evd (version 2.3-3)

plot.bvevd: Plot Diagnostics for a Bivariate EVD Object

Description

Six plots (selectable by which) are currently provided: two conditional P-P plots (1,2), conditioning on each margin, a density plot (3), a dependence function plot (4), a quantile curves plot (5) and a spectral density plot (6). Plot diagnostics for the generalized extreme value margins (selectable by mar and which) are also available.

Usage

# S3 method for bvevd
plot(x, mar = 0, which = 1:6, main, ask = nb.fig <
    length(which) && dev.interactive(), ci = TRUE, cilwd = 1,
    a = 0, grid = 50, legend = TRUE, nplty = 2, blty = 3, method = "cfg",
    convex = FALSE, rev = FALSE, p = seq(0.75, 0.95, 0.05),
    mint = 1, half = FALSE, …)

Arguments

x

An object of class "bvevd".

mar

If mar = 1 or mar = 2 diagnostics are given for the first or second genereralized extreme value margin respectively.

which

A subset of the numbers 1:6 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.

ci

Logical; if TRUE (the default), plot simulated 95% confidence intervals for the conditional P-P plots.

cilwd

Line width for confidence interval lines.

a

Passed through to ppoints for empirical estimation. Larger values give less probability for extreme events.

grid

Argument for the density plot. The (possibly transformed) data is plotted with a contour plot of the bivariate density of the fitted model. The density is evaluated at grid^2 points.

legend

If legend is TRUE and if the fitted data contained a third column of mode logical, then a legend is included in the density and quantile curve plots.

method, convex, rev

Arguments to the dependence function plot. The dependence function for the fitted model is plotted and (optionally) compared to a non-parameteric estimate. See abvnonpar for a description of the arguments.

nplty, blty

Line types for the dependence function plot. nplty is the line type of the non-parametric estimate. To omit the non-parametric estimate set nplty to zero. blty is the line type of the triangular border. To omit the border estimate set blty to zero.

p, mint

Arguments to the quantile curves plot. See qcbvnonpar for a description of the plot and the arguments.

half

Argument to the spectral density plot. See hbvevd.

…

Other arguments to be passed through to plotting functions.

Details

In all plots we assume that the fitted model is stationary. For non-stationary models the data are transformed to stationarity. The plot then corresponds to the distribution obtained when all covariates are zero. In particular, the density and quanitle curves plots will not plot the original data for non-stationary models.

A conditional P-P plot is a P-P plot for the condition distribution function of a bivariate evd object. Let \(G(.|.)\) be the conditional distribution of the first margin given the second, under the fitted model. Let \(z_1,\ldots,z_m\) be the data used in the fitted model, where \(z_j = (z_{1j}, z_{2j})\) for \(j = 1,\ldots,m\). The plot that (by default) is labelled Conditional Plot Two, conditioning on the second margin, consists of the points $$\{(p_i, c_i), i = 1,\ldots,m\}$$ where \(p_1,\ldots,p_m\) are plotting points defined by ppoints and \(c_i\) is the \(i\)th largest value from the sample \(\{G(z_{j1}|z_{j2}), j = 1,\ldots,m\}.\) The margins are reversed for Conditional Plot One, so that \(G(.|.)\) is the conditional distribution of the second margin given the first.

See Also

plot.uvevd, contour, jitter, abvnonpar, qcbvnonpar

Examples

# NOT RUN {
bvdata <- rbvevd(100, dep = 0.6, model = "log")
M1 <- fbvevd(bvdata, model = "log")
# }
# NOT RUN {
par(mfrow = c(2,2))
# }
# NOT RUN {
plot(M1, which = 1:5)
# }
# NOT RUN {
plot(M1, mar = 1)
# }
# NOT RUN {
plot(M1, mar = 2)
# }