plot.bvevd {evd} | R Documentation |
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.
## 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, ...)
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. |
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.
plot.uvevd
, contour
,
jitter
, abvnonpar
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)