evd (version 2.0-1)

plot.bvevd: Plot Diagnostics for a Bivariate EVD Object

Description

Four plots (selectable by which) are currently provided: two conditional P-P plots (conditioning on each margin), a density plot and a dependence function plot. Plot diagnostics for the generalized extreme value margins (selectable by mar and which) are also available.

Usage

## S3 method for class 'bvevd':
plot(x, mar = 0, which = 1:4, main = c("Conditional Plot One", 
    "Conditional Plot Two", "Density Plot", "Dependence Function"), 
    ask = nb.fig < length(which) && dev.interactive(), ci = TRUE, 
    jitter = FALSE, grid = 50, nplty = 2, blty = 3, method = "cfg",
    convex = FALSE, wf = function(t) t, ...)

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. The values of the remaining parameters are then passed to the plot method

which

If a subset of the plots is required, specify a subset of the numbers 1:4.

main

Title of each plot.

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.

jitter, grid

Arguments 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. If jitter is TRUE, t

nplty, blty, method, convex, wf

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 definition of th

...

Other arguments to be passed through to plotting functions.

Details

The following discussion assumes 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. 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.gev, contour, jitter, abvnonpar

Examples

bvdata <- rbvevd(100, dep = 0.6, model = "log")
M1 <- fbvevd(bvdata, model = "log")
par(mfrow = c(2,2))
plot(M1)
plot(M1, mar = 1)
plot(M1, mar = 2)