NAG Library Routine Document

g02bjf  (coeffs_pearson_subset_miss_pair)

 Contents

    1  Purpose
    7  Accuracy

1
Purpose

g02bjf computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables omitting cases with missing values from only those calculations involving the variables for which the values are missing.

2
Specification

Fortran Interface
Subroutine g02bjf ( n, m, x, ldx, miss, xmiss, nvars, kvar, xbar, std, ssp, ldssp, r, ldr, ncases, cnt, ldcnt, ifail)
Integer, Intent (In):: n, m, ldx, miss(m), nvars, kvar(nvars), ldssp, ldr, ldcnt
Integer, Intent (Inout):: ifail
Integer, Intent (Out):: ncases
Real (Kind=nag_wp), Intent (In):: x(ldx,m), xmiss(m)
Real (Kind=nag_wp), Intent (Inout):: ssp(ldssp,nvars), r(ldr,nvars), cnt(ldcnt,nvars)
Real (Kind=nag_wp), Intent (Out):: xbar(nvars), std(nvars)
C Header Interface
#include nagmk26.h
void  g02bjf_ ( const Integer *n, const Integer *m, const double x[], const Integer *ldx, const Integer miss[], const double xmiss[], const Integer *nvars, const Integer kvar[], double xbar[], double std[], double ssp[], const Integer *ldssp, double r[], const Integer *ldr, Integer *ncases, double cnt[], const Integer *ldcnt, Integer *ifail)

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, together with the subset of these variables, v1,v2,,vp, for which information is required.
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 wij=0 if the ith observation for the jth variable is a missing value, i.e., if a missing value, xmj, has been declared for the jth variable, and xij=xmj (see also Section 7); and wij=1 otherwise, for i=1,2,,n and j=1,2,,m.
The quantities calculated are:
(a) Means:
x-j=i=1nwijxij i=1nwij ,  j=v1,v2,,vp.  
(b) Standard deviations:
sj=i= 1nwij xij-x-j 2 i= 1nwij- 1 ,   j=v1,v2,,vp.  
(c) Sums of squares and cross-products of deviations from means:
Sjk=i=1nwijwikxij-x-jkxik-x-kj,  j,k=v1,v2,,vp,  
where
x-jk=i= 1nwijwikxij i= 1nwijwik   and   x-kj=i= 1nwikwijxik i= 1nwikwij ,  
(i.e., the means used in the calculation of the sum of squares and cross-products of deviations are based on the same set of observations as are the cross-products).
(d) Pearson product-moment correlation coefficients:
Rjk=SjkSjjkSkkj ,  j,k=v1,v2,,vp,  
where
Sjjk=i= 1nwijwikxij-x-jk2   and   Skkj=i= 1nwikwijxik-x-kj2,  
(i.e., the sums of squares of deviations used in the denominator are based on the same set of observations as are used in the calculation of the numerator).
If Sjjk or Skkj is zero, Rjk is set to zero.
(e) The number of cases used in the calculation of each of the correlation coefficients:
cjk=i=1nwijwik,  j,k=v1,v2,,vp.  
(The diagonal terms, cjj, for j=v1,v2,,vp, also give the number of cases used in the calculation of the means, x-j, and the standard deviations, sj.)

4
References

None.

5
Arguments

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:     xldxm – Real (Kind=nag_wp) arrayInput
On entry: xij must be set to xij, the value of the ith observation on the jth variable, for 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 g02bjf is called.
Constraint: ldxn.
5:     missm – Integer arrayInput
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.
6:     xmissm – Real (Kind=nag_wp) arrayInput
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).
7:     nvars – IntegerInput
On entry: p, the number of variables for which information is required.
Constraint: 2nvarsm.
8:     kvarnvars – Integer arrayInput
On entry: kvarj must be set to the column number in x of the jth variable for which information is required, for j=1,2,,p.
Constraint: 1kvarjm, for j=1,2,,p.
9:     xbarnvars – Real (Kind=nag_wp) arrayOutput
On exit: the mean value, x-j, of the variable specified in kvarj, for j=1,2,,p.
10:   stdnvars – Real (Kind=nag_wp) arrayOutput
On exit: the standard deviation, sj, of the variable specified in kvarj, for j=1,2,,p.
11:   sspldsspnvars – Real (Kind=nag_wp) arrayOutput
On exit: sspjk is the cross-product of deviations, Sjk, for the variables specified in kvarj and kvark, for j=1,2,,p and k=1,2,,p.
12:   ldssp – IntegerInput
On entry: the first dimension of the array ssp as declared in the (sub)program from which g02bjf is called.
Constraint: ldsspnvars.
13:   rldrnvars – Real (Kind=nag_wp) arrayOutput
On exit: rjk is the product-moment correlation coefficient, Rjk, between the variables specified in kvarj and kvark, for j=1,2,,p and k=1,2,,p.
14:   ldr – IntegerInput
On entry: the first dimension of the array r as declared in the (sub)program from which g02bjf is called.
Constraint: ldrnvars.
15:   ncases – IntegerOutput
On exit: the minimum number of cases used in the calculation of any of the sums of squares and cross-products and correlation coefficients (when cases involving missing values have been eliminated).
16:   cntldcntnvars – Real (Kind=nag_wp) arrayOutput
On exit: cntjk is the number of cases, cjk, actually used in the calculation of Sjk, and Rjk, the sum of cross-products and correlation coefficient for the variables specified in kvarj and kvark, for j=1,2,,p and k=1,2,,p.
17:   ldcnt – IntegerInput
On entry: the first dimension of the array cnt as declared in the (sub)program from which g02bjf is called.
Constraint: ldcntnvars.
18:   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, because for this routine the values of the output arguments may be useful even if ifail0 on exit, the recommended value is -1. 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).
Note: g02bjf may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
ifail=1
On entry, n=value.
Constraint: n2.
ifail=2
On entry, nvars=value and m=value.
Constraint: nvars2 and nvarsm.
ifail=3
On entry, ldcnt=value and nvars=value.
Constraint: ldcntnvars 
On entry, ldr=value and nvars=value.
Constraint: ldrnvars.
On entry, ldssp=value and nvars=value.
Constraint: ldsspnvars.
On entry, ldx=value and n=value.
Constraint: ldxn.
ifail=4
On entry, i=value, kvari=value and m=value.
Constraint: 1kvarim.
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

g02bjf 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. g02bjf 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
Parallelism and Performance

g02bjf is not threaded in any implementation.

9
Further Comments

The time taken by g02bjf depends on n and p, and the occurrence of missing values.
The routine uses a two-pass algorithm.

10
Example

This example reads in a set of data consisting of five observations on each of four variables. Missing values of -1.0, 0.0 and 0.0 are declared for the first, second and fourth variables respectively; no missing value is specified for the third variable. The means, standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for the fourth, first and second variables are then calculated and printed, omitting cases with missing values from only those calculations involving the variables for which the values are missing. The program therefore eliminates cases 4 and 5 in calculating the correlation between the fourth and first variables, and cases 3 and 4 for the fourth and second variables etc.

10.1
Program Text

Program Text (g02bjfe.f90)

10.2
Program Data

Program Data (g02bjfe.d)

10.3
Program Results

Program Results (g02bjfe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017