Java StatUtils.meanDifference方法代码示例

import org.apache.commons.math3.stat.StatUtils; //导入方法依赖的package包/类
public static double meanDifference(FloatColumn column1, FloatColumn column2) {
    return StatUtils.meanDifference(column1.toDoubleArray(), column2.toDoubleArray());
}
 

import org.apache.commons.math3.stat.StatUtils; //导入方法依赖的package包/类
/**
 * Computes a paired, 2-sample t-statistic based on the data in the input
 * arrays.  The t-statistic returned is equivalent to what would be returned by
 * computing the one-sample t-statistic {@link #t(double, double[])}, with
 * <code>mu = 0</code> and the sample array consisting of the (signed)
 * differences between corresponding entries in <code>sample1</code> and
 * <code>sample2.</code>
 * <p>
 * <strong>Preconditions</strong>: <ul>
 * <li>The input arrays must have the same length and their common length
 * must be at least 2.
 * </li></ul></p>
 *
 * @param sample1 array of sample data values
 * @param sample2 array of sample data values
 * @return t statistic
 * @throws NullArgumentException if the arrays are <code>null</code>
 * @throws NoDataException if the arrays are empty
 * @throws DimensionMismatchException if the length of the arrays is not equal
 * @throws NumberIsTooSmallException if the length of the arrays is &lt; 2
 */
public double pairedT(final double[] sample1, final double[] sample2)
    throws NullArgumentException, NoDataException,
    DimensionMismatchException, NumberIsTooSmallException {

    checkSampleData(sample1);
    checkSampleData(sample2);
    double meanDifference = StatUtils.meanDifference(sample1, sample2);
    return t(meanDifference, 0,
             StatUtils.varianceDifference(sample1, sample2, meanDifference),
             sample1.length);

}
 

import org.apache.commons.math3.stat.StatUtils; //导入方法依赖的package包/类
/**
 * Returns the <i>observed significance level</i>, or
 * <i> p-value</i>, associated with a paired, two-sample, two-tailed t-test
 * based on the data in the input arrays.
 * <p>
 * The number returned is the smallest significance level
 * at which one can reject the null hypothesis that the mean of the paired
 * differences is 0 in favor of the two-sided alternative that the mean paired
 * difference is not equal to 0. For a one-sided test, divide the returned
 * value by 2.</p>
 * <p>
 * This test is equivalent to a one-sample t-test computed using
 * {@link #tTest(double, double[])} with <code>mu = 0</code> and the sample
 * array consisting of the signed differences between corresponding elements of
 * <code>sample1</code> and <code>sample2.</code></p>
 * <p>
 * <strong>Usage Note:</strong><br>
 * The validity of the p-value depends on the assumptions of the parametric
 * t-test procedure, as discussed
 * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
 * here</a></p>
 * <p>
 * <strong>Preconditions</strong>: <ul>
 * <li>The input array lengths must be the same and their common length must
 * be at least 2.
 * </li></ul></p>
 *
 * @param sample1 array of sample data values
 * @param sample2 array of sample data values
 * @return p-value for t-test
 * @throws NullArgumentException if the arrays are <code>null</code>
 * @throws NoDataException if the arrays are empty
 * @throws DimensionMismatchException if the length of the arrays is not equal
 * @throws NumberIsTooSmallException if the length of the arrays is &lt; 2
 * @throws MaxCountExceededException if an error occurs computing the p-value
 */
public double pairedTTest(final double[] sample1, final double[] sample2)
    throws NullArgumentException, NoDataException, DimensionMismatchException,
    NumberIsTooSmallException, MaxCountExceededException {

    double meanDifference = StatUtils.meanDifference(sample1, sample2);
    return tTest(meanDifference, 0,
            StatUtils.varianceDifference(sample1, sample2, meanDifference),
            sample1.length);

}