| wavelet.plot | R Documentation | 
This function creates a filled.contour plot of a continuous
wavelet transform as output from morlet.
wavelet.plot(wave.list,
             wavelet.levels = quantile(wave.list$Power,
                                       probs = (0:10)/10),
             add.coi = TRUE, add.sig = TRUE,
             x.lab = gettext("Time", domain = "R-dplR"),
             period.lab = gettext("Period", domain = "R-dplR"),
             crn.lab = gettext("RWI", domain = "R-dplR"),
             key.cols = rev(rainbow(length(wavelet.levels)-1)),
             key.lab = parse(text=paste0("\"",
                                         gettext("Power",
                                                 domain="R-dplR"),
                                         "\"^2")),
             add.spline = FALSE, f = 0.5, nyrs = NULL,
             crn.col = "black", crn.lwd = 1,coi.col='black',
             crn.ylim = range(wave.list$y) * c(0.95, 1.05),
             side.by.side = FALSE,
             useRaster = FALSE, res = 150, reverse.y = FALSE, ...)
| wave.list | A  | 
| wavelet.levels | A  | 
| add.coi | A  | 
| add.sig | A  | 
| 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 | A  | 
| nyrs |  A number giving the rigidity of the smoothing spline,
defaults to 0.33 of series length if  | 
| 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. | 
| coi.col | Color for the COI if  | 
| crn.ylim | Axis limits for the time-series plot. | 
| side.by.side | A  | 
| useRaster | A  | 
| res | A  | 
| reverse.y | A  | 
| ... | Arguments passed to  | 
This produces a plot of a continuous wavelet transform and plots the original time series. Contours 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.
None. This function is invoked for its side effect, which is to produce a plot.
 The function morlet is a port of Torrence’s
IDL code, which can be accessed through the
Internet Archive Wayback Machine.
Andy Bunn. Patched and improved by Mikko Korpela.
Torrence, C. and Compo, G. P. (1998) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.
morlet, caps
library(stats)
library(utils)
data(ca533)
ca533.rwi <- detrend(rwl = ca533, method = "ModNegExp")
ca533.crn <- chron(ca533.rwi, prewhiten = FALSE)
Years <- time(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, useRaster = NA)
## Not run: 
# Alternative palette with better separation of colors
# via: rev(RColorBrewer::brewer.pal(10, "Spectral"))
specCols <- c("#5E4FA2", "#3288BD", "#66C2A5", "#ABDDA4", "#E6F598", 
              "#FEE08B", "#FDAE61", "#F46D43", "#D53E4F", "#9E0142")
wavelet.plot(out.wave, key.cols=specCols,useRaster = NA)
# fewer colors
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("#FFFFFF", "#ABDDA4", "#FDAE61", "#D7191C"), useRaster = NA)
             
## End(Not run)
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