private void button1_Click(object sender, EventArgs e)
{
StringBuilder Result = new StringBuilder();
int ColNum = 0;
if (Int32.TryParse(texbox_dep.Text, out ColNum))
{
string[] NumberSeries = Tabulation.ReadVector(MainForm.MainDT, ColNum - 1).ToArray();
int length = 0;
int lag = 4; //自己给定lag的值
string[][] NumberCombine = new string[lag + 1][];
BigDecimal[] corr = new BigDecimal[lag];
BigDecimal sum_Qtest = 0;
BigDecimal sum_LBtest = 0;
foreach (string Num in NumberSeries)
{
if (Num != "" && Num != null)
{
length++;
}
}
if (length < 8)
{
MessageBox.Show("数据量过少,建议使用灰色预测");
}
else
{
for (int i = 0; i < lag + 1; i++)
{
NumberCombine[i] = new string[length - lag];
for (int j = 0; j < length - lag; j++)
{
NumberCombine[i][j] = NumberSeries[j + lag - i];
}
try
{
corr[i - 1] = Stat.Corr(NumberCombine[i], NumberCombine[i - 1]);
sum_Qtest += corr[i - 1] * corr[i - 1];
sum_LBtest += corr[i - 1] * corr[i - 1] / (BigDecimal)(length - i);
}
catch (Exception ex) { }
}
string[] output = Stat.TimeseriesTest(length, sum_Qtest, sum_LBtest, lag, corr);
Result.Append(StrManipulation.PadLeftX("时间序列平稳性检验", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX("Q检验:", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("Q =", ' ', 12));
Result.Append("\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(output[lag]), ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("Prob > Q =", ' ', 12));
Result.Append("\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(output[lag + 1]), ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX("LB检验:", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("LB =", ' ', 12));
Result.Append("\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(output[lag + 2]), ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("Prob > LB =", ' ', 12));
Result.Append("\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(output[lag + 3]), ' ', 12));
Result.Append("\r\n");
if (Convert.ToDouble(output[lag + 1]) <= 0.05 || Convert.ToDouble(output[lag + 3]) <= 0.05)
{
Result.Append(StrManipulation.PadRightX("在95%的显著性水平下认为序列非白噪声，观测值间显著相关。", ' ', 50));
Result.Append("\r\n");
}
else
{
Result.Append(StrManipulation.PadRightX("在95%的显著性水平下认为序列为白噪声，观测值间相互独立。", ' ', 50));
Result.Append("\r\n");
}
Result.Append("\r\n");
int period = 0;
if (Int32.TryParse(textBox_period.Text, out period))
{
BigDecimal[] AR1 = Stat.AR1(NumberSeries, length, corr, period);
BigDecimal[] AR2 = Stat.AR2(NumberSeries, length, corr, period);
BigDecimal[] plot_forecast = new BigDecimal[period];
if (AR1[period] <= AR2[period])
{
Result.Append(StrManipulation.PadRightX("最佳预测模型：AR1", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("序号（预测）", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period; i++)
{
Result.Append(StrManipulation.PadLeftX(i.ToString(), ' ', 12));
plot_forecast[i] = AR1[i];
Result.Append("\t");
}
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("预测值", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period; i++)
