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

Introduction

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

Prototype

public double cumulativeProbability(double x) throws MathException 

Source Link

Document

For this distribution, X, this method returns P(X < x).

Usage

From source file:dr.evomodel.epidemiology.casetocase.periodpriors.NormalPeriodPriorDistribution.java

public double calculateLogPosteriorPredictiveCDF(double value, boolean upperTail) {
    double mean = currentParameters[0];
    double sd = Math.sqrt(
            currentParameters[3] * (currentParameters[1] + 1) / (currentParameters[2] * currentParameters[1]));
    double scaledValue = (value - mean) / sd;
    double out;//from   ww w.j  av  a 2  s  . co m

    if (2 * currentParameters[2] <= normalApproximationThreshold) {
        TDistributionImpl tDist = new TDistributionImpl(2 * currentParameters[2]);

        try {
            out = upperTail ? Math.log(tDist.cumulativeProbability(-scaledValue))
                    : Math.log(tDist.cumulativeProbability(scaledValue));
        } catch (MathException e) {
            throw new RuntimeException(e.toString());
        }

    } else {

        out = upperTail ? NormalDistribution.standardCDF(-scaledValue, true)
                : NormalDistribution.standardCDF(scaledValue, true);

    }
    return out;
}

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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   ww w  .  j  av  a 2  s. c om*/
        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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