dplR (version 1.4.9)

wavelet.plot: Plot a Continuous Wavelet Transform

Description

This function creates a filled.contour plot of a continuous wavelet transform as output from morlet.

Usage

wavelet.plot(wave.list,
               wavelet.levels = quantile(wave.list$Power,probs=seq(from=0, to=1, by=0.1)),
               add.coi = TRUE, add.sig = TRUE, x.lab = gettext("Time"),
               period.lab = gettext("Period"), crn.lab = gettext("RWI"),
               key.cols = rev(rainbow(length(wavelet.levels)-1)),
               key.lab = expression(paste("Power"^2)),
               add.spline = FALSE, f = 0.5, nyrs = NULL,
               crn.col = "black", crn.lwd = 1,
               crn.ylim = range(wave.list$y)*1.1, side.by.side = FALSE)

Arguments

wave.list

List. Output from morlet.

wavelet.levels

Numeric. Values for levels of the filled countours for the wavelet plot.

add.coi

Logical. Add cone of influence?

add.sig

Logical. Add countour lines for significance?

x.lab

X-axis label.

period.lab

Y-axis label for the wavelet plot.

crn.lab

Y-axis label for the time-series plot.

key.cols

A vector of colors for the wavelets and the key.

key.lab

Label for key.

add.spline

Logical. Add a spline to the time-series plot using ffcsaps if TRUE.

nyrs

A number giving the rigidity of the smoothing spline, defaults to 0.33 of series length if nyrs is NULL.

f

A number between 0 and 1 giving the frequency response or wavelength cutoff for the smoothing spline. Defaults to 0.5.

crn.col

Line color for the time-series plot.

crn.lwd

Line width for the time-series plot.

crn.ylim

Axis limits for the time-series plot.

side.by.side

Logical. Plots will be in one row if TRUE.

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 and plots the original time series. Countors are added for significance and a cone of influence polygon can be added as well. Anything within the cone of influence should not be interpreted. The time series can be plotted with a smoothing spline as well.

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

morlet ffcsaps

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]
  out.wave <- morlet(y1=CAMstd,x1=Years,p2=9,dj=0.1,siglvl=0.99)
  wavelet.plot(out.wave)
  levs <- quantile(out.wave$Power,probs=c(0,0.5,0.75,0.9,0.99))
  wavelet.plot(out.wave, wavelet.levels=levs,add.sig=FALSE, key.cols = c("white","green","blue","red"))