g05rc 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 $\frac{\nu}{\nu -2}C$.

# Syntax

C# |
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public static void g05rc( int mode, int n, int df, int m, double[,] c, double[] r, G05..::..G05State g05state, double[,] x, out int ifail ) |

Visual Basic |
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Public Shared Sub g05rc ( _ mode As Integer, _ n As Integer, _ df As Integer, _ m As Integer, _ c As Double(,), _ r As Double(), _ g05state As G05..::..G05State, _ x As Double(,), _ <OutAttribute> ByRef ifail As Integer _ ) |

Visual C++ |
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public: static void g05rc( int mode, int n, int df, int m, array<double,2>^ c, array<double>^ r, G05..::..G05State^ g05state, array<double,2>^ x, [OutAttribute] int% ifail ) |

F# |
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static member g05rc : mode : int * n : int * df : int * m : int * c : float[,] * r : float[] * g05state : G05..::..G05State * x : float[,] * ifail : int byref -> unit |

#### Parameters

- mode
- Type: System..::..Int32
*On entry*: a code for selecting the operation to be performed by the method.- ${\mathbf{mode}}=0$
- Set up reference vector only.
- ${\mathbf{mode}}=1$
- Generate variates using reference vector set up in a prior call to g05rc.
- ${\mathbf{mode}}=2$
- Set up reference vector and generate variates.

*Constraint*: ${\mathbf{mode}}=0$, $1$ or $2$.

- n
- Type: System..::..Int32
*On entry*: $n$, the number of random variates required.*Constraint*: ${\mathbf{n}}\ge 0$.

- df
- Type: System..::..Int32
*On entry*: $\nu $, the number of degrees of freedom of the distribution.*Constraint*: ${\mathbf{df}}\ge 3$.

- m
- Type: System..::..Int32
*On entry*: $m$, the number of dimensions of the distribution.*Constraint*: ${\mathbf{m}}>0$.

- c
- Type: array<System..::..Double,2>[,](,)[,][,]An array of size [dim1, m]
**Note:**dim1 must satisfy the constraint: $\mathrm{dim1}\ge {\mathbf{m}}$*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.

- r
- Type: array<System..::..Double>[]()[][]An array of size [lr]
*On entry*: if ${\mathbf{mode}}=1$, the reference vector as set up by g05rc 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 g05rc with ${\mathbf{mode}}=1$.

- g05state
- Type: NagLibrary..::..G05..::..G05StateAn Object of type G05.G05State.

- x
- Type: array<System..::..Double,2>[,](,)[,][,]An array of size [dim1,
**_tdx**]**Note:**dim1 must satisfy the constraint: $\mathrm{dim1}\ge {\mathbf{n}}$*On exit*: the array of values from a multivariate Student's $t$ copula, with ${\mathbf{x}}[i-1,j-1]$ holding the $j$th dimension for the $i$th variate.

- ifail
- Type: System..::..Int32%
*On exit*: ${\mathbf{ifail}}={0}$ unless the method detects an error or a warning has been flagged (see [Error Indicators and Warnings]).

# 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 $\frac{\nu}{\nu -2}{C}_{\mathit{ii}}$.

$$G\left({u}_{1},{u}_{2},\dots ,{u}_{m};C\right)={T}_{\nu ,C}^{m}\left({t}_{\nu ,{C}_{11}}^{-1}\left({u}_{1}\right),{t}_{\nu ,{C}_{22}}^{-1}\left({u}_{2}\right),\dots ,{t}_{\nu ,{C}_{\mathit{mm}}}^{-1}\left({u}_{m}\right)\right)$$ |

g05ry is used to generate a vector from a multivariate Student's $t$ distribution and g01eb is used to convert each element of that vector into a uniformly distributed value between zero and one.

One of the initialization methods (G05KFF not in this release) (for a repeatable sequence if computed sequentially) or (G05KGF not in this release) (for a non-repeatable sequence) must be called prior to the first call to g05rc.

# References

Nelsen R B (1998)

*An Introduction to Copulas. Lecture Notes in Statistics 139*SpringerSklar A (1973) Random variables: joint distribution functions and copulas

*Kybernetika***9**499–460# Error Indicators and Warnings

Errors or warnings detected by the method:

Some error messages may refer to parameters that are dropped from this interface
(LDC, LDX) In these
cases, an error in another parameter has usually caused an incorrect value to be inferred.

- ${\mathbf{ifail}}=1$
- 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, ${\mathbf{ldc}}<{\mathbf{m}}$.

- ${\mathbf{ifail}}=7$

- ${\mathbf{ifail}}=8$
- 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, ${\mathbf{ldx}}<{\mathbf{n}}$.

- ${\mathbf{ifail}}=-9000$
- An error occured, see message report.
- ${\mathbf{ifail}}=-6000$
- Invalid Parameters $\u2329\mathit{\text{value}}\u232a$
- ${\mathbf{ifail}}=-4000$
- Invalid dimension for array $\u2329\mathit{\text{value}}\u232a$
- ${\mathbf{ifail}}=-8000$
- Negative dimension for array $\u2329\mathit{\text{value}}\u232a$
- ${\mathbf{ifail}}=-6000$
- Invalid Parameters $\u2329\mathit{\text{value}}\u232a$

# Accuracy

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

# Parallelism and Performance

None.

# Further Comments

The time taken by g05rc 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.