 # KolmogorovSmirnovTest

### Related Doc: package stat

#### object KolmogorovSmirnovTest

:: Experimental ::

Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution. For more information on KS Test:

Annotations
@Experimental() @Since( "2.4.0" )
Source
KolmogorovSmirnovTest.scala

Kolmogorov-Smirnov test (Wikipedia)

Linear Supertypes
AnyRef, Any
Ordering
1. Alphabetic
2. By Inheritance
Inherited
1. KolmogorovSmirnovTest
2. AnyRef
3. Any
1. Hide All
2. Show All
Visibility
1. Public
2. All

### Value Members

1. #### final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
2. #### final def ##(): Int

Definition Classes
AnyRef → Any
3. #### final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
4. #### final def asInstanceOf[T0]: T0

Definition Classes
Any
5. #### def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
6. #### final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
7. #### def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
8. #### def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
9. #### final def getClass(): Class[_]

Definition Classes
AnyRef → Any
10. #### def hashCode(): Int

Definition Classes
AnyRef → Any
11. #### final def isInstanceOf[T0]: Boolean

Definition Classes
Any
12. #### final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
13. #### final def notify(): Unit

Definition Classes
AnyRef
14. #### final def notifyAll(): Unit

Definition Classes
AnyRef
15. #### final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
16. #### def test(dataset: Dataset[_], sampleCol: String, distName: String, params: Double*): DataFrame

Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.

Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality. Currently supports the normal distribution, taking as parameters the mean and standard deviation.

dataset

A `Dataset` or a `DataFrame` containing the sample of data to test

sampleCol

Name of sample column in dataset, of any numerical type

distName

a `String` name for a theoretical distribution, currently only support "norm".

params

`Double*` specifying the parameters to be used for the theoretical distribution. For "norm" distribution, the parameters includes mean and variance.

returns

DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

• `pValue: Double`
• `statistic: Double`
Annotations
@Since( "2.4.0" ) @varargs()
17. #### def test(dataset: Dataset[_], sampleCol: String, cdf: Function[Double, Double]): DataFrame

Java-friendly version of `test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)`

Java-friendly version of `test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)`

Annotations
@Since( "2.4.0" )
18. #### def test(dataset: Dataset[_], sampleCol: String, cdf: (Double) ⇒ Double): DataFrame

Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution.

Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution.

dataset

A `Dataset` or a `DataFrame` containing the sample of data to test

sampleCol

Name of sample column in dataset, of any numerical type

cdf

a `Double => Double` function to calculate the theoretical CDF at a given value

returns

DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

• `pValue: Double`
• `statistic: Double`
Annotations
@Since( "2.4.0" )
19. #### def toString(): String

Definition Classes
AnyRef → Any
20. #### final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
21. #### final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
22. #### final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )