# NAG Library Routine Document

## 1Purpose

g02bkf computes means and standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for selected variables.

## 2Specification

Fortran Interface
 Subroutine g02bkf ( n, m, x, ldx, kvar, xbar, std, sspz, rz, ldrz,
 Integer, Intent (In) :: n, m, ldx, nvars, kvar(nvars), ldsspz, ldrz Integer, Intent (Inout) :: ifail Real (Kind=nag_wp), Intent (In) :: x(ldx,m) Real (Kind=nag_wp), Intent (Inout) :: sspz(ldsspz,nvars), rz(ldrz,nvars) Real (Kind=nag_wp), Intent (Out) :: xbar(nvars), std(nvars)
#include nagmk26.h
 void g02bkf_ (const Integer *n, const Integer *m, const double x[], const Integer *ldx, const Integer *nvars, const Integer kvar[], double xbar[], double std[], double sspz[], const Integer *ldsspz, double rz[], const Integer *ldrz, Integer *ifail)

## 3Description

The input data consists of $n$ observations for each of $m$ variables, given as an array
 $xij, i=1,2,…,n n≥2,j=1,2,…,m m≥2,$
where ${x}_{ij}$ is the $i$th observation on the $j$th variable, together with the subset of these variables, ${v}_{1},{v}_{2},\dots ,{v}_{p}$, for which information is required.
The quantities calculated are:
(a) Means:
 $x-j=∑i=1nxijn, j=v1,v2,…,vp.$
(b) Standard deviations:
 $sj=1n- 1 ∑i= 1n xij-x-j 2, j=v1,v2,…,vp.$
(c) Sums of squares and cross-products about zero:
 $S~jk=∑i=1nxijxik, j,k=v1,v2,…,vp.$
(d) Correlation-like coefficients:
 $R~jk=S~jkS~jjS~kk , j,k=v1,v2,…,vp.$
If ${\stackrel{~}{S}}_{jj}$ or ${\stackrel{~}{S}}_{kk}$ is zero, ${\stackrel{~}{R}}_{jk}$ is set to zero.

None.

