private void btnRun_Click(object sender, EventArgs e)
{
try
{
if (cboFieldName.Text == "")
{
MessageBox.Show("Please select the dependent input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
if (lstIndeVar.Items.Count == 0)
{
MessageBox.Show("Please select independents input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
if (cboFieldName.Text == "")
{
MessageBox.Show("Please select the dependent input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
if (lstIndeVar.Items.Count == 0)
{
MessageBox.Show("Please select independents input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Pre-Processing:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
//Decimal places
int intDeciPlaces = 5;
//Get number of Independent variables
int nIDepen = lstIndeVar.Items.Count;
// Gets the column of the dependent variable
String dependentName = (string)cboFieldName.SelectedItem;
//sourceTable.AcceptChanges();
//DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
// Gets the columns of the independent variables
String[] independentNames = new string[nIDepen];
for (int j = 0; j < nIDepen; j++)
{
independentNames[j] = (string)lstIndeVar.Items[j];
}
int nFeature = m_dt.Rows.Count;
//Get index for independent and dependent variables
int intDepenIdx = m_dt.Columns.IndexOf(dependentName);
int[] idxes = new int[nIDepen];
for (int j = 0; j < nIDepen; j++)
{
idxes[j] = m_dt.Columns.IndexOf(independentNames[j]);
}
//Store independent values at Array
double[] arrDepen = new double[nFeature];
double[][] arrInDepen = new double[nIDepen][];
for (int j = 0; j < nIDepen; j++)
{
arrInDepen[j] = new double[nFeature];
}
int i = 0;
foreach (DataRow row in m_dt.Rows)
{
arrDepen[i] = Convert.ToDouble(row[intDepenIdx]);
for (int j = 0; j < nIDepen; j++)
{
arrInDepen[j][i] = Convert.ToDouble(row[idxes[j]]);
}
i++;
}
//Plot command for R
StringBuilder plotCommmand = new StringBuilder();
if (!m_blnCreateSWM)
{
//Get the file path and name to create spatial weight matrix
string strNameR = m_pSnippet.FilePathinRfromLayer(m_pMaplayer);
if (strNameR == null)
{
return;
}
//Create spatial weight matrix in R
if (m_pMaplayer is MapPointLayer)
{
m_pEngine.Evaluate("sample.shp <- readShapePoints('" + strNameR + "')");
}
else if (m_pMaplayer is MapPolygonLayer)
{
m_pEngine.Evaluate("sample.shp <- readShapePoly('" + strNameR + "')");
}
else
{
MessageBox.Show("This geometry type is not supported");
pfrmProgress.Close();
this.Close();
}
int intSuccess = m_pSnippet.CreateSpatialWeightMatrix(m_pEngine, m_pMaplayer, txtSWM.Text, pfrmProgress);
if (intSuccess == 0)
{
return;
}
}
//Dependent variable to R vector
NumericVector vecDepen = m_pEngine.CreateNumericVector(arrDepen);
m_pEngine.SetSymbol(dependentName, vecDepen);
if (rbtError.Checked)
{
plotCommmand.Append("errorsarlm(" + dependentName + "~");
}
else if (rbtLag.Checked || rbtDurbin.Checked)
{
plotCommmand.Append("lagsarlm(" + dependentName + "~");
}
else
{
plotCommmand.Append("spautolm(" + dependentName + "~");
}
for (int j = 0; j < nIDepen; j++)
{
//double[] arrVector = arrInDepen.GetColumn<double>(j);
NumericVector vecIndepen = m_pEngine.CreateNumericVector(arrInDepen[j]);
m_pEngine.SetSymbol(independentNames[j], vecIndepen);
plotCommmand.Append(independentNames[j] + "+");
}
plotCommmand.Remove(plotCommmand.Length - 1, 1);
//Select Method
if (rbtEigen.Checked)
{
plotCommmand.Append(", method='eigen'");
}
else if (rbtMatrix.Checked)
{
plotCommmand.Append(", method='Matrix'");
}
else if (rbtMatrixJ.Checked)
{
