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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_rand_copula_students_t (g05rc)

## Purpose

nag_rand_copula_students_t (g05rc) sets up a reference vector and generates an array of pseudorandom numbers from a Student's t$t$ copula with ν$\nu$ degrees of freedom and covariance matrix ν/(ν2) C $\frac{\nu }{\nu -2}C$.

## Syntax

[r, state, x, ifail] = g05rc(mode, n, df, c, r, state, 'm', m, 'lr', lr)
[r, state, x, ifail] = nag_rand_copula_students_t(mode, n, df, c, r, state, 'm', m, 'lr', lr)

## Description

The Student's t$t$ copula, G$G$, is defined by
 G (u1,u2, … ,um ; C) = Tν,Cm (tν,C11 − 1(u1),tν,C22 − 1(u2), … ,tν,Cmm − 1(um)) $G ( u1 , u2 ,…, um ; C ) = T ν,C m ( t ν,C11 -1 (u1) , t ν,C22 -1 (u2) ,…, t ν,Cmm -1 (um) )$
where m$m$ is the number of dimensions, Tν,Cm ${T}_{\nu ,C}^{m}$ is the multivariate Student's t$t$ density function with ν$\nu$ degrees of freedom, mean zero and covariance matrix ν/(ν2) C $\frac{\nu }{\nu -2}C$ and tν,Cii1 ${t}_{\nu ,{C}_{\mathit{ii}}}^{-1}$ is the inverse of the univariate Student's t$t$ density function with ν$\nu$ degrees of freedom, zero mean and variance ν/(ν2) Cii $\frac{\nu }{\nu -2}{C}_{\mathit{ii}}$.
nag_rand_multivar_students_t (g05ry) is used to generate a vector from a multivariate Student's t$t$ distribution and nag_stat_prob_students_t (g01eb) is used to convert each element of that vector into a uniformly distributed value between zero and one.
One of the initialization functions nag_rand_init_repeat (g05kf) (for a repeatable sequence if computed sequentially) or nag_rand_init_nonrepeat (g05kg) (for a non-repeatable sequence) must be called prior to the first call to nag_rand_copula_students_t (g05rc).

## References

Nelsen R B (1998) An Introduction to Copulas. Lecture Notes in Statistics 139 Springer
Sklar A (1973) Random variables: joint distribution functions and copulas Kybernetika 9 499–460

## Parameters

### Compulsory Input Parameters

1:     mode – int64int32nag_int scalar
A code for selecting the operation to be performed by the function.
mode = 0${\mathbf{mode}}=0$
Set up reference vector only.
mode = 1${\mathbf{mode}}=1$
Generate variates using reference vector set up in a prior call to nag_rand_copula_students_t (g05rc).
mode = 2${\mathbf{mode}}=2$
Set up reference vector and generate variates.
Constraint: mode = 0${\mathbf{mode}}=0$, 1$1$ or 2$2$.
2:     n – int64int32nag_int scalar
n$n$, the number of random variates required.
Constraint: n0${\mathbf{n}}\ge 0$.
3:     df – int64int32nag_int scalar
ν$\nu$, the number of degrees of freedom of the distribution.
Constraint: df3 ${\mathbf{df}}\ge 3$.
4:     c(ldc,m) – double array
ldc, the first dimension of the array, must satisfy the constraint ldcm$\mathit{ldc}\ge {\mathbf{m}}$.
Matrix which, along with df, defines the covariance of the distribution. Only the upper triangle need be set.
Constraint: C$C$ must be positive semidefinite to machine precision.
5:     r(lr) – double array
lr, the dimension of the array, must satisfy the constraint lrm × (m + 1) + 2${\mathbf{lr}}\ge {\mathbf{m}}×\left({\mathbf{m}}+1\right)+2$.
If mode = 1${\mathbf{mode}}=1$, the reference vector as set up by nag_rand_copula_students_t (g05rc) in a previous call with mode = 0${\mathbf{mode}}=0$ or 2$2$.
6:     state( : $:$) – int64int32nag_int array
Note: the actual argument supplied must be the array state supplied to the initialization routines nag_rand_init_repeat (g05kf) or nag_rand_init_nonrepeat (g05kg).
Contains information on the selected base generator and its current state.

### Optional Input Parameters

1:     m – int64int32nag_int scalar
Default: The first dimension of the array c and the second dimension of the array c. (An error is raised if these dimensions are not equal.)
m$m$, the number of dimensions of the distribution.
Constraint: m > 0${\mathbf{m}}>0$.
2:     lr – int64int32nag_int scalar
Default: The dimension of the array r.
The dimension of the array r as declared in the (sub)program from which nag_rand_copula_students_t (g05rc) is called. If mode = 1${\mathbf{mode}}=1$, it must be the same as the value of lr specified in the prior call to nag_rand_copula_students_t (g05rc) with mode = 0${\mathbf{mode}}=0$ or 2$2$.
Constraint: lrm × (m + 1) + 2${\mathbf{lr}}\ge {\mathbf{m}}×\left({\mathbf{m}}+1\right)+2$.

ldc ldx

### Output Parameters

1:     r(lr) – double array
If mode = 0${\mathbf{mode}}=0$ or 2$2$, the reference vector that can be used in subsequent calls to nag_rand_copula_students_t (g05rc) with mode = 1${\mathbf{mode}}=1$.
2:     state( : $:$) – int64int32nag_int array
Note: the actual argument supplied must be the array state supplied to the initialization routines nag_rand_init_repeat (g05kf) or nag_rand_init_nonrepeat (g05kg).
Contains updated information on the state of the generator.
3:     x(ldx,m) – double array
ldxn$\mathit{ldx}\ge {\mathbf{n}}$.
The array of values from a multivariate Student's t$t$ copula, with x(i,j)${\mathbf{x}}\left(i,j\right)$ holding the j$j$th dimension for the i$i$th variate.
4:     ifail – int64int32nag_int scalar
${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see [Error Indicators and Warnings]).

