public double GetSignificance()
{
if (this.n < 3L)
{
return(Double.NaN);
}
TDistribution distribution = new TDistribution(this.n - 2L);
return(2.0D * (1.0D - distribution.cumulativeProbability(Math.Abs(GetSlope()) / GetSlopeStdErr())));
}
private LeastSquaresRegressionResult getResultWithStatistics(double[][] x, double[] y, double[] betas, double[] yModel, DoubleMatrix transpose, DoubleMatrix matrix, bool useIntercept)
{
double yMean = 0.0;
foreach (double y1 in y)
{
yMean += y1;
}
yMean /= y.Length;
double totalSumOfSquares = 0.0;
double errorSumOfSquares = 0.0;
int n = x.Length;
int k = betas.Length;
double[] residuals = new double[n];
double[] stdErrorBetas = new double[k];
double[] tStats = new double[k];
double[] pValues = new double[k];
for (int i = 0; i < n; i++)
{
totalSumOfSquares += (y[i] - yMean) * (y[i] - yMean);
residuals[i] = y[i] - yModel[i];
errorSumOfSquares += residuals[i] * residuals[i];
}
double regressionSumOfSquares = totalSumOfSquares - errorSumOfSquares;
double[][] covarianceBetas = convertArray(_algebra.getInverse(_algebra.multiply(transpose, matrix)).toArray());
double rSquared = regressionSumOfSquares / totalSumOfSquares;
double adjustedRSquared = 1.0 - (1 - rSquared) * (n - 1.0) / (n - k);
double meanSquareError = errorSumOfSquares / (n - k);
TDistribution studentT = new TDistribution(n - k);
for (int i = 0; i < k; i++)
{
stdErrorBetas[i] = Math.Sqrt(meanSquareError * covarianceBetas[i][i]);
tStats[i] = betas[i] / stdErrorBetas[i];
pValues[i] = 1 - studentT.cumulativeProbability(Math.Abs(tStats[i]));
}
return(new LeastSquaresRegressionResult(betas, residuals, meanSquareError, stdErrorBetas, rSquared, adjustedRSquared, tStats, pValues, useIntercept));
}
public RealMatrix GetCorrelationPValues()
{
TDistribution tDistribution = new TDistribution(this.nObs - 2);
int nVars = this.correlationMatrix.getColumnDimension();
double[][] outVar = new double[nVars][];
for (int i = 0; i < nVars; i++)
{
for (int j = 0; j < nVars; j++)
{
if (i == j)
{
outVar[i][j] = 0.0D;
}
else
{
double r = this.correlationMatrix.getEntry(i, j);
double t = Math.Abs(r * Math.Sqrt((this.nObs - 2) / (1.0D - r * r)));
outVar[i][j] = (2.0D * tDistribution.cumulativeProbability(-t));
}
}
}
return(new BlockRealMatrix(outVar));
}