Examples of TDistribution

    protected double tTest(final double m, final double mu,
                           final double v, final double n)
        throws MaxCountExceededException, MathIllegalArgumentException {


        double t = FastMath.abs(t(m, mu, v, n));
        TDistribution distribution = new TDistribution(n - 1);
        return 2.0 * distribution.cumulativeProbability(-t);


    }

Examples of org.apache.commons.math3.distribution.TDistribution

                           final double n1, final double n2)
        throws MaxCountExceededException, NotStrictlyPositiveException {


        final double t = FastMath.abs(t(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = df(v1, v2, n1, n2);
        TDistribution distribution = new TDistribution(degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }

Examples of org.apache.commons.math3.distribution.TDistribution

                                        double n1, double n2)
        throws MaxCountExceededException, NotStrictlyPositiveException {


        final double t = FastMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = n1 + n2 - 2;
        TDistribution distribution = new TDistribution(degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }

Examples of org.apache.commons.math3.distribution.TDistribution

     * @return matrix of p-values
     * @throws org.apache.commons.math3.exception.MaxCountExceededException
     * if an error occurs estimating probabilities
     */
    public RealMatrix getCorrelationPValues() {
        TDistribution tDistribution = new TDistribution(nObs - 2);
        int nVars = correlationMatrix.getColumnDimension();
        double[][] out = new double[nVars][nVars];
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < nVars; j++) {
                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);
    }

Examples of org.apache.commons.math3.distribution.TDistribution

     * @param df the degrees of freedom of the T distribution
     * @return random value from the T(df) distribution
     * @throws NotStrictlyPositiveException if {@code df <= 0}
     */
    public double nextT(double df) throws NotStrictlyPositiveException {
        return new TDistribution(getRan(), df,
                TDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample();
    }

Examples of org.apache.commons.math3.distribution.TDistribution

        if (alpha >= 1 || alpha <= 0) {
            throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL,
                                          alpha, 0, 1);
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return getSlopeStdErr() *
            distribution.inverseCumulativeProbability(1d - alpha / 2d);
    }

Examples of org.apache.commons.math3.distribution.TDistribution

    public double getSignificance() {
        if (n < 3) {
            return Double.NaN;
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return 2d * (1.0 - distribution.cumulativeProbability(
                    FastMath.abs(getSlope()) / getSlopeStdErr()));
    }

Examples of org.apache.commons.math3.distribution.TDistribution

     * Verify that direct t-tests using standard error estimates are consistent
     * with reported p-values
     */
    @Test
    public void testStdErrorConsistency() {
        TDistribution tDistribution = new TDistribution(45);
        RealMatrix matrix = createRealMatrix(swissData, 47, 5);
        PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
        RealMatrix rValues = corrInstance.getCorrelationMatrix();
        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));
                Assert.assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

Examples of org.apache.commons.math3.distribution.TDistribution

        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }


    @Test
    public void testNextT() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new TDistribution(10));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextT(10);
            TestUtils.updateCounts(value, counts, quartiles);

Examples of org.apache.commons.math3.distribution.TDistribution


        // now compare against reference distributions to test accuracy of the observed step distributions
        NormalDistribution normalDistribution = new NormalDistribution();
        GammaDistribution gd1 = new GammaDistribution(0.2, 5);
        GammaDistribution gd2 = new GammaDistribution(1, 1);
        TDistribution tDistribution = new TDistribution(2);
        for (double q : new double[]{0.001, 0.01, 0.1, 0.2, 0.5, 0.8, 0.9, 0.99, 0.99}) {
            double uG1 = gd1.cumulativeProbability(tdG1.quantile(q));
            assertEquals(q, uG1, (1 - q) * q * 10e-2);


            double uG2 = gd2.cumulativeProbability(tdG2.quantile(q));
            assertEquals(q, uG2, (1 - q) * q * 10e-2);


            double u1 = normalDistribution.cumulativeProbability(td1.quantile(q));
            assertEquals(q, u1, (1 - q) * q * 10e-2);


            double u2 = normalDistribution.cumulativeProbability(td2.quantile(q) / 2);
            assertEquals(q, u2, (1 - q) * q * 10e-2);


            double u3 = tDistribution.cumulativeProbability(td3.quantile(q));
            assertEquals(q, u3, (1 - q) * q * 10e-2);
        }
    }