rmvn Generate from or evaluate multivariate normal or t densities.

Description

Generates multivariate normal or t random deviates, and evaluates the corresponding log densities.

Usage

rmvn(n,mu,V)
r.mvt(n,mu,V,df)
dmvn(x,mu,V,R=NULL)
d.mvt(x,mu,V,df,R=NULL)

Arguments

n	number of simulated vectors required.
mu	the mean of the vectors: either a single vector of length p=ncol(V) or an n by p matrix.
V	A positive semi definite covariance matrix.
df	The degrees of freedom for a t distribution.
x	A vector or matrix to evaluate the log density of.
R	An optional Cholesky factor of V (not pivoted).

Details

Uses a ‘square root’ of V to transform standard normal deviates to multivariate normal with the correct covariance matrix.

Value

An n row matrix, with each row being a draw from a multivariate normal or t density with covariance matrix V and mean vector mu. Alternatively each row may have a different mean vector if mu is a vector.

For density functions, a vector of log densities.

Author(s)

Simon N. Wood [email protected]

See Also

ldTweedie, Tweedie

Examples

library(mgcv)
V <- matrix(c(2,1,1,2),2,2) 
mu <- c(1,3)
n <- 1000
z <- rmvn(n,mu,V)
crossprod(sweep(z,2,colMeans(z)))/n ## observed covariance matrix
colMeans(z) ## observed mu
dmvn(z,mu,V)