g08ajf calculates the exact tail probability for the Mann–Whitney rank sum test statistic for the case where there are no ties in the two samples pooled together.

2 Specification

Fortran Interface

Subroutine g08ajf (

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

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

C Header Interface

#include <nag.h>

void	g08ajf_ (const Integer n1, const Integer n2, const char tail, const double u, double p, double wrk[], const Integer lwrk, Integer *ifail, const Charlen length_tail)

The routine may be called by the names g08ajf or nagf_nonpar_prob_mwu_noties.

3 Description

g08ajf 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 Harding (1983), and presented by Neumann (1988), for the case where there are no 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)

g08ajf 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

Harding E F (1983) An efficient minimal-storage procedure for calculating the Mann–Whitney U, generalised U and similar distributions Appl. Statist. 33 1–6

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: $n1$ – Integer Input

On entry: the number of non-tied pairs,

n_{1}

Constraint:

n1 \geq 1

2: $n2$ – Integer Input

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: $u$ – 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

5: $p$ – Real (Kind=nag_wp) Output

On exit: the exact tail probability,

p

, as specified by the argument tail.

6: $wrk (lwrk)$ – Real (Kind=nag_wp) array Workspace

7: $lwrk$ – Integer Input

On entry: the dimension of the array wrk as declared in the (sub)program from which g08ajf is called.

Constraint:

lwrk \geq (n1 \times n2) / 2 + 1

8: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 4 in the Introduction to the NAG Library FL Interface 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, $n1 = 〈value〉$ .
Constraint: $n1 \geq 1$ .

On entry, $n2 = 〈value〉$ .
Constraint: $n2 \geq 1$ .

$ifail = 2$: On entry, $tail = 〈value〉$ .
Constraint: $tail ='T'$ , $'U'$ or $'L'$ .

$ifail = 3$: On entry, $u = 〈value〉$ .
Constraint: $u \geq 0.0$ .

$ifail = 4$: On entry, $lwrk = 〈value〉$ and the minimum workspace is $〈value〉$ .
Constraint: $lwrk \geq (n1 \times n2) / 2 + 1$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface 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

g08ajf is not threaded in any implementation.

9 Further Comments

The time taken by g08ajf increases with

n_{1}

and

n_{2}

and the product

n_{1} 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. g08ajf is then called to obtain the exact two tailed probability. The exact probability is also printed.

Interfaces: FL CL AD

NAG FL Interface Introduction

G08 (Nonpar) Chapter Contents

G08 (Nonpar) Chapter Introduction

g08aj: FL CL AD

NAG FL Interfaceg08ajf (prob_​mwu_​noties)

▸▿ 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

NAG FL Interface
g08ajf (prob_mwu_noties)