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NAG Toolbox: nag_correg_coeffs_zero_subset_miss_pair (g02bm)

Purpose

nag_correg_coeffs_zero_subset_miss_pair (g02bm) computes means and standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for selected variables omitting cases with missing values from only those calculations involving the variables for which the values are missing.

Syntax

[xbar, std, sspz, rz, ncases, cnt, ifail] = g02bm(x, miss, xmiss, kvar, 'n', n, 'm', m, 'nvars', nvars)
[xbar, std, sspz, rz, ncases, cnt, ifail] = nag_correg_coeffs_zero_subset_miss_pair(x, miss, xmiss, kvar, 'n', n, 'm', m, 'nvars', nvars)
Note: the interface to this routine has changed since earlier releases of the toolbox:
Mark 22: n has been made optional
.

Description

The input data consists of nn observations for each of mm variables, given as an array
[xij],  i = 1,2,,n (n2),j = 1,2,,m(m2),
[xij],  i=1,2,,n (n2),j=1,2,,m(m2),
where xijxij is the iith observation on the jjth variable, together with the subset of these variables, v1,v2,,vpv1,v2,,vp, for which information is required.
In addition, each of the mm 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 jjth variable is denoted by xmjxmj. Missing values need not be specified for all variables.
Let wij = 0wij=0, if the iith observation for the jjth variable is a missing value, i.e., if a missing value, xmjxmj, has been declared for the jjth variable, and xij = xmjxij=xmj (see also Section [Accuracy]); and wij = 1wij=1 otherwise, for i = 1,2,,ni=1,2,,n and j = 1,2,,mj=1,2,,m.
The quantities calculated are:
(a) Means:
xj = (i = 1nwijxij)/(i = 1nwij),  j = v1,v2,,vp.
x-j=i=1nwijxij i=1nwij ,  j=v1,v2,,vp.
(b) Standard deviations:
sj = sqrt((i = 1nwij(xijxj)2)/(i = 1nwij 1)),   j = v1,v2,,vp.
sj=i= 1nwij (xij-x-j) 2 i= 1nwij- 1 ,   j=v1,v2,,vp.
(c) Sums of squares and cross-products about zero:
n
jk = wijwikxijxik,  j,k = v1,v2,,vp.
i = 1
S~jk=i=1nwijwikxijxik,  j,k=v1,v2,,vp.
(d) Correlation-like coefficients:
jk = (jk)/(sqrt(jj(k) kk(j))),   j,k = v1,v2,,vp ,
R~jk=S~jkS~jj(k) S~kk(j) ,   j,k=v1,v2,,vp ,
where jj(k) = i = 1nwijwikxij2S~jj(k)=i=1nwijwikxij2 and kk(j) = i = 1nwikwijxik2S~kk(j)=i=1nwikwijxik2 
(i.e., the sums of squares about zero are based on the same set of observations as are used in the calculation of the numerator).
If jj(k)S~jj(k) or kk(j)S~kk(j) is zero, jkR~jk is set to zero.
(e) The number of cases used in the calculation of each of the correlation-like coefficients:
n
cjk = wijwik,  j,k = v1,v2,,vp.
i = 1
cjk=i=1nwijwik,  j,k=v1,v2,,vp.
(The diagonal terms, cjjcjj, for j = 1,2,,nj=1,2,,n, also give the number of cases used in the calculation of the means xjx-j and the standard deviations sjsj.)

References

None.

Parameters

Compulsory Input Parameters

1:     x(ldx,m) – double array
ldx, the first dimension of the array, must satisfy the constraint ldxnldxn.
x(i,j)xij must be set to xijxij, the value of the iith observation on the jjth variable, for i = 1,2,,ni=1,2,,n and j = 1,2,,mj=1,2,,m.
2:     miss(m) – int64int32nag_int array
m, the dimension of the array, must satisfy the constraint m2m2.
miss(j)missj must be set equal to 11 if a missing value, xmjxmj, is to be specified for the jjth variable in the array x, or set equal to 00 otherwise. Values of miss must be given for all mm variables in the array x.
3:     xmiss(m) – double array
m, the dimension of the array, must satisfy the constraint m2m2.
xmiss(j)xmissj must be set to the missing value, xmjxmj, to be associated with the jjth variable in the array x, for those variables for which missing values are specified by means of the array miss (see Section [Accuracy]).
4:     kvar(nvars) – int64int32nag_int array
nvars, the dimension of the array, must satisfy the constraint 2nvarsm2nvarsm.
kvar(j)kvarj must be set to the column number in x of the jjth variable for which information is required, for j = 1,2,,pj=1,2,,p.
Constraint: 1kvar(j)m1kvarjm, for j = 1,2,,pj=1,2,,p.

Optional Input Parameters

1:     n – int64int32nag_int scalar
Default: The first dimension of the array x.
nn, the number of observations or cases.
Constraint: n2n2.
2:     m – int64int32nag_int scalar
Default: The dimension of the arrays miss, xmiss and the second dimension of the array x. (An error is raised if these dimensions are not equal.)
mm, the number of variables.
Constraint: m2m2.
3:     nvars – int64int32nag_int scalar
Default: The dimension of the array kvar.
pp, the number of variables for which information is required.
Constraint: 2nvarsm2nvarsm.

