ccbvevd {evd}

Calculate Conditional Copulas for Parametric Bivariate Extreme Value Distributions

Description

Conditional copula functions, conditioning on either margin, for nine parametric bivariate extreme value models.

Usage

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) 

Arguments

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.

Details

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.

Value

A numeric vector of probabilities.

References

Stephenson, A. G. and Tawn, J. A. (2004) Exploiting Occurence Times in Likelihood Inference for Componentwise Maxima. Biometrika 92(1), 213–217.

See Also

rbvevd, fbvevd, plot.bvevd