statistics-0.16.1.2: A library of statistical types, data, and functions
Copyright (c) 2010 Neil Brown
License BSD3
Maintainer bos@serpentine.com
Stability experimental
Portability portable
Safe Haskell None
Language Haskell2010

Statistics.Test.MannWhitneyU

Description

Mann-Whitney U test (also know as Mann-Whitney-Wilcoxon and Wilcoxon rank sum test) is a non-parametric test for assessing whether two samples of independent observations have different mean.

Synopsis

Mann-Whitney U test

mannWhitneyUtest Source #

Arguments

:: ( Ord a, Unbox a)
=> PositionTest

Perform one-tailed test (see description above).

-> PValue Double

The p-value at which to test (e.g. 0.05)

-> Vector a

First sample

-> Vector a

Second sample

-> Maybe TestResult

Return Nothing if the sample was too small to make a decision.

Perform Mann-Whitney U Test for two samples and required significance. For additional information check documentation of mannWhitneyU and mannWhitneyUSignificant . This is just a helper function.

One-tailed test checks whether first sample is significantly larger than second. Two-tailed whether they are significantly different.

mannWhitneyU :: ( Ord a, Unbox a) => Vector a -> Vector a -> ( Double , Double ) Source #

The Mann-Whitney U Test.

This is sometimes known as the Mann-Whitney-Wilcoxon U test, and confusingly many sources state that the Mann-Whitney U test is the same as the Wilcoxon's rank sum test (which is provided as wilcoxonRankSums ). The Mann-Whitney U is a simple transform of Wilcoxon's rank sum test.

Again confusingly, different sources state reversed definitions for U₁ and U₂, so it is worth being explicit about what this function returns. Given two samples, the first, xs₁, of size n₁ and the second, xs₂, of size n₂, this function returns (U₁, U₂) where U₁ = W₁ - (n₁(n₁+1))/2 and U₂ = W₂ - (n₂(n₂+1))/2, where (W₁, W₂) is the return value of wilcoxonRankSums xs1 xs2 .

Some sources instead state that U₁ and U₂ should be the other way round, often expressing this using U₁' = n₁n₂ - U₁ (since U₁ + U₂ = n₁n₂).

All of which you probably don't care about if you just feed this into mannWhitneyUSignificant .

mannWhitneyUCriticalValue Source #

Arguments

:: ( Int , Int )

The sample size

-> PValue Double

The p-value (e.g. 0.05) for which you want the critical value.

-> Maybe Int

The critical value (of U).

Calculates the critical value of Mann-Whitney U for the given sample sizes and significance level.

This function returns the exact calculated value of U for all sample sizes; it does not use the normal approximation at all. Above sample size 20 it is generally recommended to use the normal approximation instead, but this function will calculate the higher critical values if you need them.

The algorithm to generate these values is a faster, memoised version of the simple unoptimised generating function given in section 2 of "The Mann Whitney Wilcoxon Distribution Using Linked Lists"

mannWhitneyUSignificant Source #

Arguments

:: PositionTest

Perform one-tailed test (see description above).

-> ( Int , Int )

The samples' size from which the (U₁,U₂) values were derived.

-> PValue Double

The p-value at which to test (e.g. 0.05)

-> ( Double , Double )

The (U₁, U₂) values from mannWhitneyU .

-> Maybe TestResult

Return Nothing if the sample was too small to make a decision.

Calculates whether the Mann Whitney U test is significant.

If both sample sizes are less than or equal to 20, the exact U critical value (as calculated by mannWhitneyUCriticalValue ) is used. If either sample is larger than 20, the normal approximation is used instead.

If you use a one-tailed test, the test indicates whether the first sample is significantly larger than the second. If you want the opposite, simply reverse the order in both the sample size and the (U₁, U₂) pairs.

Wilcoxon rank sum test

wilcoxonRankSums :: ( Ord a, Unbox a) => Vector a -> Vector a -> ( Double , Double ) Source #

The Wilcoxon Rank Sums Test.

This test calculates the sum of ranks for the given two samples. The samples are ordered, and assigned ranks (ties are given their average rank), then these ranks are summed for each sample.

The return value is (W₁, W₂) where W₁ is the sum of ranks of the first sample and W₂ is the sum of ranks of the second sample. This test is trivially transformed into the Mann-Whitney U test. You will probably want to use mannWhitneyU and the related functions for testing significance, but this function is exposed for completeness.

data PositionTest Source #

Test type for test which compare positional (mean,median etc.) information of samples.

Constructors

SamplesDiffer

Test whether samples differ in position. Null hypothesis is samples are not different

AGreater

Test if first sample (A) is larger than second (B). Null hypothesis is first sample is not larger than second.

BGreater

Test if second sample is larger than first.

