https://github.com/cran/RandomFields
Tip revision: f082dc8b0950aff830aab568d89a74af74f10e14 authored by Martin Schlather on 12 August 2014, 00:00:00 UTC
version 3.0.35
version 3.0.35
Tip revision: f082dc8
RFoptions.Rd
\name{RFoptions}
\alias{RFoptions}
\title{Setting control arguments}
\description{
\command{\link{RFoptions}} sets and returns control arguments for the analysis
and the simulation of random fields
}
\usage{
RFoptions(..., no.readonly = TRUE)
}
\arguments{
\item{...}{arguments in \code{tag = value} form, or a list of tagged
values.}
\item{no.readonly}{If \command{\link{RFoptions}} is called without
argument then all arguments are returned in a list. If
\code{no.readonly=TRUE} then only rewritable arguments are returned.
}
}
\details{
The subsections below comment on options for\cr
\bold{1. \code{general}: General options}\cr
\bold{2. \code{br}: Options for Brown-Resnick
Fields}\cr
\bold{3. \code{circulant}: Options for circulant embedding methods, cf.\command{\link{RPcirculant}}}\cr
\bold{4. \code{coords}: Options for coordinates and units}
\bold{5. \code{direct}: Options for simulating by simple matrix decomposition}\cr
\bold{6. \code{distr}: Options for distributions, in particular \command{\link{RRrectangular}}}
\bold{7. \code{empvario}: Options for calculating the empirical variogram}\cr
\bold{8. \code{fit}: Options for \command{\link{RFfit}},
\command{\link{RFratiotest}}, and \command{\link{RFcrossvalidate}}}
\bold{9. \code{gauss}: Options for simulating Gaussian random fields}
\bold{10. \code{graphics}: Options for graphical output}\cr
\bold{11. \code{gui}: Options for c\command{\link{RFgui}}}\cr
\bold{12. \code{hyper}: Options for simulating hyperplane tessellations}\cr
\bold{13. \code{krige}: Options for Kriging}\cr
\bold{14. \code{maxstable}: Options for simulating max-stable random fields}
\bold{15. \code{mpp}: Options for the random coins (shot noise) methods}\cr
\bold{16. \code{nugget}: Options for the nugget effect}\cr
\bold{17. \code{registers}: Register numbers}\cr
\bold{18. \code{sequ}: Options for the sequential method}\cr
\bold{19. \code{special}: Options for some special methods}\cr
\bold{20. \code{spectral}: Options for the spectral (turning bands) method}\cr
\bold{21. \code{tbm}: Options for the turning bands method}\cr
\bold{22. \code{internal}: Internal}\cr
\bold{General comments}\cr\cr\cr
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% \bold{16. Options for RFloglikelihood}\cr
%
% "auto", "full", "composite", "selection"
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{1. General options}\cr
\describe{
% only works for RFsimulate, not for RFsimulate
\item{\code{allowdistanceZero}}{boolean. Only used in
\command{\link{RFinterpolate}} and in \command{\link{RFfit}}.
If true, then
multiple observations or identical locations
are allowed within a single data set.
In this case, the coordinates are slightly scattered, so that
the points have some tiny distances.
Default: \code{FALSE}.
}
\item{\code{cPrintlevel}}{\code{printlevel} above works
on the R level whereas \code{cPrintlevel} works on the C level.
\code{cPrintlevel} is automatically set when \code{printlevel}
is changed, namely as \code{min(printlevel, 5)}.
0 : no message\cr
1 : important (error) messages and warnings\cr
2 : less important messages\cr
3 : recursive call tracing\cr
4 : details of recursive call tracing\cr
5 : function flow information of large functions\cr
6 : errors that are internally treated\cr
7 : details on building up the covariance structure\cr
8 : details on taking the square root of the covariance matrix\cr
9 : details on intermediate calculations\cr
10 : further details on intermediate calculations\cr
Default: 1 [also do].\cr
}
\item{\code{detailed_output}}{logical.
if \code{TRUE} some function, e.g. \code{\link{RFcrossvalidate}}
will return additional information.
}
\item{\code{every}}{integer.
if greater than zero, then every \code{every}th iteration is
printed if simulated by TBM or random coin method. The value zero
means that nothing is printed.
Default: \code{0} [do].
}
\item{\code{exactness}}{logical or NA.
\itemize{
\item \code{TRUE}: add.MPP, hyperplanes and
all turning bands methods are excluded.
If the circulant embedding method is considered as badly
behaved, then the matrix decomposition methods are preferred.
\item \code{FALSE}: if the circulant embedding method is
considered as badly behaved or the number of points to be
simulated is large, the turning bands methods are
rather preferred.
\item \code{NA}: approximative non-exact methods are excluded,
i.e. TBM2 if the Abel transform of the covariance function
cannot be given explicitely.
}
Default: \code{NA} .
}
\item{\code{expected_number_simu}}{positive integer which is usally set
internally as the value of the argument \code{n} in
\command{\link{RFsimulate}}. The argument \code{expected_number_simu}
should be set only by an advanced users and only if
\command{\link{RFsimulate}} will be called with argument \code{n} allone.
}
\item{\code{gridtolerance}}{
used in \command{\link{RFsimulate}} to see if the coordinates build a
grid for x, y, z, T-values. This argument is also used
in case of conditional
simulation where the data locations might ly on
a grid.
Default: \code{1e-6}
}
\item{\code{matrix_inversion}}{
vector of integers gives the methods (and their sequence)
to inverse a matrix:
0 : Choleskey decomposition \cr
1 : QR decomposition\cr
2 : SVD\cr
This option is currently used only in \command{\link{RFinterpolate}}.% to
% do
Default: \code{c(0, 2)}.
}
\item{\code{matrix_tolerance}}{
In case of SVD, it is checked whether the eigenvalue is less than
\code{matrix_tolerance}. Then the eigenvalue is not inverted, but
the inverse is set to zero.
}
\item{\code{modus_operandi}}{character. One of the values
\code{"careless"}, \code{"sloppy"}, \code{"easygoing"},
\code{"normal"}, \code{"precise"}, \code{"pedantic"},
\code{"neurotic"} .
\bold{This argument is in an experimental stage and its definition
and effects will change very likely in near future.}%to do
This argument sets a lot of argument at once related to estimation
and simulation. \code{"careless"} prefers rather fast algorithms,
but the results
might be very rough approximations. By way of contrast,
\code{"neurotic"} will try very
hard to return exact result at the cost of hugh computing times.
Default: \code{"normal"}
}
\item{\code{na_rm_lines}}{
logical. If \code{TRUE} then a line of the data that contains a
\code{NA} value is deleted. Otherwise it is tried to deal with the
\code{NA} value at higher costs of computing time.
Default: \code{FALSE}.
}
\item{\code{pch}}{character.
