dWishart {CholWishart} | R Documentation |
Density for Random Wishart Distributed Matrices
Description
Compute the density of an observation of a random Wishart distributed matrix
(dWishart
) or an observation
from the inverse Wishart distribution (dInvWishart
).
Usage
dWishart(x, df, Sigma, log = TRUE)
dInvWishart(x, df, Sigma, log = TRUE)
Arguments
x |
positive definite |
df |
numeric parameter, "degrees of freedom". |
Sigma |
positive definite |
log |
logical, whether to return value on the log scale. |
Details
Note there are different ways of parameterizing the Inverse
Wishart distribution, so check which one you need.
Here, If X \sim IW_p(\Sigma, \nu)
then
X^{-1} \sim W_p(\Sigma^{-1}, \nu)
.
Dawid (1981) has a different definition: if
X \sim W_p(\Sigma^{-1}, \nu)
and
\nu > p - 1
, then
X^{-1} = Y \sim IW(\Sigma, \delta)
,
where
\delta = \nu - p + 1
.
Value
Density or log of density
Functions
-
dInvWishart
: density for the inverse Wishart distribution.
References
Dawid, A. (1981). Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application. Biometrika, 68(1), 265-274. doi: 10.2307/2335827
Gupta, A. K. and D. K. Nagar (1999). Matrix variate distributions. Chapman and Hall.
Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.
Examples
set.seed(20180222)
A <- rWishart(1, 10, diag(4))[, , 1]
A
dWishart(x = A, df = 10, Sigma = diag(4L), log = TRUE)
dInvWishart(x = solve(A), df = 10, Sigma = diag(4L), log = TRUE)