CramersV

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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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4. def apply[T, U](values: Seq[(T, U)]): Double

   

   Calculate Cramer's V for a collection of values co-sampled from two variables.

   Calculate Cramer's V for a collection of values co-sampled from two variables.

   values

   Sequence of 2-tuples containing co-sampled values

   returns

   Cramer's V

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def pValueEstimate[T, U](values: Seq[(T, U)], rounds: Int, seed: Long = Random.nextLong): Double

    

    Perform a permutation test to get a p-value indicating the probability of getting a higher assocation value.

    Perform a permutation test to get a p-value indicating the probability of getting a higher assocation value. Take the association level as the null hypothesis, reject if the p-value is less than your desired threshold.

    values

    Values co-sampled from variables 1 and 2

    rounds

    Number of permutations to generate

    seed

    (optional) Seed for the Random number generator used to generate permutations

    returns

    p-value giving the probability of getting a lower association value

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

    

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18. def toString(): String

    

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19. final def wait(): Unit

    

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

    

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

    

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