\command{\link{RFfit}}: shown before evaluating any method;
if \code{pch!=""} then one or two
additional steps in the MLE methods are
marked by \dQuote{+} and \dQuote{#}.
Simulation:
The character is printed after each
performed simulation if more than one simulation is performed at
once. If \code{pch='!'} then an absolute
counter is shown instead of the character.
If \code{pch='\%'} then a
counter of percentages is shown instead of the character.
Note that also \sQuote{\eqn{\mbox{\textasciicircum}}{^}H}s are printed in
the last two cases,
which may have undesirable interactions with some few other R
functions, e.g. \command{\link[utils]{Sweave}}.
Default: \code{'*'} [do].
}
\item{\code{practicalrange}}{logical or integer.
If not \code{FALSE} the range of primitive
covariance functions is
adjusted so that cov(1) is zero for models with finite range.
(Operators are too complex to be adjusted; for anisotropic
covariance the practical range is not well defined.)
The value of cov(1) is about 0.05 (for \code{scale=1})
for models without range. See \command{\link{RMmodel}} or type
\code{\link{RFgetModelNames}(type="positive definite",
domain="single variable", isotropy="isotropic", operator=FALSE, vdim=1)}
for the list of primitive models.
\itemize{
\item \code{FALSE} : the practical range ajustment is not used.
\item \code{TRUE} : \code{practicalrange} is applicable only if
the value is known exactly, or, at least, can be approximated by
a closed formula.
\item \code{2} : if the practical range is not known exactly it
is approximated numerically.
}
Default: \code{FALSE} .
}
\item{\code{printlevel}}{If \code{printlevel}\eqn{\le0}{<=0}
there is not any output on the screen. The
higher the number the more tracing information is given.
0 : no message\cr
1 : important (error) messages and warnings\cr
2 : less important messages\cr
3 : recursive call tracing\cr
4 : details of recursive call tracing\cr
5 : function flow information of large functions\cr
6 : errors that are internally treated\cr
7 : details on intermediate calculations\cr
8 : further details on intermediate calculations\cr
Default: 1 [also do].\cr
}
\item{seed}{integer. If not \code{NULL} and not \code{NA}
the \code{\link[base]{set.seed}(seed)} is set
before simulations are performed, e.g. by
\command{\link{RFsimulate}} or \command{\link{RFdistr}}.
Otherwise \command{\link{set.seed}} is \bold{not} called.
If the argument is set locally, i.e. within a function,
it has the usual local effect. If it is set globally, i.e. by
\command{RFoptions} the \code{seed} is fixed
for all subsequent calls.
If the number of simulations \code{n} is greater than one
and if \code{RFoptions(seed=seed)} is set, the \eqn{i}th
simulation is started the seed \code{seed}\eqn{+i-1}.
The function \code{set.seed} should not be used in case \code{n}
is greater than 1.
%Vgle!
%set.seed(5)
%RFsimulate(RPschlather(RMmatern(nu=2), xi=1, mu=1, s=1), x, grid=F, n=5)@data
%set.seed(5)
%RFsimulate(RPschlather(RMmatern(nu=2.01), xi=1, mu=1, s=1), x,grid=F,n=5)@data
%RFoptions(cPr=3, seed=5)
%RFsimulate(RPschlather(RMmatern(nu=2), xi=1, mu=1, s=1), x, grid=F, n=5)@data
%RFsimulate(RPschlather(RMmatern(nu=2.01), xi=1, mu=1, s=1), x, grid=F,n=5)@data
Note also that \command{\link{RFratiotest}} has its own argument
\code{seed} with a slightly different meaning.
}
\item{\code{spConform}}{logical.
If \code{TRUE} then \command{\link{RFsimulate}} and many other functions
return an \code{sp}-object. Otherwise matrices or lists are
returned as defined in RandomFields 2.0, see the manuals for the
specific functions.
Briefly, \code{spConform=TRUE} should be used by
a standard user as this allows the comfortable use of \command{plot},
for instance, while \code{spConform=FALSE} is much faster and might
be used by programmers.
Default: \code{TRUE} [do].
}
\item{\code{skipchecks}}{logical.
If \code{TRUE}, several checks whether the given parameter values
and the dimension are within the allowed range is skipped.
Do not change the value of this variable except you really
know what you do.
Default: \code{FALSE} [also do].
}
\item{\code{storing}}{Logical.
If \code{FALSE} then the intermediate results are
destroyed after the simulation of the random field(s)
or if an error had occured.
If \code{storing=TRUE}, then
additional simulations can be performed by calling
\command{\link{RFsimulate}} with at most the argument \code{n}.
This call can then be much faster, but the a rather large
amount of memory could be kept.
When \code{storing} turned from \code{TRUE} to \code{FALSE} by
global call then all registers are deleted.
Advanced:
With \code{\link{RFoptions}(storing=list(FALSE, register,
model_register))}
single registers can be deleted.
Default: \code{FALSE} [do].
}
\item{\code{vdim_close_together}}{logical. Used especially in functions that
create covariance matrices. If the model is multivariate, then two
ways of ordering the matrix exist. To consider first all variables at
a certain location (\code{vdim_close_together=TRUE}) or to consider first
all locations keeping the variable fixed
(\code{vdim_close_together=FALSE}).
Note that several simulation methods rely on the value \code{FALSE},
so that these methods will not work anymore if \code{vdim_close_together=TRUE}.
Default: \code{FALSE}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{2. Options for Brown-Resnick Fields}
\describe{
\item{\code{corr_factorr}}{
}
\item{\code{deltaAM}}{
}
\item{\code{maxtrendmem}}{
integer; the maximal number of trends for shifted locations that may
be stored at the same time when simulating BR processes via
\code{RPbrshifted}; if \code{maxtrendmem} is large, multiple trend
evaluation may be avoided.
Default: \code{1e8} .
}
\item{\code{meshsize}}{
positive; width of the grid on which the shape functions in the M3
representation of BR processes are simulated; only used for
simulation of BR processes via \code{RPbrmixed}.
Default: \code{0.1} .
}
\item{\code{optim_mixed}}{\code{0, 1, 2}; only used for simulation of BR
processes via \code{RPbrmixed}.\cr
If \code{optim_mixed=0}, the arguments
\code{\link[=RPbrmixed]{lambda}} and
\code{\link[=RPbrmixed]{areamat}} of \command{\link{RPbrshifted}}
are used for the simulation.\cr
If \code{optim_mixed=1}, \code{\link[=RPbrmixed]{lambda}} is estimated for
\code{\link[=RPbrmixed]{areamat=1}}.\cr
If \code{optim_mixed=2}, \code{\link[=RPbrmixed]{areamat}} is optimized and
\code{\link[=RPbrmixed]{lambda}} is estimated.
Default: \code{1} .
