library(copula) could be helpful as well.
## Toy example for gumbel copula with log-normal distribution
## (Taken from the documentation of copula::fitMvdc)
## Specify the copula
gumbel.cop <- gumbelCopula(3, dim=2)
myMvd <- mvdc(gumbel.cop, c("lnorm","lnorm"), list(list(meanlog = 1.17),
list(meanlog = 0.76)))
## Generate some random sample to test
x <- rmvdc(myMvd, 1000)
## Fit the random sample
fit <- fitMvdc(x, myMvd, c(1,1,2))
fit
## Jun Yan (2007). Enjoy the Joy of Copulas: With a Package copula.
## Journal of Statistical Software, 21(4), 1-21. URL
## http://www.jstatsoft.org/v21/i04/.
## Ivan Kojadinovic, Jun Yan (2010). Modeling Multivariate Distributions
## with Continuous Margins Using the copula R Package. Journal of
## Statistical Software, 34(9), 1-20. URL
## http://www.jstatsoft.org/v34/i09/.
Cheers,
On 25/11/2011 9:09 a.m., Dennis Murphy wrote:
Hi:
This is the type of question for which the sos package can come to the rescue:
library('sos')
findFn('Gumbel Clayton copula')
It appears that the QRMlib package would be a reasonable place to start.
Dennis
On Thu, Nov 24, 2011 at 7:29 PM, cahaya iman<qaisfay...@gmail.com> wrote:
Hi,
Is anybody using Copula package for fitting copulas to own data?
I have two marginals Log Normal with (parameters 1.17 and 0.76) and Gamma (
2.7 and 1.05)
Which package I should use to fit Gumbel and Clayton Copulas?
Thanks,
fayyad
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