RFoptions(..., no.readonly = TRUE)tag = value form, or a list of tagged
values.no.readonly=TRUE then only rewritable arguments are returned.NULL if any argument is given, and the full list of
arguments, otherwise. if no.readonly=FALSE then additionally,
a list called readonly is included containing
* covmaxchar: the maximum length of a model name
* covnr: number of currently implemented
variogram/covariance models (-1 means that none of the functions like
distrmaxchar: max. name length for a distribution
* distrnr: number of currently implemented
distributions
* maxdim: maximum number of dimensions for a
random field
* maxmodels: maximum number of elementary models in
definition of a complex covariance model
* methodmaxchar: max. name length for methods
* methodnr: number of currently implemented simulation methods
gauss: Options for simulating Gaussian random fieldsgraphics: Options for graphical outputgui: Options for chyper: Options for simulating hyperplane tessellationskrige: Options for Krigingmaxstable: Options for simulating max-stable random fieldsmpp: Options for the random coins (shot noise) methodsnugget: Options for the nugget effectabout_zeroabout_zero.
Default: 0.001 .n_estim_E50000 .scatter_size, scatter_maxRMscatter that calculates
$\sum_{i=1}^n f(x + h_i)$ for some function $f$ and
for some distances $h_i$. Real valued and integer valued, respectively, or NA.
Let $\varepsilon=$about_zero, $s=$scatter_size and $m=$scatter_max.
We distinguish 4 cases:
scatter_size > 0andscatter_max >= 0
Here,$n$equals$(2m)^d$.
and$h_i \in M = { (k s, \ldots, k s),\ldots, (m s, \ldots, m
s)}$with$k=-m$.scatter_size > 0andscatter_max < 0
same as the previous case, but$m$is chosen such that$f(k_i e_i s_i) \approx \varepsilon$,$-k_i\in N$,$i=1,\ldots,d$and$f(m_i e_i s_i) \approx \varepsilon$,$m \in N$.scatter_size <= 0<="" code="">andscatter_max >= 0
This option is possible only for grids.
Here$h_i$runs on the given grid$i=1,\ldots,d$,
but at mostscatter_maxsteps.scatter_size <= 0<="" code="">andscatter_max < 0
this option is possible only for grids.
Here,$h_i$runs over the whole grid.shape_powershape_power before used as
intensity function for the point process.
Default: 2.0.general: General options
2. br: Options for Brown-Resnick
Fields
3. circulant: Options for circulant embedding methods
coords: Options for coordinates and units, see
coordinate systems
5. direct: Options for simulating by simple matrix decomposition
6. distr: Options for distributions, in particular empvario: Options for calculating the empirical variogram
8. fit: Options for gauss: Options for simulating Gaussian random fields
10. graphics: Options for graphical output
11. gui: Options for hyper: Options for simulating hyperplane tessellations
13. krige: Options for Kriging
14. maxstable: Options for simulating max-stable random fields
15. mpp: Options for the random coins (shot noise) methods
16. nugget: Options for the nugget effect
17. registers: Register numbers
18. sequ: Options for the sequential method
19. special: Options for some special methods
20. spectral: Options for the spectral (turning bands) method
21. tbm: Options for the turning bands method
22. internal: Internal
General comments
1. General options
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]2. Options for Brown-Resnick Fields
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] 3. circulant: Options for circulant embedding methods, cf. coords: Options for coordinates and units
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]5. direct: Options for simulating by simple matrix decomposition
[object Object],[object Object],[object Object]6. distr: Options for distributions, in particular empvario: Options for calculating the empirical variogram
[object Object],[object Object],[object Object],[object Object],[object Object] 8. fit: Options for [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Default: 'respect bound'.
refine_onborderTRUE and an estimated parameter of the model
is close to the boundary, a second search for the optimum
is started.
Default: TRUE
}
minmixedvar
Default: 1/1000
}
solvesigmasolvesigma=TRUE.This makes sense
if the number of independent variables is very small.
If solvesigma=FALSE then the variance parameter is
treated as any other parameter to be estimated.
Default: FALSE.
}
ratiotest_approxTRUE the approximative formula that twice the
difference of the likelihoods follow about a $\chi^2$
distribution is used. The parameter of freedom equals
the number of parameters to be estimated for the covariance
function, including those for the covariates.
Default: TRUE
}
reoptimiseTRUE && !only_users then at a very last step,
the optimisation is redone with currently best parameters
and likelihood as scale parameter for TRUE.
}
scale_max_relative_factorscale_max_relative_factor
a warning is given that probably a nugget effect
is present.
Note: if scale_max_relative_factor is greater
than lowerbound_scale_ls_factor then
no warning is given as
the scale has the lower bound $(minimum distance
between different pairs of points) /$
lowerbound_scale_ls_factor.
Default: 1000
}
scale_ratioparscale and fnscale
in the calls of scale_ratio,
parscale and fnscale are reset and the optimisation
is redone.
Default: 0.1.
}
shortnamelengthshortnamelength
letters.
Default: 4.
}
sillnugget and variance
separately, they may also be estimated together under the
condition that nugget + variance = sill.
For the latter a finite value for sill has to be supplied,
and nugget and variance are set to NA.
sill is only used for the standard model.
Default: NA.
}
smalldataset2000.
}
splitTRUE then TRUE.
}
splitn_neighboursmaxn
is exceeded, then split or less, but never more than
maxn.
Default: c(3000, 200, 1000).
}
splitfactor_neighbours2.
}
split_refinedTRUE then also submodels are fitted if splitted.
This takes more time, but
Default: TRUE.
}
upperbound_scale_factor
upperbound_scale_factor * (maximum distance
between all pairs of points).
Default: 3.
}
upperbound_var_factorupperbound_var_factor * var(data)
Default: 10.
}
use_naturalscalingTRUE then internally, rescaled
covariance functions will be used for which
cov(1)$\approx$0.05.
use_naturalscaling has the advantage that scale
and the form parameters of the model get use_naturalscaling does not work for all models.
Note that this argument does not influence
the output of practicalrange
in use_naturalscaling=TRUE:
Disadvantages if scaleand the shape parameter of a parameterised
covariance model can be estimated better if they are estimated
simultaneously.upperbound_scale_factorandlowerbound_scale_factor,
etc. might be more realistic.use_naturalscaling=TRUE:
Default: TRUE.
}
use_spamspam (sparse matrices)
be used for matrix calculations?
If TRUE FALSE,
it is never used. If NA its use is determined by
the size and the sparsity of the matrix.
Default: NA.
}
eps_zhoushape_powerRandomFields,
and RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
RFoptions()
############################################################
## ##
## use of exactness ##
## ##
############################################################
x <- seq(0, 1, if (interactive()) 1/30 else 0.5)
model <- RMgauss()
for (exactness in c(NA, FALSE, TRUE)) {
readline(paste("exactness: `", exactness, "'; press return"))
z <- RFsimulate(model, x, x, exactness=exactness,
stationary_only=NA, storing=TRUE)
print(RFgetModelInfo(which="internal")$internal$name)
}
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