private void CalcRegressionCoefficientsSignificance()
{
double[] sb = new double[parametersCount];
var temperory = MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(MatrixFunction.TransposeMatrix(xMatrix), xMatrix));
for (int j = 0; j < parametersCount; j++)
{
sb[j] = ResidualDispersion * temperory[j][j];
}
RegressionCoefficientsSignificance = new double[parametersCount];
for (int i = 0; i < parametersCount; ++i)
{
RegressionCoefficientsSignificance[i] = RegressionCoefficients[i] / sb[i];
}
}
private void CalcConfidenceIntervalsOfCoefficients()
{
double[] sb = new double[parametersCount];
var temperory = MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(MatrixFunction.TransposeMatrix(xMatrix), xMatrix));
for (int j = 0; j < parametersCount; j++)
{
sb[j] = ResidualDispersion * temperory[j][j];
}
double t = 4.587;
ConfidenceIntervalsOfCoefficients = new Tuple <double, double> [parametersCount];
for (int i = 0; i < parametersCount; ++i)
{
ConfidenceIntervalsOfCoefficients[i] = new Tuple <double, double>(RegressionCoefficients[i] - t * sb[i], RegressionCoefficients[i] + t * sb[i]);
}
}
private void CalcRegressionCoefficients()
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
int parametersCount = dataMatrix[0].Length, measuresCount = dataMatrix.Length;
double[] resultParameter = transpMatrix[resultParameterNumber];
List <double>[] withoutResultParameter = new List <double> [measuresCount];
for (int i = 0; i < measuresCount; i++)
{
withoutResultParameter[i] = new List <double>();
withoutResultParameter[i].Add(1);
for (int j = 0; j < parametersCount; ++j)
{
if (j != resultParameterNumber)
{
withoutResultParameter[i].Add(dataMatrix[i][j]);
}
}
}
xMatrix = withoutResultParameter.Select(o => o.ToArray()).ToArray();
var transpWithoutMatrix = MatrixFunction.TransposeMatrix(xMatrix);
RegressionCoefficients = MatrixFunction.MatrixVectorMultiplication(MatrixFunction.MultiplicateMatrix(MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix)), transpWithoutMatrix), resultParameter);
RegressionCoefficients = RegressionCoefficients.Take(parametersCount).ToArray();
}
private void Prognoz()
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
int parametersCount = dataMatrix[0].Length, measuresCount = dataMatrix.Length;
double[] resultParameter = transpMatrix[resultParameterNumber];
List <double>[] withoutResultParameter = new List <double> [measuresCount];
for (int i = 0; i < measuresCount; i++)
{
withoutResultParameter[i] = new List <double>();
withoutResultParameter[i].Add(1);
for (int j = 0; j < parametersCount; ++j)
{
if (j != resultParameterNumber)
{
withoutResultParameter[i].Add(dataMatrix[i][j]);
}
}
}
var xMatrix = withoutResultParameter.Select(o => o.ToArray()).ToArray();
var transpWithoutMatrix = MatrixFunction.TransposeMatrix(xMatrix);
RegressionAnalysis ra = new RegressionAnalysis(dataMatrix, resultParameterNumber);
var result = ra.RegressionCoefficients;
double s = ra.ResidualDispersion;
double t = 4.587;
double[] a = new double[2];
a[0] = MatrixFunction.ScalarProductOfVectors(x0, result) - t * s * Math.Sqrt(MatrixFunction.ScalarProductOfVectors(MatrixFunction.TransposeMatrixVectorProduct(x0, MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix))), x0));
a[1] = MatrixFunction.ScalarProductOfVectors(x0, result) + t * s * Math.Sqrt(MatrixFunction.ScalarProductOfVectors(MatrixFunction.TransposeMatrixVectorProduct(x0, MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix))), x0));
Value = a;
}