public void createRegression(List <Variable> variablesD, List <Variable> variablesI, DataSet dataSet, DataSet dataSet2, bool[] graphs, List <Variable> variablesPrediction)
{
_Worksheet sheet = WorksheetHelper.NewWorksheet("Regression");
double confLevel = model.confidenceLevel;
sheet.Cells[100, 100] = "=ROWS(" + dataSet.getWorksheet().Name + "!" + variablesI[0].Range + ")";
int length = Convert.ToInt32((sheet.Cells[100, 100] as Range).Value);
sheet.Cells[100, 100] = "";
int row = 1;
sheet.Cells[1, 1] = "Regression Summary";
sheet.Cells[2, 1] = "R";
sheet.Cells[3, 1] = "R-square";
sheet.Cells[4, 1] = "adjusted R-square";
sheet.Cells[5, 1] = "stErr of Estimate";
sheet.Cells[9, 1] = "ANOVA Table";
sheet.Cells[10, 1] = "Explained";
sheet.Cells[11, 1] = "Unexplained";
sheet.Cells[8, 3] = "Sum of";
sheet.Cells[9, 3] = "Squares";
sheet.Cells[8, 2] = "Degrees of";
sheet.Cells[9, 2] = "Freedom";
sheet.Cells[8, 4] = "Mean";
sheet.Cells[9, 4] = "Squares";
sheet.Cells[9, 5] = "F-Ratio";
sheet.Cells[9, 6] = "p-Value";
sheet.Cells[15, 1] = "Regression Table";
sheet.Cells[16, 1] = "Constant";
row = 17;
foreach (Variable var in variablesI)
{
sheet.Cells[row, 1] = var.name;
row++;
}
lowestDataRow = row;
sheet.Cells[15, 2] = "Coefficient";
sheet.Cells[14, 3] = "Standard";
sheet.Cells[15, 3] = "Error";
sheet.Cells[15, 4] = "t-value";
sheet.Cells[15, 5] = "p-value";
sheet.Cells[14, 6] = "Confidence Interval " + confLevel.ToString() + "%";
sheet.Range[sheet.Cells[14, 6], sheet.Cells[14, 7]].Merge();
sheet.Cells[15, 6] = "Lower";
sheet.Cells[15, 7] = "Upper";
sheet.get_Range("B1", "J200").Cells.HorizontalAlignment = XlHAlign.xlHAlignCenter;
sheet.get_Range("A1", "B1").Borders[XlBordersIndex.xlEdgeBottom].LineStyle = XlLineStyle.xlDouble;
sheet.get_Range("A9", "F9").Borders[XlBordersIndex.xlEdgeBottom].LineStyle = XlLineStyle.xlDouble;
sheet.get_Range("A15", "G15").Borders[XlBordersIndex.xlEdgeBottom].LineStyle = XlLineStyle.xlDouble;
sheet.get_Range("B3", "B5").NumberFormat = "0.0000";
sheet.get_Range("B18", "B20").NumberFormat = "0.0000";
sheet.get_Range("D18", "E19").NumberFormat = "0.0000";
Globals.ExcelAddIn.Application.ActiveWindow.DisplayGridlines = false;
//
//calculate values of regression
//
double[] b = new double[variablesI.Count];
double[] yData = calcYdata(variablesD, dataSet, length);
double[,] xData = calcXdata(length, dataSet, variablesI);
b = calculateCoefB(yData, xData, b);
double a = b[0];
var X = DenseMatrix.OfArray(xData);
var B = new DenseVector(b);
var Y = new DenseVector(yData);
var yh = X.Multiply(B);
var er = Y.Subtract(yh);
double[] error = er.ToArray();
double[] yhat = yh.ToArray();
double R2 = sheet.Application.WorksheetFunction.Correl(yData, yhat);
double R = Math.Pow(R2, 2);
double Radj = 1.0 - ((1 - R) * (length - 1) / (length - variablesI.Count - 1));
double err = 0;
int i = 0;
while (i < error.Length)
{
err = err + Math.Pow(error[i], 2);
i++;
}
err = Math.Sqrt(err / (length - 4));
double[] anovaResults = calculateAnova(yData, yhat, variablesI.Count(), sheet);
multicollinearity(sheet, xData, variablesI.Count);
if (model.doDurbinWatson == true)
{
DurbinWatson(sheet, error);
}
//
//Calculate regressionTable
//
double MSE = anovaResults[6];
var Xt = X.Transpose();
var tempM = (Xt.Multiply(X)).Inverse();
var variancesM = tempM.Multiply(MSE);
double[] variances = variancesM.Diagonal().ToArray();
double[] std = new double[variances.Length];
int index = 0;
