RFparameters {RandomFields} | R Documentation |
RFparameters
sets and returns control parameters for the simulation
of random fields
RFparameters(...)
... |
arguments in tag = value form, or a list of tagged
values. |
The possible parameters are
Storing
TRUE
then intermediate results are kept
after each simulation; if several simulation are performed with the same
model parameters then
Storing=TRUE
accelerates the simulations, but needs additional
memory. GaussRF
intermediate changes of RFparameters with flag "[init]"
do not have any influence on the algorithm.
Hence, for studying the effects for
divers values of technical parameters like
CE.force
, CE.mmin
, etc. the parameter Storing
must be
FALSE
. See also last paragraphs in the Details.
Default: TRUE
[init, do].PrintLevel
PrintLevel
<=0
there is not any output on the screen. The
higher the number the more tracing information is given.
Default: 1 [init, do].PracticalRange
FALSE
the range of the covariance functions is
adjusted so that cov(1) is about 0.05 (for scale==1
).
TRUE
: PracticalRange
is applicable only if the value
is known exactly.
2
: PracticalRange
is applicable if the value is
known pretty well
3
: PracticalRange
is applicable if the value is
roughly known
11
: if the practical range is not known exactly it
is approximated numerically.
12
: if the practical range is not known pretty well it
is approximated numerically.
13
: if the practical range is even not known
approximately it is approximated numerically.
Note that values beyond FALSE
, TRUE
, and 11
,
are only used for specialists' purposes.
Default: FALSE
[init].
CE.force
CE.force==TRUE
) after CE.trials
number of trials.
Default: FALSE
[init].
CE.mmin
CE.mmin
the minimum number of rows and columns
of the matrix are given. If CE.mmin>=0
then the minimum
absolute size is given.CE.mmin<0
then the follow holds.
If CE.userfft==FALSE
then the matrix
has -CE.mmin
times the size of the size of the smallest
matrix. If CE.userfft==TRUE
then the matrix
has max(-CE.mmin/2,1)
times the size of the size of the smallest
matrix. Default: 0
[init].CE.tolRe
-1E-5
[init].CE.tolIm
CE.tolIm
then the eigenvalue is considered as real.
Default: 1E-3
[init].CE.trials
CE.tolRe
and
CE.tolIm
are missed then the matrix size is doubled,
and the matrix is checked again. This procedure is repeated
up to CE.trials-1
times. If there are still negative
eigenvalues, the simulation method fails if CE.force==FALSE
.
Default: 3
[init].
CE.userfft
FALSE
the columns of the circulant matrix
have length 2^k for some k. Otherwise the algorithm
tries to find a nicely factorizable number close to the size of the
given matrix. Default: FALSE
[init].CE.strategy
CE.trials
is probably too small
in that case. 0
[init].direct.checkprecision
direct.checkprecision==TRUE
then the precision is checked.
Default: FALSE
[init].direct.maxvariables
direct.maxvariables
, then any matrix decomposition
method is rejected. It is important that this option is set
conveniently if method==NULL
in GaussRF.
Default: 1800
[init]direct.method
direct.method==1
, Cholesky
decomposition will not be attempted, but singular value
decomposition
used instead.
Default: 0
[init].direct.requiredprecision
direct.checkprecision==TRUE
and
the direct.requiredprecision
is not reached then Cholesky
decomposition fails, and singular value decomposition is used.
Default: 1e-11
[init].
spectral.lines
500
[do].spectral.grid
spectral.grid==FALSE
,
and k*pi/spectral.lines
for k in 1:spectral.lines
,
otherwise. Default: TRUE
[do].TBMCE.force
CE.force
.
Default: FALSE
[init].TBMCE.mmin
CE.mmin
. Default: 0
[init].TBMCE.tolRe
CE.tolRe
. Default: -1E-5
[init].TBMCE.tolIm
CE.tolIm
. Default: 1E-3
[init].TBMCE.trials
CE.trials
. Default: 3
[init].TBMCE.userfft
CE.userfft
. Default: true
[init].TBMCE.strategy
CE.strategy
. Default: 0
[init].TBM2.lines
60
[do].TBM2.linesimufactor
TBM2.linesimufactor
or
TBM2.linesimustep
must be greater than zero. The parameter
that is zero is ignored. The grid on the line is
TBM2.linesimufactor
-times
finer than the smallest distance.
See also TBM2.linesimustep
.
Default: 2.0
[init].TBM2.linesimustep
TBM2.linesimustep
is positive the grid on the line has lag
TBM2.linesimustep
.
See also TBM2.linesimufactor
.
Default: 0.0
[init].TBM2.every
TBM2.every>0
then every
TBM2.every
th iteration is announced.
