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NAG Toolbox

NAG Toolbox: nag_tsa_multi_gain_bivar (g13cf)

Purpose

For a bivariate time series, nag_tsa_multi_gain_bivar (g13cf) calculates the gain and phase together with lower and upper bounds from the univariate and bivariate spectra.

Syntax

[gn, gnlw, gnup, ph, phlw, phup, ifail] = g13cf(xg, yg, xyrg, xyig, stats, 'ng', ng)
[gn, gnlw, gnup, ph, phlw, phup, ifail] = nag_tsa_multi_gain_bivar(xg, yg, xyrg, xyig, stats, 'ng', ng)

Description

Estimates of the gain G(ω)G(ω) and phase φ(ω)ϕ(ω) of the dependency of series yy on series xx at frequency ωω are given by
(ω) = (A(ω))/(fxx(ω))
ϕ̂(ω) = arccos((cf(ω))/(A(ω))), if ​qf(ω)0
ϕ̂(ω) = 2πarccos((cf(ω))/(A(ω))), if ​qf(ω) < 0.
G^(ω)= A(ω) fxx(ω) ϕ^(ω)=arccos( cf(ω) A(ω) ), if ​qf(ω)0 ϕ^(ω)=2π-arccos( cf(ω) A(ω) ), if ​qf(ω)<0.
The quantities used in these definitions are obtained as in Section [Description] in (g13ce).
Confidence limits are returned for both gain and phase, but should again be taken as very approximate when the coherency W(ω)W(ω), as calculated by nag_tsa_multi_spectrum_bivar (g13ce), is not significant. These are based on the assumption that both ((ω) / G(ω))1(G^(ω)/G(ω))-1 and ϕ̂(ω)ϕ^(ω) are Normal with variance
1/d (1/(W(ω))1) .
1d (1W(ω) -1) .
Although the estimate of φ(ω)ϕ(ω) is always given in the range [0,2π)[0,2π), no attempt is made to restrict its confidence limits to this range.

References

Bloomfield P (1976) Fourier Analysis of Time Series: An Introduction Wiley
Jenkins G M and Watts D G (1968) Spectral Analysis and its Applications Holden–Day

Parameters

Compulsory Input Parameters

1:     xg(ng) – double array
ng, the dimension of the array, must satisfy the constraint ng1ng1.
The ng univariate spectral estimates, fxx(ω)fxx(ω), for the xx series.
2:     yg(ng) – double array
ng, the dimension of the array, must satisfy the constraint ng1ng1.
The ng univariate spectral estimates, fyy(ω)fyy(ω), for the yy series.
3:     xyrg(ng) – double array
ng, the dimension of the array, must satisfy the constraint ng1ng1.
The real parts, cf(ω)cf(ω), of the ng bivariate spectral estimates for the xx and yy series. The xx series leads the yy series.
4:     xyig(ng) – double array
ng, the dimension of the array, must satisfy the constraint ng1ng1.
The imaginary parts, qf(ω)qf(ω), of the ng bivariate spectral estimates for the xx and yy series. The xx series leads the yy series.
Note:  the two univariate and the bivariate spectra must each have been calculated using the same method of smoothing. For rectangular, Bartlett, Tukey or Parzen smoothing windows, the same cut-off point of lag window and the same frequency division of the spectral estimates must be used. For the trapezium frequency smoothing window, the frequency width and the shape of the window and the frequency division of the spectral estimates must be the same. The spectral estimates and statistics must also be unlogged.
5:     stats(44) – double array
The four associated statistics for the univariate spectral estimates for the xx and yy series. stats(1)stats1 contains the degrees of freedom, stats(2)stats2 and stats(3)stats3 contain the lower and upper bound multiplying factors respectively and stats(4)stats4 holds the bandwidth.
Constraint: stats(1)3.0stats13.0.

Optional Input Parameters

1:     ng – int64int32nag_int scalar
Default: The dimension of the arrays xg, yg, xyrg, xyig. (An error is raised if these dimensions are not equal.)
The number of spectral estimates in each of the arrays xg, yg, xyrg and xyig. It is also the number of gain and phase estimates.
Constraint: ng1ng1.

Input Parameters Omitted from the MATLAB Interface

None.

Output Parameters

1:     gn(ng) – double array
The ng gain estimates, (ω)G^(ω), at each frequency ωω.
2:     gnlw(ng) – double array
The ng lower bounds for the ng gain estimates.
3:     gnup(ng) – double array
The ng upper bounds for the ng gain estimates.
4:     ph(ng) – double array
The ng phase estimates, ϕ̂(ω)ϕ^(ω), at each frequency ωω.
5:     phlw(ng) – double array
The ng lower bounds for the ng phase estimates.
6:     phup(ng) – double array
The ng upper bounds for the ng phase estimates.
7:     ifail – int64int32nag_int scalar
ifail = 0ifail=0 unless the function detects an error (see [Error Indicators and Warnings]).

