LaplacesDemon: dist.Multivariate.t.Precision – R documentation

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dist.Multivariate.t.Precision

Multivariate t Distribution: Precision Parameterization

Description

These functions provide the density and random number generation for the multivariate t distribution, otherwise called the multivariate Student distribution. These functions use the precision parameterization.

Usage

dmvtp(x, mu, Omega, nu=Inf, log=FALSE)
rmvtp(n=1, mu, Omega, nu=Inf)

Arguments

`x`	This is either a vector of length k or a matrix with a number of columns, k, equal to the number of columns in precision matrix Omega.
`n`	This is the number of random draws.
`mu`	This is a numeric vector representing the location parameter, mu (the mean vector), of the multivariate distribution (equal to the expected value when `df > 1`, otherwise represented as nu > 1). It must be of length k, as defined above.
`Omega`	This is a k x k positive-definite precision matrix Omega.
`nu`	This is the degrees of freedom nu, which must be positive.
`log`	Logical. If `log=TRUE`, then the logarithm of the density is returned.

Details

Application: Continuous Multivariate
Density:

p(theta) = (Gamma((nu+k)/2) / (Gamma(nu/2)*nu^(k/2)*pi^(k/2))) * |Omega|^(1/2) * (1 + (1/nu) (theta-mu)^T Omega (theta-mu))^(-(nu+k)/2)
Inventor: Unknown (to me, anyway)
Notation 1: theta ~ t[k](mu, Omega^(-1), nu)
Notation 2: p(theta) = t[k](theta | mu, Omega^(-1), ν)
Parameter 1: location vector mu
Parameter 2: positive-definite k x k precision matrix Omega
Parameter 3: degrees of freedom nu > 0
Mean: E(theta) = mu, for nu > 1, otherwise undefined
Variance: var(theta) = (nu / (nu - 2))*Omega^(-1), for nu> 2
Mode: mode(theta) = mu

The multivariate t distribution, also called the multivariate Student or multivariate Student t distribution, is a multidimensional extension of the one-dimensional or univariate Student t distribution. A random vector is considered to be multivariate t-distributed if every linear combination of its components has a univariate Student t-distribution.

It is usually parameterized with mean and a covariance matrix, or in Bayesian inference, with mean and a precision matrix, where the precision matrix is the matrix inverse of the covariance matrix. These functions provide the precision parameterization for convenience and familiarity. It is easier to calculate a multivariate t density with the precision parameterization, because a matrix inversion can be avoided.

This distribution has a mean parameter vector mu of length k, and a k x k precision matrix Omega, which must be positive-definite. When degrees of freedom nu=1, this is the multivariate Cauchy distribution.

Value

dmvtp gives the density and rmvtp generates random deviates.

Author(s)

Statisticat, LLC. software@bayesian-inference.com

Examples

library(LaplacesDemon)
x <- seq(-2,4,length=21)
y <- 2*x+10
z <- x+cos(y) 
mu <- c(1,12,2)
Omega <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
nu <- 4
f <- dmvtp(cbind(x,y,z), mu, Omega, nu)
X <- rmvtp(1000, c(0,1,2), diag(3), 5)
joint.density.plot(X[,1], X[,2], color=TRUE)

LaplacesDemon

Complete Environment for Bayesian Inference

v16.1.4

MIT + file LICENSE

Authors

Byron Hall [aut], Martina Hall [aut], Statisticat, LLC [aut], Eric Brown [ctb], Richard Hermanson [ctb], Emmanuel Charpentier [ctb], Daniel Heck [ctb], Stephane Laurent [ctb], Quentin F. Gronau [ctb], Henrik Singmann [cre]

Initial release