public void createRegression(DataSet dataSet, List <Variable> independentVariables, Variable dependentVariable)
{
_Worksheet sheet = WorksheetHelper.NewWorksheet("Simple Regression");
var matrixX = Matrix <double> .Build.Dense(dataSet.rangeSize(), independentVariables.Count + 1);
for (var i = 0; i < dataSet.rangeSize(); i++)
{
matrixX[i, 0] = 1;
for (var j = 0; j < independentVariables.Count; j++)
{
matrixX[i, j + 1] = dataSet.getValuesArray(independentVariables[j])[i];
}
}
var matrixY = Matrix <double> .Build.Dense(dataSet.rangeSize(), 1);
for (var i = 0; i < dataSet.rangeSize(); i++)
{
matrixY[i, 0] = dataSet.getValuesArray(dependentVariable)[i];
}
var matrixXt = matrixX.Transpose();
var matrixXtX = matrixXt * matrixX;
var matrixInv = matrixXtX.Inverse();
var matrixInvXt = matrixInv * matrixXt;
var matrixResult = matrixInvXt * matrixY;
const int title = 1;
const int name = 3;
const int betaCoeff = 2;
const int stdErrorName = betaCoeff + 1;
const int pValuesName = stdErrorName + 1;
const int lowerLimitName = pValuesName + 1;
const int upperLimitName = lowerLimitName + 1;
var summaryName = name + independentVariables.Count + 3;
var anovaTableName = summaryName + 3;
const int degreesOfFreedomName = 2;
const int sumOfSquaresName = 3;
const int meanOfSquaresName = 4;
const int anovaFValueName = 5;
const int anovaPValueName = 6;
var dataName = anovaTableName + 4;
sheet.Cells[title, 1] = "Regression";
sheet.Cells[name + 1, 1] = "Constant";
sheet.Cells[name, betaCoeff] = "Beta";
sheet.Cells[name, stdErrorName] = "Standard Error";
sheet.Cells[name, pValuesName] = "P-Value";
sheet.Cells[name, lowerLimitName] = "Lower Limit";
sheet.Cells[name, upperLimitName] = "Upper Limit";
sheet.Cells[summaryName + 1, 1] = "Regression Summary";
sheet.Cells[summaryName, 2] = "R-Square";
sheet.Cells[summaryName, 3] = "Adjusted R-Square";
sheet.Cells[summaryName, 4] = "Standard Error of estimation";
if (model.regressionEquation)
{
sheet.Cells[summaryName, 5] = "Regression Equation";
var regressionEquation = "" + matrixResult[0, 0];
for (var i = 0; i < independentVariables.Count; i++)
{
regressionEquation = regressionEquation + " + (" + matrixResult[i + 1, 0] + "*" + independentVariables[i].name + ")";
}
sheet.Cells[summaryName + 1, 5] = dependentVariable.name + " = " + regressionEquation;
}
sheet.Cells[anovaTableName, 1] = "Anova Table";
sheet.Cells[anovaTableName + 1, 1] = "Explained";
sheet.Cells[anovaTableName + 2, 1] = "Unexplained";
sheet.Cells[anovaTableName, degreesOfFreedomName] = "Degrees of Freedom";
sheet.Cells[anovaTableName + 1, degreesOfFreedomName] = independentVariables.Count;
var dF = dataSet.rangeSize() - 1 - independentVariables.Count;
sheet.Cells[anovaTableName + 2, degreesOfFreedomName] = dF;
sheet.Cells[anovaTableName, sumOfSquaresName] = "Sum of Squares";
var sumOfSquare = CalculateSumOfSquares(dataSet, independentVariables, dependentVariable, matrixResult);
sheet.Cells[anovaTableName + 1, sumOfSquaresName] = sumOfSquare[0];
sheet.Cells[anovaTableName + 2, sumOfSquaresName] = sumOfSquare[1];
sheet.Cells[anovaTableName, meanOfSquaresName] = "Mean of Squares";
sheet.Cells[anovaTableName + 1, meanOfSquaresName] = sumOfSquare[0] / independentVariables.Count;
sheet.Cells[anovaTableName + 2, meanOfSquaresName] = sumOfSquare[1] / (dataSet.rangeSize() - 1 - independentVariables.Count);
sheet.Cells[anovaTableName, anovaFValueName] = "F";
sheet.Cells[anovaTableName, anovaPValueName] = "P-Value";
var fTest = (sumOfSquare[0] / independentVariables.Count) / (sumOfSquare[1] / (dataSet.rangeSize() - 1 - independentVariables.Count));
sheet.Cells[anovaTableName + 1, anovaFValueName] = fTest;
sheet.Cells[anovaTableName + 1, anovaPValueName] = _functions.FDist(fTest, independentVariables.Count, dataSet.rangeSize() - 1 - independentVariables.Count);
sheet.Cells[dataName, 1] = "Data";
sheet.Cells[dataName, 2] = "Y: " + dependentVariable.name;
sheet.Cells[dataName, 3] = "Fit";
