nag_correg_coeffs_pearson_subset_miss_pair (g02bj) computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables omitting cases with missing values from only those calculations involving the variables for which the values are missing.

Syntax

[xbar, std, ssp, r, ncases, cnt, ifail] = g02bj(x, miss, xmiss, kvar, 'n', n, 'm', m, 'nvars', nvars)

[xbar, std, ssp, r, ncases, cnt, ifail] = nag_correg_coeffs_pearson_subset_miss_pair(x, miss, xmiss, kvar, 'n', n, 'm', m, 'nvars', nvars)

Note: the interface to this routine has changed since earlier releases of the toolbox:

At Mark 22:

n was made optional

Description

The input data consists of

n

observations for each of

m

variables, given as an array

[x_{i j}], i = 1, 2, \dots, n (n \geq 2), j = 1, 2, \dots, m (m \geq 2),

where

x_{i j}

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.

In addition, each of the

m

variables may optionally have associated with it a value which is to be considered as representing a missing observation for that variable; the missing value for the

j

th variable is denoted by

{xm}_{j}

. Missing values need not be specified for all variables.

Let

w_{i j} = 0

if the

i

th observation for the

j

th variable is a missing value, i.e., if a missing value,

{xm}_{j}

, has been declared for the

j

th variable, and

x_{i j} = {xm}_{j}

(see also Accuracy); and

w_{i j} = 1

otherwise, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

The quantities calculated are:

(a)

Means:

{\bar{x}}_{j} = \frac{\sum_{i = 1}^{n} w_{i j} x_{i j}}{\sum_{i = 1}^{n} w_{i j}}, j = v_{1}, v_{2}, \dots, v_{p} .

(b)

Standard deviations:

s_{j} = \sqrt{\frac{\sum_{i = 1}^{n} w_{i j} {(x_{i j} - {\bar{x}}_{j})}^{2}}{\sum_{i = 1}^{n} w_{i j} - 1}}, j = v_{1}, v_{2}, \dots, v_{p} .

(c)

Sums of squares and cross-products of deviations from means:

S_{j k} = \sum_{i = 1}^{n} w_{i j} w_{i k} (x_{i j} - {\bar{x}}_{j (k)}) (x_{i k} - {\bar{x}}_{k (j)}), j, k = v_{1}, v_{2}, \dots, v_{p},

where

{\bar{x}}_{j (k)} = \frac{\sum_{i = 1}^{n} w_{i j} w_{i k} x_{i j}}{\sum_{i = 1}^{n} w_{i j} w_{i k}} and {\bar{x}}_{k (j)} = \frac{\sum_{i = 1}^{n} w_{i k} w_{i j} x_{i k}}{\sum_{i = 1}^{n} w_{i k} w_{i j}},

(i.e., the means used in the calculation of the sum of squares and cross-products of deviations are based on the same set of observations as are the cross-products).

(d)

Pearson product-moment correlation coefficients:

R_{j k} = \frac{S_{j k}}{\sqrt{S_{j j (k)} S_{k k (j)}}}, j, k = v_{1}, v_{2}, \dots, v_{p},

where

S_{j j (k)} = \sum_{i = 1}^{n} w_{i j} w_{i k} {(x_{i j} - {\bar{x}}_{j (k)})}^{2} and S_{k k (j)} = \sum_{i = 1}^{n} w_{i k} w_{i j} {(x_{i k} - {\bar{x}}_{k (j)})}^{2},

(i.e., the sums of squares of deviations used in the denominator are based on the same set of observations as are used in the calculation of the numerator).

S_{j j (k)}

S_{k k (j)}

is zero,

R_{j k}

is set to zero.

(e)

The number of cases used in the calculation of each of the correlation coefficients:

c_{j k} = \sum_{i = 1}^{n} w_{i j} w_{i k}, j, k = v_{1}, v_{2}, \dots, v_{p} .

(The diagonal terms,

c_{j j}

, for

j = v_{1}, v_{2}, \dots, v_{p}

, also give the number of cases used in the calculation of the means,

{\bar{x}}_{j}

, and the standard deviations,

s_{j}

References

None.

