rmatrixinvt {MixMatrix} | 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 (>0, may be non-integer),
|
mean |
p * q This is really a 'shift' rather than a mean, though the expected value will be equal to this if df > 2 |
L |
p * p matrix specifying relations among the rows. By default, an identity matrix. |
R |
q * q matrix specifying relations among the columns. By default, an identity matrix. |
U |
LL^T - p * p positive definite matrix for rows, computed from L if not specified. |
V |
R^T R - q * q positive definite matrix for columns, computed from R if not specified. |
list |
Defaults to |
array |
If n = 1 and this is not specified and |
x |
quantile for density |
log |
logical; in |
rmatrixinvt
returns either a list of n
p * q matrices or
a p * q * 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. doi: 10.1214/aoms/1177698967
rmatrixnorm
, rmatrixt
,
and 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))