g02bs computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data omitting cases with missing values from only those calculations involving the variables for which the values are missing; the data array is preserved, and the ranks of the observations are not available on exit from the method.
Syntax
C# 

public static void g02bs( int n, int m, double[,] x, int[] miss, double[] xmiss, int itype, double[,] rr, out int ncases, double[,] cnt, out int ifail ) 
Visual Basic 

Public Shared Sub g02bs ( _ n As Integer, _ m As Integer, _ x As Double(,), _ miss As Integer(), _ xmiss As Double(), _ itype As Integer, _ rr As Double(,), _ <OutAttribute> ByRef ncases As Integer, _ cnt As Double(,), _ <OutAttribute> ByRef ifail As Integer _ ) 
Visual C++ 

public: static void g02bs( int n, int m, array<double,2>^ x, array<int>^ miss, array<double>^ xmiss, int itype, array<double,2>^ rr, [OutAttribute] int% ncases, array<double,2>^ cnt, [OutAttribute] int% ifail ) 
F# 

static member g02bs : n : int * m : int * x : float[,] * miss : int[] * xmiss : float[] * itype : int * rr : float[,] * ncases : int byref * cnt : float[,] * ifail : int byref > unit 
Parameters
 n
 Type: System..::..Int32On entry: $n$, the number of observations or cases.Constraint: ${\mathbf{n}}\ge 2$.
 m
 Type: System..::..Int32On entry: $m$, the number of variables.Constraint: ${\mathbf{m}}\ge 2$.
 x
 Type: array<System..::..Double,2>[,](,)[,][,]An array of size [dim1, m]Note: dim1 must satisfy the constraint: $\mathrm{dim1}\ge {\mathbf{n}}$On entry: ${\mathbf{x}}[\mathit{i}1,\mathit{j}1]$ 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 $\mathit{j}=1,2,\dots ,m$.
 miss
 Type: array<System..::..Int32>[]()[][]An array of size [m]
 xmiss
 Type: array<System..::..Double>[]()[][]An array of size [m]On entry: ${\mathbf{xmiss}}\left[j1\right]$ 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]).
 itype
 Type: System..::..Int32On entry: the type of correlation coefficients which are to be calculated.
 ${\mathbf{itype}}=1$
 Only Kendall's tau coefficients are calculated.
 ${\mathbf{itype}}=0$
 Both Kendall's tau and Spearman's coefficients are calculated.
 ${\mathbf{itype}}=1$
 Only Spearman's coefficients are calculated.
Constraint: ${\mathbf{itype}}=1$, $0$ or $1$.
 rr
 Type: array<System..::..Double,2>[,](,)[,][,]An array of size [dim1, m]Note: dim1 must satisfy the constraint: $\mathrm{dim1}\ge {\mathbf{m}}$On exit: the requested correlation coefficients.If only Kendall's tau coefficients are requested (${\mathbf{itype}}=1$), ${\mathbf{rr}}[j1,k1]$ contains Kendall's tau for the $j$th and $k$th variables.If only Spearman's coefficients are requested (${\mathbf{itype}}=1$), ${\mathbf{rr}}[j1,k1]$ 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 (${\mathbf{itype}}=0$), the upper triangle of rr contains the Spearman coefficients and the lower triangle the Kendall coefficients. That is, for the $\mathit{j}$th and $\mathit{k}$th variables, where $\mathit{j}$ is less than $\mathit{k}$, ${\mathbf{rr}}[\mathit{j}1,\mathit{k}1]$ contains the Spearman rank correlation coefficient, and ${\mathbf{rr}}[\mathit{k}1,\mathit{j}1]$ contains Kendall's tau, for $\mathit{j}=1,2,\dots ,m$ and $\mathit{k}=1,2,\dots ,m$.(Diagonal terms, ${\mathbf{rr}}[j1,j1]$, are unity for all three values of itype.)
 ncases
 Type: System..::..Int32%On exit: the minimum number of cases used in the calculation of any of the correlation coefficients (when cases involving missing values have been eliminated).
 cnt
 Type: array<System..::..Double,2>[,](,)[,][,]An array of size [dim1, m]Note: dim1 must satisfy the constraint: $\mathrm{dim1}\ge {\mathbf{m}}$On exit: the number of cases, ${n}_{\mathit{j}\mathit{k}}$, actually used in the calculation of the rank correlation coefficient for the $\mathit{j}$th and $\mathit{k}$th variables, for $\mathit{j}=1,2,\dots ,m$ and $\mathit{k}=1,2,\dots ,m$.
 ifail
 Type: System..::..Int32%On exit: ${\mathbf{ifail}}={0}$ unless the method detects an error or a warning has been flagged (see [Error Indicators and Warnings]).
Description
The input data consists of $n$ observations for each of $m$ variables, given as an array
where ${x}_{ij}$ 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 ${\mathit{xm}}_{j}$. Missing values need not be specified for all variables.
$$\left[{x}_{ij}\right]\text{, \hspace{1em}}i=1,2,\dots ,n\left(n\ge 2\right)\text{ and}j=1,2,\dots ,m\left(m\ge 2\right)\text{,}$$ 
Let ${w}_{\mathit{i}\mathit{j}}=0$ if the $\mathit{i}$th observation for the $\mathit{j}$th variable is a missing value, i.e., if a missing value, ${\mathit{xm}}_{\mathit{j}}$, has been declared for the $\mathit{j}$th variable, and ${x}_{\mathit{i}\mathit{j}}={\mathit{xm}}_{\mathit{j}}$ (see also [Accuracy]); and ${w}_{\mathit{i}\mathit{j}}=1$ otherwise, for $\mathit{i}=1,2,\dots ,n$ and $\mathit{j}=1,2,\dots ,m$.
The observations are first ranked, a pair of variables at a time as follows:
For a given pair of variables, $j$ and $l$ say, each of the observations ${x}_{\mathit{i}j}$ for which the product ${w}_{\mathit{i}j}{w}_{\mathit{i}l}=1$, for $\mathit{i}=1,2,\dots ,n$, has associated with it an additional number, the ‘rank’ of the observation, which indicates the magnitude of that observation relative to the magnitude of the other observations on variable $j$ for which ${w}_{ij}{w}_{il}=1$.
The smallest of these valid observations for variable $j$ is assigned to rank $1$, the second smallest valid observation for variable $j$ the rank $2$, the third smallest rank $3$, and so on until the largest such observation is given the rank ${n}_{jl}$, where
If a number of cases all have the same value for the 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 ${w}_{ij}{w}_{il}=1$ were found to have the same value, then instead of giving them the ranks
all $k$ observations would be assigned the rank
and the next value in ascending order would be assigned the rank
The variable $\mathit{l}$ is then ranked in a similar way. The process is then repeated for all pairs of variables $\mathit{j}$ and $\mathit{l}$, for $\mathit{j}=1,2,\dots ,m$ and $\mathit{l}=\mathit{j},\dots ,m$. Let ${y}_{\mathit{i}\mathit{j}\left(\mathit{l}\right)}$ be the rank assigned to the observation ${x}_{\mathit{i}\mathit{j}}$ when the $\mathit{j}$th and $\mathit{l}$th variables are being ranked, and ${y}_{\mathit{i}\mathit{l}\left(\mathit{j}\right)}$ be the rank assigned to the observation ${x}_{\mathit{i}\mathit{l}}$ during the same process, for $\mathit{i}=1,2,\dots ,n$, $\mathit{j}=1,2,\dots ,m$ and $\mathit{l}=j,\dots ,m$.
$${n}_{jl}=\sum _{i=1}^{n}{w}_{ij}{w}_{il}\text{.}$$ 
$$h+1,h+2,\dots ,h+k\text{,}$$ 
$$\frac{2h+k+1}{2}$$ 
$$h+k+1\text{.}$$ 
The quantities calculated are:
(a)  Kendall's tau rank correlation coefficients:


(b)  Spearman's rank correlation coefficients:
and ${T}_{j\left(k\right)}^{*}=\sum {t}_{j}\left({t}_{j}^{2}1\right)$, where ${t}_{j}$ is the number of ties of a particular value of variable $j$ when the $j$th and $k$th variables are being ranked, and the summation is over all tied values of variable $j$. 
References
Siegel S (1956) Nonparametric Statistics for the Behavioral Sciences McGraw–Hill
Error Indicators and Warnings
Note: g02bs may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the method:
Some error messages may refer to parameters that are dropped from this interface
(LDX, LDRR, LDCNT) In these
cases, an error in another parameter has usually caused an incorrect value to be inferred.
 ${\mathbf{ifail}}=1$
On entry, ${\mathbf{n}}<2$.
 ${\mathbf{ifail}}=2$
On entry, ${\mathbf{m}}<2$.
 ${\mathbf{ifail}}=4$
On entry, ${\mathbf{itype}}<1$, or ${\mathbf{itype}}>1$.
 ${\mathbf{ifail}}=5$
 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 correlation coefficients based on two or more cases are returned by the method even if ${\mathbf{ifail}}={5}$.
 ${\mathbf{ifail}}=9000$
 An error occured, see message report.
 ${\mathbf{ifail}}=6000$
 Invalid Parameters $\u2329\mathit{\text{value}}\u232a$
 ${\mathbf{ifail}}=4000$
 Invalid dimension for array $\u2329\mathit{\text{value}}\u232a$
 ${\mathbf{ifail}}=8000$
 Negative dimension for array $\u2329\mathit{\text{value}}\u232a$
 ${\mathbf{ifail}}=6000$
 Invalid Parameters $\u2329\mathit{\text{value}}\u232a$
Accuracy
You are warned of the need to exercise extreme care in your selection of missing values. g02bs treats all values in the inclusive range $\left(1\pm {0.1}^{\left({\mathbf{x02be}}2\right)}\right)\times {xm}_{j}$, where ${\mathit{xm}}_{j}$ 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.
Parallelism and Performance
None.
Further Comments
Example
This example reads in a set of data consisting of nine observations on each of three variables. Missing values of $0.99$, $9.0$ and $0.0$ are declared for the first, second and third variables respectively. The program then calculates and prints both Kendall's tau and Spearman's rank correlation coefficients for all three variables, omitting cases with missing values from only those calculations involving the variables for which the values are missing. The program therefore eliminates cases $4$, $5$, $7$ and $9$ in calculating and correlation between the first and second variables, cases $5$, $8$ and $9$ for the first and third variables, and cases $4$, $7$ and $8$ for the second and third variables.
Example program (C#): g02bse.cs