{
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(AR1[i].ToString()), ' ', 12));
Result.Append("\t");
}
}
else
{
Result.Append(StrManipulation.PadRightX("最佳预测模型：AR2", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("序号（预测）", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period; i++)
{
Result.Append(StrManipulation.PadLeftX(i.ToString(), ' ', 12));
plot_forecast[i] = AR2[i];
Result.Append("\t");
}
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("预测值", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period; i++)
{
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(AR2[i].ToString()), ' ', 12));
Result.Append("\t");
}
}
Result.Append("\r\n");
Result.Append("\r\n");
MainForm.S.richTextBox1.AppendText(Result.ToString());
//MainForm.S.richTextBox1.Select();//让RichTextBox获得焦点
chart_timeseries.Series.Clear();
Series series = new Series("原数据");
Series series2 = new Series("预测数据");
//ChartArea area = chart_timeseries.ChartAreas.Add("chartArea");
//area.AxisX.MajorGrid.LineWidth = 0;
series.Color = Color.MidnightBlue;
series2.Color = Color.Maroon;
if (length <= 50)
{
series.BorderWidth = 2;
series2.BorderWidth = 2;
}
series.ChartType = SeriesChartType.Line;
series2.ChartType = SeriesChartType.Line;
for (int i = 0; i < length; i++)
{
series.Points.AddXY(i + 1, Convert.ToDouble(NumberSeries[i].ToString()));
}
series2.Points.AddXY(length, Convert.ToDouble(NumberSeries[length - 1].ToString()));
for (int i = 0; i < period; i++)
{
series2.Points.AddXY(i + 1 + length, Convert.ToDouble(plot_forecast[i].ToString()));
}
chart_timeseries.Series.Add(series);
chart_timeseries.Series.Add(series2);
}
else
{
MessageBox.Show("请输入预测期数");
}
}
}
else
{
MessageBox.Show("无此列");
}
}
private void button_Regression_Click(object sender, EventArgs e)
{
string ColNums = textBox_Cols.Text;
char[] separator = { ',' };
//string是以逗号分隔的
string[] AllNum = ColNums.Split(separator);
//按照逗号分割
List <int> Cols = new List <int>();
foreach (string SingleNum in AllNum)
{
if (SingleNum != "")
{
Cols.Add(Convert.ToInt32(SingleNum) - 1);
}
}
int[] AllColNums = Cols.ToArray();
//MessageBox.Show(AllColNums[0].ToString());
//MessageBox.Show(AllColNums[1].ToString());
int yCol = Tabulation.FindCol(MainForm.MainDT, comboBox_y.Text);
List <List <string> > data = new List <List <string> >();
int RowsCount = MainForm.MainDT.Rows.Count;
int InputColsCount = AllColNums.Length;
//计算总共要录入的列数
int count = 0;
//计算实际录入数据数
List <string> Ydata = new List <string>();
for (int i = 0; i < InputColsCount; i++)
{
data.Add(new List <string>());
}
for (int i = 0; i < RowsCount; i++)
{
if (Tabulation.IdentifyNARow(MainForm.MainDT, i, AllColNums))
{
//确认该行无空格
if (MainForm.MainDT.Rows[i][yCol].ToString().Trim() != "")
{
Ydata.Add(MainForm.MainDT.Rows[i][yCol].ToString().Trim());
for (int j = 0; j < InputColsCount; j++)
{
data[j].Add(MainForm.MainDT.Rows[i][AllColNums[j]].ToString()); //此处队长有bug，已改正
