G02BAF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

G02BAF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

G02BAF computes means and standard deviations of variables, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for a set of data.

2  Specification

SUBROUTINE G02BAF ( N, M, X, LDX, XBAR, STD, SSP, LDSSP, R, LDR, IFAIL)
INTEGER  N, M, LDX, LDSSP, LDR, IFAIL
REAL (KIND=nag_wp)  X(LDX,M), XBAR(M), STD(M), SSP(LDSSP,M), R(LDR,M)

3  Description

The input data consist of n observations for each of m variables, given as an array
xij,  i=1,2,,nn2,j=1,2,,mm2,
where xij is the ith observation on the jth variable.
The quantities calculated are:
(a) Means:
x-j=1ni=1nxij,  j=1,2,,m.
(b) Standard deviations:
sj=1n- 1 i= 1n xij-x-j 2,   j= 1,2,,m.
(c) Sums of squares and cross-products of deviations from means:
Sjk=i=1n xij-x-j xik-x-k ,  j,k=1,2,,m.
(d) Pearson product-moment correlation coefficients:
Rjk=SjkSjjSkk ,   j,k= 1,2,,m.
If Sjj or Skk is zero, Rjk is set to zero.

4  References

None.

5  Parameters

1:     N – INTEGERInput
On entry: n, the number of observations or cases.
Constraint: N2.
2:     M – INTEGERInput
On entry: m, the number of variables.
Constraint: M2.
3:     X(LDX,M) – REAL (KIND=nag_wp) arrayInput
On entry: Xij must be set to xij, the ith observation on the jth variable, for i=1,2,,n and j=1,2,,m.
4:     LDX – INTEGERInput
On entry: the first dimension of the array X as declared in the (sub)program from which G02BAF is called.
Constraint: LDXN.
5:     XBAR(M) – REAL (KIND=nag_wp) arrayOutput
On exit: the mean value, x-j, of the jth variable, for j=1,2,,m.
6:     STD(M) – REAL (KIND=nag_wp) arrayOutput
On exit: the standard deviation, sj, of the jth variable, for j=1,2,,m.
7:     SSP(LDSSP,M) – REAL (KIND=nag_wp) arrayOutput
On exit: SSPjk is the cross-product of deviations Sjk, for j=1,2,,m and k=1,2,,m.
8:     LDSSP – INTEGERInput
On entry: the first dimension of the array SSP as declared in the (sub)program from which G02BAF is called.
Constraint: LDSSPM.
9:     R(LDR,M) – REAL (KIND=nag_wp) arrayOutput
On exit: Rjk is the product-moment correlation coefficient Rjk between the jth and kth variables, for j=1,2,,m and k=1,2,,m.
10:   LDR – INTEGERInput
On entry: the first dimension of the array R as declared in the (sub)program from which G02BAF is called.
Constraint: LDRM.
11:   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 or -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<2.
IFAIL=2
On entry,M<2.
IFAIL=3
On entry,LDX<N,
orLDSSP<M,
orLDR<M.

7  Accuracy

G02BAF does not use additional precision arithmetic for the accumulation of scalar products, so there may be a loss of significant figures for large n.

8  Further Comments

The time taken by G02BAF depends on n and m.
The routine uses a two-pass algorithm.

9  Example

This example reads in a set of data consisting of five observations on each of three variables. The means, standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for all three variables are then calculated and printed.

9.1  Program Text

Program Text (g02bafe.f90)

9.2  Program Data

Program Data (g02bafe.d)

9.3  Program Results

Program Results (g02bafe.r)


G02BAF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012