Default: 0
[do].TBM3D2.lines
500
[do].TBM3D2.linesimufactor
TBM3D2.linesimufactor
or
TBM2.linesimustep
must be greater than zero. The parameter
that is zero is ignored. The grid on the line is
TBM3D2.linesimufactor
-times
smaller than the smallest distance. See also TBM3D2.linesimustep
.
Default: 2.0
[init].TBM3D2.linesimustep
TBM3D2.linesimustep
. See also TBM3D2.linesimufactor
.
Default: 0.0
[init].TBM3D2.every
TBM3D2.every>0
then every
TBM3D2.every
th iteration is announced.
Default: 0
[do].TBM3D3.lines
500
[do].TBM3D3.linesimufactor
TBM3D3.linesimufactor
or
TBM2.linesimustep
must be greater than zero. The parameter
that is zero is ignored. The grid on the line is
TBM3D3.linesimufactor
-times smaller than the smallest
distance. See also TBM3D3.linesimustep
.
Default: 2.0
[init].TBM3D3.linesimustep
TBM3D3.linesimustep
. See also TBM3D3.linesimufactor
.
Default: 0.0
[init].TBM3D3.every
TBM3D3.every>0
then every
TBM3D3.every
th iteration is announced.
Default: 0
[do].MPP.approxzero
MPP.approxzero
.
Default: 0.001
[init].add.MPP.realisations
100
[do].MPP.radius
MPP.approxzero
.
If MPP.radius>0
the true radius r is replaced by
MPP.radius
.
Default: 0.0
[init].maxstable.maxGauss
MaxStableRF
, the upper endpoint is
approximated by maxstable.maxGauss
.
Default: 3.0
[init].
pch
pch='!'
then a counter is shown instead of the character.
Note that also '^H's are printed if the counter (pch='!'
) is shown,
which may have undesirable interactions with some few other R functions, e.g.
Sweave
.
Default: '*'
[do].
The following refers to the simulation of Gaussian random fields
(InitGaussRF
, GaussRF
), but most
parts also apply
for the simulation of max-stable random fields
(InitMaxStableRF
, MaxStableRF
).
Some of the global parameters determine the basic settings of a
simulation, e.g. direct.method
(which chooses a square
root of a positive definite matrix). The values of
such parameters are read by
InitGaussRF
and stored in an internal register.
Changing
such a parameter between calling InitGaussRF
and calling
DoSimulateRF
or between subsequent calls of
GaussRF
will not have any effect. These parameters have
the flag "[init]".
Parameters like TBM2.lines
(which determines the number of
i.i.d. processes to be simulated on the line)
are only relevant when generating
random numbers. These parameters are read by DoSimulateRF
(or by the second part of GaussRF
), and
are marked by "[do]".
Storing
has an influence on both, InitGaussRF
and
DoSimulateRF
. InitGaussRF
may reserve
more memory if Storing==TRUE
. DoSimulateRF
will
free the register
if Storing==FALSE
, whatever the value of Storing
was
when InitGaussRF
was called.
The distinction between [init] and [do] is also relevant if
GaussRF
is used and called a second time
with the same parameters for the random field and if
RFparameters()$Storing==TRUE
.
Then GaussRF
realises that the second call has the
same random field parameters, and
takes over the stored intermediate results (that have been calculated
with the RFparameters()
at that time). To prevent the use of
stored intermediate results or to take into account intermediate
changes of RFparameters
set RFparameters(Storing==FALSE)
or use
DeleteRegister()
between calls of GaussRF
.
A programme that checks whether the parameters are well
adapted to a specific simulation problem is given as an example of
EmpiricalVariogram()
.
For further details on the implemented methods, see RFMethods.
If any parameter has been given
RFparameters
returns an invisible list of
the given parameters in full name.
Otherwise the complete list of parameters is returned. Further the
values of the following internal readonly variables are returned
|
max. name length for variogram/covariance models |
|
max. name length for methods |
|
max. name length for a distribution |
|
number of currently implemented variogram/covariance models |
|
number of currently implemented variogram/covariance models |
|
number of currently implemented distributions |
|
maximum number of dimensions for a random field |
|
maximum number of models, i.e. the possible
register numbers in GaussRF for example, are
1,...,maxmodels .
|
Martin Schlather, martin.schlather@cu.lu http://www.cu.lu/~schlathe
Schlather, M. (1999) An introduction to positive definite functions and to unconditional simulation of random fields. Technical report ST 99-10, Dept. of Maths and Statistics, Lancaster University.
GaussRF
,
GetPracticalRange
,
MaxStableRF
,
RandomFields
,
and RFMethods
.
RFparameters(Storing=TRUE) str(RFparameters())