org.apache.commons.math4.stat.interval.ClopperPearsonInterval Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of virtdata-lib-curves4 Show documentation

Statistical sampling library for use in virtdata libraries, based on apache commons math 4

There is a newer version: 5.17.0

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math4.stat.interval;

import org.apache.commons.statistics.distribution.FDistribution;

/**
 * Implements the Clopper-Pearson method for creating a binomial proportion confidence interval.
 *
 * @see 
 *      Clopper-Pearson interval (Wikipedia)
 * @since 3.3
 */
public class ClopperPearsonInterval implements BinomialConfidenceInterval {

    /** {@inheritDoc} */
    @Override
    public ConfidenceInterval createInterval(int numberOfTrials,
                                             int numberOfSuccesses,
                                             double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        double lowerBound = 0;
        double upperBound = 0;

        if (numberOfSuccesses > 0) {
            final double alpha = 0.5 * (1 - confidenceLevel);

            final FDistribution distributionLowerBound = new FDistribution(2 * (numberOfTrials - numberOfSuccesses + 1),
                                                                           2 * numberOfSuccesses);
            final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha);
            lowerBound = numberOfSuccesses /
                (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);

            if (numberOfSuccesses != numberOfTrials) {
                final FDistribution distributionUpperBound = new FDistribution(2 * (numberOfSuccesses + 1),
                                                                               2 * (numberOfTrials - numberOfSuccesses));
                final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha);
                upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                    (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound);
            } else {
                upperBound = 1;
            }
        }

        return new ConfidenceInterval(lowerBound, upperBound, confidenceLevel);
    }
}