ccbvevd {evd} | R Documentation |
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 = "log", lower.tail = TRUE)
x |
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). |
mar |
One or two; conditions on this margin. |
dep |
Dependence parameter for the logistic, asymmetric logistic, Husler-Reiss, negative logistic and asymmetric negative logistic models. |
asy |
A vector of length two, containing the two asymmetry parameters for the asymmetric logistic and asymmetric negative logistic models. |
alpha, beta |
Alpha and beta parameters for the bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models. |
model |
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. |
lower.tail |
Logical; if TRUE (default), the
conditional distribution function is returned; the conditional
survivor function is returned otherwise. |
The function calculates P(U1 < x1|U2 = x2), where
(U1,U2) 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 x1 and
x2 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 x3
of mode logical, then
the function returns P(U1 < x1|U2 = x2,I = x3), 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.
A numeric vector of probabilities.
Stephenson, A. G. and Tawn, J. A. (2004) Exploiting Occurence Times in Likelihood Inference for Componentwise Maxima. Biometrika 92(1), 213–217.