plotCommmand.Append(", method='Matrix_J'");
}
else if (rbtLU.Checked)
{
plotCommmand.Append(", method='LU'");
}
else if (rbtChebyshev.Checked)
{
plotCommmand.Append(", method='Chebyshev'");
}
else if (rbtMC.Checked)
{
plotCommmand.Append(", method='MC'");
}
else
{
plotCommmand.Append(", method='eigen'");
}
if (rbtError.Checked)
{
plotCommmand.Append(", listw=sample.listw, tol.solve=1.0e-20, zero.policy=TRUE)");
}
else if (rbtLag.Checked)
{
plotCommmand.Append(", listw=sample.listw, tol.solve=1.0e-20, zero.policy=TRUE)");
}
else if (rbtCAR.Checked)
{
plotCommmand.Append(", listw=sample.listw, family='CAR', verbose=TRUE, zero.policy=TRUE)");
}
else if (rbtSMA.Checked)
{
plotCommmand.Append(", listw=sample.listw, family='SMA', verbose=TRUE, zero.policy=TRUE)");
}
else if (rbtDurbin.Checked)
{
plotCommmand.Append(", type='mixed', listw=sample.listw, tol.solve=1.0e-20, zero.policy=TRUE)");
}
else
{
return;
}
try
{
m_pEngine.Evaluate("sum.lm <- summary(" + plotCommmand.ToString() + ", Nagelkerke=T)");
}
catch
{
MessageBox.Show("Cannot solve the regression. Try again with different variables.");
pfrmProgress.Close();
return;
}
//Collect results from R
NumericMatrix matCoe = m_pEngine.Evaluate("as.matrix(sum.lm$Coef)").AsNumericMatrix();
double dblLRLambda = m_pEngine.Evaluate("as.numeric(sum.lm$LR1$statistic)").AsNumeric().First();
double dblpLambda = m_pEngine.Evaluate("as.numeric(sum.lm$LR1$p.value)").AsNumeric().First();
double dblLRErrorModel = m_pEngine.Evaluate("as.numeric(sum.lm$LR1$estimate)").AsNumeric().First();
double dblSigmasquared = 0;
double dblAIC = 0;
double dblWald = 0;
double dblpWald = 0;
if (rbtLag.Checked || rbtError.Checked || rbtDurbin.Checked)
{
dblSigmasquared = m_pEngine.Evaluate("as.numeric(sum.lm$s2)").AsNumeric().First();
//dblAIC = pEngine.Evaluate("as.numeric(sum.lm$AIC_lm.model)").AsNumeric().First();
dblWald = m_pEngine.Evaluate("as.numeric(sum.lm$Wald1$statistic)").AsNumeric().First();
dblpWald = m_pEngine.Evaluate("as.numeric(sum.lm$Wald1$p.value)").AsNumeric().First();
double dblParaCnt = m_pEngine.Evaluate("as.numeric(sum.lm$parameters)").AsNumeric().First();
dblAIC = (2 * dblParaCnt) - (2 * dblLRErrorModel);
}
else
{
dblSigmasquared = m_pEngine.Evaluate("as.numeric(sum.lm$fit$s2)").AsNumeric().First();
double dblParaCnt = m_pEngine.Evaluate("as.numeric(sum.lm$parameters)").AsNumeric().First();
dblAIC = (2 * dblParaCnt) - (2 * dblLRErrorModel);
}
//int intNObser = pEngine.Evaluate("as.numeric(nrow(sum.lm$X))").AsInteger().First();
//int intNParmeter = pEngine.Evaluate(" as.numeric(sum.lm$parameters)").AsInteger().First();
double dblLambda = 0;
double dblSELambda = 0;
double dblResiAuto = 0;
double dblResiAutoP = 0;
if (rbtLag.Checked || rbtDurbin.Checked)
{
dblLambda = m_pEngine.Evaluate("as.numeric(sum.lm$rho)").AsNumeric().First();
dblSELambda = m_pEngine.Evaluate("as.numeric(sum.lm$rho.se)").AsNumeric().First();
dblResiAuto = m_pEngine.Evaluate("as.numeric(sum.lm$LMtest)").AsNumeric().First();
dblResiAutoP = m_pEngine.Evaluate("as.numeric(sum.lm$rho.se)").AsNumeric().First();
}
else
{
dblLambda = m_pEngine.Evaluate("as.numeric(sum.lm$lambda)").AsNumeric().First();
dblSELambda = m_pEngine.Evaluate("as.numeric(sum.lm$lambda.se)").AsNumeric().First();
}
double dblRsquared = m_pEngine.Evaluate("as.numeric(sum.lm$NK)").AsNumeric().First();
//Draw result form
//Open Ouput form
frmRegResult pfrmRegResult = new frmRegResult();
if (rbtError.Checked)
{
pfrmRegResult.Text = "Spatial Autoregressive Model Summary (Error Model)";
