NAG Library Routine Document
G02BBF
1 Purpose
G02BBF computes means and standard deviations of variables, sums of squares and crossproducts of deviations from means, and Pearson productmoment correlation coefficients for a set of data omitting completely any cases with a missing observation for any variable.
2 Specification
SUBROUTINE G02BBF ( 
N, M, X, LDX, MISS, XMISS, XBAR, STD, SSP, LDSSP, R, LDR, NCASES, IFAIL) 
INTEGER 
N, M, LDX, MISS(M), LDSSP, LDR, NCASES, IFAIL 
REAL (KIND=nag_wp) 
X(LDX,M), XMISS(M), XBAR(M), STD(M), SSP(LDSSP,M), R(LDR,M) 

3 Description
The input data consist 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
$x{m}_{j}$. 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
${x}_{ij}=x{m}_{j}$ for any
$j$ for which an
$x{m}_{j}$ has been assigned (see also
Section 7); and
${w}_{i}=1$ otherwise, for
$\mathit{i}=1,2,\dots ,n$.
The quantities calculated are:
(a) 
Means:

(b) 
Standard deviations:

(c) 
Sums of squares and crossproducts of deviations from means:

(d) 
Pearson productmoment correlation coefficients:
If ${S}_{jj}$ or ${S}_{kk}$ is zero, ${R}_{jk}$ is set to zero. 
4 References
None.
5 Parameters
 1: N – INTEGERInput
On entry: $n$, the number of observations or cases.
Constraint:
${\mathbf{N}}\ge 2$.
 2: M – INTEGERInput
On entry: $m$, the number of variables.
Constraint:
${\mathbf{M}}\ge 2$.
 3: X(LDX,M) – REAL (KIND=nag_wp) arrayInput
On entry: ${\mathbf{X}}\left(\mathit{i},\mathit{j}\right)$ 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 $\mathit{j}=1,2,\dots ,m$.
 4: LDX – INTEGERInput
On entry: the first dimension of the array
X as declared in the (sub)program from which G02BBF is called.
Constraint:
${\mathbf{LDX}}\ge {\mathbf{N}}$.
 5: MISS(M) – INTEGER arrayInput/Output
On entry:
${\mathbf{MISS}}\left(j\right)$ 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:
${\mathbf{XMISS}}\left(j\right)$ 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
Section 7).
On exit: the array
XMISS is overwritten by the routine, and the information it contained on entry is lost.
 7: XBAR(M) – REAL (KIND=nag_wp) arrayOutput
On exit: the mean value,
${\stackrel{}{x}}_{\mathit{j}}$, of the $\mathit{j}$th variable, for $\mathit{j}=1,2,\dots ,m$.
 8: STD(M) – REAL (KIND=nag_wp) arrayOutput
On exit: the standard deviation,
${s}_{\mathit{j}}$, of the $\mathit{j}$th variable, for $\mathit{j}=1,2,\dots ,m$.
 9: SSP(LDSSP,M) – REAL (KIND=nag_wp) arrayOutput
On exit: ${\mathbf{SSP}}\left(\mathit{j},\mathit{k}\right)$ is the crossproduct of deviations ${S}_{\mathit{j}\mathit{k}}$, for $\mathit{j}=1,2,\dots ,m$ and $\mathit{k}=1,2,\dots ,m$.
 10: LDSSP – INTEGERInput
On entry: the first dimension of the array
SSP as declared in the (sub)program from which G02BBF is called.
Constraint:
${\mathbf{LDSSP}}\ge {\mathbf{M}}$.
 11: R(LDR,M) – REAL (KIND=nag_wp) arrayOutput
On exit: ${\mathbf{R}}\left(\mathit{j},\mathit{k}\right)$ is the productmoment correlation coefficient ${R}_{\mathit{j}\mathit{k}}$ between the $\mathit{j}$th and $\mathit{k}$th variables, for $\mathit{j}=1,2,\dots ,m$ and $\mathit{k}=1,2,\dots ,m$.
 12: LDR – INTEGERInput
On entry: the first dimension of the array
R as declared in the (sub)program from which G02BBF is called.
Constraint:
${\mathbf{LDR}}\ge {\mathbf{M}}$.
 13: NCASES – INTEGEROutput
On exit: the number of cases actually used in the calculations (when cases involving missing values have been eliminated).
 14: IFAIL – INTEGERInput/Output

On entry:
IFAIL must be set to
$0$,
$1\text{ or}1$. 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
$1\text{ or}1$ 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 $\mathbf{1}\text{ or}\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit:
${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
${\mathbf{IFAIL}}={\mathbf{0}}$ or
${{\mathbf{1}}}$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

On entry,  ${\mathbf{N}}<2$. 
 ${\mathbf{IFAIL}}=2$

On entry,  ${\mathbf{M}}<2$. 
 ${\mathbf{IFAIL}}=3$

On entry,  ${\mathbf{LDX}}<{\mathbf{N}}$, 
or  ${\mathbf{LDSSP}}<{\mathbf{M}}$, 
or  ${\mathbf{LDR}}<{\mathbf{M}}$. 
 ${\mathbf{IFAIL}}=4$

After observations with missing values were omitted, no cases remained.
 ${\mathbf{IFAIL}}=5$

After observations with missing values were omitted, only one case remained.
7 Accuracy
G02BBF does not use additional precision arithmetic for the accumulation of scalar products, so there may be a loss of significant figures for large $n$.
You are warned of the need to exercise extreme care in your selection of missing values. G02BBF treats all values in the inclusive range
$\left(1\pm {0.1}^{\left({\mathbf{X02BEF}}2\right)}\right)\times {xm}_{j}$, 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.
The time taken by G02BBF depends on $n$ and $m$, and the occurrence of missing values.
The routine uses a twopass algorithm.
9 Example
This example reads in a set of data consisting of five observations on each of three variables. Missing values of $0.0$ are declared for the first and third variables; no missing value is specified for the second variable. The means, standard deviations, sums of squares and crossproducts of deviations from means, and Pearson productmoment correlation coefficients for all three variables are then calculated and printed, omitting completely all cases containing missing values; cases $3$ and $4$ are therefore eliminated, leaving only three cases in the calculations.
9.1 Program Text
Program Text (g02bbfe.f90)
9.2 Program Data
Program Data (g02bbfe.d)
9.3 Program Results
Program Results (g02bbfe.r)