Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/1733#issuecomment-51298178
@dorx I checked R's implementation and finally figured out what is going on.
1. When only a vector `x` is given, it is treated as a vector containing
frequency counts for categories and tested against multinomial distribution.
2. When a matrix `x` is given, it is treated as a contingency table and the
test is for independence.
3. When both `x` and `y` are given, both vectors are treated as factors
(categorical values) and the test is for independence.
I want to suggest the following APIs:
~~~
// test observed frequencies against multinomial distribution with
// `p = (1/n, 1/n, ..., 1/n)`
def chiSqTest(counts: Vector)
// test observed frequencies against the given multinomial distribution
def chiSqTest(counts: Vector, p: Vector)
// test independence using the given contingency table
def chiSqTest(counts: Matrix)
// test independence using the given observed pairs (assuming categorical
values)
def chiSqTest[V1, V2](observations: RDD[(V1, V2)])
~~~
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