fitCopula {copula}R Documentation

Maximum Likelihood Estimation of Copula Models

Description

Fit a copula model to multivariate data.

Usage

loglikCopula(param, x, copula)
loglikMvdc(param, x, mvdc)
fitCopula(data, copula, start, optim.control = list(NULL), method = "BFGS")
fitMvdc(data, mvdc, start, optim.control = list(NULL), method = "BFGS")

Arguments

param a vector of parameter values
x a data matrix
copula a 'copula' object
mvdc a 'mvdc' object
data a data matrix
start a vector of starting value for param
optim.control a list of control to be passed to optim
method the method for optim

Value

The return values of 'loglikCopula' and 'loglikMvdc' are the loglikelihood evaluated at the given value of 'param'.
The return values of 'fitCopula' and 'fitMvdc' are an object of class 'fitCopula' and 'fitMvdc', respectively, containing slots:

est the estimate of the parameters
var.est variance matrix of the estimate
loglik loglikelihood at est
fit the result of optim

Note

When covariates are available for marginal distributions or copula, one can construct loglikelihood function and feed it to optim to estimate all the parameters.

Author(s)

Jun Yan <jyan@stat.uiowa.edu>

References

Yan (2006) Multivariate Modeling with Copulas and Engineering Applications. In Handbook of Engineering Statistics, Ed. Pham, Springer.

See Also

Copula, mvdc

Examples

gmb <- gumbelCopula(3, dim=2)
myMvd <- mvdc(gmb, c("exp","exp"), list(list(rate=2),list(rate=4)))
x <- rmvdc(myMvd, 1000)
fit <- fitMvdc(x, myMvd, c(1,1,2))

[Package copula version 0.3-7 Index]