Description Usage Arguments Value Author(s) References Examples
Compute multiple wavelet coherence
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y |
time series 1 in matrix format ( |
x1 |
time series 2 in matrix format ( |
x2 |
time series 3 in matrix format ( |
pad |
pad the values will with zeros to increase the speed of the transform. Default is TRUE. |
dj |
spacing between successive scales. Default is 1/12. |
s0 |
smallest scale of the wavelet. Default is |
J1 |
number of scales - 1. |
max.scale |
maximum scale. Computed automatically if left unspecified. |
mother |
type of mother wavelet function to use. Can be set to
|
param |
nondimensional parameter specific to the wavelet function. |
lag1 |
vector containing the AR(1) coefficient of each time series. |
sig.level |
significance level. Default is |
sig.test |
type of significance test. If set to 0, use a regular χ^2 test. If set to 1, then perform a time-average test. If set to 2, then do a scale-average test. |
nrands |
number of Monte Carlo randomizations. Default is 300. |
quiet |
Do not display progress bar. Default is |
Return a vectorwavelet
object containing:
coi |
matrix containg cone of influence |
rsq |
matrix of wavelet coherence |
phase |
matrix of phases |
period |
vector of periods |
scale |
vector of scales |
dt |
length of a time step |
t |
vector of times |
xaxis |
vector of values used to plot xaxis |
s0 |
smallest scale of the wavelet |
dj |
spacing between successive scales |
mother |
mother wavelet used |
type |
type of |
signif |
matrix containg |
Tunc Oygur (info@tuncoygur.com.tr)
Code based on MWC MATLAB package written by Eric K. W. Ng and Johnny C. L. Chan.
T. Oygur, G. Unal.. Vector wavelet coherence for multiple time series. Int. J. Dynam. Control (2020).
T. Oygur, G. Unal. 2017. The large fluctuations of the stock return and financial crises evidence from Turkey: using wavelet coherency and VARMA modeling to forecast stock return. Fluctuation and Noise Letters
Ng, Eric KW and Chan, Johnny CL. 2012. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. Journal of Atmospheric and Oceanic Technology 29-12:1845–1853.
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t <- (-100:100)
y <- sin(t*2*pi)+sin(t*2*pi/4)+sin(t*2*pi/8)+sin(t*2*pi/16)+sin(t*2*pi/32)+sin(t*2*pi/64)
x1 <- sin(t*2*pi/8)
x2 <- sin(t*2*pi/32)
y <- cbind(t,y)
x1 <- cbind(t,x1)
x2 <- cbind(t,x2)
## Multiple wavelet coherence
result <- mwc(y, x1, x2, nrands = 10)
result <- mwc(y, x1, x2)
## Plot wavelet coherence and make room to the right for the color bar
## Note: plot function can be used instead of plot.vectorwavelet
par(oma = c(0, 0, 0, 1), mar = c(5, 4, 4, 5) + 0.1, pin = c(3,3))
plot.vectorwavelet(result, plot.cb = TRUE, main = "Plot multiple wavelet coherence")
par(old.par)
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