G02BRF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

NAG Library Routine Document


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

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


3  Description

The input data consists of n observations for each of m variables, given as an array
xij ,   i=1,2,,n n2 ,   j=1,2,,m m2 ,
where xij is the ith observation on the jth 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 jth variable is denoted by xmj. Missing values need not be specified for all variables.
Let wi=0 if observation i contains a missing value for any of those variables for which missing values have been declared, i.e., if xij=xmj for any j for which an xmj has been assigned (see also Section 7); and wi=1 otherwise, for i=1,2,,n.
The observations are first ranked as follows.
For a given variable, j say, each of the observations xij for which wi=1, (i=1,2,,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 wi=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 nc, where nc=i=1nwi.
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 wi=1 were found to have the same value, then instead of giving them the ranks
h+1, h+2, , h+k ,
all k observations would be assigned the rank
2h+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 yij be the rank assigned to the observation xij when the jth variable is being ranked. For those observations, i, for which wi=0, yij=0, for j=1,2,,m.
The quantities calculated are:
(a) Kendall's tau rank correlation coefficients:
Rjk = h=1 n i=1 n wh wi signyhj-yij signyhk-yik ncnc-1-Tjncnc-1-Tk ,   j,k=1,2,,m ,
where nc=i=1nwi
and signu=1 if u>0
signu=0 if u=0
signu=-1 if u<0
and Tj=tjtj-1 where tj 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:
Rjk*=ncnc2-1-6i=1nwi yij-yik 2-12Tj*+Tk* ncnc2-1-Tj*ncnc2-1-Tk* ,  j,k=1,2,,m,
where nc=i=1nwi and Tj*=tjtj2-1 where tj 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  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 value of the ith observation on the jth variable, where 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 G02BRF is called.
Constraint: LDXN.
5:     MISS(M) – INTEGER arrayInput/Output
On entry: MISSj must be set equal to 1 if a missing value, xmj, is to be specified for the jth 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: XMISSj must be set to the missing value, xmj, to be associated with the jth 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.
Only Kendall's tau coefficients are calculated.
Both Kendall's tau and Spearman's coefficients are calculated.
Only Spearman's coefficients are calculated.
Constraint: ITYPE=-1, 0 or 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), RRjk contains Kendall's tau for the jth and kth variables.
If only Spearman's coefficients are requested (ITYPE=1), RRjk contains Spearman's rank correlation coefficient for the jth and kth 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 jth and kth variables, where j is less than k, RRjk contains the Spearman rank correlation coefficient, and RRkj contains Kendall's tau, for j=1,2,,m and k=1,2,,m.
(Diagonal terms, RRjj, 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: LDRRM.
10:   NCASES – INTEGEROutput
On exit: the number of cases, nc, actually used in the calculations (when cases involving missing values have been eliminated).
11:   INCASE(N) – INTEGER arrayOutput
On exit: INCASEi holds the value 1 if the ith case was included in the calculations, and the value 0 if the ith case contained a missing value for at least one variable. That is, INCASEi=wi (see Section 3), for i=1,2,,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 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:
On entry,N<2.
On entry,M<2.
On entry,LDX<N,
On entry,ITYPE<-1,
After observations with missing values were omitted, fewer than 2 cases remained.

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±0.1X02BEF-2×xmj, where xmj 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  Further Comments

The time taken by G02BRF depends on n and m, and the occurrence of missing values.

9  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.

9.1  Program Text

Program Text (g02brfe.f90)

9.2  Program Data

Program Data (g02brfe.d)

9.3  Program Results

Program Results (g02brfe.r)

G02BRF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

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