abvpar(x = 0.5, dep, asy = c(1,1), alpha, beta, model = "log",
plot = FALSE, add = FALSE, lty = 1, lwd = 1, col = 1, blty = 3,
xlim = c(0,1), ylim = c(0.5,1), xlab = "", ylab = "", ...)
TRUE
). $A(1/2)$
is returned by default since it is often a useful summary of
dependence."log"
(the default), "alog"
, "hr"
,
"neglog"
, "aneglog"
, "bilog"
,
"negbilog"
or "ct"
TRUE
the function is plotted. The
x and y values used to create the plot are returned invisibly.
If plot
and add
are FALSE
(the default),
the arguments following add
abvpar
or
abvnonpar
, the latter of which plots (or calculates)
a non-parametric estimate blty
to zero to omit the border.plot
.abvpar
calculates or plots the dependence function
for one of eight parametric bivariate extreme value models,
at specified parameter values.$A(\cdot)$ is called (by some authors) the dependence function. It follows that $A(0)=A(1)=1$, and that $A(\cdot)$ is a convex function with $\max(x,1-x) \leq A(x)\leq 1$ for all $0\leq x\leq1$. The lower and upper limits of $A$ are obtained under complete dependence and independence respectively. $A(\cdot)$ does not depend on the marginal parameters.
abvnonpar
, fbvevd
,
rbvevd
, atvpar
abvpar(dep = 2.7, model = "hr")
abvpar(seq(0,1,0.25), dep = 0.3, asy = c(.7,.9), model = "alog")
abvpar(alpha = 0.3, beta = 1.2, model = "negbi", plot = TRUE)
bvdata <- rbvevd(100, dep = 0.7, model = "log")
M1 <- fitted(fbvevd(bvdata, model = "log"))
abvpar(dep = M1["dep"], model = "log", plot = TRUE)
abvnonpar(data = bvdata, add = TRUE, lty = 2)
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