rmvss {mvpd} | R Documentation |
Multivariate Subgaussian Stable Random Variates
Description
Computes random vectors of the multivariate subgaussian stable distribution for arbitrary alpha, shape matrices, and location vectors. See Nolan (2013).
Usage
rmvss(
n,
alpha = 1,
Q = NULL,
delta = rep(0, d),
which.stable = c("libstable4u", "stabledist")[1]
)
Arguments
n |
number of observations |
alpha |
default to 1 (Cauchy). Must be 0< |
Q |
Shape matrix. See Nolan (2013). |
delta |
location vector. |
which.stable |
defaults to |
Value
Returns the n
by d
matrix containing multivariate subgaussian stable
random variates where d=nrow(Q)
.
References
Nolan JP (2013), Multivariate elliptically contoured stable distributions: theory and estimation. Comput Stat (2013) 28:2067–2089 DOI 10.1007/s00180-013-0396-7
Examples
## generate 10 random variates of a bivariate mvss
rmvss(n=10, alpha=1.71, Q=matrix(c(10,7.5,7.5,10),2))
## generate 10 random variates of a trivariate mvss
Q <- matrix(c(10,7.5,7.5,7.5,10,7.5,7.5,7.5,10),3)
rmvss(n=10, alpha=1.71, Q=Q)
[Package mvpd version 0.0.4 Index]