}
else if (rbtLag.Checked)
{
pfrmRegResult.Text = "Spatial Autoregressive Model Summary (Lag Model)";
}
else if (rbtCAR.Checked)
{
pfrmRegResult.Text = "Spatial Autoregressive Model Summary (CAR Model)";
}
else if (rbtDurbin.Checked)
{
pfrmRegResult.Text = "Spatial Autoregressive Model Summary (Spatial Durbin Model)";
}
else
{
pfrmRegResult.Text = "Spatial Autoregressive Model Summary (SMA Model)";
}
//pfrmRegResult.panel2.Visible = true;
//Create DataTable to store Result
System.Data.DataTable tblRegResult = new DataTable("SRResult");
//Assign DataTable
DataColumn dColName = new DataColumn();
dColName.DataType = System.Type.GetType("System.String");
dColName.ColumnName = "Name";
tblRegResult.Columns.Add(dColName);
DataColumn dColValue = new DataColumn();
dColValue.DataType = System.Type.GetType("System.Double");
dColValue.ColumnName = "Estimate";
tblRegResult.Columns.Add(dColValue);
DataColumn dColSE = new DataColumn();
dColSE.DataType = System.Type.GetType("System.Double");
dColSE.ColumnName = "Std. Error";
tblRegResult.Columns.Add(dColSE);
String.Format("{0:0.##}", tblRegResult.Columns["Std. Error"]);
DataColumn dColTValue = new DataColumn();
dColTValue.DataType = System.Type.GetType("System.Double");
dColTValue.ColumnName = "z value";
tblRegResult.Columns.Add(dColTValue);
DataColumn dColPvT = new DataColumn();
dColPvT.DataType = System.Type.GetType("System.Double");
dColPvT.ColumnName = "Pr(>|z|)";
tblRegResult.Columns.Add(dColPvT);
if (rbtDurbin.Checked)
{
nIDepen = nIDepen * 2;
}
//Store Data Table by R result
for (int j = 0; j < nIDepen + 1; j++)
{
DataRow pDataRow = tblRegResult.NewRow();
if (j == 0)
{
pDataRow["Name"] = "(Intercept)";
}
else
{
if (rbtDurbin.Checked)
{
if (j <= nIDepen / 2)
{
pDataRow["Name"] = independentNames[j - 1];
}
else
{
pDataRow["Name"] = "lag." + independentNames[j - (nIDepen / 2) - 1];
}
}
else
{
pDataRow["Name"] = independentNames[j - 1];
}
}
pDataRow["Estimate"] = Math.Round(matCoe[j, 0], intDeciPlaces);
pDataRow["Std. Error"] = Math.Round(matCoe[j, 1], intDeciPlaces);
pDataRow["z value"] = Math.Round(matCoe[j, 2], intDeciPlaces);
pDataRow["Pr(>|z|)"] = Math.Round(matCoe[j, 3], intDeciPlaces);
tblRegResult.Rows.Add(pDataRow);
}
//Assign Datagridview to Data Table
pfrmRegResult.dgvResults.DataSource = tblRegResult;
//Assign values at Textbox
string strDecimalPlaces = "N" + intDeciPlaces.ToString();
string[] strResults = new string[5];
if (rbtLag.Checked || rbtDurbin.Checked)
{
strResults[0] = "rho: " + dblLambda.ToString(strDecimalPlaces) +
", LR Test Value: " + dblLRLambda.ToString(strDecimalPlaces) + ", p-value: " + dblpLambda.ToString(strDecimalPlaces);
strResults[1] = "Asymptotic S.E: " + dblSELambda.ToString(strDecimalPlaces) +
", Wald: " + dblWald.ToString(strDecimalPlaces) + ", p-value: " + dblpWald.ToString(strDecimalPlaces);
strResults[2] = "Log likelihood: " + dblLRErrorModel.ToString(strDecimalPlaces) +
", Sigma-squared: " + dblSigmasquared.ToString(strDecimalPlaces);
strResults[3] = "AIC: " + dblAIC.ToString(strDecimalPlaces) + ", LM test for residuals autocorrelation: " + dblResiAuto.ToString(strDecimalPlaces);
}
else if (rbtError.Checked)
{
strResults[0] = "Lambda: " + dblLambda.ToString(strDecimalPlaces) +
", LR Test Value: " + dblLRLambda.ToString(strDecimalPlaces) + ", p-value: " + dblpLambda.ToString(strDecimalPlaces);
strResults[1] = "Asymptotic S.E: " + dblSELambda.ToString(strDecimalPlaces) +
", Wald: " + dblWald.ToString(strDecimalPlaces) + ", p-value: " + dblpWald.ToString(strDecimalPlaces);
