NAG Library Routine Document

G01DDF

. It is an adaptation of the Applied Statistics Algorithm AS R94, see Royston (1995). The full description of the theory behind this algorithm is given in Royston (1992).

Given a set of observations

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

sorted into either ascending or descending order (M01CAF may be used to sort the data) this routine calculates the value of Shapiro and Wilk's

W

statistic defined as:

W = \frac{{(\sum_{i = 1}^{n} a_{i} x_{i})}^{2}}{\sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}},

where

\bar{x} = \frac{1}{n} \sum_{1}^{n} x_{i}

is the sample mean and

a_{i}

, for

i = 1, 2, \dots, n

, are a set of ‘weights’ whose values depend only on the sample size

n

On exit, the values of

a_{i}

, for

i = 1, 2, \dots, n

, are only of interest should you wish to call the routine again to calculate

W

and its significance level for a different sample of the same size.

It is recommended that the routine is used in conjunction with a Normal

(Q - Q)

plot of the data. Routines G01DAF and G01DBF can be used to obtain the required Normal scores.

4 References

Royston J P (1982) Algorithm AS 181: the

W

test for normality Appl. Statist. 31 176–180

Royston J P (1986) A remark on AS 181: the

W

test for normality Appl. Statist. 35 232–234

Royston J P (1992) Approximating the Shapiro–Wilk's

W

test for non-normality Statistics & Computing 2 117–119

Royston J P (1995) A remark on AS R94: A remark on Algorithm AS 181: the

W

test for normality Appl. Statist. 44(4) 547–551

5 Parameters

1: X(N) – REAL (KIND=nag_wp) arrayInput: On entry: the ordered sample values, $x_{i}$ , for $i = 1, 2, \dots, n$ .
2: N – INTEGERInput: On entry: $n$ , the sample size.
Constraint: $3 \leq N \leq 5000$ .
3: CALWTS – LOGICALInput: On entry: must be set to .TRUE. if you wish G01DDF to calculate the elements of A.
CALWTS should be set to .FALSE. if you have saved the values in A from a previous call to G01DDF.

If in doubt, set CALWTS equal to .TRUE..
4: A(N) – REAL (KIND=nag_wp) arrayInput/Output: On entry: if CALWTS has been set to .FALSE. then before entry A must contain the $n$ weights as calculated in a previous call to G01DDF, otherwise A need not be set.

On exit: the $n$ weights required to calculate $W$ .
5: W – REAL (KIND=nag_wp)Output: On exit: the value of the statistic, $W$ .
6: PW – REAL (KIND=nag_wp)Output: On exit: the significance level of $W$ .
7: IFAIL – INTEGERInput/Output: On entry: IFAIL must be set to $0$ , $- 1 or 1$ . If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction 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 parameter, 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,

N < 3

$IFAIL = 2$

On entry,

N > 5000

$IFAIL = 3$

On entry,

the elements in X are not in ascending or descending order or are all equal.

7 Accuracy

There may be a loss of significant figures for large

n

8 Further Comments

The time taken by G01DDF depends roughly linearly on the value of

n

For very small samples the power of the test may not be very high.

The contents of the array A should not be modified between calls to G01DDF for a given sample size, unless CALWTS is reset to .TRUE. before each call of G01DDF.

The Shapiro and Wilk's

W

test is very sensitive to ties. If the data has been rounded the test can be improved by using Sheppard's correction to adjust the sum of squares about the mean. This produces an adjusted value of

W

W A = W \frac{\sum {x_{(i)} - \bar{x}}^{2}}{\{\sum_{i = 1}^{n} {x_{(i)} = \bar{x}}^{2} - \frac{n - 1}{12} ω^{2}\}},

where

ω

is the rounding width.

W A

can be compared with a standard Normal distribution, but a further approximation is given by Royston (1986).

N > 5000

, a value for W and PW is returned, but its accuracy may not be acceptable. See Section 4 for more details.

9 Example

This example tests the following two samples (each of size

20

) for Normality.

Sample Number	Data
1	$0.11$ , $7.87$ , $4.61$ , $10.14$ , $7.95$ , $3.14$ , $0.46$ , $4.43$ , $0.21$ , $4.75$ , $0.71$ , $1.52$ , $3.24$ , $0.93$ , $0.42$ , $4.97$ , $9.53$ , $4.55$ , $0.47$ , $6.66$
2	$1.36$ , $1.14$ , $2.92$ , $2.55$ , $1.46$ , $1.06$ , $5.27$ , $- 1.11$ , $3.48$ , $1.10$ , $0.88$ , $- 0.51$ , $1.46$ , $0.52$ , $6.20$ , $1.69$ , $0.08$ , $3.67$ , $2.81$ , $3.49$

The elements of A are calculated only in the first call of G01DDF, and are re-used in the second call.

NAG Library Routine DocumentG01DDF

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

6 Error Indicators and Warnings

7 Accuracy

8 Further Comments

9 Example

9.1 Program Text

9.2 Program Data

9.3 Program Results

NAG Library Routine Document

G01DDF