plot.bvevd {evd}R Documentation

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, legend = TRUE, 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 plot.uvevd.
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, legend 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, the data are jittered. This need only be used if the data contains repeated values. If legend is TRUE and if the fitted data contained a third column of mode logical, then a legend is included.
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 the dependence function, and for a description of the arguments method, modify and wf, which alter the behaviour of the non-parametric estimator. 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.
... 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,...,z_m be the data used in the fitted model, where z_j = (z_{1j}, z_{2j}) for j = 1,...,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,...,m}

where p_1,...,p_m are plotting points defined by ppoints and c_i is the ith largest value from the sample {G(z_{j1}|z_{j2}), j = 1,...,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

Examples

bvdata <- rbvevd(100, dep = 0.6, model = "log")
M1 <- fbvevd(bvdata, model = "log")
## Not run: par(mfrow = c(2,2))
## Not run: plot(M1)
## Not run: plot(M1, mar = 1)
## Not run: plot(M1, mar = 2)

[Package evd version 2.1-7 Index]