NAG Library Function Document
nag_rand_field_1d_predef_setup (g05znc)
1 Purpose
nag_rand_field_1d_predef_setup (g05znc) performs the setup required in order to simulate stationary Gaussian random fields in one dimension, for a preset variogram, using the
circulant embedding method. Specifically, the eigenvalues of the extended covariance matrix (or embedding matrix) are calculated, and their square roots output, for use by
nag_rand_field_1d_generate (g05zpc), which simulates the random field.
2 Specification
#include <nag.h> |
#include <nagg05.h> |
void |
nag_rand_field_1d_predef_setup (Integer ns,
double xmin,
double xmax,
Integer maxm,
double var,
Nag_Variogram cov,
Integer np,
const double params[],
Nag_EmbedPad pad,
Nag_EmbedScale corr,
double lam[],
double xx[],
Integer *m,
Integer *approx,
double *rho,
Integer *icount,
double eig[],
NagError *fail) |
|
3 Description
A one-dimensional random field in is a function which is random at every point , so is a random variable for each . The random field has a mean function and a symmetric positive semidefinite covariance function . is a Gaussian random field if for any choice of and , the random vector follows a multivariate Normal distribution, which would have a mean vector with entries and a covariance matrix with entries . A Gaussian random field is stationary if is constant for all and for all and hence we can express the covariance function as a function of one variable: . is known as a variogram (or more correctly, a semivariogram) and includes the multiplicative factor representing the variance such that .
The functions nag_rand_field_1d_predef_setup (g05znc) and
nag_rand_field_1d_generate (g05zpc) are used to simulate a one-dimensional stationary Gaussian random field, with mean function zero and variogram
, over an interval
, using an equally spaced set of
points. The problem reduces to sampling a Normal random vector
of size
, with mean vector zero and a symmetric Toeplitz covariance matrix
. Since
is in general expensive to factorize, a technique known as the
circulant embedding method is used.
is embedded into a larger, symmetric circulant matrix
of size
, which can now be factorized as
, where
is the Fourier matrix (
is the complex conjugate of
),
is the diagonal matrix containing the eigenvalues of
and
.
is known as the embedding matrix. The eigenvalues can be calculated by performing a discrete Fourier transform of the first row (or column) of
and multiplying by
, and so only the first row (or column) of
is needed – the whole matrix does not need to be formed.
As long as all of the values of are non-negative (i.e., is positive semidefinite), is a covariance matrix for a random vector , two samples of which can now be simulated from the real and imaginary parts of , where and have elements from the standard Normal distribution. Since , this calculation can be done using a discrete Fourier transform of the vector . Two samples of the random vector can now be recovered by taking the first elements of each sample of – because the original covariance matrix is embedded in , will have the correct distribution.
If
is not positive semidefinite, larger embedding matrices
can be tried; however if the size of the matrix would have to be larger than
maxm, an approximation procedure is used. We write
, where
and
contain the non-negative and negative eigenvalues of
respectively. Then
is replaced by
where
and
is a scaling factor. The error
in approximating the distribution of the random field is given by
Three choices for
are available, and are determined by the input argument
corr:
- setting sets
- setting sets
- setting sets .
nag_rand_field_1d_predef_setup (g05znc) finds a suitable positive semidefinite embedding matrix
and outputs its size,
m, and the square roots of its eigenvalues in
lam. If approximation is used, information regarding the accuracy of the approximation is output. Note that only the first row (or column) of
is actually formed and stored.
4 References
Dietrich C R and Newsam G N (1997) Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix SIAM J. Sci. Comput. 18 1088–1107
Schlather M (1999) Introduction to positive definite functions and to unconditional simulation of random fields Technical Report ST 99–10 Lancaster University
Wood A T A and Chan G (1997) Algorithm AS 312: An Algorithm for Simulating Stationary Gaussian Random Fields Journal of the Royal Statistical Society, Series C (Applied Statistics) (Volume 46) 1 171–181
5 Arguments
- 1:
ns – IntegerInput
On entry: the number of sample points to be generated in realizations of the random field.
Constraint:
.
- 2:
xmin – doubleInput
On entry: the lower bound for the interval over which the random field is to be simulated. Note that if
(for simulating fractional Brownian motion),
xmin is not referenced and the lower bound for the interval is set to zero.
Constraint:
if , .
- 3:
xmax – doubleInput
-
On entry: the upper bound for the interval over which the random field is to be simulated. Note that if
(for simulating fractional Brownian motion), the lower bound for the interval is set to zero and so
xmax is required to be greater than zero.
Constraints:
- if , ;
- if , .
- 4:
maxm – IntegerInput
On entry: the maximum size of the circulant matrix to use. For example, if the embedding matrix is to be allowed to double in size three times before the approximation procedure is used, then choose where .
Suggested value:
Constraint:
, where is the smallest integer satisfying .
