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

Introduction

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

Prototype

double inverseCumulativeProbability(double p) throws MathException;

Source Link

Document

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

Usage

From source file:de.tud.kom.p2psim.impl.util.stats.ConfidenceInterval.java

/**
 * Returns the delta between the mean and the lower(x1)/upper(x2) bound as
 * positive number. That is, the probabilistic bounds of x1 and x2 are given
 * by x1 <= mean <= x2 <=> mean-delta <= mean <= mean + delta
 * /*  w w  w.j  a v  a 2 s .co  m*/
 * @param sdev
 *            the given standard deviation
 * @param n
 *            the given sample size
 * @param alpha
 *            the given significance level
 * @return the upper/lower bound as positiv number
 */
public static double getDeltaBound(double sdev, int n, double alpha) {
    TDistribution tDist = DistributionFactory.newInstance().createTDistribution(n - 1);
    double errorConfCoeff = 1d - (alpha / 2);
    double delta;
    try {
        double t = Math.abs(tDist.inverseCumulativeProbability(errorConfCoeff));
        delta = t * sdev / Math.sqrt(n);
    } catch (MathException e) {
        throw new IllegalStateException(e);
    }
    return delta;
}

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From source file:geogebra.kernel.statistics.AlgoInverseTDistribution.java

protected final void compute() {

    if (input[0].isDefined() && input[1].isDefined()) {
        double param = a.getDouble();
        double val = b.getDouble();
        try {/*from  w  ww  . j a  v  a2 s . com*/
            TDistribution t = getTDistribution(param);
            num.setValue(t.inverseCumulativeProbability(val));

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

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From source file:geogebra.common.kernel.statistics.AlgoInverseTDistribution.java

@Override
public final void compute() {

    if (input[0].isDefined() && input[1].isDefined()) {
        double param = a.getDouble();
        double val = b.getDouble();
        try {//from  www .j  a  v a2s . com
            TDistribution t = getTDistribution(param);
            num.setValue(t.inverseCumulativeProbability(val));

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

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From source file:org.peerfact.impl.util.stats.ConfidenceInterval.java

/**
 * Returns the delta between the mean and the lower(x1)/upper(x2) bound as
 * positive number. That is, the probabilistic bounds of x1 and x2 are given
 * by x1 <= mean <= x2 <=> mean-delta <= mean <= mean + delta
 * /*from   w w w  .  j a v a 2 s  . c  o m*/
 * @param sdev
 *            the given standard deviation
 * @param n
 *            the given sample size
 * @param alpha
 *            the given significance level
 * @return the upper/lower bound as positive number
 */
public static double getDeltaBound(double sdev, int n, double alpha) {
    TDistribution tDist = new TDistributionImpl(n - 1);
    double errorConfCoeff = 1d - (alpha / 2);
    double delta;
    try {
        double t = Math.abs(tDist.inverseCumulativeProbability(errorConfCoeff));
        delta = t * sdev / Math.sqrt(n);
    } catch (MathException e) {
        throw new IllegalStateException(e);
    }
    return delta;
}

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From source file:weka.classifiers.functions.SimpleLinearRegressionIntervalEstimator.java

/**
 * Returns an N * 2 array, where N is the number of prediction
 * intervals. In each row, the first element contains the lower
 * boundary of the corresponding prediction interval and the second
 * element the upper boundary.//from   w  ww  .  j  a v  a2 s  . co m
 *
 * @param inst the instance to make the prediction for.
 * @param confidenceLevel the percentage of cases that the interval should cover.
 * @return an array of prediction intervals
 * @throws Exception if the intervals can't be computed
 */
@Override
public double[][] predictIntervals(Instance inst, double confidenceLevel) throws Exception {
    double alpha;
    double critProb;
    TDistribution td;
    double critValue;
    double marginError;

    if (m_df < 1)
        throw new WekaException("No degrees of freedom!");

    alpha = 1 - confidenceLevel;
    critProb = 1 - alpha / 2;
    td = new TDistributionImpl(m_df);
    critValue = td.inverseCumulativeProbability(critProb);
    marginError = critValue * getSlopeSE();

    return new double[][] { { getSlope() - marginError, getSlope() + marginError } };
}

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