On Sun, 6 May 2018, Andreas Bregiannis wrote:
Hello everyone,
I would like to ask you how I can speed up this for loop. In the following
code I generate a sample of 200 of the NIG distribution and then I fit to them
the NIG distribution. My aim is to estimate 1mln times the mean parameter of
this NIG distribution.
Any ideas?
library(GeneralizedHyperbolic)
mu=0;delta = 1;alpha = 1;beta = 0
m=numeric(1000000)
for(i in 1:1000000){
m[i]=coef(nigFit(rnig(200, param = c(mu, delta, alpha, beta))
))[1]
}
Thank you in advance.
Best,
Andreas
Hi, Andreas.
How about this?
library(GeneralizedHyperbolic)
mu = 0; delta = 1; alpha = 1; beta = 0
func <- function(){
random_numbers <- rnig(200, param = c(mu, delta, alpha, beta))
fit <- nigFit(d)
return(coef(fit)[1])
}
m <- as.numeric(rep(func(), 1000000)
Eivind K. Dovik
Bergen, NO
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