- 5:
var – doubleInput
On entry: the multiplicative factor of the variogram .
Constraint:
.
- 6:
cov – Nag_VariogramInput
-
On entry: determines which of the preset variograms to use. The choices are given below. Note that
, where
is the correlation length and is a parameter for most of the variograms, and
is the variance specified by
var.
- Symmetric stable variogram
where
- , ,
- , .
- Cauchy variogram
where
- , ,
- , .
- Differential variogram with compact support
where
- Exponential variogram
where
- Gaussian variogram
where
- Nugget variogram
No parameters need be set for this value of cov.
- Spherical variogram
where
- Bessel variogram
where
- is the Bessel function of the first kind,
- , ,
- , .
- Hole effect variogram
where
- Whittle-Matérn variogram
where
- is the modified Bessel function of the second kind,
- , ,
- , .
- Continuously parameterised variogram with compact support
where
- ,
- is the modified Bessel function of the second kind,
- , ,
- , (second correlation length),
- , .
- Generalized hyperbolic distribution variogram
where
- is the modified Bessel function of the second kind,
- , ,
- , no constraint on
- , ,
- , .
- Cosine variogram
where
- Used for simulating fractional Brownian motion . Fractional Brownian motion itself is not a stationary Gaussian random field, but its increments can be simulated in the same way as a stationary random field. The variogram for the so-called ‘increment process’ is
where
- ,
- , , is the Hurst parameter,
- , , normally is the (fixed) stepsize.
We scale the increments to set
; let
, then
The increments
can then be simulated using
nag_rand_field_1d_generate (g05zpc), then multiplied by
to obtain the original increments
for the fractional Brownian motion.
Constraint:
, , , , , , , , , , , , or .
- 7:
np – IntegerInput
On entry: the number of parameters to be set. Different variograms need a different number of parameters.
- np must be set to .
- , , , , or
- np must be set to .
- , , , or
- np must be set to .
- np must be set to .
- np must be set to .
- 8:
params[np] – const doubleInput
On entry: the parameters set for the variogram.
Constraint:
see
cov for a description of the individual parameter constraints.
- 9:
pad – Nag_EmbedPadInput
On entry: determines whether the embedding matrix is padded with zeros, or padded with values of the variogram. The choice of padding may affect how big the embedding matrix must be in order to be positive semidefinite.
- The embedding matrix is padded with zeros.
- The embedding matrix is padded with values of the variogram.
Suggested value:
.
Constraint:
or .
- 10:
corr – Nag_EmbedScaleInput
On entry: determines which approximation to implement if required, as described in
Section 3.
Suggested value:
.
Constraint:
, or .
- 11:
lam[maxm] – doubleOutput
On exit: contains the square roots of the eigenvalues of the embedding matrix.
- 12:
xx[ns] – doubleOutput
On exit: the points at which values of the random field will be output.
- 13:
m – Integer *Output
On exit: the size of the embedding matrix.
- 14:
approx – Integer *Output
On exit: indicates whether approximation was used.
- No approximation was used.
- Approximation was used.
- 15:
rho – double *Output
On exit: indicates the scaling of the covariance matrix. unless approximation was used with or .
- 16:
icount – Integer *Output
On exit: indicates the number of negative eigenvalues in the embedding matrix which have had to be set to zero.
- 17:
eig[] – doubleOutput
On exit: indicates information about the negative eigenvalues in the embedding matrix which have had to be set to zero. contains the smallest eigenvalue, contains the sum of the squares of the negative eigenvalues, and contains the sum of the absolute values of the negative eigenvalues.
- 18:
fail – NagError *Input/Output
-
The NAG error argument (see
Section 3.6 in the Essential Introduction).
6 Error Indicators and Warnings
- NE_BAD_PARAM
-
On entry, argument had an illegal value.
- NE_ENUM_INT
-
On entry, .
Constraint: for , .
- NE_ENUM_REAL_1
-
On entry, and .
Constraint: .
On entry,
.
Constraint: dependent on
cov.
- NE_ENUM_REAL_2
-
On entry, , and
.
Constraint: .
- NE_INT
-
On entry,
.
Constraint: the minimum calculated value for
maxm is
.
Where the minimum calculated value is given by
, where
is the smallest integer satisfying
.
On entry, .
Constraint: .
- 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.
- NE_REAL
-
On entry, .
Constraint: .
7 Accuracy
If on exit
, see the comments in
Section 3 regarding the quality of approximation; increase the value of
maxm to attempt to avoid approximation.
8 Parallelism and Performance
Not applicable.
None.
10 Example
This example calls nag_rand_field_1d_predef_setup (g05znc) to calculate the eigenvalues of the embedding matrix for sample points of a random field characterized by the symmetric stable variogram ().
10.1 Program Text
Program Text (g05znce.c)
10.2 Program Data
Program Data (g05znce.d)
10.3 Program Results
Program Results (g05znce.r)