}
count++;
}
}
}
if (count > 2)
{
BigDecimal[,] IndependentVariables = new BigDecimal[count, InputColsCount + 1];
StringBuilder Result = new StringBuilder();
if (count <= InputColsCount + 1)
{
Result.Append("样本量过少，无法估计");
}
else
{
//第一列全是1
for (int i = 0; i < count; i++)
{
IndependentVariables[i, 0] = 1;
}
for (int i = 0; i < count; i++)
{
for (int j = 0; j < InputColsCount; j++)
{
//录入时，BigDecimal数组列数要+1，因为第一列全是1
IndependentVariables[i, j + 1] = data[j][i];
}
}
//MathV.ArrayPrint(IndependentVariables);
BigDecimal[,] DependentVariable = new BigDecimal[count, 1];
for (int i = 0; i < count; i++)
{
DependentVariable[i, 0] = Ydata[i];
}
int len12 = IndependentVariables.GetLength(1);//列数
BigDecimal[,] b1 = MathV.MatTrans(IndependentVariables);
BigDecimal[,] b2 = MathV.MatTimes(b1, IndependentVariables);
try
{
BigDecimal[,] b3 = MathV.MatInv(b2, len12);
if (b3 != null)
{
BigDecimal[,] bhat = Stat.MultiRegBeta(b1, b3, IndependentVariables, DependentVariable);
BigDecimal[,] value_beta = Stat.MultiRegP(b3, bhat, IndependentVariables, DependentVariable);
string[] RandF = Stat.MultiRegR(bhat, IndependentVariables, DependentVariable).Split(separator);
//MathV.ArrayPrint(bhat);
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("拟合R^2："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF[0]));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("调整后R^2："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF[1]));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("回归F值："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF[2]));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("回归P值："), ' ', 12));
Result.Append(MathV.NumberPolish(Stat.FINV(Convert.ToDouble(RandF[2]), count - InputColsCount - 1, InputColsCount).ToString()));
Result.Append("\r\n \r\n");
Result.Append(comboBox_y.Text + " = ");
int ColumnNumberCount = 0;
foreach (BigDecimal EachNum in bhat)
{
if (ColumnNumberCount == 0)
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(" + ");
}
else if (ColumnNumberCount == InputColsCount)
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(" ");
Result.Append(MainForm.MainDT.Columns[AllColNums[ColumnNumberCount - 1]].ColumnName);
}
else
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(MainForm.MainDT.Columns[AllColNums[ColumnNumberCount - 1]].ColumnName);
Result.Append(" + ");
}
ColumnNumberCount++;
}
Result.Append("\r\n检验P值: ");
for (int i = 0; i < InputColsCount + 1; i++)
{
Result.Append(MathV.NumberPolish(value_beta[i, 0].ToString()));
Result.Append(" \t");
}
Result.Append("\r\n检验t值: ");
for (int i = 0; i < InputColsCount + 1; i++)
{
Result.Append(MathV.NumberPolish(value_beta[i, 1].ToString()));
Result.Append(" \t");
}
Result.Append("\r\n \r\n");
}
else
{
Result.Append("矩阵不可逆，回归方程无解");
}
}
catch (Exception ex)
{
MessageBox.Show("存在重复自变量");
}
}
MainForm.S.richTextBox1.AppendText(Result.ToString());
}
}