sheet.Cells[dataName, 4] = "Residuals";
// calculate r-square, adj r-square and std error of estimation
var rSquare = CalculateRsquare(dataSet, independentVariables, dependentVariable, matrixResult);
var adjRSquare = CalculateAdjRSquare(rSquare, matrixX);
var stdErrorEstimation = CalculateStdErrorEstimation(dataSet, independentVariables, dependentVariable, matrixResult);
sheet.Cells[summaryName + 1, 2] = rSquare;
sheet.Cells[summaryName + 1, 3] = adjRSquare;
sheet.Cells[summaryName + 1, 4] = stdErrorEstimation;
for (int i = 1; i < matrixResult.RowCount; i++)
{
sheet.Cells[name + i + 1, 1] = independentVariables[i - 1].name;
}
var meanOfSquaresError = sumOfSquare[1] / (dataSet.rangeSize() - 1 - independentVariables.Count);
for (var i = 0; i < matrixResult.RowCount; i++)
{
var coeff = matrixResult[i, 0];
var stdError = Math.Sqrt(matrixInv[i, i] * meanOfSquaresError);
var pValue = _functions.TDist(Math.Abs(coeff / stdError), dF, 2);
var confidenceConstant = _functions.T_Inv_2T(1 - model.confidenceLevel, dF);
var lower = coeff - stdError * confidenceConstant;
var upper = coeff + stdError * confidenceConstant;
sheet.Cells[name + i + 1, betaCoeff] = coeff;
sheet.Cells[name + i + 1, stdErrorName] = stdError;
sheet.Cells[name + i + 1, pValuesName] = pValue;
sheet.Cells[name + i + 1, lowerLimitName] = lower;
sheet.Cells[name + i + 1, upperLimitName] = upper;
}
for (var i = 0; i < independentVariables.Count; i++)
{
sheet.Cells[dataName, 5 + i] = independentVariables[i].name;
}
var nextFigure = CreateDataRegression(matrixX, matrixY, matrixResult, dataName, sheet);
if (model.fittedVSActual)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 2], sheet.Cells[dataName + dataSet.rangeSize(), 2]];
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 3], sheet.Cells[dataName + dataSet.rangeSize(), 3]];
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Fitted Values vs Actual Y-Values: " + dependentVariable.name, sheet);
}
if (model.residualsVSFitted)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 3], sheet.Cells[dataName + dataSet.rangeSize(), 3]];
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 4], sheet.Cells[dataName + dataSet.rangeSize(), 4]];
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Residuals vs Fitted Values", sheet);
}
if (model.residualsVSX)
{
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 4], sheet.Cells[dataName + dataSet.rangeSize(), 4]];
for (var i = 0; i < independentVariables.Count; i++)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 5 + i], sheet.Cells[dataName + dataSet.rangeSize(), 5 + i]];
var nameX = independentVariables[i].name;
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Residuals vs " + nameX, sheet);
}
}
}
/// <summary>
/// Print the Discriminant Analysis to a new <see cref="Microsoft.Office.Interop.Excel._Worksheet"/>.
/// </summary>
/// <param name="dataSet">The <see cref="DataSet"/> which needs (a) Scatterplot(s).</param>
/// <param name="doIncludeX">A <see cref="List{T}"/> of <see cref="bool"/>s that corresponds to which <see cref="Models.Data"/> in the <see cref="DataSet.DataList"/> should be included for X.</param>
/// <param name="doIncludeY">A <see cref="List{T}"/> of <see cref="bool"/>s that corresponds to which <see cref="Models.Data"/> in the <see cref="DataSet.DataList"/> should be included for Y.</param>
/// <param name="doCalculate">A collection of <see cref="bool"/>s that indicate which summary statistic has to be calculated.</param>
/// <param name="confidenceLevel">The confidence level.</param>
public bool Print(DataSet dataSet, List <bool> doIncludeX, List <bool> doIncludeY, SummaryStatisticsBool doCalculate, int confidenceLevel)
{
var valuesArraysX = new List <Models.Data>();
var valuesArraysY = new List <Models.Data>();
var sheet = WorksheetHelper.NewWorksheet("Regression");
// Loop to add X
for (var j = 0; j < dataSet.DataList.Count; j++)
{
// Check if the Set of Data is an X.