strResults[2] = "Log likelihood: " + dblLRErrorModel.ToString(strDecimalPlaces) +
", Sigma-squared: " + dblSigmasquared.ToString(strDecimalPlaces);
strResults[3] = "AIC: " + dblAIC.ToString(strDecimalPlaces);
}
else
{
strResults[0] = "Lambda: " + dblLambda.ToString(strDecimalPlaces) +
", LR Test Value: " + dblLRLambda.ToString(strDecimalPlaces) + ", p-value: " + dblpLambda.ToString(strDecimalPlaces);
strResults[1] = "Numerical Hessian S.E of lambda: " + dblSELambda.ToString(strDecimalPlaces);
strResults[2] = "Log likelihood: " + dblLRErrorModel.ToString(strDecimalPlaces) +
", Sigma-squared: " + dblSigmasquared.ToString(strDecimalPlaces);
strResults[3] = "AIC: " + dblAIC.ToString(strDecimalPlaces);
}
strResults[4] = "Nagelkerke pseudo-R-squared: " + dblRsquared.ToString(strDecimalPlaces);
pfrmRegResult.txtOutput.Lines = strResults;
if (chkSave.Checked)
{
pfrmProgress.lblStatus.Text = "Saving residuals:";
//The field names are related with string[] DeterminedName in clsSnippet
string strResiFldName = lstSave.Items[0].SubItems[1].Text;
//Get EVs and residuals
NumericVector nvResiduals = m_pEngine.Evaluate("as.numeric(sum.lm$residuals)").AsNumeric();
if (rbtCAR.Checked || rbtSMA.Checked)
{
nvResiduals = m_pEngine.Evaluate("as.numeric(sum.lm$fit$residuals)").AsNumeric();
}
// Create field, if there isn't
if (m_dt.Columns.IndexOf(strResiFldName) == -1)
{
//Add fields
DataColumn pColumn = new DataColumn(strResiFldName);
pColumn.DataType = Type.GetType("System.Double");
m_dt.Columns.Add(pColumn);
}
else
{
DialogResult dialogResult = MessageBox.Show("Do you want to overwrite " + strResiFldName + " field?", "Overwrite", MessageBoxButtons.YesNo);
if (dialogResult == DialogResult.No)
{
return;
}
}
//Update Field
int featureIdx = 0;
int intResiFldIdx = m_dt.Columns.IndexOf(strResiFldName);
foreach (DataRow row in m_dt.Rows)
{
//Update Residuals
row[intResiFldIdx] = nvResiduals[featureIdx];
featureIdx++;
}
//Save Result;
if (m_pMaplayer is MapPointLayer)
{
MapPointLayer pMapPointLyr = default(MapPointLayer);
pMapPointLyr = (MapPointLayer)m_pMaplayer;
pMapPointLyr.DataSet.Save();
}
else if (m_pMaplayer is MapPolygonLayer)
{
MapPolygonLayer pMapPolyLyr = default(MapPolygonLayer);
pMapPolyLyr = (MapPolygonLayer)m_pMaplayer;
pMapPolyLyr.DataSet.Save();
}
else if (m_pMaplayer is MapLineLayer)
{
MapLineLayer pMapLineLyr = default(MapLineLayer);
pMapLineLyr = (MapLineLayer)m_pMaplayer;
pMapLineLyr.DataSet.Save();
}
}
pfrmProgress.Close();
pfrmRegResult.Show();
}
catch (Exception ex)
{
frmErrorLog pfrmErrorLog = new frmErrorLog(); pfrmErrorLog.ex = ex; pfrmErrorLog.ShowDialog();
return;
}
}
private void BinomRegression(frmProgress pfrmProgress, IMapLayer pMapLayer, string strLM, int nIDepen, String[] independentNames, int intDeciPlaces)
{
try
{
pfrmProgress.lblStatus.Text = "Calculate Regression Coefficients";
m_pEngine.Evaluate("sample.glm <- glm(" + strLM + ", family='binomial')");
pfrmProgress.lblStatus.Text = "Printing Output:";
m_pEngine.Evaluate("sum.glm <- summary(sample.glm)");
m_pEngine.Evaluate("sample.n <- length(sample.nb)");
NumericMatrix matCoe = m_pEngine.Evaluate("as.matrix(sum.glm$coefficient)").AsNumericMatrix();
CharacterVector vecNames = m_pEngine.Evaluate("attributes(sum.glm$coefficients)$dimnames[[1]]").AsCharacter();
double dblNullDevi = m_pEngine.Evaluate("sum.glm$null.deviance").AsNumeric().First();
double dblNullDF = m_pEngine.Evaluate("sum.glm$df.null").AsNumeric().First();
