WaveletComp (version 1.0)

wt.avg: Plot of wavelet power averages across time of a single time series

Description

This function plots wavelet power averages across time of a single time series, which are provided by an object of class analyze.wavelet, or alternatively of class analyze.coherency. (In the latter case, the series number or name can be specified.) The vertical axis shows the Fourier periods. The horizontal axis shows the averages.

There is an option to label periods according to significance of averages (if p-values are provided by analyze.wavelet) at given levels of significance. Labels are point symbols along the line of averages which can be assigned individually.

The idea to show significance levels by colors of plotting characters and its implementation has been adopted from Huidong Tian and Bernard Cazelles (archived R package WaveletCo).

Usage

wt.avg(WT, my.series = 1,  show.siglvl = T, siglvl = c(0.05, 0.1), sigcol = c("red", "blue"), sigpch = 20,  label.avg.axis = T, averagelab = NULL,  label.period.axis = T, periodlab = NULL,  show.legend = T, legend.coords = "topright",  main = NULL, lwd = 0.5, verbose = F)

Arguments

WT

an object of class analyze.wavelet or analyze.coherency

my.series

In case class(WT) = analyze.coherency: number (1 or 2) or name of the series to be analyzed. Default: 1.

show.siglvl

Label periods according to significance of averages? (Effective only if p-values are provided by analyze.coherency.) Default: TRUE.

siglvl

a vector of significance levels (of any length and order). Default: c(0.05, 0.1).

sigcol

a vector of colors (should be of same length as and correspond to siglvl, otherwise 1:length(siglvl)). Default: c("red","blue").

sigpch

a vector of plotting characters. (It should be of same length as and correspond to siglvl to produce different labels, otherwise the default setting is used. A single input value affects all labels.) Default: 20.

label.avg.axis

Label the axis of averages? Logical. Default: TRUE.

averagelab

Label for the axis of averages. Default: "average wavelet power".

label.period.axis

Label the (Fourier) period axis? Logical. Default: TRUE.

periodlab

(Fourier) period axis label. Default: "period".

show.legend

Include legend of significance levels into the plot? Logical. Default: TRUE.

legend.coords

coordinates to position the legend (as in function legend). Default: "topright".

main

an overall title for the plot. Default: NULL.

lwd

line width. Default: 0.5.

verbose

Print verbose output on the screen? Logical. Default: FALSE.

References

Aguiar-Conraria L., and Soares M.J., 2011. The Continuous Wavelet Transform: A Primer. NIPE Working Paper Series 16/2011.

Carmona R., Hwang W.-L., and Torresani B., 1998. Practical Time Frequency Analysis. Gabor and Wavelet Transforms with an Implementation in S. Academic Press, San Diego.

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

Liu Y., Liang X.S., and Weisberg R.H., 2007. Rectification of the Bias in the Wavelet Power Spectrum. Journal of Atmospheric and Oceanic Technology 24, 2093--2102.

Tian, H., and Cazelles, B., 2012. WaveletCo. Available at http://cran.r-project.org/src/contrib/Archive/WaveletCo/, archived April 2013; accessed July 26, 2013.

Torrence C., and Compo G.P., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79 (1), 61--78.

See Also

analyze.wavelet, wt.image, wt.sel.phases, wt.phase.image, reconstruct

Examples

## Not run: 
# ## The following example is adopted from Liu et al, 2007:
# 
# series.length = 6*128*24
# x1 = periodic.series(start.period = 1*24, length = series.length)
# x2 = periodic.series(start.period = 8*24, length = series.length)
# x3 = periodic.series(start.period = 32*24, length = series.length)
# x4 = periodic.series(start.period = 128*24, length = series.length)
# x = x1 + x2 + x3 + x4
# plot(ts(x, start=0, frequency=24), type="l", 
#         xlab="time (days)", ylab="hourly data", 
#         main="a series of hourly data with periods of 1, 8, 32, and 128 days")
#         
# my.data = data.frame(x=x)
# 
# my.w = analyze.wavelet(my.data, "x", loess.span=0, dt=1/24, dj=1/20, 
#                        lowerPeriod=1/4, make.pval=T, n.sim=10)
# 
# ## Plot of wavelet power spectrum (with equidistant color breakpoints):  
# wt.image(my.w, color.key="i", 
#          legend.params=list(lab="wavelet power levels (equidistant levels)"))
# ## Plot of average wavelet power:
# wt.avg(my.w, siglvl=0.05, sigcol="red")
# ## End(Not run)