Parameters

Compulsory Input Parameters

1: $x (ldx, m)$ – double array: ldx, the first dimension of the array, must satisfy the constraint $ldx \geq n$ .
$x (i, j)$ must be set to $x_{i j}$ , the value of the $i$ th observation on the $j$ th variable, for $i = 1, 2, \dots, n$ and $j = 1, 2, \dots, m$ .
2: $miss (m)$ – int64int32nag_int array: $miss (j)$ must be set equal to $1$ if a missing value, $x m_{j}$ , is to be specified for the $j$ th variable in the array x, or set equal to $0$ otherwise. Values of miss must be given for all $m$ variables in the array x.
3: $xmiss (m)$ – double array: $xmiss (j)$ must be set to the missing value, $x m_{j}$ , to be associated with the $j$ th variable in the array x, for those variables for which missing values are specified by means of the array miss (see Accuracy).
4: $kvar (nvars)$ – int64int32nag_int array: $kvar (j)$ must be set to the column number in x of the $j$ th variable for which information is required, for $j = 1, 2, \dots, p$ .

Constraint: $1 \leq kvar (j) \leq m$ , for $j = 1, 2, \dots, p$ .

Optional Input Parameters

1: $n$ – int64int32nag_int scalar: Default: the first dimension of the array x.
$n$ , the number of observations or cases.

Constraint: $n \geq 2$ .
2: $m$ – int64int32nag_int scalar: Default: the dimension of the arrays miss, xmiss and the second dimension of the array x. (An error is raised if these dimensions are not equal.)
$m$ , the number of variables.

Constraint: $m \geq 2$ .
3: $nvars$ – int64int32nag_int scalar: Default: the dimension of the array kvar.
$p$ , the number of variables for which information is required.

Constraint: $2 \leq nvars \leq m$ .

Output Parameters

1: $xbar (nvars)$ – double array: The mean value, ${\bar{x}}_{j}$ , of the variable specified in $kvar (j)$ , for $j = 1, 2, \dots, p$ .
2: $std (nvars)$ – double array: The standard deviation, $s_{j}$ , of the variable specified in $kvar (j)$ , for $j = 1, 2, \dots, p$ .
3: $ssp (ldssp, nvars)$ – double array: $ssp (j, k)$ is the cross-product of deviations, $S_{j k}$ , for the variables specified in $kvar (j)$ and $kvar (k)$ , for $j = 1, 2, \dots, p$ and $k = 1, 2, \dots, p$ .
4: $r (ldr, nvars)$ – double array: $r (j, k)$ is the product-moment correlation coefficient, $R_{j k}$ , between the variables specified in $kvar (j)$ and $kvar (k)$ , for $j = 1, 2, \dots, p$ and $k = 1, 2, \dots, p$ .
5: $ncases$ – int64int32nag_int scalar: The minimum number of cases used in the calculation of any of the sums of squares and cross-products and correlation coefficients (when cases involving missing values have been eliminated).
6: $cnt (ldcnt, nvars)$ – double array: $cnt (j, k)$ is the number of cases, $c_{j k}$ , actually used in the calculation of $S_{j k}$ , and $R_{j k}$ , the sum of cross-products and correlation coefficient for the variables specified in $kvar (j)$ and $kvar (k)$ , for $j = 1, 2, \dots, p$ and $k = 1, 2, \dots, p$ .
7: $ifail$ – int64int32nag_int scalar: $ifail = 0$ unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Note: nag_correg_coeffs_pearson_subset_miss_pair (g02bj) may return useful information for one or more of the following detected errors or warnings.

Errors or warnings detected by the function:

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

$ifail = 1$

On entry,

n < 2

$ifail = 2$

On entry,	$nvars < 2$ ,
or	$nvars > m$ .

$ifail = 3$

On entry,	$ldx < n$ ,
or	$ldssp < nvars$ ,
or	$ldr < nvars$ ,
or	$ldcnt < nvars$ .

$ifail = 4$

On entry,	$kvar (j) < 1$ ,
or	$kvar (j) > m$ for some $j = 1, 2, \dots, nvars$ .

