Examples of org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()

Package org.apache.commons.math.distribution

Class org.apache.commons.math.distribution.TDistributionImpl

Examples of org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()

org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()
For this distribution, X, this method returns P(X < x). @param x the value at which the CDF is evaluated. @return CDF evaluated at x. @throws MathException if the cumulative probability can not becomputed due to convergence or other numerical errors.

        RealMatrix pValues = corrInstance.getCorrelationPValues();
        RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
        for (int i = 0; i < 5; i++) {
            for (int j = 0; j < i; j++) {
                double t = Math.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

View Full Code Here

                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = FastMath.abs(r * FastMath.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * tDistribution.cumulativeProbability(-t);
                }
            }
        }
        return new BlockRealMatrix(out);
    }

View Full Code Here

        RealMatrix pValues = corrInstance.getCorrelationPValues();
        RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
        for (int i = 0; i < 5; i++) {
            for (int j = 0; j < i; j++) {
                double t = FastMath.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

View Full Code Here

                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = Math.abs(r * Math.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * (1 - tDistribution.cumulativeProbability(t));
                }
            }
        }
        return new BlockRealMatrix(out);
    }

View Full Code Here

        RealMatrix pValues = corrInstance.getCorrelationPValues();
        RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
        for (int i = 0; i < 5; i++) {
            for (int j = 0; j < i; j++) {
                double t = Math.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

View Full Code Here

  public static Double tDistCDF(double x, int dof)
  {
    TDistributionImpl tdist = new TDistributionImpl(dof);
    try
    {
      return tdist.cumulativeProbability(x);
    }
    catch (MathException e)
    {
      e.printStackTrace();
    }

View Full Code Here

                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = Math.abs(r * Math.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * (1 - tDistribution.cumulativeProbability(t));
                }
            }
        }
        return new BlockRealMatrix(out);
    }

View Full Code Here

    final ContinuousDistribution studentT = new TDistributionImpl(n - k);
    for (int i = 0; i < k; i++) {
      standardErrorsOfBeta[i] = Math.sqrt(meanSquareError * covarianceBetas[i][i]);
      tStats[i] = betas[i] / standardErrorsOfBeta[i];
      try {
        pValues[i] = 1 - studentT.cumulativeProbability(Math.abs(tStats[i]));
      } catch (final org.apache.commons.math.MathException e) {
        throw new com.opengamma.analytics.math.MathException(e);
      }
    }
    return new WeightedLeastSquaresRegressionResult(betas, residuals, meanSquareError, standardErrorsOfBeta, rSquared, adjustedRSquared, tStats, pValues,

View Full Code Here

    // StudentTDistribution(n - k);
    for (int i = 0; i < k; i++) {
      stdErrorBetas[i] = Math.sqrt(meanSquareError * covarianceBetas[i][i]);
      tStats[i] = betas[i] / stdErrorBetas[i];
      try {
        pValues[i] = 1 - studentT.cumulativeProbability(Math.abs(tStats[i]));
      } catch (final org.apache.commons.math.MathException e) {
        throw new com.opengamma.analytics.math.MathException(e);
      }
    }
    return new LeastSquaresRegressionResult(betas, residuals, meanSquareError, stdErrorBetas, rSquared, adjustedRSquared, tStats, pValues, useIntercept);

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.