[R] simulating data from a multivariate dist

[Joris Meys]

Dirty hack, but it's working.

library(MASS)
mu <- aic.mv$best.model at expected.value
sigma <- aic.mv$best.model at variance

mvrnorm(100,mu,sigma)

If you'd like to follow the rules, look for the functions to extract
the expected value and the variance of the best model out of the
stepAIC.ghyp object. I didn't find them, but then again, I'm a lazy
bastard and didn't look for them either.

Cheers
Joris


On Thu, Jun 17, 2010 at 2:49 PM, suman dhara <suman.dhara89 at gmail.com> wrote:
> Sir,
> I am working on fitting distribution on multivariate financial data and then
> simulate observations from that fitted distribution. I use stepAIC.ghyp()
> function of 'ghyp' library which select the best fitted distribution from
> generalized hyperbolic distribution class on the given dataset.
>
> data(indices)
>
>  # Multivariate case:
>  aic.mv <- stepAIC.ghyp(indices, dist = c("ghyp", "hyp", "t", "gauss"),
>                         symmetric = NULL, control = list(maxit = 500),
>                         silent = TRUE, nit = 500)
>
>  summary(aic.mv$best.model)
>
> It fits asymmetic student-t dist. to the data 'indices'. Now, I want to
> simulate data from this best fitted distribution. I use
> simulate(aic.mv$best.model).
> But, it is not working. Can you give me the funtion/code to simulate data
> from this best fitted distribution.
>
>
>
> Thanks & Regards,
> Suman Dhara
>
>        [[alternative HTML version deleted]]
>
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>



-- 
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

tel : +32 9 264 59 87
Joris.Meys at Ugent.be
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