NAG Library Routine Document
G05RYF
1 Purpose
G05RYF sets up a reference vector and generates an array of pseudorandom numbers from a multivariate Student's $t$ distribution with $\nu $ degrees of freedom, mean vector $a$ and covariance matrix $\frac{\nu}{\nu 2}C$.
2 Specification
SUBROUTINE G05RYF ( 
MODE, N, DF, M, XMU, C, LDC, R, LR, STATE, X, LDX, IFAIL) 
INTEGER 
MODE, N, DF, M, LDC, LR, STATE(*), LDX, IFAIL 
REAL (KIND=nag_wp) 
XMU(M), C(LDC,M), R(LR), X(LDX,M) 

3 Description
When the covariance matrix is nonsingular (i.e., strictly positive definite), the distribution has probability density function
where
$m$ is the number of dimensions,
$\nu $ is the degrees of freedom,
$a$ is the vector of means,
$x$ is the vector of positions and
$\frac{\nu}{\nu 2}C$ is the covariance matrix.
The routine returns the value
where
$z$ is generated by
G05SKF from a Normal distribution with mean zero and covariance matrix
$C$ and
$s$ is generated by
G05SDF from a
${\chi}^{2}$distribution with
$\nu $ degrees of freedom.
One of the initialization routines
G05KFF (for a repeatable sequence if computed sequentially) or
G05KGF (for a nonrepeatable sequence) must be called prior to the first call to G05RYF.
4 References
Knuth D E (1981) The Art of Computer Programming (Volume 2) (2nd Edition) Addison–Wesley
Wilkinson J H (1965) The Algebraic Eigenvalue Problem Oxford University Press, Oxford
5 Parameters
 1: MODE – INTEGERInput
On entry: a code for selecting the operation to be performed by the routine.
 ${\mathbf{MODE}}=0$
 Set up reference vector only.
 ${\mathbf{MODE}}=1$
 Generate variates using reference vector set up in a prior call to G05RYF.
 ${\mathbf{MODE}}=2$
 Set up reference vector and generate variates.
Constraint:
${\mathbf{MODE}}=0$, $1$ or $2$.
 2: N – INTEGERInput
On entry: $n$, the number of random variates required.
Constraint:
${\mathbf{N}}\ge 0$.
 3: DF – INTEGERInput
On entry: $\nu $, the number of degrees of freedom of the distribution.
Constraint:
${\mathbf{DF}}\ge 3$.
 4: M – INTEGERInput
On entry: $m$, the number of dimensions of the distribution.
Constraint:
${\mathbf{M}}>0$.
 5: XMU(M) – REAL (KIND=nag_wp) arrayInput
On entry: $a$, the vector of means of the distribution.
 6: C(LDC,M) – REAL (KIND=nag_wp) arrayInput
On entry: matrix which, along with
DF, defines the covariance of the distribution. Only the upper triangle need be set.
Constraint:
C must be positive semidefinite to
machine precision.
 7: LDC – INTEGERInput
On entry: the first dimension of the array
C as declared in the (sub)program from which G05RYF is called.
Constraint:
${\mathbf{LDC}}\ge {\mathbf{M}}$.
 8: R(LR) – REAL (KIND=nag_wp) arrayInput/Output
On entry: if ${\mathbf{MODE}}=1$, the reference vector as set up by G05RYF in a previous call with ${\mathbf{MODE}}=0$ or $2$.
On exit: if ${\mathbf{MODE}}=0$ or $2$, the reference vector that can be used in subsequent calls to G05RYF with ${\mathbf{MODE}}=1$.
 9: LR – INTEGERInput
On entry: the dimension of the array
R as declared in the (sub)program from which G05RYF is called. If
${\mathbf{MODE}}=1$, it must be the same as the value of
LR specified in the prior call to G05RYF with
${\mathbf{MODE}}=0$ or
$2$.
Constraint:
${\mathbf{LR}}\ge {\mathbf{M}}\times \left({\mathbf{M}}+1\right)+2$.
 10: STATE($*$) – INTEGER arrayCommunication Array

Note: the actual argument supplied must be the array
STATE supplied to the initialization routines
G05KFF or
G05KGF.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
 11: X(LDX,M) – REAL (KIND=nag_wp) arrayOutput
On exit: the array of pseudorandom multivariate Student's $t$ vectors generated by the routine, with ${\mathbf{X}}\left(i,j\right)$ holding the $j$th dimension for the $i$th variate.
 12: LDX – INTEGERInput
On entry: the first dimension of the array
X as declared in the (sub)program from which G05RYF is called.
Constraint:
${\mathbf{LDX}}\ge {\mathbf{N}}$.
 13: IFAIL – INTEGERInput/Output
On entry:
IFAIL must be set to
$0$,
$1\text{ or}1$. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{ or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is
$0$.
When the value $\mathbf{1}\text{ or}\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit:
${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
${\mathbf{IFAIL}}={\mathbf{0}}$ or
${{\mathbf{1}}}$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$
On entry, ${\mathbf{MODE}}\ne 0$, $1$ or $2$.
 ${\mathbf{IFAIL}}=2$
On entry, ${\mathbf{N}}<0$.
 ${\mathbf{IFAIL}}=3$
On entry, ${\mathbf{DF}}\le 2$.
 ${\mathbf{IFAIL}}=4$
On entry, ${\mathbf{M}}<1$.
 ${\mathbf{IFAIL}}=6$
The covariance matrix
C is not positive semidefinite to
machine precision.
 ${\mathbf{IFAIL}}=7$
On entry, ${\mathbf{LDC}}<{\mathbf{M}}$.
 ${\mathbf{IFAIL}}=8$
The reference vector
R has been corrupted or
M has changed since
R was set up in a previous call to G05RYF with
${\mathbf{MODE}}=0$ or
$2$.
 ${\mathbf{IFAIL}}=9$
On entry, ${\mathbf{LR}}\le {\mathbf{M}}\times \left({\mathbf{M}}+1\right)+1$.
 ${\mathbf{IFAIL}}=10$
On entry,  STATE vector was not initialized or has been corrupted. 
 ${\mathbf{IFAIL}}=12$
On entry, ${\mathbf{LDX}}<{\mathbf{N}}$.
7 Accuracy
Not applicable.
The time taken by G05RYF is of order $n{m}^{3}$.
It is recommended that the diagonal elements of $C$ should not differ too widely in order of magnitude. This may be achieved by scaling the variables if necessary. The actual matrix decomposed is $C+E=L{L}^{\mathrm{T}}$, where $E$ is a diagonal matrix with small positive diagonal elements. This ensures that, even when $C$ is singular, or nearly singular, the Cholesky factor $L$ corresponds to a positive definite covariance matrix that agrees with $C$ within machine precision.
9 Example
This example prints ten pseudorandom observations from a multivariate Student's
$t$distribution with ten degrees of freedom, means vector
and
C matrix
generated by G05RYF. All ten observations are generated by a single call to G05RYF with
${\mathbf{MODE}}=2$. The random number generator is initialized by
G05KFF.
9.1 Program Text
Program Text (g05ryfe.f90)
9.2 Program Data
Program Data (g05ryfe.d)
9.3 Program Results
Program Results (g05ryfe.r)