private void button_chart_Click(object sender, EventArgs e)
{
StringBuilder Result = new StringBuilder();
int ColNum = 0;
if (Int32.TryParse(texbox_dep.Text, out ColNum))
{
string[] NumberSeries = Tabulation.ReadVector(MainForm.MainDT, ColNum - 1).ToArray();
int length = 0;
foreach (string Num in NumberSeries)
{
if (Num != "" && Num != null)
{
length++;
}
}
int period = 0;
if (Int32.TryParse(textBox_period.Text, out period))
{
BigDecimal[] cumsum = new BigDecimal[length];
for (int i = 0; i < length; i++)
{
cumsum[i] = 0;
for (int j = 0; j <= i; j++)
{
cumsum[i] += NumberSeries[j];
}
}
BigDecimal[] C = new BigDecimal[length - 1];
for (int i = 0; i < length - 1; i++)
{
C[i] = 0 - (cumsum[i] + cumsum[i + 1]) / 2;
}
BigDecimal[,] D = new BigDecimal[length - 1, 1];
for (int i = 0; i < length - 1; i++)
{
D[i, 0] = NumberSeries[i + 1];
}
BigDecimal[,] E = new BigDecimal[2, length - 1];
for (int i = 0; i < length - 1; i++)
{
E[1, i] = 1;
E[0, i] = C[i];
}
//BigNumber[,] c = MathV.MatTimes(MathV.MatTimes(MathV.MatInv(MathV.MatTimes(E, MathV.MatTrans(E)), 2), E), MathV.MatTrans(D));
BigDecimal[,] c = MathV.MatTimes(MathV.MatTimes(MathV.MatInv(MathV.MatTimes(E, MathV.MatTrans(E)), 2), E), D);
BigDecimal a = c[0, 0];
BigDecimal b = c[1, 0];
BigDecimal t = b / a;
BigDecimal[] F = new BigDecimal[length + period];
for (int i = 0; i < length + period; i++)
{
BigDecimal e1 = 2.718281828;
F[i] = (NumberSeries[0] - t) / Math.Exp((i) * Convert.ToDouble(a.ToString())) + t;
}
BigDecimal[] G = new BigDecimal[length + period];
G[0] = NumberSeries[0];
for (int i = 1; i < length + period; i++)
{
G[i] = F[i] - F[i - 1];
}
Result.Append(StrManipulation.PadRightX("灰色预测GM(1,1)模型", ' ', 12));
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("序号（所有）", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period; i++)
{
Result.Append(StrManipulation.PadLeftX(i.ToString(), ' ', 12));
Result.Append("\t");
}
Result.Append("\r\n");
Result.Append(StrManipulation.PadLeftX("预测值", ' ', 12));
Result.Append("\t");
for (int i = 0; i < period + length; i++)
{
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(G[i].ToString()), ' ', 12));
Result.Append("\t");
}
Result.Append("\r\n");
Result.Append("\r\n");
chart_timeseries.Series.Clear();
Series series3 = new Series("原数据(点)");
Series series4 = new Series("灰色预测数据");
series3.MarkerStyle = MarkerStyle.Circle;
series3.MarkerSize = 6;
series4.BorderWidth = 2;
series3.Color = Color.MidnightBlue;
series4.Color = Color.Maroon;
series3.ChartType = SeriesChartType.Point;
series4.ChartType = SeriesChartType.Line;
for (int i = 0; i < length; i++)
{
series3.Points.AddXY(i + 1, Convert.ToDouble(NumberSeries[i].ToString()));
}
for (int i = 0; i < period + length; i++)
{
series4.Points.AddXY(i + 1, Convert.ToDouble(G[i].ToString()));
}
chart_timeseries.Series.Add(series3);
chart_timeseries.Series.Add(series4);
}
else
{
MessageBox.Show("请输入预测期数");
}
}
else
{
MessageBox.Show("无此列");
}
}
