Package org.snpeff.probablility

Class FisherExactTest

java.lang.Object
org.snpeff.probablility.FisherExactTest
public class FisherExactTest extends Object
Calculate Fisher's exact test (based on hypergeometric distribution)
Author:
pcingola

  • Method Summary

    Modifier and Type
    Method
    Description
    boolean
    canUseChiSquareApproximation(int k, int N, int D, int n)
    Can ChiSquare approximation be used? A rule of the thumb says it can be used if every expected frequency is more than 10
    double
    chiSquareApproximation(int k, int N, int D, int n)
    Chi-Square approximation for Fisher's exact test
    double
    chiSquareCDF(double chiSquare, int nu)
    Chi-Square Cumulative Distribution Function probability that an observed chi-square value for a correct model should be less than chiSquare nu = the degrees of freedom
    double
    chiSquareCDFComplementary(double chiSquare, int nu)
    Chi-Square Complementary of Cumulative Distribution Function: 1 - chiSquareCDF(x, nu) probability that an observed chi-square value for a correct model should be greater than chiSquare nu = the degrees of freedom
    double
    fisherExactTestDown(int k, int N, int D, int n)
    Fisher's exact test for 'k' or less (lower tail)
    double
    fisherExactTestDown(int k, int N, int D, int n, double threshold)
    Fisher's exact test for less than 'k' (lower tail) It also compares to a 'threshold' value to speedup the process.
    double
    fisherExactTestUp(int k, int N, int D, int n)
    Fisher's exact test for 'k' or more (upper tail)
    double
    fisherExactTestUp(int k, int N, int D, int n, double threshold)
    Fisher's exact test for 'k' or more It also compares to a 'threshold' value to speedup the process.
    static FisherExactTest
    get()
     
    double
    mean(int k, int N, int D, int n)
    Calculate the mean References: http://en.wikipedia.org/wiki/Hypergeometric_distribution
    double
    pValueDown(int k, int N, int D, int n)
     
    double
    pValueDown(int k, int N, int D, int n, double threshold)
    Pvalue for 'k' or less Note: Includes 'k' It also compares to a 'threshold' value to speedup the process.
    double
    pValueUp(int k, int N, int D, int n)
     
    double
    pValueUp(int k, int N, int D, int n, double threshold)
    Fisher's exact test for more than 'k' It also compares to a 'threshold' value to speedup the process.
    String
    toR(int k, int N, int D, int n, boolean lowerTail)
    Convert values to Fisher's 'R' command
    double
    variance(int k, int N, int D, int n)
    Calculate the variance References: http://en.wikipedia.org/wiki/Hypergeometric_distribution

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Method Details

    • get

      public static FisherExactTest get()

    • canUseChiSquareApproximation

      public boolean canUseChiSquareApproximation(int k, int N, int D, int n)
      Can ChiSquare approximation be used? A rule of the thumb says it can be used if every expected frequency is more than 10
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      Returns:
      Chi-Square approximation

    • chiSquareApproximation

      public double chiSquareApproximation(int k, int N, int D, int n)
      Chi-Square approximation for Fisher's exact test
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      Returns:
      Chi-Square approximation

    • chiSquareCDF

      public double chiSquareCDF(double chiSquare, int nu)
      Chi-Square Cumulative Distribution Function probability that an observed chi-square value for a correct model should be less than chiSquare nu = the degrees of freedom
      Parameters:
      chiSquare -
      nu -
      Returns:

    • chiSquareCDFComplementary

      public double chiSquareCDFComplementary(double chiSquare, int nu)
      Chi-Square Complementary of Cumulative Distribution Function: 1 - chiSquareCDF(x, nu) probability that an observed chi-square value for a correct model should be greater than chiSquare nu = the degrees of freedom
      Parameters:
      chiSquare -
      nu -
      Returns:

    • fisherExactTestDown

      public double fisherExactTestDown(int k, int N, int D, int n)
      Fisher's exact test for 'k' or less (lower tail)
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      Returns:

    • fisherExactTestDown

      public double fisherExactTestDown(int k, int N, int D, int n, double threshold)
      Fisher's exact test for less than 'k' (lower tail) It also compares to a 'threshold' value to speedup the process. Whenever cumulative probability is over the threshold, 1.0 is returned
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      theshold - Threshold value
      Returns:
      Cumulative probability or 1.0 (if cumulative is over the threshold)

    • fisherExactTestUp

      public double fisherExactTestUp(int k, int N, int D, int n)
      Fisher's exact test for 'k' or more (upper tail)
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      Returns:

    • fisherExactTestUp

      public double fisherExactTestUp(int k, int N, int D, int n, double threshold)
      Fisher's exact test for 'k' or more It also compares to a 'threshold' value to speedup the process. Whenever cumulative probability is over the threshold, 1.0 is returned
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      theshold - Threshold value
      Returns:
      Cumulative probability or 1.0 (if cumulative is over the threshold)

    • mean

      public double mean(int k, int N, int D, int n)
      Calculate the mean References: http://en.wikipedia.org/wiki/Hypergeometric_distribution

    • pValueDown

      public double pValueDown(int k, int N, int D, int n)

    • pValueDown

      public double pValueDown(int k, int N, int D, int n, double threshold)
      Pvalue for 'k' or less Note: Includes 'k' It also compares to a 'threshold' value to speedup the process. Whenever cumulative probability is over the threshold, 1.0 is returned This is useful when we are interested on very small p-values
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      theshold - Threshold value
      Returns:
      Cumulative probability or 1.0 (if cumulative is over the threshold)

    • pValueUp

      public double pValueUp(int k, int N, int D, int n)

    • pValueUp

      public double pValueUp(int k, int N, int D, int n, double threshold)
      Fisher's exact test for more than 'k' It also compares to a 'threshold' value to speedup the process. Whenever cumulative probability is over the threshold, 1.0 is returned This is useful when we are interested on very small p-values
      Parameters:
      k - : white marbles drawn
      N - : Total marbles
      D - : White marbles => N-D : Black marbles
      n - : marbles drawn => N-n : not drawn
      theshold - Threshold value
      Returns:
      Cumulative probability or 1.0 (if cumulative is over the threshold)

    • toR

      public String toR(int k, int N, int D, int n, boolean lowerTail)
      Convert values to Fisher's 'R' command
      Returns:

    • variance

      public double variance(int k, int N, int D, int n)
      Calculate the variance References: http://en.wikipedia.org/wiki/Hypergeometric_distribution