ccbvevd | 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 = c("log",
"alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct",
"amix"), 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 |
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 |
lower.tail |
Logical; if |
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.
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.
rbvevd
, fbvevd
,
plot.bvevd
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