i++)
{
paramNames[i] = table[selectedColsWODependent[i]].Name;
}
}
// Fill the y and the error array
double[] yarr = new double[validRows.Count];
double[] earr = new double[validRows.Count];
var ycol = (Altaxo.Data.INumericColumn)table[selectedCols[parameters.DependentColumnIndexIntoSelection]];
for (int i = 0; i < validRows.Count; i++)
{
yarr[i] = ycol[validRows[i]];
earr[i] = 1;
}
var fit =
new LinearFitBySvd(
xbase, yarr, earr, xbase.RowCount, xbase.ColumnCount, 1E-5);
return(fit);
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, bool outputResults)
{
LinearFitBySvd fit = Regress(parameters, out var paramNames);
if (outputResults)
{
OutputFitResults(fit, paramNames);
if (parameters.GenerateRegressionValues)
{
GenerateValues(parameters, fit);
}
}
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);
}
public static LinearFitBySvd ShowDialogAndRegress(DataColumnCollection table, IAscendingIntegerCollection selectedColumns)
{
if (selectedColumns.Count < 2)
{
return(null);
}
object paramobject = new MultivariateLinearFitParameters(table, selectedColumns);
if (!Current.Gui.ShowDialog(ref paramobject, "Multivariate linear fit"))
{
return(null);
}
var parameters = (MultivariateLinearFitParameters)paramobject;
LinearFitBySvd result = Regress(parameters, true);
return(result);
}
/// <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];
var xcol = (Altaxo.Data.INumericColumn)xcolumn;
var 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++;
}
return(LinearFitBySvd.FitPolymomialDestructive(order, xarr, yarr, earr, numberOfDataPoints));
}
public static string Fit(Altaxo.Gui.Graph.Gdi.Viewing.IGraphController ctrl, int order, double fitCurveXmin, double fitCurveXmax, bool showFormulaOnGraph)
{
string error;
error = GetActivePlotPoints(ctrl, out var xarr, out var yarr);
int numberOfDataPoints = xarr.Length;
if (null != error)
{
return(error);
}
string[] plotNames = GetActivePlotName(ctrl);
int numberOfParameter = order + 1;
double[] parameter = new double[numberOfParameter];
var fit = LinearFitBySvd.FitPolymomialDestructive(order, xarr, yarr, null, numberOfDataPoints);
// 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("Condition number: {0}, Loss of precision (digits): {1}", fit.ConditionNumber, Math.Log10(fit.ConditionNumber));
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);
var 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);
}
public static string Fit(Altaxo.Graph.GUI.GraphController ctrl, int order, double fitCurveXmin, double fitCurveXmax, bool showFormulaOnGraph)
{
string error;
int numberOfDataPoints;
double[] xarr = null, yarr = null, earr = null;
error = GetActivePlotPoints(ctrl, ref xarr, ref yarr, out numberOfDataPoints);
if (null != error)
{
return(error);
}
string[] plotNames = GetActivePlotName(ctrl);
// Error-Array
earr = new double[numberOfDataPoints];
for (int i = 0; i < earr.Length; i++)
{
earr[i] = 1;
}
int numberOfParameter = order + 1;
double[] parameter = new double[numberOfParameter];
LinearFitBySvd fit =
new LinearFitBySvd(
xarr, yarr, earr, 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.ActiveLayer.PlotItems.Add(fittedCurve);
return(null);
}
public static string OutputFitResults(LinearFitBySvd fit, string[] paramNames)
{
// Output of results
Current.Console.WriteLine("");
Current.Console.WriteLine("---- " + DateTime.Now.ToString() + " -----------------------");
Current.Console.WriteLine("Multivariate regression of order {0}",fit.NumberOfParameter);
Current.Console.WriteLine("{0,-15} {1,20} {2,20} {3,20} {4,20}",
"Name","Value","Error","F-Value","Prob>F");
for(int i=0;i<fit.Parameter.Length;i++)
Current.Console.WriteLine("{0,-15} {1,20} {2,20} {3,20} {4,20}",
paramNames==null ? string.Format("A{0}",i) : paramNames[i],
fit.Parameter[i],
fit.StandardErrorOfParameter(i),
fit.TofParameter(i),
1-FDistribution.CDF(fit.TofParameter(i),fit.NumberOfParameter,fit.NumberOfData-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/fit.NumberOfParameter;
