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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