Statistics

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult]

   Java-friendly version of chiSqTest()

   Java-friendly version of chiSqTest()

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   @Since( "1.5.0" )

8. def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult]

   Conduct Pearson's independence test for every feature against the label across the input RDD.

   Conduct Pearson's independence test for every feature against the label across the input RDD. For each feature, the (feature, label) pairs are converted into a contingency matrix for which the chi-squared statistic is computed. All label and feature values must be categorical.

   data

   an RDD[LabeledPoint] containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value.

   returns

   an array containing the ChiSquaredTestResult for every feature against the label. The order of the elements in the returned array reflects the order of input features.

   Annotations

   @Since( "1.1.0" )

9. def chiSqTest(observed: Matrix): ChiSqTestResult

   Conduct Pearson's independence test on the input contingency matrix, which cannot contain negative entries or columns or rows that sum up to 0.

   Conduct Pearson's independence test on the input contingency matrix, which cannot contain negative entries or columns or rows that sum up to 0.

   observed

   The contingency matrix (containing either counts or relative frequencies).

   returns

   ChiSquaredTest object containing the test statistic, degrees of freedom, p-value, the method used, and the null hypothesis.

   Annotations

   @Since( "1.1.0" )

10. def chiSqTest(observed: Vector): ChiSqTestResult

    Conduct Pearson's chi-squared goodness of fit test of the observed data against the uniform distribution, with each category having an expected frequency of 1 / observed.size.

    Conduct Pearson's chi-squared goodness of fit test of the observed data against the uniform distribution, with each category having an expected frequency of 1 / observed.size.

    Note: observed cannot contain negative values.

    observed

    Vector containing the observed categorical counts/relative frequencies.

    returns

    ChiSquaredTest object containing the test statistic, degrees of freedom, p-value, the method used, and the null hypothesis.

    Annotations

    @Since( "1.1.0" )

11. def chiSqTest(observed: Vector, expected: Vector): ChiSqTestResult

    Conduct Pearson's chi-squared goodness of fit test of the observed data against the expected distribution.

    Conduct Pearson's chi-squared goodness of fit test of the observed data against the expected distribution.

    Note: the two input Vectors need to have the same size. observed cannot contain negative values. expected cannot contain nonpositive values.

    observed

    Vector containing the observed categorical counts/relative frequencies.

    expected

    Vector containing the expected categorical counts/relative frequencies. expected is rescaled if the expected sum differs from the observed sum.

    returns

    ChiSquaredTest object containing the test statistic, degrees of freedom, p-value, the method used, and the null hypothesis.

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    @Since( "1.1.0" )

12. def clone(): AnyRef

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13. def colStats(X: RDD[Vector]): MultivariateStatisticalSummary

    Computes column-wise summary statistics for the input RDD[Vector].

    Computes column-wise summary statistics for the input RDD[Vector].

    X

    an RDD[Vector] for which column-wise summary statistics are to be computed.

    returns

    MultivariateStatisticalSummary object containing column-wise summary statistics.

    Annotations

    @Since( "1.1.0" )

14. def corr(x: JavaRDD[Double], y: JavaRDD[Double], method: String): Double

    Java-friendly version of corr()

    Java-friendly version of corr()

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    @Since( "1.4.1" )

15. def corr(x: RDD[Double], y: RDD[Double], method: String): Double

    Compute the correlation for the input RDDs using the specified method.

    Compute the correlation for the input RDDs using the specified method. Methods currently supported: pearson (default), spearman.

    Note: the two input RDDs need to have the same number of partitions and the same number of elements in each partition.

    x

    RDD[Double] of the same cardinality as y.

    y

    RDD[Double] of the same cardinality as x.

    method

    String specifying the method to use for computing correlation. Supported: pearson (default), spearman

    returns

    A Double containing the correlation between the two input RDD[Double]s using the specified method.

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    @Since( "1.1.0" )

16. def corr(x: JavaRDD[Double], y: JavaRDD[Double]): Double

    Java-friendly version of corr()

    Java-friendly version of corr()

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    @Since( "1.4.1" )

17. def corr(x: RDD[Double], y: RDD[Double]): Double

    Compute the Pearson correlation for the input RDDs.

    Compute the Pearson correlation for the input RDDs. Returns NaN if either vector has 0 variance.

    Note: the two input RDDs need to have the same number of partitions and the same number of elements in each partition.

    x

    RDD[Double] of the same cardinality as y.

    y

    RDD[Double] of the same cardinality as x.

    returns

    A Double containing the Pearson correlation between the two input RDD[Double]s

    Annotations

    @Since( "1.1.0" )

18. def corr(X: RDD[Vector], method: String): Matrix

    Compute the correlation matrix for the input RDD of Vectors using the specified method.

    Compute the correlation matrix for the input RDD of Vectors using the specified method. Methods currently supported: pearson (default), spearman.

    Note that for Spearman, a rank correlation, we need to create an RDD[Double] for each column and sort it in order to retrieve the ranks and then join the columns back into an RDD[Vector], which is fairly costly. Cache the input RDD before calling corr with method = "spearman" to avoid recomputing the common lineage.

    X

    an RDD[Vector] for which the correlation matrix is to be computed.

    method

    String specifying the method to use for computing correlation. Supported: pearson (default), spearman

    returns

    Correlation matrix comparing columns in X.

    Annotations

    @Since( "1.1.0" )

19. def corr(X: RDD[Vector]): Matrix

    Compute the Pearson correlation matrix for the input RDD of Vectors.

    Compute the Pearson correlation matrix for the input RDD of Vectors. Columns with 0 covariance produce NaN entries in the correlation matrix.

    X

    an RDD[Vector] for which the correlation matrix is to be computed.

    returns

    Pearson correlation matrix comparing columns in X.

    Annotations

    @Since( "1.1.0" )

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

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21. def equals(arg0: Any): Boolean

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22. def finalize(): Unit

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23. final def getClass(): Class[_]

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24. def hashCode(): Int

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25. final def isInstanceOf[T0]: Boolean

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26. def kolmogorovSmirnovTest(data: JavaDoubleRDD, distName: String, params: Double*): KolmogorovSmirnovTestResult

    Java-friendly version of kolmogorovSmirnovTest()

    Java-friendly version of kolmogorovSmirnovTest()

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    @Since( "1.5.0" ) @varargs()

27. def kolmogorovSmirnovTest(data: RDD[Double], distName: String, params: Double*): KolmogorovSmirnovTestResult

    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. (distName = "norm")

    data

    an RDD[Double] containing the sample of data to test

    distName

    a String name for a theoretical distribution

    params

    Double* specifying the parameters to be used for the theoretical distribution

    returns

    org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult object containing test statistic, p-value, and null hypothesis.

    Annotations

    @Since( "1.5.0" ) @varargs()

28. def kolmogorovSmirnovTest(data: RDD[Double], cdf: (Double) ⇒ Double): KolmogorovSmirnovTestResult

    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. For more information on KS Test:

    data

    an RDD[Double] containing the sample of data to test

    cdf

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

    returns

    org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult object containing test statistic, p-value, and null hypothesis.

    Annotations

    @Since( "1.5.0" )

    See also

    https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test

29. final def ne(arg0: AnyRef): Boolean

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30. final def notify(): Unit

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31. final def notifyAll(): Unit

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32. final def synchronized[T0](arg0: ⇒ T0): T0

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33. def toString(): String

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34. final def wait(): Unit

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

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

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