double dblResiDevi = m_pEngine.Evaluate("sum.glm$deviance").AsNumeric().First();
double dblResiDF = m_pEngine.Evaluate("sum.glm$df.residual").AsNumeric().First();
//Nagelkerke r squared
double dblPseudoRsquared = m_pEngine.Evaluate("(1 - exp((sample.glm$dev - sample.glm$null)/sample.n))/(1 - exp(-sample.glm$null/sample.n))").AsNumeric().First();
double dblResiLMMC = 0;
double dblResiLMpVal = 0;
if (chkResiAuto.Checked)
{
m_pEngine.Evaluate("orgresi.mc <-moran.mc(residuals(sample.glm, type='" + cboResiType.Text + "'), listw =sample.listb, nsim=999, zero.policy=TRUE)");
dblResiLMMC = m_pEngine.Evaluate("orgresi.mc$statistic").AsNumeric().First();
dblResiLMpVal = m_pEngine.Evaluate("orgresi.mc$p.value").AsNumeric().First();
}
NumericVector nvecNonAIC = m_pEngine.Evaluate("sample.glm$aic").AsNumeric();
//Open Ouput form
frmRegResult pfrmRegResult = new frmRegResult();
pfrmRegResult.Text = "Binomial Regression Summary";
//Create DataTable to store Result
System.Data.DataTable tblRegResult = new DataTable("BinoResult");
//Assign DataTable
DataColumn dColName = new DataColumn();
dColName.DataType = System.Type.GetType("System.String");
dColName.ColumnName = "Name";
tblRegResult.Columns.Add(dColName);
DataColumn dColValue = new DataColumn();
dColValue.DataType = System.Type.GetType("System.Double");
dColValue.ColumnName = "Estimate";
tblRegResult.Columns.Add(dColValue);
DataColumn dColSE = new DataColumn();
dColSE.DataType = System.Type.GetType("System.Double");
dColSE.ColumnName = "Std. Error";
tblRegResult.Columns.Add(dColSE);
DataColumn dColTValue = new DataColumn();
dColTValue.DataType = System.Type.GetType("System.Double");
dColTValue.ColumnName = "z value";
tblRegResult.Columns.Add(dColTValue);
DataColumn dColPvT = new DataColumn();
dColPvT.DataType = System.Type.GetType("System.Double");
dColPvT.ColumnName = "Pr(>|z|)";
tblRegResult.Columns.Add(dColPvT);
int intNCoeff = nIDepen + 1;
//Store Data Table by R result
for (int j = 0; j < intNCoeff; j++)
{
DataRow pDataRow = tblRegResult.NewRow();
if (j == 0)
{
pDataRow["Name"] = "(Intercept)";
}
else
{
pDataRow["Name"] = independentNames[j - 1];
}
pDataRow["Estimate"] = Math.Round(matCoe[j, 0], intDeciPlaces);
pDataRow["Std. Error"] = Math.Round(matCoe[j, 1], intDeciPlaces);
pDataRow["z value"] = Math.Round(matCoe[j, 2], intDeciPlaces);
pDataRow["Pr(>|z|)"] = Math.Round(matCoe[j, 3], intDeciPlaces);
tblRegResult.Rows.Add(pDataRow);
}
//Assign Datagridview to Data Table
pfrmRegResult.dgvResults.DataSource = tblRegResult;
//Get Data Table
DataTable pDT = null;
if (pMapLayer is MapPointLayer)
{
MapPointLayer pMapPointLyr = default(MapPointLayer);
pMapPointLyr = (MapPointLayer)pMapLayer;
pDT = pMapPointLyr.DataSet.DataTable;
}
else if (pMapLayer is MapPolygonLayer)
{
MapPolygonLayer pMapPolyLyr = default(MapPolygonLayer);
pMapPolyLyr = (MapPolygonLayer)pMapLayer;
pDT = pMapPolyLyr.DataSet.DataTable;
}
int nFeature = pDT.Rows.Count;
//Assign values at Textbox
string strDecimalPlaces = "N" + intDeciPlaces.ToString();
string[] strResults = new string[6];
strResults[0] = "Number of rows: " + nFeature.ToString();
strResults[1] = "AIC: " + nvecNonAIC.Last().ToString(strDecimalPlaces);
strResults[2] = "Null deviance: " + dblNullDevi.ToString(strDecimalPlaces) + " on " + dblNullDF.ToString("N0") + " degrees of freedom";
strResults[3] = "Residual deviance: " + dblResiDevi.ToString(strDecimalPlaces) + " on " + dblResiDF.ToString("N0") + " degrees of freedom";
strResults[4] = "Nagelkerke pseudo R squared: " + dblPseudoRsquared.ToString(strDecimalPlaces);