}
\item{\code{optim_mixed_maxpoints}}{
positive integer; only used for simulation of BR processes via
\code{RPbrmixed} with \code{optim_mixed>0}. Maximal number of Poisson
points used for the optimization of
\code{\link[=RPbrmixed]{areamat}} and the estimation
of \code{\link[=RPbrmixed]{lambda}}.
Default: \code{10000} .
}
\item{\code{optim_mixed_tol}}{
value in \eqn{[0,1]}; only used for simulation of BR processes via
\code{RPbrmixed} with \code{optim_mixed=2}. In this case,
\code{\link[=RPbrmixed]{areamat}} is optimized under the constraint that the
probability of drawing the shape function incorrectly is bounded by
\code{optim_mixed_tol} (cf. Oesting et al., 2012).
Default: \code{0.01} .
}
\item{\code{variobound}}{
positive; the shape functions in the mixed moving maxima
representation are cut off where the variogram belonging
to \code{phi} exceeds \code{variobound}.
Default: \code{8.0} .
}
\item{\code{vertnumber}}{
positive integer; for an efficient simulation of the shape functions
in the M3 representation of BR processes, the component \eqn{E} from
of the domain \eqn{[x_0, \infty] \times E}{[x_0, Inf] x E} of the
underlying Poisson point process is sub-dividedinto cubes
(cf. Oesting et al., 2012); \code{vertical} is the number of
vertical breaks of \eqn{E}; only used for simulation of BR processes
via \code{RPbrmixed} with \code{optim_mixed=2}.
Default: \code{7} .
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{3. \code{circulant}: Options for circulant embedding methods, cf. \command{\link{RPcirculant}}}
These options influence the standard circulant embedding
method, cutoff circulant embedding intrinsic circulant embedding.
It can also influence \command{\link{RPtbm}} if the line is simulated
with any circulant embedding method.
\cr
\describe{
\item{\code{approx_maxgrid}}{See \command{\link{RPcirculant}}}
\item{\code{approx_step}}{See \command{\link{RPcirculant}}}
\item{\code{dependent}}{See \command{\link{RPcirculant}}}
\item{\code{force}}{See \command{\link{RPcirculant}}}
\item{\code{maxmem}}{See \command{\link{RPcirculant}}}
\item{\code{mmin}}{See \command{\link{RPcirculant}}}
\item{\code{strategy}}{See \command{\link{RPcirculant}}}
\item{\code{tolIm}}{See \command{\link{RPcirculant}}}
\item{\code{tolRe}}{See \command{\link{RPcirculant}}}
\item{\code{trials}}{See \command{\link{RPcirculant}}}
\item{\code{useprimes}}{See \command{\link{RPcirculant}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{4. \code{coords}: Options for coordinates and units}
\describe{
\item{\code{coordinate_system}}{
One of the values \code{"auto"}, \code{"cartesian"},
% \code{"polar"}, \code{"cylindric"}, \code{"spherical"},
\code{"earth"}
Default: \code{"auto"}
}
\item{\code{coord_units}}{any string.
If \code{coordinate_system = "earth"} and longitude and latitude
are transformed to 3d cartesian coordinates, \code{coord_units}
determines
whether the radius is given in kilometers (\code{"km"}) or miles
(\code{"miles"}).
If empty, then \code{"km"} is chosen.
Default: \code{""}
}
\item{\code{new_coord_units}}{internal and should not be set
internal.
Default: \code{""}
}
\item{\code{variab_units}}{vector of characters.
The default units of the variables
Default: \code{""}
}
\item{\code{xyz_notation}}{logical, NA
\code{NA} : automatic choice (if possible)
\code{false} : notation (x, y) should not be understood as
as kernel definition, not as xyz notation
\code{true}: xyz notation used
% \code{2}:this value is only used by calls of RFcov and should not
% be used by a user
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{5. \code{direct}: Options for simulating by simple matrix decomposition}\cr
\describe{
\item{\code{max_variab}}{See \command{\link{RPdirect}}}
\item{\code{root_method}}{See \command{\link{RPdirect}}}
\item{\code{svdtolerance}}{See \command{\link{RPdirect}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{6. \code{distr}: Options for distributions, in particular \command{\link{RRrectangular}}}
\describe{
\item{\code{innermin}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
The minimal length of the interval where the Taylor expansion shall
be valid.
Default: \code{1e-20} .
}
\item{\code{maxit}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
The number of iterative steps where the
the constant of the Taylor development is increased,
to find an upper bound for the given function.
Default: \code{20} .
}
\item{\code{maxsteps}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
\code{maxsteps} is usually the number of steps in the middle part of
the approximation. From this value and the length between
the determined endpoints for the approximation at the origin and in
the tail, the step length is calculated. If the step length is less
than \code{minsteplen} the number of steps is reduced.
Default: \code{1000} .
}
\item{\code{mcmc_n}}{
In case of the use of MCMC it leaves out \eqn{n-1}
member of the Markov chain bevor the \eqn{n} member
is returned. See also maxsteps.
Default: \code{15} .
}
\item{\code{minsteplen}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
The minimal step length
for the middle part of approximation, which is a step function,
Default: \code{0} (i.e. not used as a criterion.)
}
\item{\code{outermax}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
The largest possible endpoint for the middle part that
approximates the function by a step function. See also \code{innermax}.
Default: 20.
}
\item{\code{parts}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
\code{parts} determines the number of tests that are performed to
check whether a proposed power function is an upper bound for
the given function, at the origin and the tail.
Default: \code{8} .
}
\item{\code{repetitions}}{
Minimal number of realisations to determine a quantity of the
distribution by MCMC. E.g. zu determine integral \eqn{c}
in the paper of Oesting, Schlather, Zhou.
Default: 1000
}
\item{\code{safety}}{
Default value to simulate from the
\command{\link{RRrectangular}} distribution.
First, at the origin, the first power function of the Taylor
expansion is taken as potential upper function.
The constant of the power function are increased by factor
\eqn{1 + }\code{safety} and the exponent of the function
similarly decreased. A number of test evaluations
is performed to check whether this modified function is indeed
a upper bound. If not, the considered interval at the origin
is reduced iteratively, the constants of the power function
further increased and the exponent decreased.
If \code{maxit} iteration have been performed without success,
the search for an upper bound fails.
The search at the origin also fails if the interval around
the origin has become less than \code{innermin}.
Similar procedure is performed for the tail.
Default: \code{0.08} .
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{7. \code{empvario}: Options for calculating the empirical variogram}\cr
\describe{
\item{\code{fft}}{
Logical. Determines whether FFT should be used for data on a grid
Default: \code{TRUE}.
}
\item{\code{phi0}}{
numeric. In case of anisotropic fields directional cones are
considered. The argument \code{phi0} determines one of the
boundaries, hence all boundaries for a given fixed number of cones.
