condMVN {condMVNorm} R Documentation

## Conditional Mean and Variance of Multivariate Normal Distribution

### Description

These functions provide the conditional mean and variance-covariance matrix of [Y given X], where Z = (X,Y) is the fully-joint multivariate normal distribution with mean equal to mean and covariance matrix sigma.

### Usage

condMVN(mean, sigma, dependent.ind, given.ind, X.given, check.sigma=TRUE)


### Arguments

 mean mean vector, which must be specified. sigma a symmetric, positive-definte matrix of dimension n x n, which must be specified. dependent.ind a vector of integers denoting the indices of dependent variable Y. given.ind a vector of integers denoting the indices of conditioning variable X. If specified as integer vector of length zero or left unspecified, the unconditional density is returned. X.given a vector of reals denoting the conditioning value of X. This should be of the same length as given.ind check.sigma logical; if TRUE, the variance-covariance matrix is checked for appropriateness (symmetry, positive-definiteness). This could be set to FALSE if the user knows it is appropriate.

dcmvnorm, pcmvnorm, pmvnorm, dmvnorm, qmvnorm

### Examples

# 10-dimensional multivariate normal distribution
n <- 10
A <- matrix(rnorm(n^2), n, n)
A <- A %*% t(A)

condMVN(mean=rep(1,n), sigma=A, dependent=c(2,3,5), given=c(1,4,7,9),
X.given=c(1,1,0,-1))

condMVN(mean=rep(1,n), sigma=A, dep=3, given=c(1,4,7,9), X=c(1,1,0,-1))

condMVN(mean=rep(1,n), sigma=A, dep=3, given=integer())
# or simply the following

condMVN(mean=rep(1,n), sigma=A, dep=3)



[Package condMVNorm version 2020.1 Index]