EllDistrSimCond: Simulation of elliptically symmetric random vectors...

In AlexisDerumigny/ElliptCopulas: Inference of Elliptical Distributions and Copulas

Simulation of elliptically symmetric random vectors conditionally to some observed part.

Description

Simulation of elliptically symmetric random vectors conditionally to some observed part.

Usage

EllDistrSimCond(
  n,
  xobs,
  d,
  Sigma = diag(d),
  mu = 0,
  density_R2_,
  genR = list(method = "pinv")
)

Arguments

n	number of observations to be simulated from the conditional distribution.
xobs	observed value of X that we condition on. NA represent unknown components of the vectors to be simulated.
d	dimension of the random vector
Sigma	(unconditional) covariance matrix
mu	(unconditional) mean
density_R2_	(unconditional) density of the squared radius.
genR	additional arguments for the generation of the squared radius. It must be a list with a component method: If genR$method == "pinv", the radius is generated using the function Runuran::pinv.new(). If genR$method == "MH", the generation is done using the Metropolis-Hasting algorithm, with a N(0,1) move at each step.

Value

a matrix of size (n,d) of simulated observations.

References

Cambanis, S., Huang, S., & Simons, G. (1981). On the Theory of Elliptically Contoured Distributions, Journal of Multivariate Analysis. (Corollary 5, p.376)

See Also

EllDistrSim for the (unconditional) simulation of elliptically distributed random vectors.

Examples

d = 3
Sigma = rbind(c(1, 0.8, 0.9),
              c(0.8, 1, 0.7),
              c(0.9, 0.7, 1))
mu = c(0, 0, 0)
result = EllDistrSimCond(n = 100, xobs = c(NA, 2, NA), d = d,
  Sigma = Sigma, mu = mu, density_R2_ = function(x){stats::dchisq(x=x,df=3)})
plot(result)

result2 = EllDistrSimCond(n = 1000, xobs = c(1.3, 2, NA), d = d,
  Sigma = Sigma, mu = mu, density_R2_ = function(x){stats::dchisq(x=x,df=3)})
hist(result2)

AlexisDerumigny/ElliptCopulas documentation built on Sept. 13, 2024, 11:01 p.m.