Default: \code{0}.
}
\item{\code{pseudovariogram}}{
logical. Only in the multivariate case. Whether the
pseudovariogram or the crossvariogram should be calculated.
Default: \code{FALSE}.
}
\item{\code{theta0}}{
numeric. In case of anisotropic fields directional cones are
considered. The argument \code{theta0} determines one of the
boundaries, hence all boundaries for a given fixed number of cones.
The argument \code{theta0} refers to the second angle in a polar
coordinate representation in 3 dimensions.
Default: \code{0}.
}
\item{\code{tol0}}{
numeric. Estimated values of the empirical variogram
below \code{tol0} times the grid step in the third dimension
are considered to be zero. Hence the respective values are set
to zero.
Default: \code{1e-13}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{8. \code{fit}: Options for \command{\link{RFfit}},
\command{\link{RFratiotest}}, and \command{\link{RFcrossvalidate}}}
The following comments on \command{\link{RFfit}} apply
also to \command{\link{RFratiotest}}.
\describe{
\item{\code{algorithm}}{
Default: \code{-1}.
}
\item{\code{approximate_functioncalls}}{
In case the parameter vector is too close to the given
bounds, the ML target function is evaluated on a grid
to get a new initial value for the ML estimation.
The number of points of the grid is approximately
\code{approximate_functioncalls}.
Default: \code{50}
}
\item{\code{bc_lambda_lb}}{
lower bound for the Box-Cox transformation
Default: \code{-10}.
}
\item{\code{bc_lambda_ub}}{
upper bound for the Box-Cox transformation
Default: \code{10}.
}
\item{\code{bin_dist_factor}}{
numeric. The empirical variogram is calculated up the distance
\code{bin_dist_factor} times (maximum distance among any pair of locations)
Default: \code{0.5}.
}
\item{\code{bins}}{vector of explicit boundaries for the bins or the
number of bins for the empirical variogram (used in the
LSQ target function, which is described at the beginning
of the Details).
Note that for anisotropic models, the value of \code{bins} might
be enlarged.
Default: \code{20}.
}
\item{\code{critical}}{logical or signed integer.
If \code{critical=FALSE} and if the result of
any maximum likelihood method
is on a borderline, then the optimisation is redone
in a modified way (which takes about double extra time)
If \code{critical=TRUE} and if the result of
any maximum likelihood method
is on a borderline, then a kind of profile likelihood
optimization is done (which takes about 10 times extra time)
If \code{critical>=2} then a kind of profile likelihood
optimization is always done (which takes about \code{n_crit}
times extra time) for an automatically chosen selection
of the model parameters.
If \code{critical>=3} then a kind of profile likelihood
optimization is always done (which takes about \code{n_crit}
times extra time) for all the parameters.
If \code{critical<0} then none of the refined methods
are performed.
Default: \code{TRUE}.
}
\item{\code{cross_refit}}{logical.
For each of the subset of the cross-validation method
the parameters have to be fitted to the given model.
If \code{cross_refit} is \code{TRUE}, this is done, but takes a huge
amount of time. If \code{FALSE}, the model is fitted only once to
the data and the value at
each point is predicted with the same model given
the values of the other points.
Default: \code{FALSE}.
}
\item{likelihood}{character.
types of likelihood are \code{"auto"}, \code{"full"},
\code{"composite"}, \code{"selection"};
Default: \code{"auto"}
}
\item{\code{lowerbound_scale_factor}}{
The lower bound for the scale is determined as
\eqn{(minimum distance
between different pairs of points) / }
\code{lowerbound_scale_factor}.
Default: \code{3}.
}
\item{\code{lowerbound_scale_ls_factor}}{ For the LSQ target
function a different lower bound
for the scale is used. It is determined as
\eqn{(minimum distance between different pairs of points) / }
\code{lowerbound_scale_ls_factor}.
Default: \code{5}.
}
\item{\code{lowerbound_sill}}{absolute lower bound for variance
and nugget. See \code{lowerbound_var_factor}.
Default: \code{1E-10}.
}
\item{\code{lowerbound_var_factor}}{
The lower bound for the nugget and the variance is determined
as var(\code{data}) / \code{lowerbound_var_factor}.
If a standard model definition is given and
either the nugget or the variance is fixed,
the parameter to be estimated
must also be greater than \code{lowerbound_sill}.
Default: \code{10000}.
}
\item{\code{maxmixedvar}}{
upper bound for variance in a mixed model;
so, the covariance model for mixed model part might
be calibrated appropriately
}
\item{\code{max_neighbours}}{integer.
Maximum number of locations (with depending values)
that are allowed.
Default: \code{5000}.
}
\item{\code{minbounddistance}}{
If any value of the parameter vector
returned from the ML estimation
is closer than \code{minbounddistance}
to any of the bounds or if any value
has a relative distance smaller than
\code{minboundreldist}, then it is assumed that
the MLE algorithm has dropped into a local minimum,
and it will be continued with evaluating the
ML target function on a grid, cf. the beginning paragraphs
of the Details.
Default: \code{0.001}.
}
\item{\code{minboundreldist}}{relative distance to the bounds
below which a part of the algorithm is considered as
having failed. See \code{minbounddistance}.
Default: \code{0.02}.
}
\item{\code{min_diag}}{
Minimal value of any estimated diagonal matrix element.
Default: \code{1e-7}.
}
\item{\code{n_crit}}{integer.
The approximate profiles that are considered.
Default: \code{10}.
}
\item{\code{nphi}}{scalar or vector of 2 components.
If it is a vector then the first component gives the first angle
of the xy plane
and the second one gives the number of directions on the half circle.
If scalar then the first angle is assumed to be zero.
Note that a good estimation of the variogramm by LSQ with a
anisotropic model a large value for \code{ntheta} might be needed
(about 20).
Default: \code{1}.
}
\item{\code{ntheta}}{scalar or vector of 2 components.
If it is a vector then the first component gives the first angle
in the third direction
and the second one gives the number of directions on the half circle.
If scalar then the first angle is assumed to be zero.
Note that a good estimation of the variogramm by LSQ with a
anisotropic model a large value for \code{ntheta} might be needed
(about 20).
Default: \code{1}.
}
\item{\code{ntime}}{scalar or vector of 2 components.
if \code{ntimes} is a vector, then the first component are the
maximum time distance (in units of the grid length \code{T[3]}) and the
second component gives the step size (in units of the grid length
\code{T[3]}). If scalar then the step size is assumed to 1 (in units
of the grid length \code{T[3]}).
Default: \code{20}.
}
\item{\code{only_users}}{boolean.
If true then only \code{users_guess} is used as a
starting point for the fitting algorithms
Default: \code{FALSE}.
