Last chance! 50% off unlimited learning
Sale ends in
randmvn(N, d, method = c("normwish", "parsimonious"),
mup=list(mu = 0, s2 = 1), s2p=list(a = 0.5, b = 1),
pnz=0.1, nu=Inf)"norwish"
uses the direct method described in the details section below,
whereas the "parsimonious" method builds up the random mean
vector and covariance via regression coefficients, list with entries $mu and $s2:
$mu is the prior mean for the independent components
of the normally distributed mean vector; $s2 is the prior
variancelist with entries $a and $b
only valid for method = "parsimonious":
$a > 0 is the baseline inverse gamma prior scale parameter
for the regression variances (the actual paramet0 <= pnz="" <="1, only valid for
method = "parsimonious": determines the binomial
proportion of non-zero regression coefficients in the sequential
build-up of mu and S, thereby indirect>= 1 indicating the degrees of freedom
of a Student-t distribution to be used instead of an MVN
when not infinitelist with the following components:dmatrix with d
rows and d columnsN > 0 then x is an N*d
matrix of N samples from the MVN with mean vector
mu and covariance matrix S; otherwise when
N = 0 this component is not included"normwish") the components of the
mean vector mu are iid from a standard normal distribution,
and the covariance matrix S is
drawn from an inverse--Wishart distribution with degrees of freedom
d + 2 and mean (centering matrix) diag(d) In the "parsimonious" method mu and S are
built up sequentially by randomly sampling intercepts, regression
coefficients (of length i-1 for i in 1:d) and variances
by applying the monomvn equations. A unique prior results
when a random number of the regression coefficients are set to zero.
When none are set to zero the direct method results
rwish, rmvnorm,
rmono