org.apache.commons.math3.stat.inference

## Class GTest

- java.lang.Object
- - org.apache.commons.math3.stat.inference.GTest

    public class GTest
    extends java.lang.Object

Implements G Test statistics.

This is known in statistical genetics as the McDonald-Kreitman test. The implementation handles both known and unknown distributions.

Two samples tests can be used when the distribution is unknown a priori but provided by one sample, or when the hypothesis under test is that the two samples come from the same underlying distribution.

Since:
  3.1
Version:
  $Id: GTest.java 1416643 2012-12-03 19:37:14Z tn $

### Constructor Summary

Constructors
Constructor and Description
`GTest()`

### Method Summary

Methods
`private double` `entropy(long[] k)` Calculates the Shannon entropy for a vector.
`private double` `entropy(long[][] k)` Calculates the Shannon entropy for 2 Dimensional Matrix.
`double` `g(double[] expected, long[] observed)` Computes the G statistic for Goodness of Fit comparing `observed` and `expected` frequency counts.
`double` `gDataSetsComparison(long[] observed1, long[] observed2)` Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts in `observed1` and `observed2`.
`double` `gTest(double[] expected, long[] observed)` Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the `observed` frequency counts to those in the `expected` array.
`boolean` `gTest(double[] expected, long[] observed, double alpha)` Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level `alpha`.
`double` `gTestDataSetsComparison(long[] observed1, long[] observed2)` Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in `observed1` and `observed2`.
`boolean` `gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)` Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
`double` `gTestIntrinsic(double[] expected, long[] observed)` Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
`double` `rootLogLikelihoodRatio(long k11, long k12, long k21, long k22)` Calculates the root log-likelihood ratio for 2 state Datasets.

### Methods inherited from class java.lang.Object

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

### Constructor Detail

#### GTest

    public GTest()

### Method Detail

#### g

    public double g(double[] expected,
           long[] observed)
             throws NotPositiveException,
                    NotStrictlyPositiveException,
                    DimensionMismatchException

Computes the G statistic for Goodness of Fit comparing `observed` and `expected` frequency counts.

This statistic can be used to perform a G test (Log-Likelihood Ratio Test) evaluating the null hypothesis that the observed counts follow the expected distribution.

Preconditions:

- Expected counts must all be positive.
- Observed counts must all be ≥ 0.
- The observed and expected arrays must have the same length and their common length must be at least 2.

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Note:This implementation rescales the `expected` array if necessary to ensure that the sum of the expected and observed counts are equal.

Parameters:
  `observed` - array of observed frequency counts
  `expected` - array of expected frequency counts
Returns:
  G-Test statistic
Throws:
  `NotPositiveException` - if `observed` has negative entries
  `NotStrictlyPositiveException` - if `expected` has entries that are not strictly positive
  `DimensionMismatchException` - if the array lengths do not match or are less than 2.

#### gTest

    public double gTest(double[] expected,
               long[] observed)
                 throws NotPositiveException,
                        NotStrictlyPositiveException,
                        DimensionMismatchException,
                        MaxCountExceededException

Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the `observed` frequency counts to those in the `expected` array.

The number returned is the smallest significance level at which one can reject the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts.

The probability returned is the tail probability beyond `g(expected, observed)` in the ChiSquare distribution with degrees of freedom one less than the common length of `expected` and `observed`.

Preconditions:

- Expected counts must all be positive.
- Observed counts must all be ≥ 0.
- The observed and expected arrays must have the same length and their common length must be at least 2.

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Note:This implementation rescales the `expected` array if necessary to ensure that the sum of the expected and observed counts are equal.

Parameters:
  `observed` - array of observed frequency counts
  `expected` - array of expected frequency counts
Returns:
  p-value
Throws:
  `NotPositiveException` - if `observed` has negative entries
  `NotStrictlyPositiveException` - if `expected` has entries that are not strictly positive
  `DimensionMismatchException` - if the array lengths do not match or are less than 2.
  `MaxCountExceededException` - if an error occurs computing the p-value.

#### gTestIntrinsic

    public double gTestIntrinsic(double[] expected,
                        long[] observed)
                          throws NotPositiveException,
                                 NotStrictlyPositiveException,
                                 DimensionMismatchException,
                                 MaxCountExceededException

Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H. 2009. Handbook of Biological Statistics (2nd ed.). Sparky House Publishing, Baltimore, Maryland.

