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Update javadoc; use += for jitter.

This commit is contained in:
Phil Steitz 2015-11-27 13:00:01 -07:00
parent 49bb370a6c
commit 2bfd539dba
1 changed files with 23 additions and 6 deletions
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29
src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java
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@ -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();
}
}
}