double residualmeansquare = fit.ResidualSumOfSquares/(fit.NumberOfData-fit.NumberOfParameter-1);
Current.Console.WriteLine("Regression {0,10} {1,20} {2,20} {3,20} {4,20}",
fit.NumberOfParameter,
fit.RegressionCorrectedSumOfSquares,
fit.RegressionCorrectedSumOfSquares/fit.NumberOfParameter,
regressionmeansquare/residualmeansquare,
1-FDistribution.CDF(regressionmeansquare/residualmeansquare,fit.NumberOfParameter,fit.NumberOfData-1)
);
Current.Console.WriteLine("Residual {0,10} {1,20} {2,20}",
fit.NumberOfData-1-fit.NumberOfParameter,
fit.ResidualSumOfSquares,
residualmeansquare
);
Current.Console.WriteLine("Total {0,10} {1,20}",
fit.NumberOfData-1,
fit.TotalCorrectedSumOfSquares
);
Current.Console.WriteLine("------------------------------------------------------------");
return null;
}
public static void GenerateValues(MultivariateLinearFitParameters parameters, LinearFitBySvd fit)
{
DataColumn dependentColumn = parameters.Table[parameters.SelectedDataColumns[parameters.DependentColumnIndexIntoSelection]];
if(parameters.GenerateRegressionValues)
{
DoubleColumn col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.PredictedValues),DataColumnWrapper.ToVector(col,parameters.SelectedDataRows));
parameters.Table.Add(col,dependentColumn.Name + "(predicted)",ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
if(parameters.GenerateResidualValues)
{
DoubleColumn col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.ResidualValues),DataColumnWrapper.ToVector(col,parameters.SelectedDataRows));
parameters.Table.Add(col,dependentColumn.Name + "(residual)",ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, out string[] paramNames)
{
DataColumnCollection table = parameters.Table;
IAscendingIntegerCollection selectedCols = parameters.SelectedDataColumns;
AscendingIntegerCollection selectedColsWODependent = new AscendingIntegerCollection(selectedCols);
selectedColsWODependent.RemoveAt(parameters.DependentColumnIndexIntoSelection);
IAscendingIntegerCollection validRows = DataTableWrapper.GetCollectionOfValidNumericRows(parameters.Table,selectedCols);
parameters.SelectedDataRows = validRows;
IROMatrix xbase;
if(parameters.IncludeIntercept)
{
xbase = DataTableWrapper.ToROColumnMatrixWithIntercept(parameters.Table,selectedColsWODependent,validRows);
}
else
{
xbase = DataTableWrapper.ToROColumnMatrix(parameters.Table,selectedColsWODependent,validRows);
}
paramNames = new string[xbase.Columns];
if(parameters.IncludeIntercept)
{
paramNames[0] = "Intercept";
for(int i=0;i<selectedColsWODependent.Count;i++)
paramNames[i+1]=table[selectedColsWODependent[i]].Name;
}
else
{
for(int i=0;i<selectedColsWODependent.Count;i++)
paramNames[i]=table[selectedColsWODependent[i]].Name;
}
// Fill the y and the error array
double[] yarr = new double[validRows.Count];
double[] earr = new double[validRows.Count];
Altaxo.Data.INumericColumn ycol = (Altaxo.Data.INumericColumn)table[selectedCols[parameters.DependentColumnIndexIntoSelection]];
for(int i=0;i<validRows.Count;i++)
{
yarr[i] = ycol[validRows[i]];
earr[i] = 1;
}
LinearFitBySvd fit =
new LinearFitBySvd(
xbase,yarr,earr, xbase.Rows, xbase.Columns, 1E-5);
return fit;
}
public static void GenerateValues(MultivariateLinearFitParameters parameters, LinearFitBySvd fit)
{
DataColumn dependentColumn = parameters.Table[parameters.SelectedDataColumns[parameters.DependentColumnIndexIntoSelection]];
if (parameters.GenerateRegressionValues)
{
var col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.PredictedValues), DataColumnWrapper.ToVector(col, parameters.SelectedDataRows));
parameters.Table.Add(col, dependentColumn.Name + "(predicted)", ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
if (parameters.GenerateResidualValues)
{
var col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.ResidualValues), DataColumnWrapper.ToVector(col, parameters.SelectedDataRows));
parameters.Table.Add(col, dependentColumn.Name + "(residual)", ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
}
