Conditional copula functions, conditioning on either margin, for nine parametric bivariate extreme value models.
ccbvevd(x, mar = 2, dep, asy = c(1, 1), alpha, beta, model = c("log",
"alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct",
"amix"), lower.tail = TRUE)
A numeric vector of probabilities.
A matrix or data frame, ordinarily with two columns,
which may contain missing values. A data frame may also
contain a third column of mode logical
, which
itself may contain missing values (see Details).
One or two; conditions on this margin.
Dependence parameter for the logistic, asymmetric logistic, Husler-Reiss, negative logistic and asymmetric negative logistic models.
A vector of length two, containing the two asymmetry parameters for the asymmetric logistic and asymmetric negative logistic models.
Alpha and beta parameters for the bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models.
The specified model; a character string. Must be
either "log"
(the default), "alog"
, "hr"
,
"neglog"
, "aneglog"
, "bilog"
,
"negbilog"
, "ct"
or "amix"
(or any unique
partial match), for the logistic, asymmetric logistic,
Husler-Reiss, negative logistic, asymmetric negative logistic,
bilogistic, negative bilogistic, Coles-Tawn and asymmetric
mixed models respectively. If parameter arguments are given
that do not correspond to the specified model those arguments
are ignored, with a warning.
Logical; if TRUE
(default), the
conditional distribution function is returned; the conditional
survivor function is returned otherwise.
The function calculates \(P(U_1 < x_1|U_2 = x_2)\), where \((U_1,U_2)\) is a random
vector with Uniform(0,1) margins and with a dependence structure
given by the specified parametric model. By default, the values
of \(x_1\) and \(x_1\) are given by the first and second
columns of the argument x
. If mar = 1
then this is
reversed.
If x
has a third column \(x_3\) of mode logical, then
the function returns \(P(U_1 < x_1|U_2 = x_2,I = x_3)\), according to inference proceedures derived
by Stephenson and Tawn (2004).
See fbvevd
. This requires numerical integration,
and hence will be slower.
This function is mainly for internal use. It is used by
plot.bvevd
to calculate the conditional P-P
plotting diagnostics.
Stephenson, A. G. and Tawn, J. A. (2004) Exploiting Occurence Times in Likelihood Inference for Componentwise Maxima. Biometrika 92(1), 213--217.
rbvevd
, fbvevd
,
plot.bvevd