private void button1_Click(object sender, EventArgs e)
{
string ColNums = textBox_Cols.Text;
char[] separator = { ',' };
//string是以逗号分隔的
string[] AllNum = ColNums.Split(separator);
//按照逗号分割
List <int> Cols = new List <int>();
foreach (string SingleNum in AllNum)
{
if (SingleNum != "")
{
Cols.Add(Convert.ToInt32(SingleNum) - 1);
}
}
int[] AllColNums = Cols.ToArray();
//MessageBox.Show(AllColNums[0].ToString());
//MessageBox.Show(AllColNums[1].ToString());
int yCol = Tabulation.FindCol(MainForm.MainDT, comboBox_y.Text);
List <List <string> > data = new List <List <string> >();
int RowsCount = MainForm.MainDT.Rows.Count;
int InputColsCount = AllColNums.Length;
//计算总共要录入的列数
int count = 0;
//计算实际录入数据数
List <string> Ydata = new List <string>();
for (int i = 0; i < InputColsCount; i++)
{
data.Add(new List <string>());
}
for (int i = 0; i < RowsCount; i++)
{
if (Tabulation.IdentifyNARow(MainForm.MainDT, i, AllColNums))
{
//确认该行无空格
if (MainForm.MainDT.Rows[i][yCol].ToString().Trim() != "")
{
Ydata.Add(MainForm.MainDT.Rows[i][yCol].ToString().Trim());
for (int j = 0; j < InputColsCount; j++)
{
data[j].Add(MainForm.MainDT.Rows[i][AllColNums[j]].ToString()); //此处队长有bug，已改正
}
count++;
}
}
}
if (count > 2)
{
BigDecimal[,] IndependentVariables = new BigDecimal[count, InputColsCount + 1];
StringBuilder Result = new StringBuilder();
if (count <= InputColsCount + 1)
{
Result.Append("样本量过少，无法估计");
}
else
{
//第一列全是1
for (int i = 0; i < count; i++)
{
IndependentVariables[i, 0] = 1;
}
for (int i = 0; i < count; i++)
{
for (int j = 0; j < InputColsCount; j++)
{
//录入时，BigDecimal数组列数要+1，因为第一列全是1
IndependentVariables[i, j + 1] = data[j][i];
}
}
//MathV.ArrayPrint(IndependentVariables);
BigDecimal[,] DependentVariable = new BigDecimal[count, 1];
for (int i = 0; i < count; i++)
{
DependentVariable[i, 0] = Ydata[i];
}
int len12 = IndependentVariables.GetLength(1); //列数
BigDecimal[,] b1 = MathV.MatTrans(IndependentVariables);
BigDecimal[,] b2 = MathV.MatTimes(b1, IndependentVariables);
BigDecimal[,] b3 = MathV.MatInv(b2, len12);
if (b3 != null)
{
BigDecimal[,] bhat = Stat.MultiRegBeta(b1, b3, IndependentVariables, DependentVariable);
BigDecimal[,] value_beta = Stat.MultiRegP(b3, bhat, IndependentVariables, DependentVariable);
string[] RandF = Stat.MultiRegR(bhat, IndependentVariables, DependentVariable).Split(separator);
//回归诊断开始
//#初步判断 ： 根据R^2 adjR^2 Fvalue tvalue
Result.Append("1.初步诊断 \r\n");
if ((BigDecimal)RandF[1] < 0.70)
{
Result.Append(" 调整后R^2较小，可能存在遗漏变量\r\n");
}
if ((BigDecimal)RandF[0] - (BigDecimal)RandF[1] > 0.1)
{
Result.Append(" 调整后R^2与R^2差值较大，可能存在冗余变量\r\n");
}
if (Stat.FDIST(Convert.ToDouble(RandF[2]), InputColsCount, count - 1) > 0.01)
{
Result.Append(" 模型拟合情况不好，建议更改解释变量\r\n");
}
else
{
Result.Append(" 模型拟合情况较好\r\n");
}
int count_nonsignifi = 0;
for (int i = 0; i < InputColsCount + 1; i++)
{
if (i == 0)
{
if (value_beta[i, 0] > 0.05)
{
Result.Append(" 常数项在95%置信水平下不显著\r\n");
count_nonsignifi++;
