NAG Library Routine Document
G05RCF
1 Purpose
G05RCF sets up a reference vector and generates an array of pseudorandom numbers from a Student's $t$ copula with $\nu $ degrees of freedom and covariance matrix $\frac{\nu}{\nu 2}C$.
2 Specification
SUBROUTINE G05RCF ( 
MODE, N, DF, M, C, LDC, R, LR, STATE, X, LDX, IFAIL) 
INTEGER 
MODE, N, DF, M, LDC, LR, STATE(*), LDX, IFAIL 
REAL (KIND=nag_wp) 
C(LDC,M), R(LR), X(LDX,M) 

3 Description
The Student's
$t$ copula,
$G$, is defined by
where
$m$ is the number of dimensions,
${T}_{\nu ,C}^{m}$ is the multivariate Student's
$t$ density function with
$\nu $ degrees of freedom, mean zero and covariance matrix
$\frac{\nu}{\nu 2}C$ and
${t}_{\nu ,{C}_{\mathit{ii}}}^{1}$ is the inverse of the univariate Student's
$t$ density function with
$\nu $ degrees of freedom, zero mean and variance
$\frac{\nu}{\nu 2}{C}_{\mathit{ii}}$.
G05RYF is used to generate a vector from a multivariate Student's
$t$ distribution and
G01EBF is used to convert each element of that vector into a uniformly distributed value between zero and one.
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 G05RCF.
4 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
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 G05RCF.
 ${\mathbf{MODE}}=2$
 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: 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.
 6: LDC – INTEGERInput
On entry: the first dimension of the array
C as declared in the (sub)program from which G05RCF is called.
Constraint:
${\mathbf{LDC}}\ge {\mathbf{M}}$.
 7: R(LR) – REAL (KIND=nag_wp) arrayCommunication Array
On entry: if ${\mathbf{MODE}}=1$, the reference vector as set up by G05RCF 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 G05RCF with ${\mathbf{MODE}}=1$.
 8: LR – INTEGERInput
On entry: the dimension of the array
R as declared in the (sub)program from which G05RCF is called. If
${\mathbf{MODE}}=1$, it must be the same as the value of
LR specified in the prior call to G05RCF with
${\mathbf{MODE}}=0$ or
$2$.
Constraint:
${\mathbf{LR}}\ge {\mathbf{M}}\times \left({\mathbf{M}}+1\right)+2$.
 9: 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.
 10: X(LDX,M) – REAL (KIND=nag_wp) arrayOutput
On exit: the array of values from a multivariate Student's $t$ copula, with ${\mathbf{X}}\left(i,j\right)$ holding the $j$th dimension for the $i$th variate.
 11: LDX – INTEGERInput
On entry: the first dimension of the array
X as declared in the (sub)program from which G05RCF is called.
Constraint:
${\mathbf{LDX}}\ge {\mathbf{N}}$.
 12: 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, ${\mathbf{M}}<1$.
 ${\mathbf{IFAIL}}=5$
The covariance matrix $C$ is not positive semidefinite to machine precision.
 ${\mathbf{IFAIL}}=6$
On entry, ${\mathbf{LDC}}<{\mathbf{M}}$.
 ${\mathbf{IFAIL}}=7$
The reference vector
R has been corrupted or
M has changed since
R was set up in a previous call to G05RCF with
${\mathbf{MODE}}=0$ or
$2$.
 ${\mathbf{IFAIL}}=8$
On entry, ${\mathbf{LR}}\le {\mathbf{M}}\times \left({\mathbf{M}}+1\right)+1$.
 ${\mathbf{IFAIL}}=9$
On entry,  STATE vector was not initialized or has been corrupted. 
 ${\mathbf{IFAIL}}=11$
On entry, ${\mathbf{LDX}}<{\mathbf{N}}$.
7 Accuracy
See
Section 7 in G05RYF for an indication of the accuracy of the underlying multivariate Student's
$t$distribution.
The time taken by G05RCF 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 Student's
$t$ copula with ten degrees of freedom and
$C$ matrix
generated by G05RCF. All ten observations are generated by a single call to G05RCF with
${\mathbf{MODE}}=2$. The random number generator is initialized by
G05KFF.
9.1 Program Text
Program Text (g05rcfe.f90)
9.2 Program Data
Program Data (g05rcfe.d)
9.3 Program Results
Program Results (g05rcfe.r)