zzz-mvt function

Multivariate Student-t Distribution

Alternative density, distribution function, and random generation for the multivariate Student-t distribution.

Details

The functions to compute densities dmvt, probabilities pmvt, and to generate random numbers rmvt are available from the contributed R package mvtnorm. The function qmvt computes the equicoordinate quantile function of the multivariate normal distribution for arbitrary correlation matrices based on inversion of pmvt.

dmvt(x, delta, sigma, df, <<...>>)

pmvt(<<...>>)

rmvt(n, sigma, df, delta, <<...>>

NOTE: The function are not builtin in the package fMultivar. Fur details we refer to the help page of mvnorm.

References

McNeil, A. J., Frey, R., and Embrechts, P. (2005), Quantitative Risk Management: Concepts, Techniques, Tools, Princeton University Press.

Author(s)

Alan Genz, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch, Fabian Scheipl, Bjoern Bornkamp, Torsten Hothorn.

Examples

## Not run:

## Load Libray:
   require(mvtnorm)
   
## dmvt - 
   # basic evaluation
   dmvt(x = c(0,0), sigma = diag(2))

## dmvt | dmvnorm - 
   # check behavior for df=0 and df=Inf
   x <- c(1.23, 4.56)
   mu <- 1:2
   Sigma <- diag(2)
   x0 <- dmvt(x, delta = mu, sigma = Sigma, df = 0) # default log = TRUE!
   x8 <- dmvt(x, delta = mu, sigma = Sigma, df = Inf) # default log = TRUE!
   xn <- dmvnorm(x, mean = mu, sigma = Sigma, log = TRUE)
   stopifnot(identical(x0, x8), identical(x0, xn))

## rmvt -
   # X ~ t_3(0, diag(2))
   x <- rmvt(100, sigma = diag(2), df = 3) # t_3(0, diag(2)) sample
   plot(x)

## rmvt -
   # X ~ t_3(mu, Sigma)
   n <- 1000
   mu <- 1:2
   Sigma <- matrix(c(4, 2, 2, 3), ncol=2)
   set.seed(271)
   x <- rep(mu, each=n) + rmvt(n, sigma=Sigma, df=3)
   plot(x)

## rmvt -
   # Note that the call rmvt(n, mean=mu, sigma=Sigma, df=3) does *not*
   # give a valid sample from t_3(mu, Sigma)! [and thus throws an error]
   try(rmvt(n, mean=mu, sigma=Sigma, df=3))

## rmvnorm - 
   # df=Inf correctly samples from a multivariate normal distribution
   set.seed(271)
   x <- rep(mu, each=n) + rmvt(n, sigma=Sigma, df=Inf)
   set.seed(271)
   x. <- rmvnorm(n, mean=mu, sigma=Sigma)
   stopifnot(identical(x, x.))
## End(Not run)