}
\item{\code{optimiser}}{
Optimiser used in \command{\link{RFfit}} and takes
one of the values
\code{"optim"}, \code{"optimx"}, \code{"soma"}, \code{"nloptr"},
\code{"GenSA"}, \code{"minqa"}, \code{"pso"} or \code{"DEoptim"}.
Default: \code{"optim"}.
}
\item{\code{optim_var_elimination}}{This argument takes the values
\code{'never'}, \code{'respect bound'}, \code{'try'},
\code{'yes'}, and should only be
set by the advanced user.
The meaning of the values is as follows.
\itemize{
\item{\code{'never'}}{
A global variance is never tried to be eliminated
}
\item{\code{'respect bound'}}{
A global variance is eliminated if such a variance
is detected and the user did not indicate bounds
for the parameters.
}
\item{\code{'try'}}{
A global variance is eliminated if such a variance
is detected.
}
\item{\code{'yes'}}{
A global variance is tried to be eliminated altough
the algorithm dis not find an indication. Here, the full
responsibility is left to the user. (This option might
make sense if \code{transform} is given.)
This option is only overwritten when it does not make sense,
e.g. no variance is estimated.
}
}
Default: \code{'respect bound'}.
}
\item{\code{refine_onborder}}{
}
\item{\code{minmixedvar}}{
lower bound for variance in a mixed model;
so, the covariance model for mixed model part might
be calibrated appropriately
}
\item{\code{solvesigma}}{Boolean -- experimental stage!
If a mixed effect part is present where the variance
has to be estimated, then this variance parameter is solved
iteratively within the profile likelihood function, if
\code{solvesigma=TRUE}.This makes sense
if the number of independent variables is very small.
If \code{solvesigma=FALSE} then the variance parameter is
treated as any other parameter to be estimated.
}
\item{\code{ratiotest_approx}}{logical.
if \code{TRUE} the approximative formula that twice the
difference of the likelihoods follow about a \eqn{\chi^2}
distribution is used. The parameter of freedom equals
the number of parameters to be estimated for the covariance
function, including those for the covariates.
}
\item{\code{reoptimise}}{logical.
If \code{TRUE && !only_users} then at a very last step,
the optimisation is redone with currently best parameters
and likelihood as scale parameter for \command{\link{optim}}.
Default: \code{TRUE}.
}
\item{\code{scale_max_relative_factor}}{ If the initial scale
value for the ML estimation
obtained by the LSQ target function is
less than
\eqn{(minimum distance
between different pairs of points) / }
\code{scale_max_relative_factor}
a warning is given that probably a nugget effect
is present.
Note: if \code{scale_max_relative_factor} is greater
than \code{lowerbound_scale_ls_factor} then
no warning is given as
the scale has the lower bound \eqn{(minimum distance
between different pairs of points) / }
\code{lowerbound_scale_ls_factor}.
Default: \code{1000}
}
\item{\code{scale_ratio}}{
\command{\link{RFfit}} uses \code{parscale} and \code{fnscale}
in the calls of \command{\link{optim}}. As these arguments should
have the magnitude of the estimated values, \command{\link{RFfit}}
checks this by calculating the absolute log ratios.
If they are larger than \code{scale_ratio},
\code{parscale} and \code{fnscale} are reset and the optimisation
is redone.
Default: \code{0.1}.
}
\item{\code{shortnamelength}}{
The names of the variables in the returned table are
abbreviated by taking the first \code{shortnamelength}
letters.
Default: \code{4}.
}
\item{\code{sill}}{
Additionally to estimating \code{nugget} and \code{variance}
separately, they may also be estimated together under the
condition that \code{nugget} + \code{variance} = \code{sill}.
For the latter a finite value for \code{sill} has to be supplied,
and \code{nugget} and \code{variance} are set to \code{NA}.
\code{sill} is only used for the standard model.
Default: \code{NA}.
}
\item{\code{smalldataset}}{
Default: \code{2000}.
}
\item{\code{split}}{logical.
If \code{TRUE} then \command{\link{RFfit}} checks whether a
space-time covariance model or a multivariate covariance model
can be split into components, so that certain parameters
can be estimated separately.
Default: \code{TRUE}.
}
\item{\code{splitn_neighbours}}{integer.
In case the maximum number of locations \code{maxn}
is exceeded, then \command{\link{RFfit}} tries to split the data set
into parts of size \code{split} or less, but never more than
\code{maxn}.
Default: \code{c(3000, 200, 1000)}.
}
\item{\code{splitfactor_neighbours}}{
The total number of neighbouring boxes in each direction
\eqn{1 + 2\code{splitfactor}}, including the current box itself.
Default: \code{2}.
}
\item{\code{split_refined}}{logical.
If \code{TRUE} then also submodels are fitted if splitted.
This takes more time, but \command{\link[=anova.RF_fit]{anova}} and
\command{\link{RFratiotest}}, for instance,
will give additional information.
Default: \code{TRUE}.
}
\item{\code{upperbound_scale_factor}}{
The upper bound for the scale is determined
as \code{upperbound_scale_factor}\eqn{ * (maximum distance
between all pairs of points)}.
Default: \code{3}.
}
\item{\code{upperbound_var_factor}}{ The upper bound for the
variance and the nugget is determined
as \code{upperbound_var_factor} * var(\code{data})
Default: \code{10}.
}
\item{\code{use_naturalscaling}}{
logical. Only used if model is given in standard (simple) way.
If \code{TRUE} then \emph{internally}, rescaled
covariance functions will be used for which
cov(1)\eqn{\approx}{~=}0.05.
\code{use_naturalscaling} has the advantage that \code{scale}
and the form parameters of the model get \sQuote{orthogonal},
but \code{use_naturalscaling} does not work for all models.
Note that this argument does not influence
the output of \command{\link{RFfit}}: the parameter vector
returned by \command{\link{RFfit}} refers
\emph{always} to the standard covariance model as given in
\command{\link{RMmodel}}. (In contrast to \code{practicalrange}
in \command{\link{RFoptions}}.)\cr
Advantages if \code{use_naturalscaling=TRUE}:
\itemize{
\item \code{scale} and the shape parameter of a parameterised
covariance model can be estimated better if they are estimated
simultaneously.
\item The estimated bounds calculated by means of
\code{upperbound_scale_factor} and \code{lowerbound_scale_factor},
etc. might be more realistic.
\item in case of anisotropic models, the inverse of the elements
of the anisotropy matrix should be in the above bounds.
}
Disadvantages if \code{use_naturalscaling=TRUE}:
\itemize{
\item For some covariance models with additional parameters, the
rescaling factor has to be determined numerically.
Then, more time is needed to perform \command{\link{RFfit}}.
}
Default: \code{TRUE}.
}
\item{\code{use_spam}}{
Should the package \code{spam} (sparse matrices)
be used for matrix calculations?
