com.itemanalysis.psychometrics.reliability.AbstractScoreReliability.java Source code

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Here is the source code for com.itemanalysis.psychometrics.reliability.AbstractScoreReliability.java

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/**
 * Copyright 2014 J. Patrick Meyer
 * <p/>
 * Licensed 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
 * <p/>
 * http://www.apache.org/licenses/LICENSE-2.0
 * <p/>
 * 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 com.itemanalysis.psychometrics.reliability;

import com.itemanalysis.psychometrics.polycor.CovarianceMatrix;
import org.apache.commons.math3.distribution.FDistribution;

import java.util.Formatter;

/**
 * Methods implemented by all instances of ScoreReliability.
 *
 */
public abstract class AbstractScoreReliability implements ScoreReliability, Comparable<ScoreReliability> {

    protected CovarianceMatrix matrix = null;

    protected boolean unbiased = false;

    protected int nItems = 0;

    public double totalVariance() {
        return matrix.totalVariance(unbiased);
    }

    public void isUnbiased(boolean unbiased) {
        this.unbiased = unbiased;
    }

    public double[] confidenceInterval() {
        double numberOfExaminees = matrix.getMaxSampleSize();
        double[] confidenceInterval = new double[2];
        double numberOfItems = (double) nItems;
        double df1 = numberOfExaminees - 1.0;
        double df2 = (numberOfExaminees - 1.0) * (numberOfItems - 1.0);
        FDistribution fDist = new FDistribution(df1, df2);
        try {
            confidenceInterval[0] = 1.0 - ((1.0 - this.value()) * fDist.inverseCumulativeProbability(0.975));
            confidenceInterval[1] = 1.0 - ((1.0 - this.value()) * fDist.inverseCumulativeProbability(0.025));
        } catch (Exception ex) {
            confidenceInterval[0] = Double.NaN;
            confidenceInterval[1] = Double.NaN;
        }
        return confidenceInterval;
    }

    public String confidenceIntervalToString(double[] confidenceInterval) {
        StringBuilder builder = new StringBuilder();
        Formatter f = new Formatter(builder);
        f.format("(%6.4f, ", confidenceInterval[0]);
        f.format("%6.4f)", confidenceInterval[1]);
        return f.toString();
    }

    public int compareTo(ScoreReliability that) {
        double v1 = this.value();
        double v2 = that.value();
        if (v1 > v2)
            return 1;
        if (v1 < v2)
            return -1;
        return 0;
    }

    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;
        if (obj instanceof ScoreReliability) {
            return compareTo((ScoreReliability) obj) == 0;
        } else {
            return false;
        }
    }

    public int hashCode() {
        Double v = new Double(value());
        return v.hashCode();
    }

}