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

    /**
     * Verify that direct t-tests using standard error estimates are consistent
     * with reported p-values
     */
    public void testStdErrorConsistency() throws Exception {
        TDistribution tDistribution = new TDistributionImpl(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 = 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);
            }
        }
    }

     *
     * @return matrix of p-values
     * @throws MathException if an error occurs estimating probabilities
     */
    public RealMatrix getCorrelationPValues() throws MathException {
        TDistribution tDistribution = new TDistributionImpl(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);
    }

    //EXCEL: TDIST(x,degrees_freedom,tails)
    double x = CommonFns.toNumber(args[0]).doubleValue();
    double degree_freedom = CommonFns.toNumber(args[1]).doubleValue();
    int tails = CommonFns.toNumber(args[2]).intValue();
    DistributionFactory factory = DistributionFactory.newInstance();
    TDistribution td = factory.createTDistribution(degree_freedom );
    return UtilFns.validateNumber(1 - td.cumulativeProbability(x));
  }

   */
  public static Object statTinv(Object[] args, XelContext ctx) throws MathException{
    double p = CommonFns.toNumber(args[0]).doubleValue();
    double degree_freedom = CommonFns.toNumber(args[1]).doubleValue();
    DistributionFactory factory = DistributionFactory.newInstance();
    TDistribution td = factory.createTDistribution(degree_freedom );
    return UtilFns.validateNumber(td.inverseCumulativeProbability((1-p)));
  }

     * @throws MathException if an error occurs computing the p-value
     */
    protected double tTest(double m, double mu, double v, double n)
    throws MathException {
        double t = Math.abs(t(m, mu, v, n));
        TDistribution tDistribution = 
            getDistributionFactory().createTDistribution(n - 1);
        return 1.0 - tDistribution.cumulativeProbability(-t, t);
    }

            double n1, double n2)
    throws MathException {
        double t = Math.abs(t(m1, m2, v1, v2, n1, n2));
        double degreesOfFreedom = 0;
        degreesOfFreedom= df(v1, v2, n1, n2);
        TDistribution tDistribution =
            getDistributionFactory().createTDistribution(degreesOfFreedom);
        return 1.0 - tDistribution.cumulativeProbability(-t, t);
    }

            double v2, double n1, double n2)
    throws MathException {
        double t = Math.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
        double degreesOfFreedom = 0;
            degreesOfFreedom = (double) (n1 + n2 - 2);
        TDistribution tDistribution =
            getDistributionFactory().createTDistribution(degreesOfFreedom);
        return 1.0 - tDistribution.cumulativeProbability(-t, t);
    }

    /**
     * Verify that direct t-tests using standard error estimates are consistent
     * with reported p-values
     */
    public void testStdErrorConsistency() throws Exception {
        TDistribution tDistribution = new TDistributionImpl(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));
                assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

     * 
     * @return matrix of p-values
     * @throws MathException if an error occurs estimating probabilities
     */
    public RealMatrix getCorrelationPValues() throws MathException {
        TDistribution tDistribution = new TDistributionImpl(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 = Math.abs(r * Math.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * (1 - tDistribution.cumulativeProbability(t));
                }
            }
        }
        return new BlockRealMatrix(out);
    }

    /**
     * Verify that direct t-tests using standard error estimates are consistent
     * with reported p-values
     */
    public void testStdErrorConsistency() throws Exception {
        TDistribution tDistribution = new TDistributionImpl(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 = 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);
            }
        }
    }

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

Related Classes of org.apache.commons.math.distribution.TDistribution