nag_rand_matrix_multi_students_t (g05ryc) (PDF version)
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NAG Library Manual

NAG Library Function Document

nag_rand_matrix_multi_students_t (g05ryc)

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_rand_matrix_multi_students_t (g05ryc) sets up a reference vector and generates an array of pseudorandom numbers from a multivariate Student's t distribution with ν degrees of freedom, mean vector a and covariance matrix ν ν-2 C .

2  Specification

#include <nag.h>
#include <nagg05.h>
void  nag_rand_matrix_multi_students_t (Nag_OrderType order, Nag_ModeRNG mode, Integer n, Integer df, Integer m, const double xmu[], const double c[], Integer pdc, double r[], Integer lr, Integer state[], double x[], Integer pdx, NagError *fail)

3  Description

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, ν is the degrees of freedom, a is the vector of means, x is the vector of positions and ν ν-2 C  is the covariance matrix.
The function returns the value
x = a + νs z  
where z is generated by nag_rand_normal (g05skc) from a Normal distribution with mean zero and covariance matrix C and s is generated by nag_rand_chi_sq (g05sdc) from a χ2-distribution with ν degrees of freedom.
One of the initialization functions nag_rand_init_repeatable (g05kfc) (for a repeatable sequence if computed sequentially) or nag_rand_init_nonrepeatable (g05kgc) (for a non-repeatable sequence) must be called prior to the first call to nag_rand_matrix_multi_students_t (g05ryc).

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  Arguments

1:     order Nag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     mode Nag_ModeRNGInput
On entry: a code for selecting the operation to be performed by the function.
mode=Nag_InitializeReference
Set up reference vector only.
mode=Nag_GenerateFromReference
Generate variates using reference vector set up in a prior call to nag_rand_matrix_multi_students_t (g05ryc).
mode=Nag_InitializeAndGenerate
Set up reference vector and generate variates.
Constraint: mode=Nag_InitializeReference, Nag_GenerateFromReference or Nag_InitializeAndGenerate.
3:     n IntegerInput
On entry: n, the number of random variates required.
Constraint: n0.
4:     df IntegerInput
On entry: ν, the number of degrees of freedom of the distribution.
Constraint: df3 .
5:     m IntegerInput
On entry: m, the number of dimensions of the distribution.
Constraint: m>0.
6:     xmu[m] const doubleInput
On entry: a, the vector of means of the distribution.
7:     c[dim] const doubleInput
Note: the dimension, dim, of the array c must be at least pdc×m.
The i,jth element of the matrix C is stored in
  • c[j-1×pdc+i-1] when order=Nag_ColMajor;
  • c[i-1×pdc+j-1] when order=Nag_RowMajor.
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.
8:     pdc IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array c.
Constraint: pdcm.
9:     r[lr] doubleInput/Output
On entry: if mode=Nag_GenerateFromReference, the reference vector as set up by nag_rand_matrix_multi_students_t (g05ryc) in a previous call with mode=Nag_InitializeReference or Nag_InitializeAndGenerate.
On exit: if mode=Nag_InitializeReference or Nag_InitializeAndGenerate, the reference vector that can be used in subsequent calls to nag_rand_matrix_multi_students_t (g05ryc) with mode=Nag_GenerateFromReference.
10:   lr IntegerInput
On entry: the dimension of the array r. If mode=Nag_GenerateFromReference, it must be the same as the value of lr specified in the prior call to nag_rand_matrix_multi_students_t (g05ryc) with mode=Nag_InitializeReference or Nag_InitializeAndGenerate.
Constraint: lrm×m+1+2.
11:   state[dim] IntegerCommunication Array
Note: the dimension, dim, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
12:   x[dim] doubleOutput
Note: the dimension, dim, of the array x must be at least
  • max1,pdx×m when order=Nag_ColMajor;
  • max1,n×pdx when order=Nag_RowMajor.
Where Xi,j appears in this document, it refers to the array element
  • x[j-1×pdx+i-1] when order=Nag_ColMajor;
  • x[i-1×pdx+j-1] when order=Nag_RowMajor.
On exit: the array of pseudorandom multivariate Student's t vectors generated by the function, with Xi,j holding the jth dimension for the ith variate.
13:   pdx IntegerInput
On entry: the stride used in the array x.
Constraints:
  • if order=Nag_ColMajor, pdxn;
  • if order=Nag_RowMajor, pdxm.
14:   fail NagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.2.1.2 in the Essential Introduction for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, df=value.
Constraint: df3.
On entry, lr is not large enough, lr=value: minimum length required =value.
On entry, m=value.
Constraint: m > 0.
On entry, n=value.
Constraint: n0.
NE_INT_2
On entry, pdc=value and m=value.
Constraint: pdcm.
On entry, pdx=value and m=value.
Constraint: pdxm.
On entry, pdx=value and n=value.
Constraint: pdxn.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
An unexpected error has been triggered by this function. Please contact NAG.
See Section 3.6.6 in the Essential Introduction for further information.
NE_INVALID_STATE
On entry, state vector has been corrupted or not initialized.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.
NE_POS_DEF
On entry, the covariance matrix C is not positive semidefinite to machine precision.
NE_PREV_CALL
m is not the same as when r was set up in a previous call.
Previous value of m=value and m=value.

7  Accuracy

Not applicable.

8  Parallelism and Performance

nag_rand_matrix_multi_students_t (g05ryc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_rand_matrix_multi_students_t (g05ryc) 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

The time taken by nag_rand_matrix_multi_students_t (g05ryc) is of order nm3.
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=LLT, 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.

10  Example

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 nag_rand_matrix_multi_students_t (g05ryc). All ten observations are generated by a single call to nag_rand_matrix_multi_students_t (g05ryc) with mode=Nag_InitializeAndGenerate. The random number generator is initialized by nag_rand_init_repeatable (g05kfc).

10.1  Program Text

Program Text (g05ryce.c)

10.2  Program Data

None.

10.3  Program Results

Program Results (g05ryce.r)


nag_rand_matrix_multi_students_t (g05ryc) (PDF version)
g05 Chapter Contents
g05 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015