Github user josepablocam commented on a diff in the pull request:
https://github.com/apache/spark/pull/7278#discussion_r34308925
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
---
@@ -158,4 +158,30 @@ object Statistics {
def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
ChiSqTest.chiSquaredFeatures(data)
}
+
+ /**
+ * Conduct a 1-sample Anderson-Darling test for the null hypothesis that
the data
+ * comes from a given theoretical distribution. The Anderson-Darling
test is an alternative
+ * to the Kolmogorov-Smirnov test, and is more adequate at identifying
departures from the
+ * theoretical distribution at the tails. The implementation returns an
`ADTestResult`, which
+ * includes the AD statistic, the critical values at varying
significance levels, and
+ * the null hypothesis. Note that the critical values are calculated
assuming the parameters
+ * have been calculated from the data sample. If the parameters for the
theoretical distribution
+ * are not in a valid domain, throws an exception.
+ * @param data `RDD[Double]` the data to be test
--- End diff --
Ah I see. I was going off of what I saw in Statistics.chiSqTest that seemed
to note in some occasions. I'll remove. Thanks!
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