}
}
else
{
if (value_beta[i, 0] > 0.05)
{
Result.Append(" 变量" + (i + 1).ToString() + "在95%置信水平下不显著\r\n");
count_nonsignifi++;
}
}
}
if ((BigDecimal)RandF[0] > 0.7 && count_nonsignifi > ((InputColsCount + 1) / 2))
{
Result.Append(" 高拟合优度伴随着大量非显著解释变量，存在多重共线性\r\n");
}
Result.Append("\r\n");
//#遗漏变量 : 拉姆齐检验
Result.Append("2.模型设定偏误检验（拉姆齐检验）\r\n");
BigDecimal[,] Y_hat = new BigDecimal[count, 1];
Y_hat = MathV.MatTimes(IndependentVariables, bhat);
BigDecimal[,] Ramsey_indep = new BigDecimal[count, InputColsCount + 3];
for (int i = 0; i < count; i++)
{
for (int j = 0; j < InputColsCount + 3; j++)
{
if (j < InputColsCount + 1)
{
Ramsey_indep[i, j] = IndependentVariables[i, j];
}
else if (j == InputColsCount + 1)
{
Ramsey_indep[i, j] = Y_hat[i, 0] * Y_hat[i, 0];
}
else
{
Ramsey_indep[i, j] = Y_hat[i, 0] * Y_hat[i, 0] * Y_hat[i, 0];
}
}
}
BigDecimal[,] b1_ramsey = MathV.MatTrans(Ramsey_indep);
BigDecimal[,] b2_ramsey = MathV.MatTimes(b1_ramsey, Ramsey_indep);
BigDecimal[,] b3_ramsey = MathV.MatInv(b2_ramsey, InputColsCount + 3);
if (b3_ramsey != null && count > InputColsCount + 3)
{
BigDecimal[,] bhat_ramsey = Stat.MultiRegBeta(b1_ramsey, b3_ramsey, Ramsey_indep, DependentVariable);
string[] RandF_ramsey = Stat.MultiRegR(bhat_ramsey, Ramsey_indep, DependentVariable).Split(separator);
double R2_ramsey = Convert.ToDouble(RandF_ramsey[0]);
double F_ramsey = (R2_ramsey - Convert.ToDouble(RandF[0])) * (count - InputColsCount - 2) / ((1 - R2_ramsey) * 2);
double F_P_ramsey = Stat.FDIST(Convert.ToDouble(RandF_ramsey[2]), 2, count - InputColsCount - 2);
if (F_P_ramsey <= 0.001)
{
Result.Append(" 模型在99.9%的置信水平下认为存在偏误\r\n");
}
else
{
Result.Append(" 模型设定在99.9%的置信水平下认为无偏误\r\n");
}
}
else
{
Result.Append(" ！样本量过少，无法进行拉姆齐检验\r\n");
}
Result.Append("");
Result.Append("\r\n3.多重共线性检验：方差膨胀因子(VIF)\r\n");
//#多重共线性检验：方差膨胀因子（VIF）
int count_vif;
if (InputColsCount == 1)
{
Result.Append(" ！一个自变量无法进行方差膨胀因子检验\r\n");
}
else
{
for (int i = 0; i < InputColsCount; i++)
{
BigDecimal[,] Vif_dep = new BigDecimal[count, 1];
BigDecimal[,] Vif_indep = new BigDecimal[count, InputColsCount];
for (int j = 0; j < count; j++)
{
count_vif = 0;
Vif_dep[j, 0] = IndependentVariables[j, i + 1];
for (int k = 0; k < InputColsCount + 1; k++)
{
if (k != i + 1)
{
Vif_indep[j, count_vif] = IndependentVariables[j, k];
count_vif++;
}
}
}
//MathV.ArrayPrint(Vif_indep);
// MathV.ArrayPrint(Vif_dep);
BigDecimal[,] b1_vif = MathV.MatTrans(Vif_indep);
BigDecimal[,] b2_vif = MathV.MatTimes(b1_vif, Vif_indep);
BigDecimal[,] b3_vif = MathV.MatInv(b2_vif, InputColsCount);
if (b3_vif != null)
{
BigDecimal[,] bhat_vif = Stat.MultiRegBeta(b1_vif, b3_vif, Vif_indep, Vif_dep);
string[] RandF_vif = Stat.MultiRegR(bhat_vif, Vif_indep, Vif_dep).Split(separator);
//MessageBox.Show(RandF_vif[0]);
BigDecimal VIF = 1 / (1 - Convert.ToDouble(RandF_vif[0]));
if (VIF <= 10)
{
Result.Append(" 变量" + (i + 1).ToString() + "在模型中共线性较弱，可以保留\r\n");
}
