dplR (version 1.2.4)

wavelet.plot: Plot a Continuous Wavelet Transform

Description

This function creates a filled.contour plot of a continuous wavelet transform using the Morlet wavelet.

Usage

wavelet.plot(crn.vec,yr.vec,p2,dj=0.25,siglvl=0.99,...)

Arguments

crn.vec

a vector of values for the wavelet transform.

yr.vec

a vector of values giving the years for the plot. Must be the same length as length(crn.vec).

p2

the numbers of power of two to be computed for the wavelet transform.

dj

sub-octaves per octave calculated.

siglvl

level for the significance test. Defaults to 0.99.

...

other arguments to pass to filled.contour.

Value

• None. This function is invoked for its side effect, which is to produce a plot.

Details

This produces a plot of a continuous wavelet transform. Its implementation very closely follows Torrence and Compo (1998). The user provides a tree-ring chronology (although detrended series are conceivably useful as well), the years for the plot, the powers of two (for the scale parameter), and the confidence level for the significance test. The function assumes that the data are yearly and defaults to calculating four sub-octaves per octave (four voices per power of two). The input crn.vec is padded up to the next power of two before the transform and the padding is removed before plotting. Currently the Morlet wavelet is the only wavelet implemented; the wavenumber (k0) is fixed at six. In future releases, other wavelets will be available (Dog, Paul, etc.). Similarly, a chi-square distribution is used to assess significance at the level indicated. In future versions, significance will be calculated against the global wavelet spectrum, or a red-noise background. The filled.contour levels are determined using quantile(Power,probs=seq(0,1,0.1)). A contour for significance is displayed as is the cone of influence. Anything within the cone of influence should not be interpreted. Refer to Torrence and Compo (1998) for details on the transform, significance, etc.

References

Torrence, C. and Compo, G.P. (1998) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79: 61--78.

See Also

chron

Examples

data(ca533)
  ca533.rwi <- detrend(rwl = ca533, method = "ModNegExp")
  ca533.crn <- chron(ca533.rwi, prefix = "CAM", prewhiten = FALSE)
  Years <- as.numeric(rownames(ca533.crn))
  CAMstd <- ca533.crn[,1]
  wavelet.plot(CAMstd,Years,p2=9,siglvl=0.99,main="CAMstd")