## Error Indicators and Warnings

Errors or warnings detected by the function:
ifail = 1${\mathbf{ifail}}=1$
On entry, mode0${\mathbf{mode}}\ne 0$, 1$1$ or 2$2$.
ifail = 2${\mathbf{ifail}}=2$
On entry, n < 0${\mathbf{n}}<0$.
ifail = 3${\mathbf{ifail}}=3$
On entry, df2${\mathbf{df}}\le 2$.
ifail = 4${\mathbf{ifail}}=4$
On entry, m < 1${\mathbf{m}}<1$.
ifail = 5${\mathbf{ifail}}=5$
The covariance matrix C$C$ is not positive semidefinite to machine precision.
ifail = 6${\mathbf{ifail}}=6$
On entry, ldc < m$\mathit{ldc}<{\mathbf{m}}$.
ifail = 7${\mathbf{ifail}}=7$
The reference vector r has been corrupted or m has changed since r was set up in a previous call to nag_rand_copula_students_t (g05rc) with mode = 0${\mathbf{mode}}=0$ or 2$2$.
ifail = 8${\mathbf{ifail}}=8$
On entry, lrm × (m + 1) + 1${\mathbf{lr}}\le {\mathbf{m}}×\left({\mathbf{m}}+1\right)+1$.
ifail = 9${\mathbf{ifail}}=9$
 On entry, state vector was not initialized or has been corrupted.
ifail = 11${\mathbf{ifail}}=11$
On entry, ldx < n$\mathit{ldx}<{\mathbf{n}}$.

## Accuracy

See Section [Accuracy] in (g05ry) for an indication of the accuracy of the underlying multivariate Student's t$t$-distribution.

The time taken by nag_rand_copula_students_t (g05rc) is of order nm3$n{m}^{3}$.
It is recommended that the diagonal elements of C$C$ 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 = LLT$C+E=L{L}^{\mathrm{T}}$, where E$E$ is a diagonal matrix with small positive diagonal elements. This ensures that, even when C$C$ is singular, or nearly singular, the Cholesky factor L$L$ corresponds to a positive definite covariance matrix that agrees with C$C$ within machine precision.

## Example

```function nag_rand_copula_students_t_example
% Initialize the seed
seed = [int64(1762543)];
% genid and subid identify the base generator
genid = int64(1);
subid =  int64(1);

mode = int64(2);
n = int64(10);
df = int64(10);
c = [1.69, 0.39, -1.86, 0.07;
0, 98.01, -7.07, -0.71;
0, 0, 11.56, 0.03;
0, 0, 0, 0.01];
r = zeros(32, 1);
% Initialize the generator to a repeatable sequence
[state, ifail] = nag_rand_init_repeat(genid, subid, seed);
[r, state, x, ifail] = nag_rand_copula_students_t(mode, n, df, c, r, state)
```
```

r =

4.5000
1.3000
0.3000
-1.4308
0.0538
0
9.8955
-0.6711
-0.0734
0
0
3.0104
0.0192
0
0
0
0.0367
1.3000
9.9000
3.4000
0.1000
10.5000
0
0
0
0
0
0
0
0
0
0

state =

17
1234
1
0
8662
18721
15161
5250
17917
13895
19930
8
0
1234
1
1
1234

x =

0.6445    0.0527    0.4082    0.8876
0.0701    0.1988    0.8471    0.3521
0.7988    0.6664    0.2194    0.5541
0.8202    0.0492    0.7059    0.9341
0.1786    0.5594    0.7810    0.2836
0.4920    0.2677    0.3427    0.5169
0.4139    0.2978    0.8762    0.7145
0.7437    0.9714    0.8931    0.2487
0.4971    0.9687    0.8142    0.1965
0.6464    0.5304    0.5817    0.4565

ifail =

0

```
```function g05rc_example
% Initialize the seed
seed = [int64(1762543)];
% genid and subid identify the base generator
genid = int64(1);
subid =  int64(1);

mode = int64(2);
n = int64(10);
df = int64(10);
c = [1.69, 0.39, -1.86, 0.07;
0, 98.01, -7.07, -0.71;
0, 0, 11.56, 0.03;
0, 0, 0, 0.01];
r = zeros(32, 1);
% Initialize the generator to a repeatable sequence
[state, ifail] = g05kf(genid, subid, seed);
[r, state, x, ifail] = g05rc(mode, n, df, c, r, state)
```
```

r =

4.5000
1.3000
0.3000
-1.4308
0.0538
0
9.8955
-0.6711
-0.0734
0
0
3.0104
0.0192
0
0
0
0.0367
1.3000
9.9000
3.4000
0.1000
10.5000
0
0
0
0
0
0
0
0
0
0

state =

17
1234
1
0
8662
18721
15161
5250
17917
13895
19930
8
0
1234
1
1
1234

x =

0.6445    0.0527    0.4082    0.8876
0.0701    0.1988    0.8471    0.3521
0.7988    0.6664    0.2194    0.5541
0.8202    0.0492    0.7059    0.9341
0.1786    0.5594    0.7810    0.2836
0.4920    0.2677    0.3427    0.5169
0.4139    0.2978    0.8762    0.7145
0.7437    0.9714    0.8931    0.2487
0.4971    0.9687    0.8142    0.1965
0.6464    0.5304    0.5817    0.4565

ifail =

0

```