nntmvn (version 1.3.0)

rptmvn: Draw one sample of the underlying GP responses for a partially censored Gaussian process using sequential nearest neighbor (SNN) method

Description

Draw one sample of the underlying GP responses for a partially censored Gaussian process using sequential nearest neighbor (SNN) method

Usage

rptmvn(
  y,
  cens_lb,
  cens_ub,
  mask_cens,
  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

y

uncensored responses of length n, where n is the number of all responses

cens_lb

lower bound vector for TMVN of length n

cens_ub

upper bound vector for TMVN of length n

mask_cens

mask for censored responses (also locations) 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(GpGp)
library(RANN)
library(nntmvn)
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)
y <- t(chol(covmat)) %*% rnorm(length(x))
mask <- y < 0.3
y_cens <- y
y_cens[mask] <- NA
lb <- rep(-Inf, 100)
ub <- rep(0.3, 100)
m <- 10
y_samp_mtd1 <- rptmvn(y_cens, lb, ub, mask,
  m = m, locs = x,
  cov_name = cov_name, cov_parm = cov_parm, seed = 123
)
y_samp_mtd2 <- rptmvn(y_cens, lb, ub, mask,
  m = m, covmat = covmat,
  seed = 123
)
plot(x, y_cens, ylim = range(y))
points(x[mask, ], y[mask], col = "blue")
plot(x, y_cens, ylim = range(y))
points(x[mask, ], y_samp_mtd1[mask], col = "red")
plot(x, y_cens, ylim = range(y))
points(x[mask, ], y_samp_mtd2[mask], col = "brown")