fitCopula {copula} | R Documentation |
Fit a copula model to multivariate data.
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")
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 |
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 a list of:
est |
the estimate of the parameters |
var.est |
variance matrix of the estimate |
loglik |
loglikelihood at est |
fit |
the result of optim |
When covariates are available for marginal distributions or copula, one can construct loglikelihood function and feed it to optim to estimate all the parameters.
Jun Yan <jyan@stat.uiowa.edu>
Yan (2006) Multivariate Modeling with Copulas and Engineering Applications. In Handbook of Engineering Statistics, Ed. Pham, Springer.
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))