if (chkResiAuto.Checked)
{
strResults[5] = "MC of residuals: " + dblResiLMMC.ToString("N3") + ", p-value: " + dblResiLMpVal.ToString("N3");
}
else
{
strResults[5] = "";
}
pfrmRegResult.txtOutput.Lines = strResults;
if (chkSave.Checked)
{
pfrmProgress.lblStatus.Text = "Saving residuals:";
//The field names are related with string[] DeterminedName in clsSnippet
string strResiFldName = lstSave.Items[0].SubItems[1].Text;
//Get EVs and residuals
NumericVector nvResiduals = m_pEngine.Evaluate("as.numeric(residuals(sample.glm, type='" + cboResiType.Text + "'))").AsNumeric();
// Create field, if there isn't
// Create field, if there isn't
if (m_dt.Columns.IndexOf(strResiFldName) == -1)
{
//Add fields
DataColumn pColumn = new DataColumn(strResiFldName);
pColumn.DataType = Type.GetType("System.Double");
m_dt.Columns.Add(pColumn);
}
else
{
DialogResult dialogResult = MessageBox.Show("Do you want to overwrite " + strResiFldName + " field?", "Overwrite", MessageBoxButtons.YesNo);
if (dialogResult == DialogResult.No)
{
return;
}
}
//Update Field
int featureIdx = 0;
int intResiFldIdx = m_dt.Columns.IndexOf(strResiFldName);
foreach (DataRow row in m_dt.Rows)
{
//Update Residuals
row[intResiFldIdx] = nvResiduals[featureIdx];
featureIdx++;
}
//Save Result;
if (m_pMaplayer is MapPointLayer)
{
MapPointLayer pMapPointLyr = default(MapPointLayer);
pMapPointLyr = (MapPointLayer)m_pMaplayer;
pMapPointLyr.DataSet.Save();
}
else if (m_pMaplayer is MapPolygonLayer)
{
MapPolygonLayer pMapPolyLyr = default(MapPolygonLayer);
pMapPolyLyr = (MapPolygonLayer)m_pMaplayer;
pMapPolyLyr.DataSet.Save();
}
else if (m_pMaplayer is MapLineLayer)
{
MapLineLayer pMapLineLyr = default(MapLineLayer);
pMapLineLyr = (MapLineLayer)m_pMaplayer;
pMapLineLyr.DataSet.Save();
}
}
pfrmRegResult.Show();
}
catch (Exception ex)
{
frmErrorLog pfrmErrorLog = new frmErrorLog(); pfrmErrorLog.ex = ex; pfrmErrorLog.ShowDialog();
return;
}
}
private void btnRun_Click(object sender, EventArgs e)
{
try
{
if (cboFieldName.Text == "")
{
MessageBox.Show("Please select the dependent input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
if (lstIndeVar.Items.Count == 0)
{
MessageBox.Show("Please select independents input variables to be used in the regression model.",
"Please choose at least one input variable");
return;
}
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Pre-Processing:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
//Decimal places
int intDeciPlaces = 5;
//Get number of Independent variables
int nIDepen = lstIndeVar.Items.Count;
// Gets the column of the dependent variable
String dependentName = (string)cboFieldName.SelectedItem;
//sourceTable.AcceptChanges();
//DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
// Gets the columns of the independent variables
String[] independentNames = new string[nIDepen];
for (int j = 0; j < nIDepen; j++)
{
independentNames[j] = (string)lstIndeVar.Items[j];
}
int nFeature = m_dt.Rows.Count;
//Get index for independent and dependent variables
int intDepenIdx = m_dt.Columns.IndexOf(dependentName);
int[] idxes = new int[nIDepen];
for (int j = 0; j < nIDepen; j++)
{
idxes[j] = m_dt.Columns.IndexOf(independentNames[j]);
}
//Store independent values at Array
double[] arrDepen = new double[nFeature];
double[][] arrInDepen = new double[nIDepen][];
for (int j = 0; j < nIDepen; j++)
{
arrInDepen[j] = new double[nFeature];
}
int i = 0;
foreach (DataRow row in m_dt.Rows)
{
arrDepen[i] = Convert.ToDouble(row[intDepenIdx]);
