Repository: commons-math Updated Branches: refs/heads/master 49bb370a6 -> 2bfd539db
Update javadoc; use += for jitter. Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/2bfd539d Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/2bfd539d Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/2bfd539d Branch: refs/heads/master Commit: 2bfd539dba6e69a35d75a301d8b4a1feffcfcd3b Parents: 49bb370 Author: Phil Steitz <phil.ste...@gmail.com> Authored: Fri Nov 27 13:00:01 2015 -0700 Committer: Phil Steitz <phil.ste...@gmail.com> Committed: Fri Nov 27 13:00:01 2015 -0700 ---------------------------------------------------------------------- .../stat/inference/KolmogorovSmirnovTest.java | 29 ++++++++++++++++---- 1 file changed, 23 insertions(+), 6 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/2bfd539d/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 9569cae..312acc0 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 @@ -80,7 +80,12 @@ import org.apache.commons.math4.util.MathUtils; * <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> + * </ul></p><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)} + * 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> * <p> * In the two-sample case, \(D_{n,m}\) has a discrete distribution. This makes the p-value @@ -107,6 +112,9 @@ import org.apache.commons.math4.util.MathUtils; * George Marsaglia, Wai Wan Tsang, and Jingbo Wang</li> * <li>[2] <a href="http://www.jstatsoft.org/v39/i11/";> Computing the Two-Sided Kolmogorov-Smirnov * Distribution</a> by Richard Simard and Pierre L'Ecuyer</li> + * <li>[3] Jasjeet S. Sekhon. 2011. <a href="http://www.jstatsoft.org/article/view/v042i07";> + * Multivariate and Propensity Score Matching Software with Automated Balance Optimization: + * The Matching package for R</a> Journal of Statistical Software, 42(7): 1-52.</li> * </ul> * <br/> * Note that [1] contains an error in computing h, refer to <a @@ -233,7 +241,15 @@ public class KolmogorovSmirnovTest { * <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> + * </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)} + * may be used as an alternative method for estimating the p-value.</p> * * @param x first sample dataset * @param y second sample dataset @@ -244,6 +260,7 @@ public class KolmogorovSmirnovTest { * @throws InsufficientDataException if either {@code x} or {@code y} does not have length at * least 2 * @throws NullArgumentException if either {@code x} or {@code y} is null + * @see #bootstrap(double[], double[], int, boolean) */ public double kolmogorovSmirnovTest(double[] x, double[] y, boolean strict) { final long lengthProduct = (long) x.length * y.length; @@ -397,9 +414,9 @@ public class KolmogorovSmirnovTest { * probability distribution. This method estimates the p-value by repeatedly sampling sets of size * {@code x.length} and {@code y.length} from the empirical distribution of the combined sample. * When {@code strict} is true, this is equivalent to the algorithm implemented in the R function - * ks.boot, described in <pre> - * Jasjeet S. Sekhon. 2011. `Multivariate and Propensity Score Matching - * Software with Automated Balance Optimization: The Matching package for R.` + * {@code ks.boot}, described in <pre> + * Jasjeet S. Sekhon. 2011. 'Multivariate and Propensity Score Matching + * Software with Automated Balance Optimization: The Matching package for R.' * Journal of Statistical Software, 42(7): 1-52. * </pre> * @param x first sample @@ -1250,7 +1267,7 @@ public class KolmogorovSmirnovTest { */ private static void jitter(double[] data, RealDistribution dist) { for (int i = 0; i < data.length; i++) { - data[i] = data[i] + dist.sample(); + data[i] += dist.sample(); } } }
