# Mvt

##### The Multivariate t Distribution

These functions provide information about the multivariate \(t\)
distribution with non-centrality parameter (or mode) `delta`

,
scale matrix `sigma`

and degrees of freedom `df`

.
`dmvt`

gives the density and `rmvt`

generates random deviates.

- Keywords
- multivariate, distribution

##### Usage

```
rmvt(n, sigma = diag(2), df = 1, delta = rep(0, nrow(sigma)),
type = c("shifted", "Kshirsagar"), ...)
dmvt(x, delta = rep(0, p), sigma = diag(p), df = 1, log = TRUE,
type = "shifted")
```

##### Arguments

- x
vector or matrix of quantiles. If

`x`

is a matrix, each row is taken to be a quantile.- n
number of observations.

- delta
the vector of noncentrality parameters of length n, for

`type = "shifted"`

delta specifies the mode.- sigma
scale matrix, defaults to

`diag(ncol(x))`

.- df
degrees of freedom.

`df = 0`

or`df = Inf`

corresponds to the multivariate normal distribution.- log
`logical`

indicating whether densities \(d\) are given as \(\log(d)\).- type
type of the noncentral multivariate \(t\) distribution.

`type = "Kshirsagar"`

corresponds to formula (1.4) in Genz and Bretz (2009) (see also Chapter 5.1 in Kotz and Nadarajah (2004)). This is the noncentral t-distribution needed for calculating the power of multiple contrast tests under a normality assumption.`type = "shifted"`

corresponds to the formula right before formula (1.4) in Genz and Bretz (2009) (see also formula (1.1) in Kotz and Nadarajah (2004)). It is a location shifted version of the central t-distribution. This noncentral multivariate \(t\) distribution appears for example as the Bayesian posterior distribution for the regression coefficients in a linear regression. In the central case both types coincide. Note that the defaults differ from the default in`pmvt()`

(for reasons of backward compatibility).- …
additional arguments to

`rmvnorm()`

, for example`method`

.

##### Details

If \(\bm{X}\) denotes a random vector following a \(t\) distribution
with location vector \(\bm{0}\) and scale matrix
\(\Sigma\) (written \(X\sim t_\nu(\bm{0},\Sigma)\)), the scale matrix (the argument
`sigma`

) is not equal to the covariance matrix \(Cov(\bm{X})\)
of \(\bm{X}\). If the degrees of freedom \(\nu\) (the
argument `df`

) is larger than 2, then
\(Cov(\bm{X})=\Sigma\nu/(\nu-2)\). Furthermore,
in this case the correlation matrix \(Cor(\bm{X})\) equals
the correlation matrix corresponding to the scale matrix
\(\Sigma\) (which can be computed with
`cov2cor()`

). Note that the scale matrix is sometimes
referred to as “dispersion matrix”;
see McNeil, Frey, Embrechts (2005, p. 74).

For `type = "shifted"`

the density
$$c(1+(x-\delta)'S^{-1}(x-\delta)/\nu)^{-(\nu+m)/2}$$
is implemented, where
$$c = \Gamma((\nu+m)/2)/((\pi \nu)^{m/2}\Gamma(\nu/2)|S|^{1/2}),$$
\(S\) is a positive definite symmetric matrix (the matrix
`sigma`

above), \(\delta\) is the
non-centrality vector and \(\nu\) are the degrees of freedom.

`df=0`

historically leads to the multivariate normal
distribution. From a mathematical point of view, rather
`df=Inf`

corresponds to the multivariate normal
distribution. This is (now) also allowed for `rmvt()`

and
`dmvt()`

.

Note that `dmvt()`

has default `log = TRUE`

, whereas
`dmvnorm()`

has default `log = FALSE`

.

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
## basic evaluation
dmvt(x = c(0,0), sigma = diag(2))
## 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))
## X ~ t_3(0, diag(2))
x <- rmvt(100, sigma = diag(2), df = 3) # t_3(0, diag(2)) sample
plot(x)
## 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)
## 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))
## 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.))
# }
```

*Documentation reproduced from package mvtnorm, version 1.0-10, License: GPL-2*