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NAG Library Routine Document

g08akf (prob_mwu_ties)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

g08akf calculates the exact tail probability for the Mann–Whitney rank sum test statistic for the case where there are ties in the two samples pooled together.

2

Specification

Fortran Interface

Subroutine g08akf (

n1, n2, tail, ranks, u, p, wrk, lwrk, iwrk, ifail)

Integer, Intent (In)	::	n1, n2, lwrk
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	iwrk(2*(n1+n2+1))
Real (Kind=nag_wp), Intent (In)	::	ranks(n1+n2), u
Real (Kind=nag_wp), Intent (Out)	::	p, wrk(lwrk)
Character (1), Intent (In)	::	tail

C Header Interface

#include nagmk26.h

void	g08akf_ ( const Integer n1, const Integer n2, const char tail, const double ranks[], const double u, double p, double wrk[], const Integer lwrk, Integer iwrk[], Integer *ifail, const Charlen length_tail)

3

Description

g08akf computes the exact tail probability for the Mann–Whitney

U

test statistic (calculated by g08ahf and returned through the argument u) using a method based on an algorithm developed by Neumann (1988), for the case where there are ties in the pooled sample.

The Mann–Whitney

U

test investigates the difference between two populations defined by the distribution functions

F (x)

and

G (y)

respectively. The data consist of two independent samples of size

n_{1}

and

n_{2}

, denoted by

x_{1}, x_{2}, \dots, x_{n_{1}}

and

y_{1}, y_{2}, \dots, y_{n_{2}}

, taken from the two populations.

The hypothesis under test,

H_{0}

, often called the null hypothesis, is that the two distributions are the same, that is

F (x) = G (x)

, and this is to be tested against an alternative hypothesis

H_{1}

which is

$H_{1}$ : $F (x) \neq G (y)$ ; or
$H_{1}$ : $F (x) < G (y)$ , i.e., the $x$ 's tend to be greater than the $y$ 's; or
$H_{1}$ : $F (x) > G (y)$ , i.e., the $x$ 's tend to be less than the $y$ 's,

using a two tailed, upper tailed or lower tailed probability respectively. You select the alternative hypothesis by choosing the appropriate tail probability to be computed (see the description of argument tail in Section 5).

Note that when using this test to test for differences in the distributions one is primarily detecting differences in the location of the two distributions. That is to say, if we reject the null hypothesis

H_{0}

in favour of the alternative hypothesis

H_{1}

F (x) > G (y)

we have evidence to suggest that the location, of the distribution defined by

F (x)

, is less than the location of the distribution defined by

G (y)

g08akf returns the exact tail probability,

p

, corresponding to

U

, depending on the choice of alternative hypothesis,

H_{1}

The value of

p

can be used to perform a significance test on the null hypothesis

H_{0}

against the alternative hypothesis

H_{1}

. Let

α

be the size of the significance test (that is

α

is the probability of rejecting

H_{0}

when

H_{0}

is true). If

p < α

then the null hypothesis is rejected. Typically

α

might be

0.05

0.01

4

References

Conover W J (1980) Practical Nonparametric Statistics Wiley

Neumann N (1988) Some procedures for calculating the distributions of elementary nonparametric teststatistics Statistical Software Newsletter 14(3) 120–126

Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill

5

Arguments

1: $n1$ – IntegerInput

On entry: the number of non-tied pairs,

n_{1}

Constraint:

n1 \geq 1

2: $n2$ – IntegerInput

On entry: the size of the second sample,

n_{2}

Constraint:

n2 \geq 1

3: $tail$ – Character(1)Input

On entry: indicates the choice of tail probability, and hence the alternative hypothesis.

$tail ='T'$: A two tailed probability is calculated and the alternative hypothesis is $H_{1} : F (x) \neq G (y)$ .
$tail ='U'$: An upper tailed probability is calculated and the alternative hypothesis $H_{1} : F (x) < G (y)$ , i.e., the $x$ 's tend to be greater than the $y$ 's.
$tail ='L'$: A lower tailed probability is calculated and the alternative hypothesis $H_{1} : F (x) > G (y)$ , i.e., the $x$ 's tend to be less than the $y$ 's.

Constraint:

tail ='T'

'U'

'L'

4: $ranks (n1 + n2)$ – Real (Kind=nag_wp) arrayInput

On entry: the ranks of the pooled sample. These ranks are output in the array ranks by g08ahf and should not be altered in any way if you are using the same

n_{1}

n_{2}

and

u

as used in g08ahf.

5: $u$ – Real (Kind=nag_wp)Input

On entry:

U

, the value of the Mann–Whitney rank sum test statistic. This is the statistic returned through the argument u by g08ahf.

Constraint:

u \geq 0.0

6: $p$ – Real (Kind=nag_wp)Output

On exit: the tail probability,

p

, as specified by the argument tail.

7: $wrk (lwrk)$ – Real (Kind=nag_wp) arrayWorkspace

8: $lwrk$ – IntegerInput

On entry: the dimension of the array wrk as declared in the (sub)program from which g08akf is called.

Constraint:

lwrk \geq n + n (n + 1) (n + m) - \frac{n (n + 1) (2 \times n + 1)}{3} + 1

, where

n = \min (n1, n2)

and

m = \max (n1, n2)

9: $iwrk (2 \times (n1 + n2 + 1))$ – Integer arrayWorkspace

10: $ifail$ – IntegerInput/Output

On entry: ifail must be set to

0

- 1 ​ or ​ 1

. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 ​ or ​ 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this argument, the recommended value is

0

. When the value $- 1 or 1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6

Error Indicators and Warnings

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$

On entry,	$n1 < 1$ ,
or	$n2 < 1$ .

$ifail = 2$

On entry,

tail \neq'T'

'U'

'L'

$ifail = 3$

On entry,

u < 0.0

$ifail = 4$

On entry,

lwrk is too small.

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

The exact tail probability,

p

, is computed to an accuracy of at least

4

significant figures.

8

Parallelism and Performance

g08akf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9

Further Comments

The time taken by g08akf increases with

n_{1}

and

n_{2}

and the product

n_{1} n_{2}

. Note that the amount of workspace required becomes very large for even moderate sizes of

n_{1}

and

n_{2}

10

Example

This example finds the Mann–Whitney test statistic, using g08ahf for two independent samples of size

16

and

23

respectively. This is used to test the null hypothesis that the distributions of the two populations from which the samples were taken are the same against the alternative hypothesis that the distributions are different. The test statistic, the approximate Normal statistic and the approximate two tail probability are printed. g08akf is then called to obtain the exact two tailed probability. The exact probability is also printed.

NAG Library Routine Document

g08akf (prob_mwu_ties)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

6

Error Indicators and Warnings

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

10.2

Program Data

10.3

Program Results