Example usage for org.apache.commons.math.distribution TDistributionImpl inverseCumulativeProbability

Introduction

In this page you can find the example usage for org.apache.commons.math.distribution TDistributionImpl inverseCumulativeProbability.

Prototype

@Override
public double inverseCumulativeProbability(final double p) throws MathException 

Source Link

Document

For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

Usage

From source file:boa.aggregators.StatisticsAggregator.java

/** {@inheritDoc} */
@Override/*from w  w w.  j ava 2  s.  co  m*/
public void finish() throws IOException, InterruptedException {
    if (this.isCombining()) {
        String s = "";
        for (final Long key : map.keySet())
            s += key + ":" + map.get(key) + ";";
        this.collect(s, null);
        return;
    }

    float median = 0;

    long medianPos = count / 2L;
    long curPos = 0;
    long prevPos = 0;
    long prevKey = 0;

    for (final Long key : map.keySet()) {
        curPos = prevPos + map.get(key);

        if (prevPos <= medianPos && medianPos < curPos) {
            if (curPos % 2 == 0 && prevPos == medianPos)
                median = (float) (key + prevKey) / 2.0f;
            else
                median = key;
            break;
        }

        prevKey = key;
        prevPos = curPos;
    }

    double s1 = 0;
    double s2 = 0;
    double s3 = 0;
    double s4 = 0;

    final SummaryStatistics summaryStatistics = new SummaryStatistics();

    for (final Long key : map.keySet()) {
        s1 += key * map.get(key);
        s2 += key * key * map.get(key);
        s3 += key * key * key * map.get(key);
        s4 += key * key * key * key * map.get(key);
        for (int i = 0; i < map.get(key); i++)
            summaryStatistics.addValue(key);
    }

    final double mean = s1 / (double) count;
    final double var = s2 / (double) (count - 1) - s1 * s1 / (double) (count * (count - 1));
    final double stdev = Math.sqrt(var);
    final double skewness = (s3 - 3 * s1 * s2 / (double) count + s1 * s1 * s1 * 2 / (count * count))
            / (count * stdev * var);
    final double kurtosis = (s4 - s3 * s1 * 4 / count + s2 * s1 * s1 * 6 / (double) (count * count)
            - s1 * s1 * s1 * s1 * 3 / (double) (count * count * count)) / (count * var * var);

    double ci = 0.0;
    try {
        final TDistributionImpl tDist = new TDistributionImpl(summaryStatistics.getN() - 1);
        final double a = tDist.inverseCumulativeProbability(1.0 - 0.025);
        ci = a * summaryStatistics.getStandardDeviation() / Math.sqrt(summaryStatistics.getN());
    } catch (final MathException e) {
    }

    this.collect(s1 + ", " + mean + ", " + median + ", " + stdev + ", " + var + ", " + kurtosis + ", "
            + skewness + ", " + ci);
}

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From source file:org.apache.mahout.freqtermsets.fpgrowth.FPGrowth.java

private double getConfidenceIntervalHalfWidth(SummaryStatistics summaryStatistics, double significance)
        throws MathException {
    TDistributionImpl tDist = new TDistributionImpl(summaryStatistics.getN() - 1);
    double a = tDist.inverseCumulativeProbability(1.0 - significance / 2);
    return a * summaryStatistics.getStandardDeviation() / Math.sqrt(summaryStatistics.getN());
}

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From source file:org.gwaspi.statistics.ChiSqrBoundaryCalculator.java

protected static void calculateChisqrBoundaryByFormula() throws IOException, MathException {

    FileWriter repFW = new FileWriter(boundaryPath);
    BufferedWriter repBW = new BufferedWriter(repFW);

    NetcdfFile ncfile = NetcdfFile.open(netCDFFile);
    List<Dimension> dims = ncfile.getDimensions();
    Dimension sizeDim = dims.get(0);
    Dimension simsDim = dims.get(1);

    String varName = "distributions";
    Variable distributions = ncfile.findVariable(varName);

    try {//from  www  .j  a  va  2  s.  co  m
        for (int i = 0; i < pointsNb; i++) {
            //distributions(i:i:1, 0:simsNb:1)
            ArrayDouble.D2 rdDoubleArrayD2 = (ArrayDouble.D2) distributions
                    .read(i + ":" + i + ":1, 0:" + (simsDim.getLength() - 1) + ":1");
            ArrayDouble.D1 rdDoubleArrayD1 = (D1) rdDoubleArrayD2.reduce();

            double sampleSize = rdDoubleArrayD2.getSize();
            double currentTot = 0;

            double[] allValues = new double[(int) sampleSize];
            for (int j = 0; j < sampleSize; j++) {
                allValues[j] = rdDoubleArrayD1.get(j);
                currentTot += rdDoubleArrayD1.get(j);
            }

            StandardDeviation stdDev = new StandardDeviation();
            double stdDevValue = stdDev.evaluate(allValues);

            double currentAvg = currentTot / simNb;

            TDistributionImpl tDistImpl = new TDistributionImpl(sampleSize - 1);
            double tInvCumulProb = tDistImpl.inverseCumulativeProbability(0.05d);
            double tCumulProb = tDistImpl.cumulativeProbability(0.05d);

            //            confidenceInterval = (STDEV(Ys) / SQRT(COUNT(Ys))) * TINV(0.05, COUNT(Ys) - 1)

            double confidenceInterval = (stdDevValue / Math.sqrt(sampleSize)) * tInvCumulProb;

            double low95 = currentAvg - confidenceInterval;
            double top95 = currentAvg + confidenceInterval;

            StringBuilder sb = new StringBuilder();
            sb.append(top95);
            sb.append(",");
            sb.append(currentAvg);
            sb.append(",");
            sb.append(low95);
            repBW.append(sb + "\n");
        }
    } catch (IOException ex) {
        log.error("Cannot read data", ex);
    } catch (InvalidRangeException ex) {
        log.error("Cannot read data", ex);
    }

    repBW.close();
    repFW.close();

    log.info("Confidence boundary created for {} points", N);
}

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