Comparison between classical and robust Wavelet Variances

Displays a plot of the wavelet variances (classical and robust) for a given time series accounting for CI values.

robust_eda(
  x,
  eff = 0.6,
  units = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  col_wv = NULL,
  col_ci = NULL,
  nb_ticks_x = NULL,
  nb_ticks_y = NULL,
  legend_position = NULL,
  ...
)

Arguments

x: A time series objects.
eff: An integer that specifies the efficiency of the robust estimator.
units: A string that specifies the units of time plotted on the x axis.
xlab: A string that gives a title for the x axis.
ylab: A string that gives a title for the y axis.
main: A string that gives an overall title for the plot.
col_wv: A string that specifies the color of the wavelet variance line.
col_ci: A string that specifies the color of the confidence interval shade.
nb_ticks_x: An integer that specifies the maximum number of ticks for the x-axis.
nb_ticks_y: An integer that specifies the maximum number of ticks for the y-axis.
legend_position: A string that specifies the position of the legend (use legend_position = NA to remove legend).
...: Additional arguments affecting the plot.

Value

Plot of wavelet variance and confidence interval for each scale.

Author

Stephane Guerrier, Nathanael Claussen, and Justin Lee

Examples

set.seed(999)
n = 10^4
Xt = rnorm(n)
wv = wvar(Xt)

plot(wv)

plot(wv, main = "Simulated white noise", xlab = "Scales")

plot(wv, units = "sec", legend_position = "topright")

plot(wv, col_wv = "darkred", col_ci = "pink")