Fast computation of the multivariate Student's t density.

Fast computation of the multivariate Student's t density.

dmvt(X, mu, sigma, df, log = FALSE, ncores = 1, isChol = FALSE)

Arguments

X	matrix n by d where each row is a d dimensional random vector. Alternatively X can be a d-dimensional vector.
mu	vector of length d, representing the mean of the distribution.
sigma	scale matrix (d x d). Alternatively it can be the cholesky decomposition of the scale matrix. In that case isChol should be set to TRUE. Notice that ff the degrees of freedom (the argument df) is larger than 2, the Cov(X)=sigma*df/(df-2).
df	a positive scalar representing the degrees of freedom.
log	boolean set to true the logarithm of the pdf is required.
ncores	Number of cores used. The parallelization will take place only if OpenMP is supported.
isChol	boolean set to true is sigma is the cholesky decomposition of the covariance matrix.

Value

A vector of length n where the i-the entry contains the pdf of the i-th random vector.

Details

There are many candidates for the multivariate generalization of Student's t-distribution, here we use the parametrization described here https://en.wikipedia.org/wiki/Multivariate_t-distribution. NB: at the moment the parallelization does not work properly on Solaris OS when ncores>1. Hence, dmvt() checks if the OS is Solaris and, if this the case, it imposes ncores==1.

Examples

# NOT RUN {
N <- 100
d <- 5
mu <- 1:d
df <- 4
X <- t(t(matrix(rnorm(N*d), N, d)) + mu)
tmp <- matrix(rnorm(d^2), d, d)
mcov <- tcrossprod(tmp, tmp)  + diag(0.5, d)
myChol <- chol(mcov)

head(dmvt(X, mu, mcov, df = df), 10)
head(dmvt(X, mu, myChol, df = df, isChol = TRUE), 10)

# }