plot.bvevd | R Documentation |
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.
## S3 method for class '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, ...)
x |
An object of class |
mar |
If |
which |
A subset of the numbers |
main |
Title of each plot. If given, should be a
character vector with the same length as |
ask |
Logical; if |
ci |
Logical; if |
cilwd |
Line width for confidence interval lines. |
a |
Passed through to |
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 |
legend |
If |
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
|
nplty , blty |
Line types for the dependence function plot.
|
p , mint |
Arguments to the quantile curves plot. See
|
half |
Argument to the spectral density plot. See
|
... |
Other arguments to be passed through to plotting functions. |
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.
plot.uvevd
, contour
,
jitter
, abvnonpar
,
qcbvnonpar
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)
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