# NAG Library Routine Document

## 1Purpose

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 $\frac{\nu }{\nu -2}C$.

## 2Specification

Fortran Interface
 Subroutine g05ryf ( mode, n, df, m, xmu, c, ldc, r, lr, x, ldx,
 Integer, Intent (In) :: mode, n, df, m, ldc, lr, ldx Integer, Intent (Inout) :: state(*), ifail Real (Kind=nag_wp), Intent (In) :: xmu(m), c(ldc,m) Real (Kind=nag_wp), Intent (Inout) :: r(lr), x(ldx,m)
#include nagmk26.h
 void g05ryf_ (const Integer *mode, const Integer *n, const Integer *df, const Integer *m, const double xmu[], const double c[], const Integer *ldc, double r[], const Integer *lr, Integer state[], double x[], const Integer *ldx, Integer *ifail)

## 3Description

When the covariance matrix is nonsingular (i.e., strictly positive definite), the distribution has probability density function
 $fx = Γ ν+m 2 πv m/2 Γ ν/2 C 12 1 + x-aT C-1 x-a ν -ν+m 2$
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
 $x = a + νs z$
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 non-repeatable sequence) must be called prior to the first call to g05ryf.

## 4References

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

## 5Arguments

1:     $\mathbf{mode}$ – 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:     $\mathbf{n}$ – IntegerInput
On entry: $n$, the number of random variates required.
Constraint: ${\mathbf{n}}\ge 0$.
3:     $\mathbf{df}$ – IntegerInput
On entry: $\nu$, the number of degrees of freedom of the distribution.
Constraint: ${\mathbf{df}}\ge 3$.
4:     $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of dimensions of the distribution.
Constraint: ${\mathbf{m}}>0$.
5:     $\mathbf{xmu}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: $a$, the vector of means of the distribution.
6:     $\mathbf{c}\left({\mathbf{ldc}},{\mathbf{m}}\right)$ – 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:     $\mathbf{ldc}$ – 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:     $\mathbf{r}\left({\mathbf{lr}}\right)$ – 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:     $\mathbf{lr}$ – 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: ${\mathbf{lr}}\ge {\mathbf{m}}×\left({\mathbf{m}}+1\right)+2$.
10:   $\mathbf{state}\left(*\right)$ – 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:   $\mathbf{x}\left({\mathbf{ldx}},{\mathbf{m}}\right)$ – 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:   $\mathbf{ldx}$ – 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:   $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, $-1\text{​ or ​}1$. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation 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 argument, 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).

## 6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, 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, ${\mathbf{mode}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{mode}}=0$, $1$ or $2$.
${\mathbf{ifail}}=2$
On entry, ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{n}}\ge 0$.
${\mathbf{ifail}}=3$
On entry, ${\mathbf{df}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{df}}\ge 3$.
${\mathbf{ifail}}=4$
On entry, ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{m}}>0$.
${\mathbf{ifail}}=6$
On entry, the covariance matrix $C$ is not positive semidefinite to machine precision.
${\mathbf{ifail}}=7$
On entry, ${\mathbf{ldc}}=〈\mathit{\text{value}}〉$ and ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldc}}\ge {\mathbf{m}}$.
${\mathbf{ifail}}=8$
m is not the same as when r was set up in a previous call.
Previous value of ${\mathbf{m}}=〈\mathit{\text{value}}〉$ and ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
${\mathbf{ifail}}=9$
On entry, lr is not large enough, ${\mathbf{lr}}=〈\mathit{\text{value}}〉$: minimum length required $\text{}=〈\mathit{\text{value}}〉$.
${\mathbf{ifail}}=10$
On entry, state vector has been corrupted or not initialized.
${\mathbf{ifail}}=12$
On entry, ${\mathbf{ldx}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldx}}\ge {\mathbf{n}}$.
${\mathbf{ifail}}=-99$
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

Not applicable.

## 8Parallelism and Performance

g05ryf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g05ryf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

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.

## 10Example

This example prints ten pseudorandom observations from a multivariate Student's $t$-distribution with ten degrees of freedom, means vector
 $1.0 2.0 -3.0 0.0$
and c matrix
 $1.69 0.39 -1.86 0.07 0.39 98.01 -7.07 -0.71 -1.86 -7.07 11.56 0.03 0.07 -0.71 0.03 0.01 ,$
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.

### 10.1Program Text

Program Text (g05ryfe.f90)

### 10.2Program Data

Program Data (g05ryfe.d)

### 10.3Program Results

Program Results (g05ryfe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017