KolmogorovSmirnovTest

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

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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6. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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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

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    @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)

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    @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

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    @Since( "2.4.0" )

19. def toString(): String

    

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20. final def wait(): Unit

    

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21. final def wait(arg0: Long, arg1: Int): Unit

    

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22. final def wait(arg0: Long): Unit

    

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