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, 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` | 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.
`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)

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