wtc {biwavelet}

Compute wavelet coherence

Description

Compute wavelet coherence

Usage

wtc(
  d1,
  d2,
  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

d1

Time series 1 in matrix format (n rows x 2 columns). The first column should contain the time steps and the second column should contain the values.

d2

Time series 2 in matrix format (n rows x 2 columns). The first column should contain the time steps and the second column should contain the values.

pad

Pad the values will with zeros to increase the speed of the transform.

dj

Spacing between successive scales.

s0

Smallest scale of the wavelet.

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.

param

Nondimensional parameter specific to the wavelet function.

lag1

Vector containing the AR(1) coefficient of each time series.

sig.level

Significance level.

sig.test

Type of significance test. If set to 0, use a regular \chi^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.

quiet

Do not display progress bar.

Value

Return a biwavelet object containing:

coi

matrix containg cone of influence

wave

matrix containing the cross-wavelet transform

wave.corr

matrix containing the bias-corrected cross-wavelet transform using the method described by Veleda et al. (2012)

power

matrix of power

power.corr

matrix of bias-corrected cross-wavelet power using the method described by Veleda et al. (2012)

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

d1.sigma

standard deviation of time series 1

d2.sigma

standard deviation of time series 2

mother

mother wavelet used

type

type of biwavelet object created (wtc)

signif

matrix containing sig.level percentiles of wavelet coherence based on the Monte Carlo AR(1) time series

Note

The Monte Carlo randomizations can be extremely slow for large datasets. For instance, 1000 randomizations of a dataset consisting of 1000 samples will take ~30 minutes on a 2.66 GHz dual-core Xeon processor.

Author(s)

Tarik C. Gouhier ([e-mail])

Code based on WTC MATLAB package written by Aslak Grinsted.

References

Cazelles, B., M. Chavez, D. Berteaux, F. Menard, J. O. Vik, S. Jenouvrier, and N. C. Stenseth. 2008. Wavelet analysis of ecological time series. Oecologia 156:287-304.

Grinsted, A., J. C. Moore, and S. Jevrejeva. 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics 11:561-566.

Torrence, C., and G. P. Compo. 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79:61-78.

Torrence, C., and P. J. Webster. 1998. The annual cycle of persistence in the El Nino/Southern Oscillation. Quarterly Journal of the Royal Meteorological Society 124:1985-2004.

Veleda, D., R. Montagne, and M. Araujo. 2012. Cross-Wavelet Bias Corrected by Normalizing Scales. Journal of Atmospheric and Oceanic Technology 29:1401-1408.

Examples

t1 <- cbind(1:100, rnorm(100))
t2 <- cbind(1:100, rnorm(100))

## Wavelet coherence
wtc.t1t2 <- wtc(t1, t2, nrands = 10)

## Plot wavelet coherence and phase difference (arrows)
## Make room to the right for the color bar
par(oma = c(0, 0, 0, 1), mar = c(5, 4, 4, 5) + 0.1)
plot(wtc.t1t2, plot.cb = TRUE, plot.phase = TRUE)