W $ifail = 5$: After observations with missing values were omitted, fewer than two cases remained for at least one pair of variables. (The pairs of variables involved can be determined by examination of the contents of the array cnt.) All means, standard deviations, sums of squares and cross-products, and correlation coefficients based on two or more cases are returned by the function even if $ifail = 5$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.

$ifail = - 999$: Dynamic memory allocation failed.

Accuracy

nag_correg_coeffs_pearson_subset_miss_pair (g02bj) does not use additional precision arithmetic for the accumulation of scalar products, so there may be a loss of significant figures for large

n

You are warned of the need to exercise extreme care in your selection of missing values. nag_correg_coeffs_pearson_subset_miss_pair (g02bj) treats all values in the inclusive range

(1 \pm {0.1}^{(x02be - 2)}) \times {x m}_{j}

, where

{xm}_{j}

is the missing value for variable

j

specified in xmiss.

You must therefore ensure that the missing value chosen for each variable is sufficiently different from all valid values for that variable so that none of the valid values fall within the range indicated above.

Further Comments

The time taken by nag_correg_coeffs_pearson_subset_miss_pair (g02bj) depends on

n

and

p

, and the occurrence of missing values.

The function uses a two-pass algorithm.

Example

This example reads in a set of data consisting of five observations on each of four variables. Missing values of

- 1.0

0.0

and

0.0

are declared for the first, second and fourth variables respectively; no missing value is specified for the third variable. The means, standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for the fourth, first and second variables are then calculated and printed, omitting cases with missing values from only those calculations involving the variables for which the values are missing. The program therefore eliminates cases

4

and

5

in calculating the correlation between the fourth and first variables, and cases

3

and

4

for the fourth and second variables etc.

Open in the MATLAB editor: g02bj_example

function g02bj_example


fprintf('g02bj example results\n\n');

x = [ 3,  3,  1,  2;
      6,  4, -1,  4;
      9,  0,  5,  9;
     12,  2,  0,  0;
     -1,  5,  4, 12];
[n,m] = size(x);
fprintf('Number of variables (columns) = %d\n', m);
fprintf('Number of cases     (rows)    = %d\n\n', n);
disp('Data matrix is:-');
disp(x);

miss  = [int64(1); 1; 0; 1];
xmiss = [        -1; 0; 0; 0];
kvar  = [int64(4);1;2];
nvar   = size(kvar,1);

[xbar, std, ssp, r, ncases, count, ifail] = ...
  g02bj( ...
         x, miss, xmiss, kvar);

fprintf('Variable   Mean     St. dev.\n');
fprintf('%5d%11.4f%11.4f\n',[double(kvar) xbar(1:nvar) std(1:nvar)]');
fprintf('\nSums of squares and cross-products of deviations\n');
disp(ssp)
fprintf('Correlation coefficients\n');
disp(r);
fprintf('Number of cases used for any pair of variables = %d\n\n', ncases);
fprintf('Numbers used for each pair are:\n   ');
fprintf('%10d',kvar);
for j=1:nvar
  fprintf('\n%3d:',kvar(j));
  fprintf('%10.1f',count(j,:));
end
fprintf('\n')

g02bj example results

Number of variables (columns) = 4
Number of cases     (rows)    = 5

Data matrix is:-
     3     3     1     2
     6     4    -1     4
     9     0     5     9
    12     2     0     0
    -1     5     4    12

Variable   Mean     St. dev.
    4     6.7500     4.5735
    1     7.5000     3.8730
    2     3.5000     1.2910

Sums of squares and cross-products of deviations
   62.7500   21.0000   10.0000
   21.0000   45.0000   -6.0000
   10.0000   -6.0000    5.0000

Correlation coefficients
    1.0000    0.9707    0.9449
    0.9707    1.0000   -0.6547
    0.9449   -0.6547    1.0000

Number of cases used for any pair of variables = 3

Numbers used for each pair are:
            4         1         2
  4:       4.0       3.0       3.0
  1:       3.0       4.0       3.0
  2:       3.0       3.0       4.0

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox