evd (version 2.3-7.1)

plot.bvpot: Plot Diagnostics for a Bivariate POT EVD Object

Description

Four plots (selectable by which) are currently provided: a density plot (1), a dependence function plot (2), a quantile curves plot (3) and a spectral density plot (4). Plot diagnostics for the generalized Pareto peaks-over-threshold margins (selectable by mar and which) are also available.

Usage

# S3 method for bvpot
plot(x, mar = 0, which = 1:4, main, ask = nb.fig <
    length(which) && dev.interactive(), grid = 50, above = FALSE,
    levels = NULL, tlty = 1, blty = 3, rev = FALSE, p = seq(0.75,
    0.95, 0.05), half = FALSE, ...)

Arguments

x

An object of class "bvpot".

mar

If mar = 1 or mar = 2 diagnostics are given for the first or second generalized Pareto margin respectively.

which

A subset of the numbers 1:4 selecting the plots to be shown. By default all are plotted.

main

Title of each plot. If given, should be a character vector with the same length as which.

ask

Logical; if TRUE, the user is asked before each plot.

grid, levels

Arguments for the density plot. The data is plotted with a contour plot of the bivariate density of the fitted model in the tail region. The density is evaluated at grid^2 points, and contours are plotted at the values given in the numeric vector levels. If levels is NULL (the default), the routine attempts to find sensible values.

above

Logical; if TRUE, only data points above both marginal thresholds are plotted.

tlty

Line type for the lines identifying the thresholds.

rev, blty

Arguments to the dependence function plot. See abvevd.

p

Lower tail probabilities for the quantile curves plot. The plot is of the same type as given by the function qcbvnonpar, but applied to the parametric bivariate threshold model.

half

Argument to the spectral density plot. See hbvevd.

...

Other arguments to be passed through to plotting functions.

See Also

plot.bvevd, contour, abvnonpar, qcbvnonpar, hbvevd

Examples

bvdata <- rbvevd(500, dep = 0.6, model = "log")
M1 <- fbvpot(bvdata, threshold = c(0,0), model = "log")
if (FALSE) plot(M1)
if (FALSE) plot(M1, mar = 1)
if (FALSE) plot(M1, mar = 2)