| Title: | Draw Samples of Truncated Multivariate Normal Distributions |
| Version: | 1.2.0 |
| Date: | 2025-02-06 |
| Author: | Jian Cao [aut, cre], Matthias Katzfuss [aut] |
| Maintainer: | Jian Cao <jcao2416@gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Description: | Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" <doi:10.48550/arXiv.2406.17307>. |
| Encoding: | UTF-8 |
| Imports: | GpGp, TruncatedNormal, RANN |
| LinkingTo: | Rcpp |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-02-08 17:03:19 UTC; jiancao |
| Repository: | CRAN |
| Date/Publication: | 2025-02-08 18:50:02 UTC |
Find ordered nearest neighbors based on correlation, assuming the absolute value of the correlation is monotonically decreasing with distance. Returns an n X (m + 1) matrix, each row indicating the m + 1 nearest neighbors including itself.
Description
Find ordered nearest neighbors based on correlation, assuming the absolute value of the correlation is monotonically decreasing with distance. Returns an n X (m + 1) matrix, each row indicating the m + 1 nearest neighbors including itself.
Usage
corr_nn(covmat, m)
Arguments
covmat |
the covariance matrix |
m |
the number of nearest neighbors |
Value
an n X (m + 1) matrix
Examples
library(RANN)
library(nntmvn)
set.seed(123)
d <- 3
n <- 100
locs <- matrix(runif(d * n), n, d)
covparms <- c(2, 0.01, 0)
covmat <- GpGp::matern15_isotropic(covparms, locs)
m <- 10
NNarray_test <- RANN::nn2(locs, k = m + 1)[[1]]
NNarray <- nntmvn::corr_nn(covmat, m)
cat("Number of mismatch is", sum(NNarray != NNarray_test, na.rm = TRUE))
nntmvn
Description
Draw Samples of Truncated Multivariate Normal Distributions
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
)
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 |
locs |
location matrix n X d |
cov_name |
covariance function name from the |
cov_parm |
parameters for the covariance function from the |
NN |
n X m matrix for nearest neighbors. i-th row is the nearest neighbor indices of y_i. |
ordering |
|
seed |
set seed for reproducibility |
Value
a vector of length n representing the underlying GP responses
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")
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
)
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 |
locs |
location matrix n X d |
cov_name |
covariance function name from the |
cov_parm |
parameters for the covariance function from the |
NN |
n X m matrix for nearest neighbors. i-th row is the nearest neighbor indices of y_i. |
ordering |
|
seed |
set seed for reproducibility |
Value
a vector of length n representing the underlying GP responses
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")