for (int j = 0; j < nIDepen; j++)
{
arrInDepen[j][i] = Convert.ToDouble(row[idxes[j]]);
}
i++;
}
//Plot command for R
StringBuilder plotCommmand = new StringBuilder();
//Dependent variable to R vector
NumericVector vecDepen = m_pEngine.CreateNumericVector(arrDepen);
m_pEngine.SetSymbol(dependentName, vecDepen);
plotCommmand.Append("lm(" + dependentName + "~");
for (int j = 0; j < nIDepen; j++)
{
//double[] arrVector = arrInDepen.GetColumn<double>(j);
NumericVector vecIndepen = m_pEngine.CreateNumericVector(arrInDepen[j]);
m_pEngine.SetSymbol(independentNames[j], vecIndepen);
plotCommmand.Append(independentNames[j] + "+");
}
plotCommmand.Remove(plotCommmand.Length - 1, 1);
plotCommmand.Append(")");
m_pEngine.Evaluate("sum.lm <- summary(" + plotCommmand + ")");
NumericMatrix matCoe = m_pEngine.Evaluate("as.matrix(sum.lm$coefficient)").AsNumericMatrix();
NumericVector vecF = m_pEngine.Evaluate("as.numeric(sum.lm$fstatistic)").AsNumeric();
m_pEngine.Evaluate("fvalue <- as.numeric(sum.lm$fstatistic)");
double dblPValueF = m_pEngine.Evaluate("pf(fvalue[1],fvalue[2],fvalue[3],lower.tail=F)").AsNumeric().First();
double dblRsqaure = m_pEngine.Evaluate("sum.lm$r.squared").AsNumeric().First();
double dblAdjRsqaure = m_pEngine.Evaluate("sum.lm$adj.r.squared").AsNumeric().First();
double dblResiSE = m_pEngine.Evaluate("sum.lm$sigma").AsNumeric().First();
NumericVector vecResiDF = m_pEngine.Evaluate("sum.lm$df").AsNumeric();
double dblResiMC = 0;
double dblResiMCpVal = 0;
if (chkResiAuto.Checked)
{
if (!m_blnCreateSWM)
{
//Get the file path and name to create spatial weight matrix
string strNameR = m_pSnippet.FilePathinRfromLayer(m_pMaplayer);
if (strNameR == null)
{
return;
}
//Create spatial weight matrix in R
if (m_pMaplayer is MapPointLayer)
{
m_pEngine.Evaluate("sample.shp <- readShapePoints('" + strNameR + "')");
}
else if (m_pMaplayer is MapPolygonLayer)
{
m_pEngine.Evaluate("sample.shp <- readShapePoly('" + strNameR + "')");
}
else
{
MessageBox.Show("This geometry type is not supported");
pfrmProgress.Close();
this.Close();
}
int intSuccess = m_pSnippet.CreateSpatialWeightMatrix(m_pEngine, m_pMaplayer, txtSWM.Text, pfrmProgress);
if (intSuccess == 0)
{
return;
}
}
m_pEngine.Evaluate("sample.n <- length(sample.nb)");
//Calculate MC
m_pEngine.Evaluate("zmc <- lm.morantest(" + plotCommmand.ToString() + ", listw=sample.listw, zero.policy=TRUE)");
dblResiMC = m_pEngine.Evaluate("zmc$estimate[1]").AsNumeric().First();
dblResiMCpVal = m_pEngine.Evaluate("zmc$p.value").AsNumeric().First();
}
pfrmProgress.lblStatus.Text = "Printing Output:";
//Open Ouput form
frmRegResult pfrmRegResult = new frmRegResult();
pfrmRegResult.Text = "Linear Regression Summary";
//Create DataTable to store Result
System.Data.DataTable tblRegResult = new DataTable("OLSResult");
//Assign DataTable
DataColumn dColName = new DataColumn();
dColName.DataType = System.Type.GetType("System.String");
dColName.ColumnName = "Name";
tblRegResult.Columns.Add(dColName);
DataColumn dColValue = new DataColumn();
dColValue.DataType = System.Type.GetType("System.Double");
dColValue.ColumnName = "Estimate";
tblRegResult.Columns.Add(dColValue);
DataColumn dColSE = new DataColumn();
dColSE.DataType = System.Type.GetType("System.Double");
dColSE.ColumnName = "Std. Error";
tblRegResult.Columns.Add(dColSE);
DataColumn dColTValue = new DataColumn();
dColTValue.DataType = System.Type.GetType("System.Double");
dColTValue.ColumnName = "t value";
tblRegResult.Columns.Add(dColTValue);
DataColumn dColPvT = new DataColumn();