The probability returned is the tail probability beyond `g(expected, observed)` in the ChiSquare distribution with degrees of freedom two less than the common length of `expected` and `observed`.

Parameters:
  `observed` - array of observed frequency counts
  `expected` - array of expected frequency counts
Returns:
  p-value
Throws:
  `NotPositiveException` - if `observed` has negative entries
  `NotStrictlyPositiveException` - `expected` has entries that are not strictly positive
  `DimensionMismatchException` - if the array lengths do not match or are less than 2.
  `MaxCountExceededException` - if an error occurs computing the p-value.

#### gTest

    public boolean gTest(double[] expected,
                long[] observed,
                double alpha)
                  throws NotPositiveException,
                         NotStrictlyPositiveException,
                         DimensionMismatchException,
                         OutOfRangeException,
                         MaxCountExceededException

Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level `alpha`. Returns true iff the null hypothesis can be rejected with `100 * (1 - alpha)` percent confidence.

Example:
To test the hypothesis that `observed` follows `expected` at the 99% level, use

`gTest(expected, observed, 0.01)`

Returns true iff `gTestGoodnessOfFitPValue(expected, observed)` < alpha

Preconditions:

- Expected counts must all be positive.
- Observed counts must all be ≥ 0.
- The observed and expected arrays must have the same length and their common length must be at least 2.
- `0 < alpha < 0.5`

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Note:This implementation rescales the `expected` array if necessary to ensure that the sum of the expected and observed counts are equal.

Parameters:
  `observed` - array of observed frequency counts
  `expected` - array of expected frequency counts
  `alpha` - significance level of the test
Returns:
  true iff null hypothesis can be rejected with confidence 1 - alpha
Throws:
  `NotPositiveException` - if `observed` has negative entries
  `NotStrictlyPositiveException` - if `expected` has entries that are not strictly positive
  `DimensionMismatchException` - if the array lengths do not match or are less than 2.
  `MaxCountExceededException` - if an error occurs computing the p-value.
  `OutOfRangeException` - if alpha is not strictly greater than zero and less than or equal to 0.5

#### entropy

    private double entropy(long[][] k)

Calculates the Shannon entropy for 2 Dimensional Matrix. The value returned is the entropy of the vector formed by concatenating the rows (or columns) of `k` to form a vector. See `entropy(long[])`.

Parameters:
  `k` - 2 Dimensional Matrix of long values (for ex. the counts of a trials)
Returns:
  Shannon Entropy of the given Matrix

#### entropy

    private double entropy(long[] k)

Calculates the Shannon entropy for a vector. The values of `k` are taken to be incidence counts of the values of a random variable. What is returned is
∑pilog(pi
where pi = k[i] / (sum of elements in k)

Parameters:
  `k` - Vector (for ex. Row Sums of a trials)
Returns:
  Shannon Entropy of the given Vector

#### gDataSetsComparison

    public double gDataSetsComparison(long[] observed1,
                             long[] observed2)
                               throws DimensionMismatchException,
                                      NotPositiveException,
                                      ZeroException

Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts in `observed1` and `observed2`. The sums of frequency counts in the two samples are not required to be the same. The formula used to compute the test statistic is

`2 * totalSum * [H(rowSums) + H(colSums) - H(k)]`

where `H` is the Shannon Entropy of the random variable formed by viewing the elements of the argument array as incidence counts;
`k` is a matrix with rows `[observed1, observed2]`;
`rowSums, colSums` are the row/col sums of `k`;
and `totalSum` is the overall sum of all entries in `k`.

This statistic can be used to perform a G test evaluating the null hypothesis that both observed counts are independent

Preconditions:

- Observed counts must be non-negative.
- Observed counts for a specific bin must not both be zero.
- Observed counts for a specific sample must not all be 0.
- The arrays `observed1` and `observed2` must have the same length and their common length must be at least 2.

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Parameters:
  `observed1` - array of observed frequency counts of the first data set
  `observed2` - array of observed frequency counts of the second data set
Returns:
  G-Test statistic
Throws:
  `DimensionMismatchException` - the the lengths of the arrays do not match or their common length is less than 2
  `NotPositiveException` - if any entry in `observed1` or `observed2` is negative
  `ZeroException` - if either all counts of `observed1` or `observed2` are zero, or if the count at the same index is zero for both arrays.