else if (VIF > 10 && VIF < 100)
{
Result.Append(" 变量" + (i + 1).ToString() + "在模型中共线性较强，建议剔除\r\n");
}
else
{
Result.Append(" 变量" + (i + 1).ToString() + "在模型中共线性极强，强烈建议剔除\r\n");
}
}
}
}
//#异方差 : White 检验
Result.Append("\r\n4.异方差检验：怀特检验与LM统计量\r\n");
//Y_hat2 与 u^hat2
BigDecimal[,] u_hat2 = new BigDecimal[count, 1];
BigDecimal[,] u_hat = new BigDecimal[count, 1];
BigDecimal[,] Y_hat2 = new BigDecimal[count, 1];
BigDecimal[,] White_indep = new BigDecimal[count, 3];
BigDecimal[,] White_dep = new BigDecimal[count, 1];
for (int i = 0; i < count; i++)
{
u_hat2[i, 0] = (DependentVariable[i, 0] - Y_hat[i, 0]) * (DependentVariable[i, 0] - Y_hat[i, 0]);
Y_hat2[i, 0] = Y_hat[i, 0] * Y_hat[i, 0];
u_hat[i, 0] = (DependentVariable[i, 0] - Y_hat[i, 0]);
White_indep[i, 0] = 1;
White_indep[i, 1] = Y_hat[i, 0];
White_indep[i, 2] = Y_hat2[i, 0];
White_dep[i, 0] = u_hat2[i, 0];
}
BigDecimal[,] b1_white = MathV.MatTrans(White_indep);
BigDecimal[,] b2_white = MathV.MatTimes(b1_white, White_indep);
BigDecimal[,] b3_white = MathV.MatInv(b2_white, 3);
if (b3_white != null)
{
BigDecimal[,] bhat_white = Stat.MultiRegBeta(b1_white, b3_white, White_indep, White_dep);
string[] RandF_white = Stat.MultiRegR(bhat_white, White_indep, White_dep).Split(separator);
BigDecimal LM_white = count * Convert.ToDouble(RandF_white[0]);
//MessageBox.Show(LM_white.ToString());
BigDecimal P_white = 1 - Stat.chi2(Convert.ToDouble(LM_white.ToString()), 2);
//MessageBox.Show(P_white2.ToString());
Result.Append(" LM统计量 \t");
Result.Append(" =\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(LM_white.ToString()), ' ', 12));
Result.Append("\r\n");
Result.Append(" LM > chi(2)\t");
Result.Append(" =\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(P_white.ToString()), ' ', 12));
Result.Append("\r\n");
if (P_white > 0.05)
{
Result.Append(" 在95%的置信度上认为样本同方差\r\n \r\n");
}
else
{
Result.Append(" 在95%的置信度上认为样本异方差\r\n");
Result.Append(" 解决方法：变量取对数回归,结果如下：\r\n");
BigDecimal[,] ln_indep = new BigDecimal[count, InputColsCount + 1];
BigDecimal[,] ln_dep = new BigDecimal[count, 1];
for (int i = 0; i < count; i++)
{
for (int j = 0; j < InputColsCount; j++)
{
ln_indep[i, j + 1] = Math.Log(Convert.ToDouble(IndependentVariables[i, j + 1].ToString()));
}
ln_indep[i, 0] = 1;
ln_dep[i, 0] = Math.Log(Convert.ToDouble(DependentVariable[i, 0].ToString()));
}
BigDecimal[,] b1_ln = MathV.MatTrans(ln_indep);
BigDecimal[,] b2_ln = MathV.MatTimes(b1_ln, ln_indep);
BigDecimal[,] b3_ln = MathV.MatInv(b2, len12);
if (b3_ln != null)
{
BigDecimal[,] bhat_ln = Stat.MultiRegBeta(b1_ln, b3_ln, ln_indep, ln_dep);
BigDecimal[,] value_beta_ln = Stat.MultiRegP(b3_ln, bhat_ln, ln_indep, ln_dep);
string[] RandF_ln = Stat.MultiRegR(bhat_ln, ln_indep, ln_dep).Split(separator);
//MathV.ArrayPrint(bhat);
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("拟合R^2："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF_ln[0]));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("调整后R^2："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF_ln[1]));