## 5Arguments

1:     $\mathbf{n}$ – IntegerInput
On entry: $n$, the number of observations or cases.
Constraint: ${\mathbf{n}}\ge 2$.
2:     $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of variables.
Constraint: ${\mathbf{m}}\ge 2$.
3:     $\mathbf{x}\left({\mathbf{ldx}},{\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: ${\mathbf{x}}\left(\mathit{i},\mathit{j}\right)$ must be set to ${x}_{\mathit{i}\mathit{j}}$, the value of the $\mathit{i}$th observation on the $\mathit{j}$th variable, for $\mathit{i}=1,2,\dots ,n$ and $\mathit{j}=1,2,\dots ,m$.
4:     $\mathbf{ldx}$ – IntegerInput
On entry: the first dimension of the array x as declared in the (sub)program from which g02bkf is called.
Constraint: ${\mathbf{ldx}}\ge {\mathbf{n}}$.
5:     $\mathbf{nvars}$ – IntegerInput
On entry: $p$, the number of variables for which information is required.
Constraint: $2\le {\mathbf{nvars}}\le {\mathbf{m}}$.
6:     $\mathbf{kvar}\left({\mathbf{nvars}}\right)$ – Integer arrayInput
On entry: ${\mathbf{kvar}}\left(\mathit{j}\right)$ must be set to the column number in x of the $\mathit{j}$th variable for which information is required, for $\mathit{j}=1,2,\dots ,p$.
Constraint: $1\le {\mathbf{kvar}}\left(\mathit{j}\right)\le {\mathbf{m}}$, for $\mathit{j}=1,2,\dots ,p$.
7:     $\mathbf{xbar}\left({\mathbf{nvars}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the mean value, ${\stackrel{-}{x}}_{\mathit{j}}$, of the variable specified in ${\mathbf{kvar}}\left(\mathit{j}\right)$, for $\mathit{j}=1,2,\dots ,p$.
8:     $\mathbf{std}\left({\mathbf{nvars}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the standard deviation, ${s}_{\mathit{j}}$, of the variable specified in ${\mathbf{kvar}}\left(\mathit{j}\right)$, for $\mathit{j}=1,2,\dots ,p$.
9:     $\mathbf{sspz}\left({\mathbf{ldsspz}},{\mathbf{nvars}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: ${\mathbf{sspz}}\left(\mathit{j},\mathit{k}\right)$ is the cross-product about zero, ${\stackrel{~}{S}}_{\mathit{j}\mathit{k}}$, for the variables specified in ${\mathbf{kvar}}\left(\mathit{j}\right)$ and ${\mathbf{kvar}}\left(\mathit{k}\right)$, for $\mathit{j}=1,2,\dots ,p$ and $\mathit{k}=1,2,\dots ,p$.
10:   $\mathbf{ldsspz}$ – IntegerInput
On entry: the first dimension of the array sspz as declared in the (sub)program from which g02bkf is called.
Constraint: ${\mathbf{ldsspz}}\ge {\mathbf{nvars}}$.
11:   $\mathbf{rz}\left({\mathbf{ldrz}},{\mathbf{nvars}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: ${\mathbf{rz}}\left(\mathit{j},\mathit{k}\right)$ is the correlation-like coefficient, ${\stackrel{~}{R}}_{\mathit{j}\mathit{k}}$, between the variables specified in ${\mathbf{kvar}}\left(\mathit{j}\right)$ and ${\mathbf{kvar}}\left(\mathit{k}\right)$, for $\mathit{j}=1,2,\dots ,p$ and $\mathit{k}=1,2,\dots ,p$.
12:   $\mathbf{ldrz}$ – IntegerInput
On entry: the first dimension of the array rz as declared in the (sub)program from which g02bkf is called.
Constraint: ${\mathbf{ldrz}}\ge {\mathbf{nvars}}$.
13:   $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, $-1\text{​ or ​}1$. 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 $-1\text{​ or ​}1$ 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 $-\mathbf{1}\text{​ or ​}\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}=1$
On entry, ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{n}}\ge 2$.
${\mathbf{ifail}}=2$
On entry, ${\mathbf{nvars}}=〈\mathit{\text{value}}〉$ and ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{nvars}}\ge 2$ and ${\mathbf{nvars}}\le {\mathbf{m}}$.
${\mathbf{ifail}}=3$
On entry, ${\mathbf{ldrz}}=〈\mathit{\text{value}}〉$ and ${\mathbf{nvars}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldrz}}\ge {\mathbf{nvars}}$.
On entry, ${\mathbf{ldsspz}}=〈\mathit{\text{value}}〉$ and ${\mathbf{nvars}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldsspz}}\ge {\mathbf{nvars}}$.
On entry, ${\mathbf{ldx}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldx}}\ge {\mathbf{n}}$.
${\mathbf{ifail}}=4$
On entry, $\mathit{i}=〈\mathit{\text{value}}〉$, ${\mathbf{kvar}}\left(\mathit{i}\right)=〈\mathit{\text{value}}〉$ and ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: $1\le {\mathbf{kvar}}\left(\mathit{i}\right)\le {\mathbf{m}}$.
${\mathbf{ifail}}=-99$
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-399$
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.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

## 7Accuracy

g02bkf does not use additional precision arithmetic for the accumulation of scalar products, so there may be a loss of significant figures for large $n$.

## 8Parallelism and Performance

g02bkf is not threaded in any implementation.

The time taken by g02bkf depends on $n$ and $p$.
The routine uses a two-pass algorithm.

## 10Example

This example reads in a set of data consisting of five observations on each of four variables. The means, standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for the fourth, first and second variables are then calculated and printed.

### 10.1Program Text

Program Text (g02bkfe.f90)

### 10.2Program Data

Program Data (g02bkfe.d)

### 10.3Program Results

Program Results (g02bkfe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017