Default: \code{NA}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{9. \code{gauss}: Options for simulating Gaussian random fields}
\describe{
\item{\code{approx_zero}}{
Value below which a correlation is considered to be essentially zero.
This argument is used to determine the practical range of covariance
function with non-compact support.
Default: \code{0.05}
}
\item{\code{direct_bestvar}}{integer.
When searching for an appropriate simuation method
the matrix decomposition method (\code{method="direct"})
is preferred if the number of variables is less than or equal to
\code{direct_bestvariables}.
Default is \code{800}.
}
\item{\code{loggauss}}{
See \command{\link{RPgauss}}
}
\item{\code{paired}}{
(\dQuote{Antithetic pairs}.)
Logical. If \code{TRUE} then the second half of the
simulations is logical. If \code{TRUE} then the second half of the
simulations is obtained by
only changing the signs of all the standard Gaussian random variables,
on which the first half of the
simulations is based. Default is \code{FALSE}.
}
\item{\code{stationary_only}}{
See \command{\link{RPgauss}}
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{10. \code{graphics}: Options for graphical output}\cr
\describe{
\item{\code{always_close_screen}}{logical.
If \code{FALSE} the current device is kept as it is.
Otherwise the action depends on the value of
\code{height}: if \code{height} is not positive
then \command{\link[graphics]{close.screen}}
is performed on the current device.
Else the current device is closed.
Default: \code{TRUE}.
}
\item{\code{grPrintlevel}}{
integer values 0, 1, 2. The higher the more text is shown in the plot.
Default: \code{1}.
}
\item{\code{height}}{real number.
if \code{height} is greater than zero then it gives the height
of a single figure in a plot create by \pkg{RandomFields};
always a new window is opened. See also \code{always_close_screen}.
If plots with multiple figures are
shown, the height and width of the plot
will be increased by a factor up the
ones given by \code{increase_upto}.
Default: \code{6}.
}
\item{\code{increase_upto}}{
See \code{height}.
Default: \code{c(3,4)}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{11. \code{gui}: Options for c\command{\link{RFgui}}}\cr
\describe{
\item{\code{alwaysSimulate}}{
logical. If \code{TRUE} then a new random field is simulated
whenever a parameter is changed. Otherwise only the covariance
function or the variogram is re-plotted; simulations are performed
only when the correponding button is pressed.
Default: \code{TRUE}.
}
\item{\code{simu_method}}{
\code{"RPcirculant"},
\code{"RPcutoff"},
\code{"RPintrinsic"},
\code{"RPtbm"},
\code{"RPspectral"},
\code{"RPdirect"},
\code{"RPsequential"},
\code{"RPaverage"},
\code{"RPnugget"},
\code{"RPcoins"},
\code{"RPhyperplane"},
\code{"RPspecific"},
\code{"any method"}.
Default: \code{"RPcirculant"}.
}
\item{\code{size}}{vector of 2 components.
Grid size of the simulated stochastic processes.
The two components of the vector correspond to one-dimensional and
two-dimensional processes, respectively.
Default: \code{c(512, 64)}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{12. \code{hyper}: Options for simulating hyperplane tessellations}\cr
\describe{
\item{\code{mar_distr}}{integer.
This argument should not be changed yet.
It codes the marginal distribution used in the
simulation:
0 : uniform distribution\cr
1 : Frechet distribution with form argument \code{mar.param}\cr
2 : Bernoulli distribution (Binomial with \eqn{n=1}) with
argument \code{mar.param}
Default: \code{0} .
}
\item{\code{mar_param}}{Argument used for the marginal
distribution. The argument should not be changed yet.
Default: \code{NA} .
}
\item{\code{maxlines}}{integer.
Maximum number of allowed lines.
Default: \code{1000} .
}
\item{\code{superpos}}{integer.
number of superposed hyperplane tessellations.
Default: \code{300} .
}
}
\bold{13. \code{krige}: Options for Kriging}\cr
\describe{
\item{\code{cholesky_R}}{
if \code{TRUE} cholesky decomposition is used instead of LU.
Default: \code{FALSE}.
}
\item{\code{fillall}}{
logical value for imputing.
If true all the components are estimated whether they are
\code{NA} or not.
Default: \code{TRUE}.
}
\item{\code{locmaxn}}{
Kriging is conditions on maximal \code{locmaxn} points.
If the data contain more points, neighbourhood kriging is performed.
Default: \code{8000}.
}
\item{\code{locsplitfactor}}{
In case of neighbourhood kriging, the area is split into small
boxes. The complete neighbourhood contains (2 *
\code{locsplitfactor} +1) boxes in each direction.
Default: \code{2}.
}
\item{\code{locsplitn}}{vector of 3 components.
A box should contain no more than \code{locsplitn[1]}
points, but never less than \code{locsplitn[2]}. If
a box had originally less than \code{locsplitn[2]} points,
then the box is increased until at least \code{locsplitn[3]}
points are in the box.
Default: \code{c(5000, 200, 1000)}.
}
\item{\code{method}}{
\code{'S'} : simple kriging\cr
\code{'O'} : ordinary kriging\cr
\code{'M'} : kriging the mean\cr
\code{'U'} : universal kriging\cr
\code{'I'} : intrinsic kriging\cr
\code{'A'} : automatic choice determined on the given
model. In some situation an automatic choice is not possible and
the \code{method} must be given explicitely.
Default: \code{'A'}.
}
\item{\code{return.variance}}{logical.
If \code{FALSE} the kriged field is
returned. If \code{TRUE} a list of two elements, \code{estim} and
\code{var}, i.e. the kriged field and the kriging variances,
is returned.
Default: \code{FALSE}.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{14. \code{maxstable}: Options for simulating max-stable random fields}
\describe{
\item{\code{check_every}}{
integer. In order to get a precise simulation result, by definition,
the maximum must be taken, for each shape function, over alle
locations of interest. Clearly, small values will not play a role.
To this end, the global minimum has to be determined.
The calculation of the global minimum is expensive and therefor
should not be done too frequently. On the other hand,
rare updates increases the computing times for taking the maximum
over a single shape functions. Here, after every \code{check_every}
considered shape function, the global minimum is calculated.
It is expected that a good choice for \code{check_every} is in
in the interval \eqn{[10, 100]}.
(For ease and for concerns of efficiency, the more adequate, local
minimum is not considered.)
Default: 30 .
}
\item{\code{density_ratio}}{
value in \eqn{[0,1]}. This argument is considered only
if \code{flat=-1} and the simulation is performed on a grid.
Then, the ratio between the highest and the lowest value is
calculated within the convex hull of the grid. If the
value is less than \code{density_ratio} then the grid points
are considered separately. Else the density is considered to be
constant in the convex hull of the grid.
Default: 0.0.
}
\item{\code{eps_zhou}}{positive real number, which
gives the aimed relative precision.
