rmatrixinvt | R Documentation |
Generate random samples from the inverted matrix variate t distribution or compute densities.
rmatrixinvt(
n,
df,
mean,
L = diag(dim(as.matrix(mean))[1]),
R = diag(dim(as.matrix(mean))[2]),
U = L %*% t(L),
V = t(R) %*% R,
list = FALSE,
array = NULL
)
dmatrixinvt(
x,
df,
mean = matrix(0, p, n),
L = diag(p),
R = diag(n),
U = L %*% t(L),
V = t(R) %*% R,
log = FALSE
)
n |
number of observations for generation |
df |
degrees of freedom ( |
mean |
|
L |
|
R |
|
U |
|
V |
|
list |
Defaults to |
array |
If |
x |
quantile for density |
log |
logical; in |
rmatrixinvt
returns either a list of n
p \times q
matrices or
a p \times q \times n
array.
dmatrixinvt
returns the density at x
.
Gupta, Arjun K, and Daya K Nagar. 1999. Matrix Variate Distributions. Vol. 104. CRC Press. ISBN:978-1584880462
Dickey, James M. 1967. “Matricvariate Generalizations of the Multivariate t Distribution and the Inverted Multivariate t Distribution.” Ann. Math. Statist. 38 (2): 511–18. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/aoms/1177698967")}
rmatrixnorm()
, rmatrixt()
,
and stats::Distributions()
.
# an example of drawing from the distribution and computing the density.
A <- rmatrixinvt(n = 2, df = 10, diag(4))
dmatrixinvt(A[, , 1], df = 10, mean = diag(4))
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