TruncatedNormal (version 2.3)

tmvnorm: Multivariate truncated normal distribution

Description

Density, distribution function and random generation for the multivariate truncated normal distribution with mean vector mu, covariance matrix sigma, lower truncation limit lb and upper truncation limit ub. The truncation limits can include infinite values. The Monte Carlo (type = "mc") uses a sample of size B, while the quasi Monte Carlo (type = "qmc") uses a pointset of size ceiling(n/12) and estimates the relative error using 12 independent randomized QMC estimators.

Value

dtmvnorm gives the density, ptmvnorm and pmvnorm give the distribution function of respectively the truncated and multivariate Gaussian distribution and rtmvnorm generate random deviates.

Arguments

n

number of observations

x, q

vector of quantiles

B

number of replications for the (quasi)-Monte Carlo scheme

log

logical; if TRUE, probabilities and density are given on the log scale.

mu

vector of location parameters

sigma

covariance matrix

lb

vector of lower truncation limits

ub

vector of upper truncation limits

check

logical; if TRUE (default), the code checks that the scale matrix sigma is positive definite and symmetric

...

additional arguments, currently ignored

type

string, either of mc or qmc for Monte Carlo and quasi Monte Carlo, respectively

Usage

dtmvnorm(x, mu, sigma, lb, ub, type = c("mc", "qmc"), log = FALSE, B = 1e4)
ptmvnorm(q, mu, sigma, lb, ub, type = c("mc", "qmc"), log = FALSE, B = 1e4)
rtmvnorm(n, mu, sigma, lb, ub)

Author

Zdravko I. Botev, Leo Belzile (wrappers)

References

Z. I. Botev (2017), The normal law under linear restrictions: simulation and estimation via minimax tilting, Journal of the Royal Statistical Society, Series B, 79 (1), pp. 1--24.

Examples

d <- 4; lb <- rep(0, d)
mu <- runif(d)
sigma <- matrix(0.5, d, d) + diag(0.5, d)
samp <- rtmvnorm(n = 10, mu = mu, sigma = sigma, lb = lb)
loglik <- dtmvnorm(samp, mu = mu, sigma = sigma, lb = lb, log = TRUE)
cdf <- ptmvnorm(samp, mu = mu, sigma = sigma, lb = lb, log = TRUE, type = "q")

# Exact Bayesian Posterior Simulation Example
# Vignette, example 5
if (FALSE) {
data("lupus"); # load lupus data
Y <- lupus[,1]; # response data
X <- as.matrix(lupus[,-1])  # construct design matrix
n <- nrow(X)
d <- ncol(X)
X <- diag(2*Y-1) %*% X; # incorporate response into design matrix
nusq <- 10000; # prior scale parameter
C <- solve(diag(d)/nusq + crossprod(X))
sigma <- diag(n) + nusq*tcrossprod(X) # this is covariance of Z given beta
est <- pmvnorm(sigma = sigma, lb = 0)
# estimate acceptance probability of crude Monte Carlo
print(attributes(est)$upbnd/est[1])
# reciprocal of acceptance probability
Z <- rtmvnorm(sigma = sigma, n = 1e3, lb = rep(0, n))
# sample exactly from auxiliary distribution
beta <- rtmvnorm(n = nrow(Z), sigma = C) + Z %*% X %*% C
# simulate beta given Z and plot boxplots of marginals
boxplot(beta, ylab = expression(beta))
# output the posterior means
colMeans(beta)
}