nag_sort_realmat_rank_rows (m01de) ranks rows m1 to m2 of a matrix, using the data in columns n1 to n2 of those rows. The ordering is determined by first ranking the data in column n1, then ranking any tied rows according to the data in column

n1 + 1

, and so on up to column n2.

nag_sort_realmat_rank_rows (m01de) uses a variant of list-merging, as described on pages 165–166 in Knuth (1973). The function takes advantage of natural ordering in the data, and uses a simple list insertion in a preparatory pass to generate ordered lists of length at least

10

. The ranking is stable: equal rows preserve their ordering in the input data.

References

Knuth D E (1973) The Art of Computer Programming (Volume 3) (2nd Edition) Addison–Wesley

Parameters

Compulsory Input Parameters

1: $rm (ldm, n2)$ – double array: ldm, the first dimension of the array, must satisfy the constraint $ldm \geq m2$ .
Columns n1 to n2 of rows m1 to m2 of rm must contain double data to be ranked.
2: $m1$ – int64int32nag_int scalar: The index of the first row of rm to be ranked.

Constraint: $m1 > 0$ .
3: $n1$ – int64int32nag_int scalar: The index of the first column of rm to be used.

Constraint: $n1 > 0$ .
4: $order$ – string (length ≥ 1): If $order ='A'$ , the rows will be ranked in ascending (i.e., nondecreasing) order.
If $order ='D'$ , into descending order.

Constraint: $order ='A'$ or $'D'$ .

Optional Input Parameters

1: $m2$ – int64int32nag_int scalar: Default: the first dimension of the array rm.
The index of the last row of rm to be ranked.

Constraint: $m2 \geq m1$ .
2: $n2$ – int64int32nag_int scalar: Default: the second dimension of the array rm.
The index of the last column of rm to be used.

Constraint: $n2 \geq n1$ .

Output Parameters

1: $irank (m2)$ – int64int32nag_int array: Elements m1 to m2 of irank contain the ranks of the corresponding rows of rm. Note that the ranks are in the range m1 to m2: thus, if the $i$ th row of rm is the first in the rank order, $irank (i)$ is set to m1.
2: $ifail$ – int64int32nag_int scalar: $ifail = 0$ unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:

$ifail = 1$

On entry,	$m2 < 1$ ,
or	$n2 < 1$ ,
or	$m1 < 1$ ,
or	$m1 > m2$ ,
or	$n1 < 1$ ,
or	$n1 > n2$ ,
or	$ldm < m2$ .

$ifail = 2$

On entry,

order is not 'A' or 'D'.

$ifail = - 99$: 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

Not applicable.

Further Comments

The average time taken by the function is approximately proportional to

n \times \log (n)

, where

n = m2 - m1 + 1

Example

This example reads a matrix of double numbers and ranks the rows in ascending order.

Open in the MATLAB editor: m01de_example

function m01de_example


fprintf('m01de example results\n\n');

rm = [6, 5, 4;
      5, 2, 1;
      2, 4, 9;
      4, 9, 6;
      4, 9, 5;
      4, 1, 2;
      3, 4, 1;
      2, 4, 6;
      1, 6, 4;
      9, 3, 2;
      6, 2, 5;
      4, 9, 6];

m1 = int64(1);
n1 = int64(1);
order = 'Ascending';

[irank, ifail] = m01de( ...
                        rm, m1, n1, order);

fprintf('          Data             Ranks\n\n');
for i = 1:size(rm,1)
  fprintf('%7.1f',rm(i,:));
  fprintf('    %7d\n',irank(i));
end

m01de example results

          Data             Ranks

    6.0    5.0    4.0         11
    5.0    2.0    1.0          9
    2.0    4.0    9.0          3
    4.0    9.0    6.0          7
    4.0    9.0    5.0          6
    4.0    1.0    2.0          5
    3.0    4.0    1.0          4
    2.0    4.0    6.0          2
    1.0    6.0    4.0          1
    9.0    3.0    2.0         12
    6.0    2.0    5.0         10
    4.0    9.0    6.0          8

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

Chapter Contents

Chapter Introduction

NAG Toolbox