nntmvn (version 1.3.0)

rtmvn: Draw one sample from a truncated multivariate normal (TMVN) distribution using sequential nearest neighbor (SNN) method

Description

Draw one sample from a truncated multivariate normal (TMVN) distribution using sequential nearest neighbor (SNN) method

Usage

rtmvn(
  cens_lb,
  cens_ub,
  m = 30,
  covmat = NULL,
  locs = NULL,
  cov_name = NULL,
  cov_parm = NULL,
  NN = NULL,
  ordering = 0,
  seed = NULL
)

Value

a vector of length n representing the underlying GP responses

Arguments

cens_lb

lower bound vector for TMVN of length n

cens_ub

upper bound vector for TMVN of length n

m

positive integer for the number of nearest neighbors used

covmat

n-by-n dense covariance matrix, either covmat or locs, cov_name, and cov_parms need to be provided

locs

location matrix n X d

cov_name

covariance function name from the GpGp package

cov_parm

parameters for the covariance function from the GpGp package

NN

n X m matrix for nearest neighbors. i-th row is the nearest neighbor indices of y_i. NN[i, 1] should be i

ordering

0 for do not reorder, 1 for variance descending order, 2 for maximin ordering

seed

set seed for reproducibility

Examples

library(nntmvn)
library(TruncatedNormal)
set.seed(123)
x <- matrix(seq(from = 0, to = 1, length.out = 51), ncol = 1)
cov_name <- "matern15_isotropic"
cov_parm <- c(1.0, 0.1, 0.001) #'' variance, range, nugget
cov_func <- getFromNamespace(cov_name, "GpGp")
covmat <- cov_func(cov_parm, x)
lb <- rep(-Inf, nrow(x))
ub <- rep(-1, nrow(x))
m <- 30
samp_SNN <- matrix(NA, 3, nrow(x))
for (i in 1:3) {
  samp_SNN[i, ] <- nntmvn::rtmvn(lb, ub, m = m, covmat = covmat, locs = x, ordering = 0)
}
samp_TN <- TruncatedNormal::rtmvnorm(3, rep(0, nrow(x)), covmat, lb, ub)
qqplot(samp_SNN, samp_TN, xlim = range(samp_SNN, samp_TN), ylim = range(samp_SNN, samp_TN))
abline(a = 0, b = 1, lty = "dashed", col = "red")