org.drugis.mtc.summary.QuantileSummary.java Source code

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Here is the source code for org.drugis.mtc.summary.QuantileSummary.java

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/*
 * This file is part of the GeMTC software for MTC model generation and
 * analysis. GeMTC is distributed from http://drugis.org/gemtc.
 * Copyright (C) 2009-2012 Gert van Valkenhoef.
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

package org.drugis.mtc.summary;

import java.util.List;

import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.drugis.common.beans.AbstractObservable;
import org.drugis.mtc.MCMCResults;
import org.drugis.mtc.MCMCResultsEvent;
import org.drugis.mtc.MCMCResultsListener;
import org.drugis.mtc.Parameter;

/**
 * Summary of data based on sample quantiles. A list (p_1, ..., p_n) of 
 * probabilities in [0, 1] is given, and the summary will estimate quantiles
 * that correspond to these probabilities. 
 * Note that the quantiles correspond to "type 6" quantiles in R.
 */
public class QuantileSummary extends AbstractObservable implements MCMCResultsListener, Summary {
    private static final double[] DEFAULT_PROBABILITIES = new double[] { 0.025, 0.5, 0.975 };
    private double[] d_probabilities;
    private Parameter d_parameter;
    private MCMCResults d_results;
    private double[] d_quantiles;
    private boolean d_defined = false;

    public QuantileSummary(MCMCResults results, Parameter parameter, double[] probabilities) {
        d_probabilities = probabilities;
        d_results = results;
        d_parameter = parameter;
        d_results.addResultsListener(this);
        calculateResults();
    }

    public QuantileSummary(double[] probabilities, double[] quantiles) {
        int length = probabilities.length;
        d_probabilities = new double[length];
        d_quantiles = new double[length];
        System.arraycopy(probabilities, 0, d_probabilities, 0, length);
        System.arraycopy(quantiles, 0, d_quantiles, 0, length);
        d_defined = true;
    }

    /**
     * Default probabilities (0.025, 0.5, 0.975): median and 95% interval.
     */
    public QuantileSummary(MCMCResults results, Parameter parameter) {
        this(results, parameter, DEFAULT_PROBABILITIES);
    }

    public void resultsEvent(MCMCResultsEvent event) {
        calculateResults();
    }

    /**
     * Get the index at which the quantile with probability p is stored.
     * @return The index, or -1 if p is not a calculated quantile.
     */
    public int indexOf(double p) {
        for (int i = 0; i < d_probabilities.length; i++) {
            if (d_probabilities[i] == p) {
                return i;
            }
        }
        return -1;
    }

    /**
     * Return the probability for which the quantile is stored at index idx.
     */
    public double getProbability(int idx) {
        return d_probabilities[idx];
    }

    /**
     * Return the quantile stored at index idx.
     */
    public double getQuantile(int idx) {
        return d_quantiles[idx];
    }

    public int getSize() {
        return (d_quantiles == null) ? 0 : d_quantiles.length;
    }

    private double calculateQuantile(int i, Percentile q) {
        double p = getProbability(i);
        return q.evaluate(p * 100);
    }

    private Percentile getSamples() {
        List<Double> list = SummaryUtil.getAllChainsLastHalfSamples(d_results, d_parameter);
        double[] arr = new double[list.size()];
        for (int i = 0; i < list.size(); ++i) {
            arr[i] = list.get(i);
        }

        Percentile percentile = new Percentile();
        percentile.setData(arr);
        return percentile;
    }

    private synchronized void calculateResults() {
        if (!isReady())
            return;
        Percentile samples = getSamples();
        d_quantiles = new double[d_probabilities.length];
        for (int i = 0; i < d_quantiles.length; i++) {
            d_quantiles[i] = calculateQuantile(i, samples);
        }

        d_defined = true;
        firePropertyChange(PROPERTY_DEFINED, null, d_defined);
    }

    private boolean isReady() {
        if (d_results.getNumberOfSamples() > 0) {
            return true;
        } else {
            return false;
        }
    }

    public boolean getDefined() {
        return d_defined;
    }
}