Description Usage Arguments Details Value References See Also Examples
Density and random generation for the matrix variate t distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 
n 
number of observations for generation 
df 
degrees of freedom (>0, may be noninteger),

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 
force 
In 
x 
quantile for density 
log 
logical; in 
The matrix tdistribution is parameterized slightly differently from the univariate and multivariate tdistributions
the variance is scaled by a factor of 1/df
.
In this parameterization, the variance for a 1 * 1 matrix
variate tdistributed random variable with identity variance matrices
is 1/(df2) instead of df/(df2). A Central Limit Theorem
for the matrix variate T is then that as df
goes to
infinity, MVT(0, df, I_p, df*I_q) converges to
MVN(0,I_p,I_q).
rmatrixt
returns either a list of n
p * q matrices or a
p * q * n
array.
dmatrixt
returns the density at x
.
Gupta, Arjun K, and Daya K Nagar. 1999. Matrix Variate Distributions. Vol. 104. CRC Press. ISBN:9781584880462
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()
,
rmatrixinvt()
,rt()
and
stats::Distributions()
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  set.seed(20180202)
# random matrix with df = 10 and the given mean and L matrix
rmatrixt(
n = 1, df = 10, mean = matrix(c(100, 0, 100, 0, 25, 1000), nrow = 2),
L = matrix(c(2, 1, 0, .1), nrow = 2), list = FALSE
)
# comparing 1D distribution of t to matrix
summary(rt(n = 100, df = 10))
summary(rmatrixt(n = 100, df = 10, matrix(0)))
# demonstrating equivalence of 1x1 matrix t to usual t
set.seed(20180204)
x < rmatrixt(n = 1, mean = matrix(0), df = 1)
dt(x, 1)
dmatrixt(x, df = 1)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.