foreach (double elem in variances)
{
std[index] = Math.Sqrt(elem);
//System.Diagnostics.Debug.WriteLine(std[index]);
index++;
}
double[] tValue = new double[b.Length];
double[] pValue = new double[b.Length];
double[] lowerInt = new double[b.Length];
double[] higherInt = new double[b.Length];
for (index = 0; index < b.Length; index++)
{
tValue[index] = b[index] / std[index];
pValue[index] = sheet.Application.WorksheetFunction.TDist(Math.Abs(tValue[index]), length - variablesI.Count - 1, 2);
lowerInt[index] = b[index] - sheet.Application.WorksheetFunction.TInv(1 - confLevel / 100, length - variablesI.Count - 1) * std[index];
higherInt[index] = b[index] + sheet.Application.WorksheetFunction.TInv(1 - confLevel / 100, length - variablesI.Count - 1) * std[index];
//System.Diagnostics.Debug.WriteLine("{0}, p={1}, lower = {2}, n={3}, inv={4}, std={5}, inv2={6}",real_p, pValue[index], lowerInt[index], length - 1, sheet.Application.WorksheetFunction.TInv(1-0.975, length - 1), std[index], sheet.Application.WorksheetFunction.TInv(1 - 0.95, length - 1));
}
//
//Prediction
//
if (model.doPrediction)
{
double[,] xDataPred = calcXdata(dataSet2.getNrDataRows(), dataSet2, variablesPrediction);
int[] rightCell = { dataSet2.getRange().Row, dataSet2.getRange().Column + dataSet2.amountOfVariables() }; //{row, column}
prediction(dataSet2.getWorksheet(), xDataPred, b, length, variablesI.Count, X, MSE, rightCell, variablesD[0].name);
setPrediction(false);
}
//
//Draw graphs
//
if (graphs[0])
{
drawGraphs(sheet, yData, yhat, "Scatter plot of fitted values vs. actual values");
}
if (graphs[1])
{
drawGraphs(sheet, yData, error, "Scatter plot of residuals vs. fitted values");
}
if (graphs[2])
{
int varNumber = 1;
foreach (Variable var in variablesI)
{
double[] xDataTemp = new double[xData.GetLength(0)];
for (int x = 0; x < xData.GetLength(0); x++)
{
xDataTemp[x] = xData[x, varNumber];
}
drawGraphs(sheet, xDataTemp, error, "Scatter plot of residuals vs. " + var.name);
varNumber++;
}
}
if (graphs[3])
{
int varNumber = 1;
foreach (Variable var in variablesI)
{
double[] xDataTemp = new double[xData.GetLength(0)];
for (int x = 0; x < xData.GetLength(0); x++)
{
xDataTemp[x] = xData[x, varNumber];
}
drawGraphs(sheet, xDataTemp, yData, "Scatter plot of actual Y values vs. " + var.name);
varNumber++;
}
}
if (graphs[4])
{
int varNumber = 1;
foreach (Variable var in variablesI)
{
double[] xDataTemp = new double[xData.GetLength(0)];
for (int x = 0; x < xData.GetLength(0); x++)
{
xDataTemp[x] = xData[x, varNumber];
}
drawGraphs(sheet, xDataTemp, yhat, "Scatter plot of fitted Y values vs. " + var.name);
varNumber++;
}
}
//
//print results to excel sheet
//
sheet.Cells[16, 2] = a;
row = 17;
foreach (Variable var in variablesI)
{
sheet.Cells[row, 2] = b[row - 16];
row++;
}
FillR(sheet, R2, R, Radj, err);
FillAnova(sheet, anovaResults);
FillRegressionTable(sheet, std, tValue, pValue, lowerInt, higherInt);
((Range)sheet.Cells[1, 1]).EntireColumn.AutoFit();
((Range)sheet.Cells[1, 2]).EntireColumn.AutoFit();
((Range)sheet.Cells[1, 3]).EntireColumn.AutoFit();
((Range)sheet.Cells[1, 4]).EntireColumn.AutoFit();
((Range)sheet.Cells[1, 5]).EntireColumn.AutoFit();
((Range)sheet.Cells[1, 6]).EntireColumn.ColumnWidth = 13;
((Range)sheet.Cells[1, 7]).EntireColumn.ColumnWidth = 13;
((Range)sheet.Cells[1, 8]).EntireColumn.ColumnWidth = 13;
((Range)sheet.Cells[1, 9]).EntireColumn.ColumnWidth = 13;
((Range)sheet.Cells[1, 10]).EntireColumn.AutoFit();
}
