plot.tos: Produces a graphical representation of the results of a test of stationarity contained in a `tos` object.

Description

After a test of stationarity for dyadic data (e.g. hwtos2) is applied to a time series it generates a results object of class tos. This function takes objects of that class and produces a graphical representation of the test.

Usage

"plot"(x, mctype = "FDR", sub = NULL, xlab = "Time", arrow.length = 0.05, verbose = FALSE, ...)

Arguments

The tos class object, the results of the test of stationarity that you wish to plot.

mctype

Whether you wish to see rejections (if they exist) according to a Bonferroni assessment ("BON") or according to FDR ("FDR")

sub

An argument to change the subtitle.

xlab

An argument to change the x-axis label.

arrow.length

The length of the edges of the arrow head (in inches). Note that this is the argument that is supplied as the length argument of the arrow function that is called by this routine to draw the arrows.

verbose

If TRUE then some meaningless debugging information is printed.

...

Other arguments to the main ts.plot routine that does the plotting.

Value

Details

The following things are usually plotted. 1. The time series that was investigated. The left-hand axes is that for the time series. The horizontal axis is time (but just integers indexing). If the series was deemed stationary by the test then that's it except that the subtitle indicates that no Haar wavelet coefficients were rejected as being nonzero.

If the test indicated that the series was nonstationary then the subtitle indicates this by stating the number of rejections (this might be according to FDR or Bonferroni depending on the setting of the mctype argument. Then graphical representations of any significant Haar wavelet coefficients are plotted as double-headed red horizontal arrows on the plot. The horizontal extent corresponds to the support of the underlying wavelet. The vertical position of the arrows gives an indication of the wavelet periodogram scale where the significant coefficient was found. The wavelet periodogram scales are indexed by the right hand axis, and beware, the numbers might not be consecutive, but they will be ordered (so e.g. if no signficant coefficients were discovered at wavelet periodogram scale level 6, then that scale/axis label will not appear). The scale within the Haar wavelet transform is indicated by the vertical position WITHIN ticks between wavelet periodogram scales (ie, there are TWO scales: the wavelet periodogram scale that is currently being analyzed, and the Haar wavelet transform scale within the periodogram scale). So, if two right hand axis labels are, e.g., 4 and 5, and horizontal arrows appear between these two they actually correspond to different Haar wavelet transform scales AT wavelet periodogram level 4. It is not usually possible to tell precisely which Haar wavelet transform scale the coefficients can come from, but the information can be extracted from the summary.tos function which lists this.

References

Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904.

Examples

Run this code

#
# Produces an interesting plot with high probability
#
#
# Note that the input time series is two concatenated white noise
# sequences with very different variances.
#
answer <- hwtos2(c(rnorm(256), rnorm(256, sd=5)))
## Not run: plot(answer)

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