E.g. if \code{eps_zhou=0.01} then the first 2 digits should be
correct.
Default: 0.01
}
\item{\code{flat}}{\code{-1, FALSE, TRUE}.
The argument is considered only if the simulation is performed on a
grid.
If \code{flat} is logical, then the density
is considered to \code{flat} in the convex hull of the grid.
If \code{flat=-1} the choice is done automatically.
Default: -1 .
}
\item{\code{max_gauss}}{
The simulation of the max-stable process based on random fields uses
a stopping rule that necessarily needs a finite upper endpoint
of the marginal distribution of the random field.
In the case of Brown-Resnick processes and extremal Gaussian fields
(\command{\link{RPbrownresnick}} and
\command{\link{RPschlather}}, respectively), the upper endpoint is
approximated by \code{standardmax}.
Default: \code{3.0} .
}
\item{\code{max_n_zhou}}{positive integer.
The overall constant \eqn{c} in the paper of
Oesting, Schlather, Zhou (2014) has to be determined
by MCMC, if the shape functions are random.
The two arguments, \code{min_n_zhou} and \code{max_n_zhou},
give the minimal and the maximal
number of simulations that are performed. To economize
computer time the values of \eqn{c} is partially estimated
when the shape functions are simulated. If the number
of shape functions is larger than the number of simulations
given by \code{eps_zhou} then
no further simulation is performed to determine \eqn{c}.
Default: 1000 and 10000000, respectively.
}
\item{\code{maxpoints}}{
positive integer; the maximal number of Poisson points to be simulated
for one realization of the max-stable random field. This option will
not be considered for most of the users.
Default: \code{2e9} .
}
\item{\code{mcmc_zhou}}{positive integer.
In case of random shape functions, an MCMC step is required.
\code{mcmc_zhou}-1 equals the number of members of the MCMC chain
that are left out before the next value of the chain is returned.
Default: 20
}
\item{\code{min_n_zhou}}{see \code{max_n_zhou}}
\item{\code{xi}}{
Extreme value index.
Default: \code{1.0} .
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{15. \code{mpp}: Options for the random coins (shot noise) methods}\cr
\describe{
\item{\code{n_estim_E}}{integer. Number of draws from the
distribution of the scale to estimate the mean of the distribution.
This is used only if the mean of the scale distribution
is not explicitely given.
Default: \code{50000} .
}
\item{\code{intensity}}{real.
Number of superposed
realisations (to approximate the normal distribution; total number
for all (additive) components with same anisotropy);
if \code{intensity<=0.0} then only a single
value is simulated (for checking).
Default: \code{100} .
}
\item{\code{about_zero}}{
In certain cases (\link{Coins},\link{RMtruncsupport}),
functions are assumed to zero if the value is less than \code{about_zero}.
Default: \code{0.001} .
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{16. \code{nugget}: Options for the nugget effect}\cr
Simulating a nugget effect is per se trivial.
However, it gets complicated
and best methods (including \code{direct} and \code{circulant
embedding}!) fail if zonal anisotropies are considered,
where sets of points have to be identified that belong to the
same subspace of eigenvalue 0 of the anisotropy matrix.
\describe{
\item{\code{tol}}{
see \command{\link{RPnugget}}
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{17. \code{registers}: Register numbers}\cr
Model for different purposes are or can be stored at
different places. They are called registers and have non-negative
numbers up to 21 (currently). Some of these predefined registers
are given here. Except for \code{register} it is hard to see why users
should change the values.
\describe{
\item{\code{condregister}}{Register number for conditional simulation
Default: \code{20} [also do].
}
\item{\code{errregister}}{Additional register for conditional
simulation if error term is envolved.
Default: \code{21} [also do].
}
\item{\code{guiregister}}{Register number for \command{\link{RFgui}}.
Default: \code{14} [also do].
}
\item{\code{interpolregister}}{Register number for kriging
Default: \code{19} [also do].
}
\item{\code{register}}{0:9; place where intermediate calculation
for random field simulation are stored;
the number refers to 10 internal registers 0..9.
Changing the register number only makes sense, when
two different random fields, say, are to simulated in
alternatingly, several times in a row. Then the
simlulation speed can be increased if several registers
are used and \code{storing=TRUE}.
Default: \code{0} [also do].
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{18. \code{sequ}: Options for the sequential method}\cr
\describe{
\item{\code{back_steps}}{See \command{\link{RPsequential}}}
\item{\code{initial}}{See \command{\link{RPsequential}}}
\item{\code{max_variables}}{See \command{\link{RPsequential}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{19. \code{special}: Options for the turning bands method}\cr
\describe{
\item{\code{multicopies}}{
The covariance functions are multiplied if the corresponding
independent random fields are multiplied. To get
an approximative Gaussian random fields with a multiplicative
covariance functions the average over \code{multicopies}
products of random fields is calculated.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{20. \code{spectral}: Options for the spectral (turning bands) method}\cr
\describe{
\item{\code{ergodic}}{
In case of an additive model and \code{ergodic=FALSE},
the additive component are chosen proportional to their
variance. In total \code{lines} are simulated. If
\code{ergodic=TRUE}, the components are simulated
separately and then added.
Default: \code{FALSE} .
}
\item{\code{prop_factor}}{see \command{\link{RPspectral}}
}
\item{\code{sigma}}{see \command{\link{RPspectral}}
}
\item{\code{sp_grid}}{ see \command{\link{RPspectral}}
}
\item{\code{sp_lines}}{
see \command{\link{RPspectral}}
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{21. \code{tbm}: Options for the turning bands method}\cr
\describe{
\item{\code{center}}{Scalar or vector.
If not \code{NA}, the \code{center} is used as the center of
the turning bands for \code{TBM2} and \code{TBM3}.
Otherwise the center is determined
automatically such that the line length is minimal.
See also \code{points} and the examples below.
Default: \code{NA} .
}
\item{\code{fulldim}}{
positiv integer.
}
\item{\code{grid}}{Logical.
The angle of the lines is random if
\code{grid=FALSE},
and \eqn{k\pi/}{k*pi/}\code{lines}
for \eqn{k}{k} in \code{1:lines},
otherwise. This argument is only considered
ift he spectral measure, not the density is used.
Default: \code{TRUE} .
}
\item{\code{layers}}{
Logical or integer. If \code{TRUE} then the turning layers are used whenever
a time component is given.
If \code{NA} the turning layers are used only when the
traditional TBM is not applicable.
If \code{FALSE} then turning layers may never be used.
Default: \code{TRUE} .
}
\item{\code{lines}}{
Number of lines used.
Default: \code{60} .
}
\item{\code{linesimustep}}{
If \code{linesimustep} is positive the grid on the line has lag
\code{linesimustep}.
See also \code{linesimufactor}.