Result.Append("\r\n");
Result.Append(StrManipulation.PadRightX(StrManipulation.VariableNamePolish("回归F值："), ' ', 12));
Result.Append(MathV.NumberPolish(RandF_ln[2]));
Result.Append("\r\n");
Result.Append("\r\n");
Result.Append(comboBox_y.Text);
Result.Append(" = ");
int ColumnNumberCount = 0;
foreach (BigDecimal EachNum in bhat_ln)
{
if (ColumnNumberCount == 0)
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(" + ");
}
else if (ColumnNumberCount == InputColsCount)
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(" ");
Result.Append(" ln " + MainForm.MainDT.Columns[AllColNums[ColumnNumberCount - 1]].ColumnName);
}
else
{
Result.Append(MathV.NumberPolish(EachNum.ToString()));
Result.Append(" ln " + MainForm.MainDT.Columns[AllColNums[ColumnNumberCount - 1]].ColumnName);
Result.Append(" + ");
}
ColumnNumberCount++;
}
Result.Append("\r\n检验P值: ");
for (int i = 0; i < InputColsCount + 1; i++)
{
Result.Append(MathV.NumberPolish(value_beta_ln[i, 0].ToString()));
Result.Append(" \t");
}
Result.Append("\r\n检验t值: ");
for (int i = 0; i < InputColsCount + 1; i++)
{
Result.Append(MathV.NumberPolish(value_beta_ln[i, 1].ToString()));
Result.Append(" \t");
}
Result.Append("\r\n\r\n");
}
else
{
Result.Append("矩阵不可逆，回归方程无解 \r\n");
}
}
}
//#自相关(3阶内） ：
Result.Append("5.自相关检验：拉格朗日乘子(LM) \r\n");
BigDecimal[,] u_hat_Auto = new BigDecimal[count - 3, 1];
BigDecimal[,] indep_Auto = new BigDecimal[count - 3, InputColsCount + 4];
for (int i = 0; i < count - 3; i++)
{
u_hat_Auto[i, 0] = u_hat[i + 3, 0];
for (int j = 0; j < InputColsCount + 1; j++)
{
indep_Auto[i, j] = IndependentVariables[i + 3, j];
}
indep_Auto[i, InputColsCount + 1] = u_hat[i + 2, 0];
indep_Auto[i, InputColsCount + 2] = u_hat[i + 1, 0];
indep_Auto[i, InputColsCount + 3] = u_hat[i, 0];
}
BigDecimal[,] b1_Auto = MathV.MatTrans(indep_Auto);
BigDecimal[,] b2_Auto = MathV.MatTimes(b1_Auto, indep_Auto);
BigDecimal[,] b3_Auto = MathV.MatInv(b2_Auto, InputColsCount + 4);
if (b3_Auto != null)
{
BigDecimal[,] bhat_Auto = Stat.MultiRegBeta(b1_Auto, b3_Auto, indep_Auto, u_hat_Auto);
string[] RandF_Auto = Stat.MultiRegR(bhat_Auto, indep_Auto, u_hat_Auto).Split(separator);
BigDecimal LM_Auto = (count - 3) * Convert.ToDouble(RandF_Auto[0].ToString());
BigDecimal P_Auto = 1 - Stat.chi2(Convert.ToDouble(LM_Auto.ToString()), 3);
Result.Append(" LM统计量 \t");
Result.Append(" =\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(LM_Auto.ToString()), ' ', 12));
Result.Append("\r\n");
Result.Append(" LM > chi(2)\t");
Result.Append(" =\t");
Result.Append(StrManipulation.PadLeftX(MathV.NumberPolish(P_Auto.ToString()), ' ', 12));
Result.Append("\r\n");
if (P_Auto <= 0.05)
{
Result.Append(" 在95%的置信水平下拒绝原假设，认为模型序列存在自相关\r\n \r\n");
}
else
{
Result.Append(" 在95%的置信水平下接受原假设，认为模型序列不存在自相关\r\n \r\n");
}
}
MainForm.S.richTextBox1.AppendText(Result.ToString());
}
}
}
}