plot.tosANYN: Produces a graphical representation of the results of a test...

In locits: Test of Stationarity and Localized Autocovariance

Produces a graphical representation of the results of a test of stationarity from a tosANYN object.

Description

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

Usage

## S3 method for class 'tosANYN'
plot(x, sub = NULL, xlab = "Time",
    arrow.length = 0.05, verbose = FALSE, ...)

Arguments

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

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. 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 the 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.tosANYN function which lists this.

Value

None.

Author(s)

Guy Nason.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}

See Also

hwtos, summary.tosANYN

Examples

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

locits documentation built on Sept. 8, 2023, 5:07 p.m.