vwc: Compute n-dimensional vector wavelet coherence

In vectorwavelet: Vector Wavelet Coherence for Multiple Time Series

Description

Compute n-dimensional vector wavelet coherence

Usage

vwc(
  y,
  x,
  pad = TRUE,
  dj = 1/12,
  s0 = 2 * dt,
  J1 = NULL,
  max.scale = NULL,
  mother = "morlet",
  param = -1,
  lag1 = NULL,
  sig.level = 0.95,
  sig.test = 0,
  nrands = 300,
  quiet = FALSE
)

Arguments

y	time series y in matrix format (m rows x 2 columns). The first column should contain the time steps and the second column should contain the values.
x	multivariate time series x in matrix format (m rows x n columns). The first column should contain the time steps and the other columns should contain the values.
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 2*dt.
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 morlet, dog, or paul. Default is morlet. Significance testing is only available for morlet wavelet.
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 0.95.
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 FALSE

Value

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 vectorwavelet object created (vwc)
signif	matrix containg sig.level percentiles of wavelet coherence based on the Monte Carlo AR(1) time series

Author(s)

Tunc Oygur ([e-mail])

References

T. Oygur, G. Unal.. Vector wavelet coherence for multiple time series. Int. J. Dynam. Control (2020).

Examples

old.par <- par(no.readonly=TRUE)

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/16)
x3 <- sin(t*2*pi/32)
x4 <- sin(t*2*pi/64)

y <- cbind(t,y)
x <- cbind(t,x1,x2,x3,x4)

## n-dimensional multiple wavelet coherence
result <- vwc(y, x, nrands = 10)

result <- vwc(y, x)


## 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 n-dimensional vwc (n=5)")

par(old.par)

vectorwavelet documentation built on Jan. 16, 2021, 5:38 p.m.