SpatialTools (version 0.5.8)

rcondnorm: Generate from conditional normal distribution

Description

Generates realizations from a multivariate normal distribution conditional on observed data vector

Usage

rcondnorm(nsim = 1, y, mu, mup, V, Vp, Vop, method = "eigen")

Arguments

nsim

An integer indicating the number of realizations from the distribution.

y

A vector of length n contained the observed data.

mu

The mean vector of the observed data. Should be a vector of length n.

mup

The mean vector of the responses to be generated. Should be a vector of length np.

V

The covariance matrix of the observed data. The matrix should be symmetric and positive definite. The size must be $n times n$.

Vp

The covariance matrix of the responses to be generated. The matrix should be symmetric and positive definite. The size must be $np times np$.

Vop

The cross-covariance matrix between the observed data and the responses to be generated. The size must be $n times np$.

method

The method for performing a decomposition of the covariance matrix. Possible values are "eigen", "chol", and "svd", Eigen value decomposition, Cholesky decomposition, or Singular Value Decomposoition, respectively.

Value

• An $np \times nsim$ matrix containing the nsim realizations of the conditional normal distribution. Each column of the matrix represents a realization of the multivariate normal distribution.

See Also

rmvnorm

Examples

n <- 100
np <- 100

mu <- rep(1, 100)
mup <- rep(2, 100)

coords <- matrix(runif(2 * n), ncol = 2)
pcoords <- matrix(runif(2 * np), ncol = 2)

myV <- cov.sp(coords, sp.type = "exponential", c(1, 2), error.var = 1, pcoords = pcoords)

y <- rmvnorm(1, mu = mu, V = myV$V)

rcondnorm(3, y = y, mu = mu, mup = mup, V = myV$V, Vp = myV$Vp, Vop = myV$Vop, method = "chol")