WaveletComp (version 1.0)

wc.avg: Plot cross-wavelet power averages and wavelet coherence averages across time of two time series

Description

This function plots cross-wavelet power averages across time, or alternatively wavelet coherence averages, of two time series, which are provided by an object of class analyze.coherency. 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.coherency) 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

wc.avg(WC, which.avg = "wp", 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

WC

an object of class analyze.coherency.

which.avg

Which averages should be plotted?

	"wp"	:
cross-wavelet power		"wp"

Default: "wp"

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 cross-wavelet power" (if which.avg="wp"), "average coherence" (if which.avg="wc").

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. Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics 33 (3), 477--489.

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

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 P.C., 1994. Wavelet spectrum analysis and ocean wind waves. In: Foufoula-Georgiou E., and Kumar P., (eds.), Wavelets in Geophysics, Academic Press, San Diego, 151--166.

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.

Veleda D., Montagne R., and Araujo M., 2012. Cross-Wavelet Bias Corrected by Normalizing Scales. Journal of Atmospheric and Oceanic Technology 29, 1401--1408.

See Also

analyze.coherency, wc.image, wc.sel.phases, wc.phasediff.image

Examples

## Not run: 
# ## The following example is adopted from Veleda et al, 2012:
# 
# add.noise=TRUE
# 
# series.length = 3*128*24
# x1 = periodic.series(start.period = 1*24, length = series.length)
# x2 = periodic.series(start.period = 2*24, length = series.length)
# x3 = periodic.series(start.period = 4*24, length = series.length)
# x4 = periodic.series(start.period = 8*24, length = series.length)
# x5 = periodic.series(start.period = 16*24, length = series.length)
# x6 = periodic.series(start.period = 32*24, length = series.length)
# x7 = periodic.series(start.period = 64*24, length = series.length)
# x8 = periodic.series(start.period = 128*24, length = series.length)
# 
# x = x1 + x2 + x3 + x4 + 3*x5 + x6 + x7 + x8 
# y = x1 + x2 + x3 + x4 + 3*x5 + x6 + 3*x7 + x8
# 
# if (add.noise == TRUE){
#     x = x + rnorm(length(x))
#     y = y + rnorm(length(y))
# }
# 
# my.data = data.frame(x=x, y=y)
# 
# ts.plot(ts(my.data$x, start=0, frequency=24), 
#      ts(my.data$y, start=0, frequency=24),
#      type="l", col=1:2, 
#      xlab="time (days)", ylab="hourly data", 
#      main="a series of hourly data with periods of 1, 2, 4, 8, 16, 32, 64, and 128 days",
#      sub="(different amplitudes at periods 16 and 64)")
# legend("topright", legend=c("x","y"), col=1:2, lty=1)
# 
# ## computation of cross-wavelet power and wavelet coherence:
# my.wc = analyze.coherency(my.data, c("x","y"), loess.span=0, 
#                            dt=1/24, dj=1/20, 
#                            window.size.t=1, window.size.s=1/2, 
#                            lowerPeriod=1/4, 
#                            make.pval=T, n.sim=10)
# 
# ## plot of cross-wavelet power (with color breakpoints according to quantiles):
# wc.image(my.wc, timelab="time (days)", periodlab="period (days)", 
#          main="cross-wavelet power")
# ## plot of average cross-wavelet power:
# wc.avg(my.wc, siglvl=0.05, sigcol="red")
# 
# ## plot of wavelet coherence (with color breakpoints according to quantiles):
# wc.image(my.wc, which.image="wc", timelab="time (days)", periodlab="period (days)", 
#          main="wavelet coherence",
#          legend.params=list(label.digits=3))
# ## plot of average wavelet coherence:
# wc.avg(my.wc, which.avg="wc", siglvl=0.05, sigcol="red", legend.coords="topleft")
# ## End(Not run)