if (!doIncludeX[j])
{
continue;
}
var safe = true;
foreach (var value in dataSet.DataList[j].GetValuesList())
{
if (value != null)
{
continue;
}
MessageBox.Show(dataSet.DataList[j].Name + " has null data and will not be included.", "NoruST - Discriminant Analysis", MessageBoxButtons.OK, MessageBoxIcon.Warning);
safe = false;
}
// If the Set of Data is an X, add it to the list.
if (safe)
{
valuesArraysX.Add(dataSet.DataList[j]);
}
}
for (var j = 0; j < dataSet.DataList.Count; j++)
{
// Check if the Set of Data is an Y.
if (!doIncludeY[j])
{
continue;
}
// If the Set of Data is Y add to list.
valuesArraysY.Add(dataSet.DataList[j]);
// only one Y (currently) so break loop if Y is found.
break;
}
// create X matrix
var matrixX = Matrix <double> .Build.Dense(valuesArraysX[0].GetValuesList().Count, valuesArraysX.Count + 1);
// create 1 column with 1
for (var i = 0; i < valuesArraysX[0].GetValuesList().Count; i++)
{
matrixX[i, 0] = 1;
for (var j = 0; j < valuesArraysX.Count; j++)
{
matrixX[i, j + 1] = valuesArraysX[j].GetValuesArray()[i];
}
}
// create Y matrix
var matrixY = Matrix <double> .Build.Dense(valuesArraysY[0].GetValuesList().Count, 1);
// create 1 column with 1
for (var i = 0; i < valuesArraysY[0].GetValuesList().Count; i++)
{
matrixY[i, 0] = valuesArraysY[0].GetValuesArray()[i];
}
var matrixXt = matrixX.Transpose();
var matrixXtX = matrixXt * matrixX;
var matrixInv = matrixXtX.Inverse();
var matrixInvXt = matrixInv * matrixXt;
var matrixResult = matrixInvXt * matrixY;
// variables for sheet
const int title = 1;
const int name = 3;
const int betaCoeff = 2;
const int stdErrorName = betaCoeff + 1;
const int pValuesName = stdErrorName + 1;
const int lowerLimitName = pValuesName + 1;
const int upperLimitName = lowerLimitName + 1;
var summaryName = name + valuesArraysX.Count + 3;
var anovaTableName = summaryName + 3;
const int degreesOfFreedomName = 2;
const int sumOfSquaresName = 3;
const int meanOfSquaresName = 4;
const int anovaFValueName = 5;
const int anovaPValueName = 6;
var dataName = anovaTableName + 4;
// names of variables on sheet
sheet.Cells[title, 1] = "Regression";
sheet.Cells[name + 1, 1] = "Constant";
sheet.Cells[name, betaCoeff] = "Beta";
sheet.Cells[name, stdErrorName] = "Standard Error";
sheet.Cells[name, pValuesName] = "P-Value";
sheet.Cells[name, lowerLimitName] = "Lower Limit";
sheet.Cells[name, upperLimitName] = "Upper Limit";
sheet.Cells[summaryName + 1, 1] = "Regression Summary";
sheet.Cells[summaryName, 2] = "R-Square";
sheet.Cells[summaryName, 3] = "Adjusted R-Square";
sheet.Cells[summaryName, 4] = "Standard Error of estimation";
if (doCalculate.DisplayRegressionEquation)
{
sheet.Cells[summaryName, 5] = "Regression Equation";
var regressionEquation = "" + matrixResult[0, 0];
for (var i = 0; i < valuesArraysX.Count; i++)
{
regressionEquation = regressionEquation + " + (" + matrixResult[i + 1, 0] + "*" + valuesArraysX[i].Name + ")";
}
sheet.Cells[summaryName + 1, 5] = valuesArraysY[0].Name + " = " + regressionEquation;
}
sheet.Cells[anovaTableName, 1] = "Anova Table";
sheet.Cells[anovaTableName + 1, 1] = "Explained";
sheet.Cells[anovaTableName + 2, 1] = "Unexplained";
sheet.Cells[anovaTableName, degreesOfFreedomName] = "Degrees of Freedom";
sheet.Cells[anovaTableName + 1, degreesOfFreedomName] = valuesArraysX.Count;
var dF = valuesArraysY[0].GetValuesList().Count - 1 - valuesArraysX.Count;
sheet.Cells[anovaTableName + 2, degreesOfFreedomName] = dF;
sheet.Cells[anovaTableName, sumOfSquaresName] = "Sum of Squares";
var sumOfSquare = CalculateSumOfSquares(valuesArraysX, valuesArraysY, matrixResult);
sheet.Cells[anovaTableName + 1, sumOfSquaresName] = sumOfSquare[0];
sheet.Cells[anovaTableName + 2, sumOfSquaresName] = sumOfSquare[1];
