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NAG Library Routine Document

g02brf (coeffs_kspearman_miss_case)

▸▿ 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

g02brf computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data, omitting completely any cases with a missing observation for any variable; the data array is preserved, and the ranks of the observations are not available on exit from the routine.

2

Specification

Fortran Interface

Subroutine g02brf (

n, m, x, ldx, miss, xmiss, itype, rr, ldrr, ncases, incase, kworka, kworkb, kworkc, work1, work2, ifail)

Integer, Intent (In)	::	n, m, ldx, itype, ldrr
Integer, Intent (Inout)	::	miss(m), ifail
Integer, Intent (Out)	::	ncases, incase(n), kworka(n), kworkb(n), kworkc(n)
Real (Kind=nag_wp), Intent (In)	::	x(ldx,m)
Real (Kind=nag_wp), Intent (Inout)	::	xmiss(m), rr(ldrr,m)
Real (Kind=nag_wp), Intent (Out)	::	work1(n), work2(n)

C Header Interface

#include nagmk26.h

void

g02brf_ ( const Integer *n, const Integer *m, const double x[], const Integer *ldx, Integer miss[], double xmiss[], const Integer *itype, double rr[], const Integer *ldrr, Integer *ncases, Integer incase[], Integer kworka[], Integer kworkb[], Integer kworkc[], double work1[], double work2[], Integer *ifail)

3

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. 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} = 0

if observation

i

contains a missing value for any of those variables for which missing values have been declared, i.e., if

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

for any

j

for which an

{xm}_{j}

has been assigned (see also Section 7); and

w_{i} = 1

otherwise, for

i = 1, 2, \dots, n

The observations are first ranked as follows.

For a given variable,

j

say, each of the observations

x_{i j}

for which

w_{i} = 1

, (

i = 1, 2, \dots, n

) has associated with it an additional number, the ‘rank’ of the observation, which indicates the magnitude of that observation relative to the magnitudes of the other observations on that same variable for which

w_{i} = 1

The smallest of these valid observations for variable

j

is assigned the rank

1

, the second smallest observation for variable

j

the rank

2

, the third smallest the rank

3

, and so on until the largest such observation is given the rank

n_{c}

, where

n_{c} = \sum_{i = 1}^{n} w_{i}

If a number of cases all have the same value for the given variable,

j

, then they are each given an ‘average’ rank, e.g., if in attempting to assign the rank

h + 1

k

observations for which

w_{i} = 1

were found to have the same value, then instead of giving them the ranks

h + 1, h + 2, \dots, h + k,

all

k

observations would be assigned the rank

\frac{2 h + k + 1}{2}

and the next value in ascending order would be assigned the rank

h + k + 1 .

The process is repeated for each of the

m

variables.

Let

y_{i j}

be the rank assigned to the observation

x_{i j}

when the

j

th variable is being ranked. For those observations,

i

, for which

w_{i} = 0

y_{i j} = 0

, for

j = 1, 2, \dots, m

The quantities calculated are:

(a)

Kendall's tau rank correlation coefficients:

R_{j k} = \frac{\sum_{h = 1}^{n} \sum_{i = 1}^{n} w_{h} w_{i} sign (y_{h j} - y_{i j}) sign (y_{h k} - y_{i k})}{\sqrt{[n_{c} (n_{c} - 1) - T_{j}] [n_{c} (n_{c} - 1) - T_{k}]}}, j, k = 1, 2, \dots, m,

where	$n_{c} = \sum_{i = 1}^{n} w_{i}$
and	$sign u = 1$ if $u > 0$
	$sign u = 0$ if $u = 0$
	$sign u = - 1$ if $u < 0$

and

T_{j} = \sum t_{j} (t_{j} - 1)

where

t_{j}

is the number of ties of a particular value of variable

j

, and the summation is over all tied values of variable

j

(b)

Spearman's rank correlation coefficients:

R_{j k}^{*} = \frac{n_{c} (n_{c}^{2} - 1) - 6 \sum_{i = 1}^{n} w_{i} {(y_{i j} - y_{i k})}^{2} - \frac{1}{2} (T_{j}^{*} + T_{k}^{*})}{\sqrt{[n_{c} (n_{c}^{2} - 1) - T_{j}^{*}] [n_{c} (n_{c}^{2} - 1) - T_{k}^{*}]}}, j, k = 1, 2, \dots, m,

where

n_{c} = \sum_{i = 1}^{n} w_{i}

and

T_{j}^{*} = \sum t_{j} (t_{j}^{2} - 1)

where

t_{j}

is the number of ties of a particular value of variable

j

, and the summation is over all tied values of variable

j

4

References

Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill

5

Arguments

1: $n$ – IntegerInput

On entry:

n

, the number of observations or cases.

