Repository: commons-math Updated Branches: refs/heads/master 08986d79d -> d9979fa97
Javadoc errors. Fields that are "private" cannot be referenced with the "@value" tag. Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/44ff5b57 Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/44ff5b57 Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/44ff5b57 Branch: refs/heads/master Commit: 44ff5b57494725b52d4849e3ac3e0d227fa7f6b2 Parents: 08986d7 Author: Gilles <er...@apache.org> Authored: Mon May 29 02:16:05 2017 +0200 Committer: Gilles <er...@apache.org> Committed: Mon May 29 02:16:05 2017 +0200 ---------------------------------------------------------------------- .../stat/inference/KolmogorovSmirnovTest.java | 36 +++++--------------- 1 file changed, 9 insertions(+), 27 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/44ff5b57/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java index 40d9d91..4b850c0 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java @@ -67,16 +67,15 @@ import org.apache.commons.math4.util.MathUtils; * default 2-sample test method, {@link #kolmogorovSmirnovTest(double[], double[])} works as * follows: * <ul> - * <li>For small samples (where the product of the sample sizes is less than - * {@value #LARGE_SAMPLE_PRODUCT}), the method presented in [4] is used to compute the - * exact p-value for the 2-sample test.</li> - * <li>When the product of the sample sizes exceeds {@value #LARGE_SAMPLE_PRODUCT}, the asymptotic + * <li>When the product of the sample sizes is less than 10000, the method presented in [4] + * is used to compute the exact p-value for the 2-sample test.</li> + * <li>When the product of the sample sizes is larger, the asymptotic * distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)} for details on * the approximation.</li> * </ul><p> - * If the product of the sample sizes is less than {@value #LARGE_SAMPLE_PRODUCT} and the sample - * data contains ties, random jitter is added to the sample data to break ties before applying - * the algorithm above. Alternatively, the {@link #bootstrap(double[],double[],int,boolean,UniformRandomProvider)} + * For small samples (former case), if the data contains ties, random jitter is added + * to the sample data to break ties before applying the algorithm above. Alternatively, + * the {@link #bootstrap(double[],double[],int,boolean,UniformRandomProvider)} * method, modeled after <a href="http://sekhon.berkeley.edu/matching/ks.boot.html";>ks.boot</a> * in the R Matching package [3], can be used if ties are known to be present in the data. * </p> @@ -187,23 +186,7 @@ public class KolmogorovSmirnovTest { * that the {@link #kolmogorovSmirnovStatistic(double[], double[])} associated with a randomly * selected partition of the combined sample into subsamples of sizes {@code x.length} and * {@code y.length} will strictly exceed (if {@code strict} is {@code true}) or be at least as - * large as {@code strict = false}) as {@code kolmogorovSmirnovStatistic(x, y)}. - * <ul> - * <li>For small samples (where the product of the sample sizes is less than - * {@value #LARGE_SAMPLE_PRODUCT}), the exact p-value is computed using the method presented - * in [4], implemented in {@link #exactP(double, int, int, boolean)}. </li> - * <li>When the product of the sample sizes exceeds {@value #LARGE_SAMPLE_PRODUCT}, the - * asymptotic distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)} - * for details on the approximation.</li> - * </ul><p> - * If {@code x.length * y.length <} {@value #LARGE_SAMPLE_PRODUCT} and the combined set of values in - * {@code x} and {@code y} contains ties, random jitter is added to {@code x} and {@code y} to - * break ties before computing \(D_{n,m}\) and the p-value. The jitter is uniformly distributed - * on (-minDelta / 2, minDelta / 2) where minDelta is the smallest pairwise difference between - * values in the combined sample.</p> - * <p> - * If ties are known to be present in the data, {@link #bootstrap(double[],double[],int,boolean,UniformRandomProvider)} - * may be used as an alternative method for estimating the p-value.</p> + * large as (if {@code strict} is {@code false}) as {@code kolmogorovSmirnovStatistic(x, y)}. * * @param x first sample dataset. * @param y second sample dataset. @@ -215,6 +198,7 @@ public class KolmogorovSmirnovTest { * not have length at least 2. * @throws NullArgumentException if either {@code x} or {@code y} is null. * @throws NotANumberException if the input arrays contain NaN values. + * * @see #bootstrap(double[],double[],int,boolean,UniformRandomProvider) */ public double kolmogorovSmirnovTest(double[] x, double[] y, boolean strict) { @@ -969,9 +953,7 @@ public class KolmogorovSmirnovTest { * <p> * Specifically, what is returned is \(1 - k(d \sqrt{mn / (m + n)})\) where \(k(t) = 1 + 2 * \sum_{i=1}^\infty (-1)^i e^{-2 i^2 t^2}\). See {@link #ksSum(double, double, int)} for - * details on how convergence of the sum is determined. This implementation passes {@code ksSum} - * {@value #KS_SUM_CAUCHY_CRITERION} as {@code tolerance} and - * {@value #MAXIMUM_PARTIAL_SUM_COUNT} as {@code maxIterations}. + * details on how convergence of the sum is determined. * </p> * * @param d D-statistic value