sheet.Cells[anovaTableName, meanOfSquaresName] = "Mean of Squares";
sheet.Cells[anovaTableName + 1, meanOfSquaresName] = sumOfSquare[0] / valuesArraysX.Count;
sheet.Cells[anovaTableName + 2, meanOfSquaresName] = sumOfSquare[1] / (valuesArraysY[0].GetValuesList().Count - 1 - valuesArraysX.Count);
sheet.Cells[anovaTableName, anovaFValueName] = "F";
sheet.Cells[anovaTableName, anovaPValueName] = "P-Value";
var fTest = (sumOfSquare[0] / valuesArraysX.Count) / (sumOfSquare[1] / (valuesArraysY[0].GetValuesList().Count - 1 - valuesArraysX.Count));
sheet.Cells[anovaTableName + 1, anovaFValueName] = fTest;
sheet.Cells[anovaTableName + 1, anovaPValueName] = _functions.FDist(fTest, valuesArraysX.Count, valuesArraysY[0].GetValuesList().Count - 1 - valuesArraysX.Count);
sheet.Cells[dataName, 1] = "Data";
sheet.Cells[dataName, 2] = "Y: " + valuesArraysY[0].Name;
sheet.Cells[dataName, 3] = "Fit";
sheet.Cells[dataName, 4] = "Residuals";
// calculate r-square, adj r-square and std error of estimation
var rSquare = CalculateRsquare(valuesArraysX, valuesArraysY, matrixResult);
var adjRSquare = CalculateAdjRSquare(rSquare, matrixX);
var stdErrorEstimation = CalculateStdErrorEstimation(valuesArraysX, valuesArraysY, matrixResult);
sheet.Cells[summaryName + 1, 2] = rSquare;
sheet.Cells[summaryName + 1, 3] = adjRSquare;
sheet.Cells[summaryName + 1, 4] = stdErrorEstimation;
for (int i = 1; i < matrixResult.RowCount; i++)
{
sheet.Cells[name + i + 1, 1] = valuesArraysX[i - 1].Name;
}
var meanOfSquaresError = sumOfSquare[1] / (valuesArraysY[0].GetValuesList().Count - 1 - valuesArraysX.Count);
for (var i = 0; i < matrixResult.RowCount; i++)
{
var coeff = matrixResult[i, 0];
var stdError = Math.Sqrt(matrixInv[i, i] * meanOfSquaresError);
var pValue = _functions.TDist(Math.Abs(coeff / stdError), dF, 2);
var confidenceConstant = _functions.T_Inv_2T(1 - confidenceLevel / 100.0, dF);
var lower = coeff - stdError * confidenceConstant;
var upper = coeff + stdError * confidenceConstant;
sheet.Cells[name + i + 1, betaCoeff] = coeff;
sheet.Cells[name + i + 1, stdErrorName] = stdError;
sheet.Cells[name + i + 1, pValuesName] = pValue;
sheet.Cells[name + i + 1, lowerLimitName] = lower;
sheet.Cells[name + i + 1, upperLimitName] = upper;
}
for (var i = 0; i < valuesArraysX.Count; i++)
{
sheet.Cells[dataName, 5 + i] = valuesArraysX[i].Name;
}
var nextFigure = CreateDataRegression(matrixX, matrixY, matrixResult, dataName, sheet);
if (doCalculate.FittedValuesVsActualYValues)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 2], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 2]];
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 3], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 3]];
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Fitted Values vs Actual Y-Values: " + valuesArraysY[0].Name, sheet);
}
if (doCalculate.ResidualsVsFittedValues)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 3], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 3]];
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 4], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 4]];
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Residuals vs Fitted Values", sheet);
}
if (doCalculate.ResidualsVsXValues)
{
var rangeY = sheet.Range[sheet.Cells[dataName + 1, 4], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 4]];
for (var i = 0; i < valuesArraysX.Count; i++)
{
var rangeX = sheet.Range[sheet.Cells[dataName + 1, 5 + i], sheet.Cells[dataName + valuesArraysY[0].GetValuesList().Count, 5 + i]];
var nameX = valuesArraysX[i].Name;
nextFigure = CreateNewFigure(rangeX, rangeY, nextFigure, "Residuals vs " + nameX, sheet);
}
}
return(true);
}
