Packages

final class StatVec extends AnyVal

The StatVec value class provides methods for computing statistics on data vectors. Both maximum likelihood and unbiased estimators are supported. Unbiased should only be used on sample (not population) data. Ex: It can be used to support the Method of Independent Replications (MIR). For efficiency, StatVec is a value class that enriches the VectorD. The corresponding implicit conversion in the package object.

See also

stat.StatVector for more complete statistical functions

stackoverflow.com/questions/14861862/how-do-you-enrich-value-classes-without-overhead -----------------------------------------------------------------------------

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Instance Constructors

  1. new StatVec(self: VectorD)

    self

    the underlying object to be accessed via the 'self' accessor

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    Any
  2. final def ##(): Int
    Definition Classes
    Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def corr(y: VectorD): Double

    Compute Pearson's correlation of 'self' vector with vector 'y'.

    Compute Pearson's correlation of 'self' vector with vector 'y'. If either variance is zero, will result in Not-a-Number (NaN), ruturn one if the vectors are the same, or -0 (indicating undefined).

    y

    the other vector

  6. def cov(y: VectorD): Double

    Compute the sample covariance of 'self' vector with vector 'y'.

    Compute the sample covariance of 'self' vector with vector 'y'.

    y

    the other vector

  7. def getClass(): Class[_ <: AnyVal]
    Definition Classes
    AnyVal → Any
  8. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  9. def pcorr(y: VectorD): Double

    Compute the population Pearson's correlation of 'self' vector with vector 'y'.

    Compute the population Pearson's correlation of 'self' vector with vector 'y'. Note: should only differ from 'corr' due to round-off errors and NaN issue.

    y

    the other vector

  10. def pcov(y: VectorD): Double

    Compute the population covariance of 'self' vector with vector 'y'.

    Compute the population covariance of 'self' vector with vector 'y'.

    y

    the other vector

  11. def pstddev: Double

    Compute the population standard deviation of 'self' vector.

    Compute the population standard deviation of 'self' vector.

    See also

    VectorD for pvariance

  12. val self: VectorD
  13. def standardize: VectorD

    Produce a standardized version of the vector by subtracting the mean and dividing by the standard deviation (e.g., Normal -> Standard Normal).

  14. def stddev: Double

    Compute the standard deviation of 'self' vector.

    Compute the standard deviation of 'self' vector.

    See also

    VectorD for variance

  15. def toString(): String
    Definition Classes
    Any

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