Default: \code{0.0} .
}
\item{\code{linesimufactor}}{ \code{linesimufactor} or
\code{linesimustep} must be non-negative; if
\code{linesimustep}
is positive then \code{linesimufactor} is ignored.
If both
arguments are naught then \code{points} is used (and must be
positive).
The grid on the line is \code{linesimufactor}-times
finer than the smallest distance.
See also \code{linesimustep}.
Default: \code{2.0} .
}
\item{\code{points}}{integer. If greater than 0,
\code{points} gives the number of points simulated on the TBM
line, hence
must be greater than the minimal number of points given by
the size of the simulated field and the two paramters
\code{TBMx.linesimufactor} and \code{TBMx.linesimustep}.
If \code{points} is not positive the number of points is
determined automatically.
The use of \code{center} and \code{points} is highlighted
in an example below.
Default: \code{0} .
}
\item{\code{tbmdim}}{
if positiv integer, then the value itself. If negativ, then
the value is substracted from fulldim.
}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bold{22. \code{internal}: Internal options mostly for warnings and messages}\cr
\describe{
\item{\code{stored.init}}{internally used logical argument.
\code{general.storing} controls whether intermediate calculations
should be stored to have faster repeated simulations.
This user option is internally overwritten if the user calls several
simulation at once. This current value is stored in \code{stored.init}.
Default: \code{TRUE}.
}
\item{\code{warn_ambiguous}}{internally used logical argument.
Usually, the argument \code{grid} in \command{\link{RFsimulate}},
for instance, should be given. If not, the system usually guesses
correctly what was meant by the user, and displays a message if
\code{ambiguous=TRUE}.
Default: \code{TRUE}.
}
\item{\code{warn_aspect_ratio}}{internally used logical argument.
if \code{TRUE} then a warning is given not a standard graphical
device is used and the package plots try to keep a certain aspect
ratio.
Default: \code{TRUE}
}
\item{\code{warn_colour_palette}}{internally used logical argument.
If none of the packages \pkg{RColorBrewer} and \pkg{colorspace}
are available and graphics are displayed, a message is displayed.
Default: \code{TRUE}.
}
\item{\code{warn_coordinates}}{internally used logical argument.
If \code{TRUE} then a transformation from earth coordinates to
cartesian coordinates is reported.
Default: \code{TRUE}.
}
\item{\code{warn_newAniso}}{internally used logical argument.
If \code{newAniso=TRUE} and the argument \code{Aniso} is used in the model
definition, then a message is displayed that the matrix \code{Aniso}
is multiplied from the right by \eqn{x}, where up to Version 2.0
the argument \code{aniso} was available which was multiplied from
the left by \eqn{x}.
Default: \code{TRUE}.
}
\item{\code{warn_new_definitions}}{internally used logical argument.
If \code{warn_new_defintions=TRUE} then a warning is returned when
models are used whose definition has changed recently.
Default: \code{TRUE}.
}
\item{\code{warn_newstyle}}{internally used logical argument.
If \code{TRUE} a message is displayed the by the argument
\code{spConform=FALSE} oldstyle return values are obtained instead
of S4 objects.
Default: \code{TRUE}.
}
\item{\code{warn_normal_mode}}{internally used logical argument.
if \code{TRUE} then the function \command{\link{RFfit}}
displays the message that other values for the option
\code{modus_operandi} are available.
Default: \code{TRUE}.
}
\item{\code{warn_scale}}{internally used logical argument.
If \code{warn_scale=TRUE} then a scale less than 10 [km] is reported
if earth coordinates are transformed to cartesian coordinates.
Default: \code{TRUE}.
}
\item{\code{warn_oldstyle}}{internally used logical argument.
If \code{TRUE} a warning is given if an obsolete function
from Version 2 is used.
Default: \code{TRUE}.
}
\item{\code{warn_on_grid}}{internally used logical argument.
If a (one-dimensional) grid is given, but the argument
\code{grid=FALSE}, e.g. in \code{RFsimulate}, this contraction is
reported if \code{warn_on_grid=TRUE}
Default: \code{TRUE}.
}
}
\bold{General comments}\cr
Most of the global arguments determine the basic settings of a
simulation, e.g. \code{root_method} (which chooses the method to
calculate a square
root of a positive definite matrix). The values of
such arguments are read by
\command{\link{RFsimulate}} and stored in an internal register
and used in preliminary, deterministic calculations (e.g. calculation
of the square of a covariance matrix)
Some few other arguments like \code{lines} (which determines the number of
i.i.d. processes to be simulated on the line)
are only relevant when generating
random numbers.
These arguments are read only when actually simulations are performed, and
are marked by \dQuote{[do]}.
The difference of these two classes of arguments might become
important only if \code{general.storing = TRUE} and
\command{\link{RFsimulate}} is called only if with the argument
\code{n}.
}
\value{
\code{NULL} if any argument is given, and the full list of
arguments, otherwise.
if \code{no.readonly=FALSE} then additionally, the last element
of the list is itself a list containing
\cr
* \code{covnr}: number of currently implemented
variogram/covariance models (-1 means that none of the functions like
\command{\link{RFsimulate}}, \command{\link{RFfit}}
, etc., have been called yet.)
\cr
* \code{distrmaxchar}: max. name length for a distribution
\cr
* \code{distrnr}: number of currently implemented
distributions
\cr
* \code{maxdim}: maximum number of dimensions for a
random field
\cr
* \code{maxmodels}: maximum number of elementary models in
definition of a complex covariance model
\cr
* \code{methodmaxchar}: max. name length for methods
\cr
* \code{methodnr}: number of currently implemented simulation methods
}
\references{
Schlather, M. (1999) \emph{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.
Oesting, M., Schlather, M. and Zhou, C. (2013) On the Normalized Spectral Representation of Max-Stable Processes on a compact set.
\emph{arXiv}, \bold{1310.1813}
}
\author{Martin Schlather, \email{schlather@math.uni-mannheim.de}
\url{http://ms.math.uni-mannheim.de/de/publications/software}}
\seealso{\command{\link{RFsimulate}},
\link{RFoptionsAdvanced},
\code{\link[=RandomFields-package]{RandomFields}},
and \command{\link{RFgetMethodNames}}.}
\examples{
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
RFoptions()
############################################################
## ##
## use of exactness ##
## ##
############################################################
x <- seq(0, 1, if (interactive()) 1/30 else 0.5)
model <- RMgauss()
for (exactness in c(NA, FALSE, TRUE)) {
readline(paste("\n\nexactness: `", exactness, "'; press return"))
z <- RFsimulate(model, x, x, exactness=exactness,
stationary_only=NA, storing=TRUE)
print(RFgetModelInfo(which="internal")$internal$name)
}
\dontshow{FinalizeExample()}
}
\keyword{spatial}