Constraint:

n \geq 2

2: $m$ – IntegerInput

On entry:

m

, the number of variables.

Constraint:

m \geq 2

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

On entry:

x (i, j)

must be set to

x_{i j}

, the value of the

i

th observation on the

j

th variable, where

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m .

4: $ldx$ – IntegerInput

On entry: the first dimension of the array x as declared in the (sub)program from which g02brf is called.

Constraint:

ldx \geq n

5: $miss (m)$ – Integer arrayInput/Output

On entry:

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.

On exit: the array miss is overwritten by the routine, and the information it contained on entry is lost.

6: $xmiss (m)$ – Real (Kind=nag_wp) arrayInput/Output

On entry:

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 Section 7).

On exit: the array xmiss is overwritten by the routine, and the information it contained on entry is lost.

7: $itype$ – IntegerInput

On entry: the type of correlation coefficients which are to be calculated.

$itype = - 1$: Only Kendall's tau coefficients are calculated.
$itype = 0$: Both Kendall's tau and Spearman's coefficients are calculated.
$itype = 1$: Only Spearman's coefficients are calculated.

Constraint:

itype = - 1

0

1

8: $rr (ldrr, m)$ – Real (Kind=nag_wp) arrayOutput

On exit: the requested correlation coefficients.

If only Kendall's tau coefficients are requested (

itype = - 1

rr (j, k)

contains Kendall's tau for the

j

th and

k

th variables.

If only Spearman's coefficients are requested (

itype = 1

rr (j, k)

contains Spearman's rank correlation coefficient for the

j

th and

k

th variables.

If both Kendall's tau and Spearman's coefficients are requested (

itype = 0

), the upper triangle of rr contains the Spearman coefficients and the lower triangle the Kendall coefficients. That is, for the

j

th and

k

th variables, where

j

is less than

k

rr (j, k)

contains the Spearman rank correlation coefficient, and

rr (k, j)

contains Kendall's tau, for

j = 1, 2, \dots, m

and

k = 1, 2, \dots, m

(Diagonal terms,

rr (j, j)

, are unity for all three values of itype.)

9: $ldrr$ – IntegerInput

On entry: the first dimension of the array rr as declared in the (sub)program from which g02brf is called.

Constraint:

ldrr \geq m

10: $ncases$ – IntegerOutput

On exit: the number of cases,

n_{c}

, actually used in the calculations (when cases involving missing values have been eliminated).

11: $incase (n)$ – Integer arrayOutput

On exit:

incase (i)

holds the value

1

if the

i

th case was included in the calculations, and the value

0

if the

i

th case contained a missing value for at least one variable. That is,

incase (i) = w_{i}

(see Section 3), for

i = 1, 2, \dots, n

12: $kworka (n)$ – Integer arrayWorkspace

13: $kworkb (n)$ – Integer arrayWorkspace

14: $kworkc (n)$ – Integer arrayWorkspace

15: $work1 (n)$ – Real (Kind=nag_wp) arrayWorkspace

16: $work2 (n)$ – Real (Kind=nag_wp) arrayWorkspace

17: $ifail$ – 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, $n = 〈value〉$ .
Constraint: $n \geq 2$ .

$ifail = 2$: On entry, $m = 〈value〉$ .
Constraint: $m \geq 2$ .

$ifail = 3$: On entry, $ldrr = 〈value〉$ and $m = 〈value〉$ .
Constraint: $ldrr \geq m$ .

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

$ifail = 4$: On entry, $itype = 〈value〉$ .
Constraint: $itype = - 1$ or $1$ .

$ifail = 5$: After observations with missing values were omitted, fewer than two cases remained.

$ifail = - 99$: 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

You are warned of the need to exercise extreme care in your selection of missing values. g02brf treats all values in the inclusive range

(1 \pm {0.1}^{(x02bef - 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.

8

Parallelism and Performance

g02brf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9

Further Comments

The time taken by g02brf depends on

n

and

m

, and the occurrence of missing values.

10

Example

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

0.99

and

0.0

are declared for the first and third variables respectively; no missing value is specified for the second variable. The program then calculates and prints both Kendall's tau and Spearman's rank correlation coefficients for all three variables, omitting completely all cases containing missing values; cases

5

8

and

9

are therefore eliminated, leaving only six cases in the calculations.

NAG Library Routine Document

g02brf (coeffs_kspearman_miss_case)

▸▿ 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