Error Indicators and 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,ng < 1ng<1,
orstats(1) < 3.0stats1<3.0.
W ifail = 2ifail=2
A bivariate spectral estimate is zero. For this frequency the gain and the phase and their bounds are set to zero.
W ifail = 3ifail=3
A univariate spectral estimate is negative. For this frequency the gain and the phase and their bounds are set to zero.
W ifail = 4ifail=4
A univariate spectral estimate is zero. For this frequency the gain and the phase and their bounds are set to zero.
W ifail = 5ifail=5
A calculated value of the squared coherency exceeds 1.01.0. For this frequency the squared coherency is reset to 1.01.0 in the formulae for the gain and phase bounds.
If more than one failure of types 22, 33, 44 and 55 occurs then the failure type which occurred at lowest frequency is returned in ifail. However the actions indicated above are also carried out for failures at higher frequencies.

Accuracy

All computations are very stable and yield good accuracy.

Further Comments

The time taken by nag_tsa_multi_gain_bivar (g13cf) is approximately proportional to ng.

Example

function nag_tsa_multi_gain_bivar_example
xg = [2.0349;
     0.51554;
     0.0764;
     0.01068;
     0.00093;
     0.001;
     0.00076;
     0.00037;
     0.00021];
yg = [21.97712;
     3.29761;
     0.28782;
     0.0248;
     0.00285;
     0.00203;
     0.00125;
     0.00107;
     0.00191];
xyrg = [-6.54995;
     0.34107;
     0.12335;
     -0.00514;
     -0.00033;
     -0.00039;
     -0.00026;
     0.00011;
     7e-05];
xyig = [0;
     -1.1903;
     0.04087;
     0.00842;
     0.00032;
     -1e-05;
     0.00018;
     -0.00016;
     0];
stats = [30;
     0.63858;
     1.7867;
     0.33288];
[gn, gnlw, gnup, ph, phlw, phup, ifail] = nag_tsa_multi_gain_bivar(xg, yg, xyrg, xyig, stats)
 

gn =

    3.2188
    2.4018
    1.7008
    0.9237
    0.4943
    0.3901
    0.4161
    0.5248
    0.3333


gnlw =

    2.9722
    2.1138
    1.3748
    0.5558
    0.1327
    0.1002
    0.1346
    0.1591
    0.0103


gnup =

    3.4859
    2.7290
    2.1042
    1.5350
    1.8415
    1.5196
    1.2863
    1.7306
   10.8301


ph =

    3.1416
    4.9915
    0.3199
    2.1189
    2.3716
    3.1672
    2.5360
    5.3147
         0


phlw =

    3.0619
    4.8637
    0.1071
    1.6109
    1.0563
    1.8075
    1.4074
    4.1214
   -3.4809


phup =

    3.2213
    5.1192
    0.5328
    2.6268
    3.6868
    4.5270
    3.6647
    6.5079
    3.4809


ifail =

                    0


function g13cf_example
xg = [2.0349;
     0.51554;
     0.0764;
     0.01068;
     0.00093;
     0.001;
     0.00076;
     0.00037;
     0.00021];
yg = [21.97712;
     3.29761;
     0.28782;
     0.0248;
     0.00285;
     0.00203;
     0.00125;
     0.00107;
     0.00191];
xyrg = [-6.54995;
     0.34107;
     0.12335;
     -0.00514;
     -0.00033;
     -0.00039;
     -0.00026;
     0.00011;
     7e-05];
xyig = [0;
     -1.1903;
     0.04087;
     0.00842;
     0.00032;
     -1e-05;
     0.00018;
     -0.00016;
     0];
stats = [30;
     0.63858;
     1.7867;
     0.33288];
[gn, gnlw, gnup, ph, phlw, phup, ifail] = g13cf(xg, yg, xyrg, xyig, stats)
 

gn =

    3.2188
    2.4018
    1.7008
    0.9237
    0.4943
    0.3901
    0.4161
    0.5248
    0.3333


gnlw =

    2.9722
    2.1138
    1.3748
    0.5558
    0.1327
    0.1002
    0.1346
    0.1591
    0.0103


gnup =

    3.4859
    2.7290
    2.1042
    1.5350
    1.8415
    1.5196
    1.2863
    1.7306
   10.8301


ph =

    3.1416
    4.9915
    0.3199
    2.1189
    2.3716
    3.1672
    2.5360
    5.3147
         0


phlw =

    3.0619
    4.8637
    0.1071
    1.6109
    1.0563
    1.8075
    1.4074
    4.1214
   -3.4809


phup =

    3.2213
    5.1192
    0.5328
    2.6268
    3.6868
    4.5270
    3.6647
    6.5079
    3.4809


ifail =

                    0



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