EllDistrSimCond {ElliptCopulas}  R Documentation 
Simulation of elliptically symmetric random vectors conditionally to some observed part.
EllDistrSimCond(
n,
xobs,
d,
Sigma = diag(d),
mu = 0,
density_R2_,
genR = list(method = "pinv")
)
n 
number of observations to be simulated from the conditional distribution. 
xobs 
observed value of X that we condition on.

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:

a matrix of size (n,d) of simulated observations.
Cambanis, S., Huang, S., & Simons, G. (1981). On the Theory of Elliptically Contoured Distributions, Journal of Multivariate Analysis. (Corollary 5, p.376)
EllDistrSim
for the (unconditional) simulation of
elliptically distributed random vectors.
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)