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NAG Library Routine Document

g02buf (ssqmat)

▸▿ 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

g02buf calculates the sample means and sums of squares and cross-products, or sums of squares and cross-products of deviations from the mean, in a single pass for a set of data. The data may be weighted.

2

Specification

Fortran Interface

Subroutine g02buf (

mean, weight, n, m, x, ldx, wt, sw, wmean, c, ifail)

Integer, Intent (In)	::	n, m, ldx
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(ldx,m), wt(*)
Real (Kind=nag_wp), Intent (Out)	::	sw, wmean(m), c((m*m+m)/2)
Character (1), Intent (In)	::	mean, weight

C Header Interface

#include nagmk26.h

void	g02buf_ ( const char mean, const char weight, const Integer n, const Integer m, const double x[], const Integer ldx, const double wt[], double sw, double wmean[], double c[], Integer *ifail, const Charlen length_mean, const Charlen length_weight)

3

Description

g02buf is an adaptation of West's WV2 algorithm; see West (1979). This routine calculates the (optionally weighted) sample means and (optionally weighted) sums of squares and cross-products or sums of squares and cross-products of deviations from the (weighted) mean for a sample of

n

observations on

m

variables

X_{j}

, for

j = 1, 2, \dots, m

. The algorithm makes a single pass through the data.

For the first

i - 1

observations let the mean of the

j

th variable be

{\bar{x}}_{j} (i - 1)

, the cross-product about the mean for the

j

th and

k

th variables be

c_{j k} (i - 1)

and the sum of weights be

W_{i - 1}

. These are updated by the

i

th observation,

x_{i j}

, for

j = 1, 2, \dots, m

, with weight

w_{i}

as follows:

\begin{matrix} W_{i} = W_{i - 1} + w_{i} \\ {\bar{x}}_{j} (i) = {\bar{x}}_{j} (i - 1) + \frac{w_{i}}{W_{i}} (x_{j} - {\bar{x}}_{j} (i - 1)), j = 1, 2, \dots, m \end{matrix}

and

c_{j k} (i) = c_{j k} (i - 1) + \frac{w_{i}}{W_{i}} (x_{j} - {\bar{x}}_{j} (i - 1)) (x_{k} - {\bar{x}}_{k} (i - 1)) W_{i - 1}, j = 1, 2, \dots, m ​ and ​ k = j, j + 1, \dots, m .

The algorithm is initialized by taking

{\bar{x}}_{j} (1) = x_{1 j}

, the first observation, and

c_{i j} (1) = 0.0

For the unweighted case

w_{i} = 1

and

W_{i} = i

for all

i

Note that only the upper triangle of the matrix is calculated and returned packed by column.

4

References

Chan T F, Golub G H and Leveque R J (1982) Updating Formulae and a Pairwise Algorithm for Computing Sample Variances Compstat, Physica-Verlag

West D H D (1979) Updating mean and variance estimates: An improved method Comm. ACM 22 532–555

5

Arguments

1: $mean$ – Character(1)Input

On entry: indicates whether g02buf is to calculate sums of squares and cross-products, or sums of squares and cross-products of deviations about the mean.

$mean ='M'$: The sums of squares and cross-products of deviations about the mean are calculated.
$mean ='Z'$: The sums of squares and cross-products are calculated.

Constraint:

mean ='M'

'Z'

2: $weight$ – Character(1)Input

On entry: indicates whether the data is weighted or not.

$weight ='U'$: The calculations are performed on unweighted data.
$weight ='W'$: The calculations are performed on weighted data.

Constraint:

weight ='W'

'U'

3: $n$ – IntegerInput

On entry:

n

, the number of observations in the dataset.

Constraint:

n \geq 1

4: $m$ – IntegerInput

On entry:

m

, the number of variables.

Constraint:

m \geq 1

5: $x (ldx, m)$ – Real (Kind=nag_wp) arrayInput

On entry:

x (i, j)

must contain the

i

th observation on the

j

th variable, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

6: $ldx$ – IntegerInput

On entry: the first dimension of the array x as declared in the (sub)program from which g02buf is called.

Constraint:

ldx \geq n

7: $wt (*)$ – Real (Kind=nag_wp) arrayInput

Note: the dimension of the array wt must be at least

n

weight ='W'

, and at least

1

otherwise.

On entry: the optional weights of each observation.

weight ='U'

, wt is not referenced.

weight ='W'

wt (i)

must contain the weight for the

i

th observation.

Constraint: if

weight ='W'

wt (i) \geq 0.0

, for

i = 1, 2, \dots, n

8: $sw$ – Real (Kind=nag_wp)Output

On exit: the sum of weights.

weight ='U'

, sw contains the number of observations,

n

9: $wmean (m)$ – Real (Kind=nag_wp) arrayOutput

On exit: the sample means.

wmean (j)

contains the mean for the

j

th variable.

10: $c ((m \times m + m) / 2)$ – Real (Kind=nag_wp) arrayOutput

On exit: the cross-products.

mean ='M'

, c contains the upper triangular part of the matrix of (weighted) sums of squares and cross-products of deviations about the mean.

mean ='Z'

, c contains the upper triangular part of the matrix of (weighted) sums of squares and cross-products.

These are stored packed by columns, i.e., the cross-product between the

j

th and

k

th variable,

k \geq j

, is stored in

c (k \times (k - 1) / 2 + j)

11: $ifail$ – 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, $ldx = 〈value〉$ and $n = 〈value〉$ .
Constraint: $ldx \geq n$ .

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

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

$ifail = 2$: On entry, $mean = 〈value〉$ .
Constraint: $mean ='M'$ or $'Z'$ .

$ifail = 3$: On entry, $weight = 〈value〉$ .
Constraint: $weight ='W'$ or $'U'$ .

$ifail = 4$: On entry, $wt (〈value〉) < 0.0$ .
Constraint: $wt (i) \geq 0.0$ , for $i = 1, 2, \dots, n$ .

$ifail = - 99$: 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

For a detailed discussion of the accuracy of this algorithm see Chan et al. (1982) or West (1979).

8

Parallelism and Performance

g02buf is not threaded in any implementation.

9

Further Comments

g02bwf may be used to calculate the correlation coefficients from the cross-products of deviations about the mean. The cross-products of deviations about the mean may be scaled using f06edf (dscal) or f06fdf to give a variance-covariance matrix.

The means and cross-products produced by g02buf may be updated by adding or removing observations using g02btf.

Two sets of means and cross-products, as produced by g02buf, can be combined using g02bzf.

10

Example

A program to calculate the means, the required sums of squares and cross-products matrix, and the variance matrix for a set of

3

observations of

3

variables.

NAG Library Routine Document

g02buf (ssqmat)

▸▿ 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