plot.stability: Plot diagnostics for a stability object

Description Usage Arguments Details Value See Also Examples

View source: R/plot_and_summary.R

Description

plot method for objects of class "stability" returned from stability

Usage

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## S3 method for class 'stability'
plot(x, y, ..., prob = TRUE, top_scale = c("none",
  "excesses", "opposite"), vertical = TRUE)

Arguments

x

an object of class "stability", a result of a call to stability.

y

Not used.

...

Additional arguments passed on to matplot, axis and/or segments.

prob

A logical scalar. If TRUE then the levels of thresholds on the lower horizontal axis are represented by the proportion of observations that lie below a threshold. If prob = FALSE then the values of the thresholds are used.

top_scale

A character scalar. If top_scale = "none" then no axis labels appear on the upper horizontal axis. If top_scale = "excesses" then the number of threshold excesses at each threshold are indicated. If top_scale = "opposite" then the type of threshold level not chosen using prob is indicated.

vertical

A logical scalar. Should the confidence intervals be depicted using a vertical line for each threshold (TRUE) or by joining up confidence limits across thresholds (FALSE)?

Details

Produces a simple threshold diagnostic plot based on the object returned from stability. The MLEs of the GP shape parameter $ξ$ and approximate conf% confidence intervals for ξ are plotted against the threshold used to fit the GP model. This plot is used to choose a threshold above which the underlying GP shape parameter may be approximately constant. See Chapter 4 of Coles (2001). See also the vignette "Introducing threshr". as described in . See also the vignette "Introducing threshr".

Value

In addition to producing the plot a list of the arguments used by matplot, axis is returned (invisibly).

See Also

stability.

Examples

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u_vec_gom <- quantile(gom, probs = seq(0, 0.95, by = 0.05))
gom_stab <- stability(data = gom, u_vec = u_vec_gom)
plot(gom_stab)

threshr documentation built on Sept. 4, 2017, 9:03 a.m.