#### rootLogLikelihoodRatio

    public double rootLogLikelihoodRatio(long k11,
                                long k12,
                                long k21,
                                long k22)

Calculates the root log-likelihood ratio for 2 state Datasets. See `gDataSetsComparison(long[], long[] )`.

Given two events A and B, let k11 be the number of times both events occur, k12 the incidence of B without A, k21 the count of A without B, and k22 the number of times neither A nor B occurs. What is returned by this method is

`(sgn) sqrt(gValueDataSetsComparison({k11, k12}, {k21, k22})`

where `sgn` is -1 if `k11 / (k11 + k12) < k21 / (k21 + k22))`;
1 otherwise.

Signed root LLR has two advantages over the basic LLR: a) it is positive where k11 is bigger than expected, negative where it is lower b) if there is no difference it is asymptotically normally distributed. This allows one to talk about "number of standard deviations" which is a more common frame of reference than the chi^2 distribution.

Parameters:
  `k11` - number of times the two events occurred together (AB)
  `k12` - number of times the second event occurred WITHOUT the first event (notA,B)
  `k21` - number of times the first event occurred WITHOUT the second event (A, notB)
  `k22` - number of times something else occurred (i.e. was neither of these events (notA, notB)
Returns:
  root log-likelihood ratio

#### gTestDataSetsComparison

    public double gTestDataSetsComparison(long[] observed1,
                                 long[] observed2)
                                   throws DimensionMismatchException,
                                          NotPositiveException,
                                          ZeroException,
                                          MaxCountExceededException

Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in `observed1` and `observed2`.

The number returned is the smallest significance level at which one can reject the null hypothesis that the observed counts conform to the same distribution.

See `gTest(double[], long[])` for details on how the p-value is computed. The degrees of of freedom used to perform the test is one less than the common length of the input observed count arrays.

Preconditions:

- Observed counts must be non-negative.
- Observed counts for a specific bin must not both be zero.
- Observed counts for a specific sample must not all be 0.
- The arrays `observed1` and `observed2` must have the same length and their common length must be at least 2.

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Parameters:
  `observed1` - array of observed frequency counts of the first data set
  `observed2` - array of observed frequency counts of the second data set
Returns:
  p-value
Throws:
  `DimensionMismatchException` - the the length of the arrays does not match or their common length is less than 2
  `NotPositiveException` - if any of the entries in `observed1` or `observed2` are negative
  `ZeroException` - if either all counts of `observed1` or `observed2` are zero, or if the count at some index is zero for both arrays
  `MaxCountExceededException` - if an error occurs computing the p-value.

#### gTestDataSetsComparison

    public boolean gTestDataSetsComparison(long[] observed1,
                                  long[] observed2,
                                  double alpha)
                                    throws DimensionMismatchException,
                                           NotPositiveException,
                                           ZeroException,
                                           OutOfRangeException,
                                           MaxCountExceededException

Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets. The test evaluates the null hypothesis that the two lists of observed counts conform to the same frequency distribution, with significance level `alpha`. Returns true iff the null hypothesis can be rejected with 100 * (1 - alpha) percent confidence.

See `gDataSetsComparison(long[], long[])` for details on the formula used to compute the G (LLR) statistic used in the test and `gTest(double[], long[])` for information on how the observed significance level is computed. The degrees of of freedom used to perform the test is one less than the common length of the input observed count arrays.

Preconditions:

- Observed counts must be non-negative.
- Observed counts for a specific bin must not both be zero.
- Observed counts for a specific sample must not all be 0.
- The arrays `observed1` and `observed2` must have the same length and their common length must be at least 2.
- `0 < alpha < 0.5`

If any of the preconditions are not met, a `MathIllegalArgumentException` is thrown.

Parameters:
  `observed1` - array of observed frequency counts of the first data set
  `observed2` - array of observed frequency counts of the second data set
  `alpha` - significance level of the test
Returns:
  true iff null hypothesis can be rejected with confidence 1 - alpha
Throws:
  `DimensionMismatchException` - the the length of the arrays does not match
  `NotPositiveException` - if any of the entries in `observed1` or `observed2` are negative
  `ZeroException` - if either all counts of `observed1` or `observed2` are zero, or if the count at some index is zero for both arrays
  `OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
  `MaxCountExceededException` - if an error occurs performing the test