Input Parameters Omitted from the MATLAB Interface

ldx ldsspz ldrz ldcnt

Output Parameters

1:     xbar(nvars) – double array
The mean value, xjx-j, of the variable specified in kvar(j)kvarj, for j = 1,2,,pj=1,2,,p.
2:     std(nvars) – double array
The standard deviation, sjsj, of the variable specified in kvar(j)kvarj, for j = 1,2,,pj=1,2,,p.
3:     sspz(ldsspz,nvars) – double array
ldsspznvarsldsspznvars.
sspz(j,k)sspzjk is the cross-product about zero, jkS~jk, for the variables specified in kvar(j)kvarj and kvar(k)kvark, for j = 1,2,,pj=1,2,,p and k = 1,2,,pk=1,2,,p.
4:     rz(ldrz,nvars) – double array
ldrznvarsldrznvars.
rz(j,k)rzjk is the correlation-like coefficient, jkR~jk, between the variables specified in kvar(j)kvarj and kvar(k)kvark, for j = 1,2,,pj=1,2,,p and k = 1,2,,pk=1,2,,p.
5:     ncases – int64int32nag_int scalar
The minimum number of cases used in the calculation of any of the sums of squares and cross-products and correlation-like coefficients (when cases involving missing values have been eliminated).
6:     cnt(ldcnt,nvars) – double array
ldcntnvarsldcntnvars.
cnt(j,k)cntjk is the number of cases, cjkcjk, actually used in the calculation of the sum of cross-product and correlation-like coefficient for the variables specified in kvar(j)kvarj and kvar(k)kvark, for j = 1,2,,pj=1,2,,p and k = 1,2,,pk=1,2,,p.
7:     ifail – int64int32nag_int scalar
ifail = 0ifail=0 unless the function detects an error (see [Error Indicators and Warnings]).

Error Indicators and Warnings

Note: nag_correg_coeffs_zero_subset_miss_pair (g02bm) may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the function:

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

  ifail = 1ifail=1
On entry,n < 2n<2.
  ifail = 2ifail=2
On entry,nvars < 2nvars<2,
ornvars > mnvars>m.
  ifail = 3ifail=3
On entry,ldx < nldx<n,
orldsspz < nvarsldsspz<nvars,
orldrz < nvarsldrz<nvars,
orldcnt < nvarsldcnt<nvars.
  ifail = 4ifail=4
On entry,kvar(j) < 1kvarj<1,
orkvar(j) > mkvarj>m for some j = 1,2,,nvarsj=1,2,,nvars.
W ifail = 5ifail=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 means, standard deviations, sums of squares and cross-products, and correlation-like coefficients based on two or more cases are returned by the function even if ifail = 5ifail=5.

Accuracy

nag_correg_coeffs_zero_subset_miss_pair (g02bm) does not use additional precision arithmetic for the accumulation of scalar products, so there may be a loss of significant figures for large nn.
You are warned of the need to exercise extreme care in your selection of missing values. nag_correg_coeffs_zero_subset_miss_pair (g02bm) treats all values in the inclusive range (1 ± 0.1(x02be2)) × xmj(1±0.1(x02be-2))×xmj, where xmjxmj is the missing value for variable jj 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.

Further Comments

The time taken by nag_correg_coeffs_zero_subset_miss_pair (g02bm) depends on nn and pp, and the occurrence of missing values.
The function uses a two-pass algorithm.

Example

function nag_correg_coeffs_zero_subset_miss_pair_example
x = [3, 3, 1, 2;
     6, 4, -1, 4;
     9, 0, 5, 9;
     12, 2, 0, 0;
     -1, 5, 4, 12];
miss = [int64(1);1;0;1];
xmiss = [-1;
     0;
     0;
     0];
kvar = [int64(4);1;2];
[xbar, std, sspz, rz, ncases, count, ifail] = ...
    nag_correg_coeffs_zero_subset_miss_pair(x, miss, xmiss, kvar)
 

xbar =

    6.7500
    7.5000
    3.5000


std =

    4.5735
    3.8730
    1.2910


sspz =

   245   111    82
   111   270    57
    82    57    54


rz =

    1.0000    0.9840    0.9055
    0.9840    1.0000    0.7699
    0.9055    0.7699    1.0000


ncases =

                    3


count =

     4     3     3
     3     4     3
     3     3     4


ifail =

                    0


function g02bm_example
x = [3, 3, 1, 2;
     6, 4, -1, 4;
     9, 0, 5, 9;
     12, 2, 0, 0;
     -1, 5, 4, 12];
miss = [int64(1);1;0;1];
xmiss = [-1;
     0;
     0;
     0];
kvar = [int64(4);1;2];
[xbar, std, sspz, rz, ncases, count, ifail] = g02bm(x, miss, xmiss, kvar)
 

xbar =

    6.7500
    7.5000
    3.5000


std =

    4.5735
    3.8730
    1.2910


sspz =

   245   111    82
   111   270    57
    82    57    54


rz =

    1.0000    0.9840    0.9055
    0.9840    1.0000    0.7699
    0.9055    0.7699    1.0000


ncases =

                    3


count =

     4     3     3
     3     4     3
     3     3     4


ifail =

                    0



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