dColPvT.DataType = System.Type.GetType("System.Double");
dColPvT.ColumnName = "Pr(>|t|)";
tblRegResult.Columns.Add(dColPvT);
//Store Data Table by R result
for (int j = 0; j < nIDepen + 1; j++)
{
DataRow pDataRow = tblRegResult.NewRow();
if (j == 0)
{
pDataRow["Name"] = "(Intercept)";
}
else
{
pDataRow["Name"] = independentNames[j - 1];
}
pDataRow["Estimate"] = Math.Round(matCoe[j, 0], intDeciPlaces);
pDataRow["Std. Error"] = Math.Round(matCoe[j, 1], intDeciPlaces);
pDataRow["t value"] = Math.Round(matCoe[j, 2], intDeciPlaces);
pDataRow["Pr(>|t|)"] = Math.Round(matCoe[j, 3], intDeciPlaces);
tblRegResult.Rows.Add(pDataRow);
}
//Assign Datagridview to Data Table
pfrmRegResult.dgvResults.DataSource = tblRegResult;
//Assign values at Textbox
string[] strResults = null;
if (chkResiAuto.Checked)
{
strResults = new string[4];
strResults[3] = "MC of residuals: " + dblResiMC.ToString("N3") + ", p-value: " + dblResiMCpVal.ToString("N3");
}
else
{
strResults = new string[3];
}
strResults[0] = "Residual standard error: " + dblResiSE.ToString("N" + intDeciPlaces.ToString()) +
" on " + vecResiDF[1].ToString() + " degrees of freedom";
strResults[1] = "Multiple R-squared: " + dblRsqaure.ToString("N" + intDeciPlaces.ToString()) +
", Adjusted R-squared: " + dblAdjRsqaure.ToString("N" + intDeciPlaces.ToString());
strResults[2] = "F-Statistic: " + vecF[0].ToString("N" + intDeciPlaces.ToString()) +
" on " + vecF[1].ToString() + " and " + vecF[2].ToString() + " DF, p-value: " + dblPValueF.ToString("N" + intDeciPlaces.ToString());
pfrmRegResult.txtOutput.Lines = strResults;
pfrmRegResult.Show();
//Create Plots for Regression
if (chkPlots.Checked)
{
string strTitle = "Linear Regression Results";
string strCommand = "plot(" + plotCommmand + ");";
m_pSnippet.drawPlottoForm(strTitle, strCommand);
}
//Save Outputs in SHP
if (chkSave.Checked)
{
//The field names are related with string[] DeterminedName in clsSnippet
string strResiFldName = lstSave.Items[0].SubItems[1].Text;
//Get EVs and residuals
NumericVector nvResiduals = m_pEngine.Evaluate("as.numeric(sum.lm$residuals)").AsNumeric();
// Create field, if there isn't
// Create field, if there isn't
if (m_dt.Columns.IndexOf(strResiFldName) == -1)
{
//Add fields
DataColumn pColumn = new DataColumn(strResiFldName);
pColumn.DataType = Type.GetType("System.Double");
m_dt.Columns.Add(pColumn);
}
else
{
DialogResult dialogResult = MessageBox.Show("Do you want to overwrite " + strResiFldName + " field?", "Overwrite", MessageBoxButtons.YesNo);
if (dialogResult == DialogResult.No)
{
return;
}
}
//Update Field
int featureIdx = 0;
int intResiFldIdx = m_dt.Columns.IndexOf(strResiFldName);
foreach (DataRow row in m_dt.Rows)
{
//Update Residuals
row[intResiFldIdx] = nvResiduals[featureIdx];
featureIdx++;
}
//Save Result;
if (m_pMaplayer is MapPointLayer)
{
MapPointLayer pMapPointLyr = default(MapPointLayer);
pMapPointLyr = (MapPointLayer)m_pMaplayer;
pMapPointLyr.DataSet.Save();
}
else if (m_pMaplayer is MapPolygonLayer)
{
MapPolygonLayer pMapPolyLyr = default(MapPolygonLayer);
pMapPolyLyr = (MapPolygonLayer)m_pMaplayer;
pMapPolyLyr.DataSet.Save();
}
else if (m_pMaplayer is MapLineLayer)
{
MapLineLayer pMapLineLyr = default(MapLineLayer);
pMapLineLyr = (MapLineLayer)m_pMaplayer;
pMapLineLyr.DataSet.Save();
}
MessageBox.Show("Residuals are stored in the shape file");
}
pfrmProgress.Close();
}
catch (Exception ex)
{
frmErrorLog pfrmErrorLog = new frmErrorLog(); pfrmErrorLog.ex = ex; pfrmErrorLog.ShowDialog();
return;
}
}