g02bp 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 overwritten with the ranks of the observations.
Syntax
C# 

public static void g02bp( int n, int m, double[,] x, int[] miss, double[] xmiss, int itype, double[,] rr, out int ncases, int[] incase, out int ifail ) 
Visual Basic 

Public Shared Sub g02bp ( _ 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, _ incase As Integer(), _ <OutAttribute> ByRef ifail As Integer _ ) 
Visual C++ 

public: static void g02bp( int n, int m, array<double,2>^ x, array<int>^ miss, array<double>^ xmiss, int itype, array<double,2>^ rr, [OutAttribute] int% ncases, array<int>^ incase, [OutAttribute] int% ifail ) 
F# 

static member g02bp : n : int * m : int * x : float[,] * miss : int[] * xmiss : float[] * itype : int * rr : float[,] * ncases : int byref * incase : int[] * 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 $\mathit{j}=1,2,\dots ,m$.On exit: ${\mathbf{x}}[\mathit{i}1,\mathit{j}1]$ contains the rank ${y}_{\mathit{i}\mathit{j}}$ of the observation ${x}_{\mathit{i}\mathit{j}}$, for $\mathit{i}=1,2,\dots ,n$ and $\mathit{j}=1,2,\dots ,m$. (For those observations containing missing values, and therefore excluded from the calculation, ${y}_{\mathit{i}\mathit{j}}=0$, for $\mathit{j}=1,2,\dots ,m$.)
 miss
 Type: array<System..::..Int32>[]()[][]An array of size [m]On entry: ${\mathbf{miss}}\left[j1\right]$ must be set 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 method, and the information it contained on entry is lost.
 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]).On exit: the array xmiss is overwritten by the method, and the information it contained on entry is lost.
 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 number of cases, ${n}_{\mathrm{c}}$, actually used in the calculations (when cases involving missing values have been eliminated).
 incase
 Type: array<System..::..Int32>[]()[][]An array of size [n]On exit: ${\mathbf{incase}}\left[\mathit{i}1\right]$ holds the value $1$ if the $\mathit{i}$th case was included in the calculations, and the value $0$ if the $\mathit{i}$th case contained a missing value for at least one variable. That is, ${\mathbf{incase}}\left[\mathit{i}1\right]={w}_{\mathit{i}}$ (see [Description]), for $\mathit{i}=1,2,\dots ,n$.
 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.
$$\left[{x}_{ij}\right]\text{, \hspace{1em}}i=1,2,\dots ,n\text{}\left(n\ge 2\right),\text{\hspace{1em}}j=1,2,\dots ,m\text{}\left(m\ge 2\right)\text{,}$$ 
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 ${x}_{ij}={\mathit{xm}}_{j}$ for any $j$ for which an ${\mathit{xm}}_{j}$ has been assigned (see also [Accuracy]); and ${w}_{i}=1$ otherwise, for $\mathit{i}=1,2,\dots ,n$.
The quantities calculated are:
(a)  Ranks
For a given variable, $j$ say, each of the observations ${x}_{ij}$ for which ${w}_{i}=1$, 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 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 ${n}_{c}={\displaystyle \sum _{i=1}^{n}}{w}_{i}$.
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
Let ${y}_{ij}$ be the rank assigned to the observation ${x}_{ij}$ when the $j$th variable is being ranked. For those observations, $i$, for which ${w}_{i}=0$, ${y}_{ij}=0$, for $j=1,2,\dots ,m$.
The actual observations ${x}_{ij}$ are replaced by the ranks ${y}_{ij}$, for $\mathit{i}=1,2,\dots ,n$ and $\mathit{j}=1,2,\dots ,m$. 

(b)  Nonparametric rank correlation coefficients

References
Siegel S (1956) Nonparametric Statistics for the Behavioral Sciences McGraw–Hill
Error Indicators and Warnings
Errors or warnings detected by the method:
Some error messages may refer to parameters that are dropped from this interface
(LDX, LDRR) 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 $2$ cases remained.
 ${\mathbf{ifail}}=9000$
 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. g02bp 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 $0.99$ 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 the rank of each observation, and 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.
Example program (C#): g02bpe.cs