Instances

Instances details
Eq PositionTest Source #
Instance details

Defined in Statistics.Test.Types

Data PositionTest Source #
Instance details

Defined in Statistics.Test.Types

Methods

gfoldl :: ( forall d b. Data d => c (d -> b) -> d -> c b) -> ( forall g. g -> c g) -> PositionTest -> c PositionTest Source #

gunfold :: ( forall b r. Data b => c (b -> r) -> c r) -> ( forall r. r -> c r) -> Constr -> c PositionTest Source #

toConstr :: PositionTest -> Constr Source #

dataTypeOf :: PositionTest -> DataType Source #

dataCast1 :: Typeable t => ( forall d. Data d => c (t d)) -> Maybe (c PositionTest ) Source #

dataCast2 :: Typeable t => ( forall d e. ( Data d, Data e) => c (t d e)) -> Maybe (c PositionTest ) Source #

gmapT :: ( forall b. Data b => b -> b) -> PositionTest -> PositionTest Source #

gmapQl :: (r -> r' -> r) -> r -> ( forall d. Data d => d -> r') -> PositionTest -> r Source #

gmapQr :: forall r r'. (r' -> r -> r) -> r -> ( forall d. Data d => d -> r') -> PositionTest -> r Source #

gmapQ :: ( forall d. Data d => d -> u) -> PositionTest -> [u] Source #

gmapQi :: Int -> ( forall d. Data d => d -> u) -> PositionTest -> u Source #

gmapM :: Monad m => ( forall d. Data d => d -> m d) -> PositionTest -> m PositionTest Source #

gmapMp :: MonadPlus m => ( forall d. Data d => d -> m d) -> PositionTest -> m PositionTest Source #

gmapMo :: MonadPlus m => ( forall d. Data d => d -> m d) -> PositionTest -> m PositionTest Source #

Ord PositionTest Source #
Instance details

Defined in Statistics.Test.Types

Show PositionTest Source #
Instance details

Defined in Statistics.Test.Types

Generic PositionTest Source #
Instance details

Defined in Statistics.Test.Types

ToJSON PositionTest Source #
Instance details

Defined in Statistics.Test.Types

FromJSON PositionTest Source #
Instance details

Defined in Statistics.Test.Types

Binary PositionTest Source #
Instance details

Defined in Statistics.Test.Types

NFData PositionTest Source #
Instance details

Defined in Statistics.Test.Types

type Rep PositionTest Source #
Instance details

Defined in Statistics.Test.Types

type Rep PositionTest = D1 (' MetaData "PositionTest" "Statistics.Test.Types" "statistics-0.16.1.2-IkOne9g3oJ1vhHVSRLPUO" ' False ) ( C1 (' MetaCons "SamplesDiffer" ' PrefixI ' False ) ( U1 :: Type -> Type ) :+: ( C1 (' MetaCons "AGreater" ' PrefixI ' False ) ( U1 :: Type -> Type ) :+: C1 (' MetaCons "BGreater" ' PrefixI ' False ) ( U1 :: Type -> Type )))

data TestResult Source #

Result of hypothesis testing

Constructors

Significant

Null hypothesis should be rejected

NotSignificant

Data is compatible with hypothesis

Instances

Instances details
Eq TestResult Source #
Instance details

Defined in Statistics.Test.Types

Data TestResult Source #
Instance details

Defined in Statistics.Test.Types

Methods

gfoldl :: ( forall d b. Data d => c (d -> b) -> d -> c b) -> ( forall g. g -> c g) -> TestResult -> c TestResult Source #

gunfold :: ( forall b r. Data b => c (b -> r) -> c r) -> ( forall r. r -> c r) -> Constr -> c TestResult Source #

toConstr :: TestResult -> Constr Source #

dataTypeOf :: TestResult -> DataType Source #

dataCast1 :: Typeable t => ( forall d. Data d => c (t d)) -> Maybe (c TestResult ) Source #

dataCast2 :: Typeable t => ( forall d e. ( Data d, Data e) => c (t d e)) -> Maybe (c TestResult ) Source #

gmapT :: ( forall b. Data b => b -> b) -> TestResult -> TestResult Source #

gmapQl :: (r -> r' -> r) -> r -> ( forall d. Data d => d -> r') -> TestResult -> r Source #

gmapQr :: forall r r'. (r' -> r -> r) -> r -> ( forall d. Data d => d -> r') -> TestResult -> r Source #

gmapQ :: ( forall d. Data d => d -> u) -> TestResult -> [u] Source #

gmapQi :: Int -> ( forall d. Data d => d -> u) -> TestResult -> u Source #

gmapM :: Monad m => ( forall d. Data d => d -> m d) -> TestResult -> m TestResult Source #

gmapMp :: MonadPlus m => ( forall d. Data d => d -> m d) -> TestResult -> m TestResult Source #

gmapMo :: MonadPlus m => ( forall d. Data d => d -> m d) -> TestResult -> m TestResult Source #

Ord TestResult Source #
Instance details

Defined in Statistics.Test.Types

Show TestResult Source #
Instance details

Defined in Statistics.Test.Types

Generic TestResult Source #
Instance details

Defined in Statistics.Test.Types

ToJSON TestResult Source #
Instance details

Defined in Statistics.Test.Types

FromJSON TestResult Source #
Instance details

Defined in Statistics.Test.Types

Binary TestResult Source #
Instance details

Defined in Statistics.Test.Types

NFData TestResult Source #
Instance details

Defined in Statistics.Test.Types

type Rep TestResult Source #
Instance details

Defined in Statistics.Test.Types

type Rep TestResult = D1 (' MetaData "TestResult" "Statistics.Test.Types" "statistics-0.16.1.2-IkOne9g3oJ1vhHVSRLPUO" ' False ) ( C1 (' MetaCons "Significant" ' PrefixI ' False ) ( U1 :: Type -> Type ) :+: C1 (' MetaCons "NotSignificant" ' PrefixI ' False ) ( U1 :: Type -> Type ))

significant :: Bool -> TestResult Source #

significant if parameter is True , not significant otherwise

References