object of class RMmodel;
specifies the covariance model to be simulated.
boxcox
the one or two parameters of the box cox transformation.
If not given, the globally defined parameters are used.
see RFboxcox for Details.
max_variab
integer less than 30000.
If the number of variables to generate is
greater than max_variab, neither SVD nor Cholesky decomposition
are performed. If the given covariance structure has finite range and
use_spam != false then spam is tried.
It is important that this option is set
conveniently to avoid great losses of time during the internal
search of an appropriate method by RPgauss.
Default: 8192
max_variables
The maximum size of the conditional covariance matrix
(default to 5000)
back_steps
Number of previous instances on which
the algorithm should condition.
If less than one then the number of previous instances
equals max / (number of spatial points).
Default: 10 .
initial
First, N=(number of spatial points) * back_steps
number of points are simulated. Then, sequentially,
all spatial points for the next time instance
are simulated at once, based on the previous back_steps
instances. The distribution of the first N points
is the correct distribution, but
differs, in general, from the distribution of the sequentially
simulated variables. We prefer here to have the same distribution
all over (although only approximatively the correct one),
hence do some initial sequential steps first.
If initial is non-negative, then initial
first steps are performed.
If initial is negative, then
back_steps - initial
initial steps are performed. The latter ensures that
none of the very first N variables are returned.
Default: -10 .
Details
RPdirect
is based on the well-known method for simulating
any multivariate Gaussian distribution, using the square root of the
covariance matrix. The method is pretty slow and limited to
about 8000 points, i.e. a 20x20x20 grid in three dimensions.
This implementation can use the Cholesky decomposition and
the singular value decomposition.
It allows for arbitrary points and arbitrary grids.
RPsequential
is programmed for spatio-temporal models
where the field is modelled sequentially in the time direction
conditioned on the previous k instances.
For k=5 the method has its limits for about 1000 spatial
points. It is an approximative method. The larger k the
better.
It also works for certain grids where the last dimension should
contain the highest number of grid points.
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.
See Also
Gaussian,
RP,
Examples
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
model <- RMgauss(var=10, s=10) + RMnugget(var=0.01)
plot(model, xlim=c(-25, 25))
z <- RFsimulate(model=RPdirect(model), 0:10, 0:10, n=4)
plot(z)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(RandomFields)
Loading required package: sp
Loading required package: RandomFieldsUtils
This is RandomFieldsUtils Version: 0.2.1
This is RandomFields Version: 3.1.16
Attaching package: 'RandomFields'
The following object is masked from 'package:RandomFieldsUtils':
RFoptions
The following objects are masked from 'package:base':
abs, acosh, asin, asinh, atan, atan2, atanh, cos, cosh, exp, expm1,
floor, gamma, lgamma, log, log1p, log2, logb, max, min, round, sin,
sinh, sqrt, tan, tanh, trunc
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RandomFields/RPsequential.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Square roots
> ### Title: Methods relying on square roots of the covariance matrix
> ### Aliases: Direct RPdirect Sequential RPsequential
> ### Keywords: methods
>
> ### ** Examples
>
> RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
> ## RFoptions(seed=NA) to make them all random again
> model <- RMgauss(var=10, s=10) + RMnugget(var=0.01)
> plot(model, xlim=c(-25, 25))
NULL
>
> z <- RFsimulate(model=RPdirect(model), 0:10, 0:10, n=4)
NOTE: simulation is performed with fixed random seed 0.
Set 'RFoptions(seed=NA)' to make the seed arbitrary.
....
New output format of RFsimulate: S4 object of class 'RFsp';
for a bare, but faster array format use 'RFoptions(spConform=FALSE)'.
> plot(z)
>
> ## Don't show:
> FinalizeExample()
> ## End(Don't show)
>
>
>
>
>
> dev.off()
null device
1
>