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## Authors
## Martin Schlather, schlather@math.uni-mannheim.de
##
##
## Copyright (C) 2017 -- 2017 Martin Schlather
##
## This program is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License
## as published by the Free Software Foundation; either version 3
## of the License, or (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
CHOICE <- c("low", "medium", "high")
TAILCHOICE <- c("compact", "exponential", "power")
UNIVARIATE <- 1
## make sure that the results can be reobtained in the future
## -> use explicite the method for simiulation, not RPgauss
## get peoples suggestion on nu(x) and also other
## non-stationary cov fcts
## can covariates be involved?
## non-Gaussian marginal distribution: empirically based; Box-Cox based?!
## Gneiting, whittle, Cauchy, ex(?!) multivariate verallg. von cauchy?
xRFdatasets <- function(nonstationarity=CHOICE,
trendnonstationarity=CHOICE,
anisotropy=CHOICE,
differentiability=CHOICE,
tail = TAILCHOICE,
grid= c(FALSE, TRUE),
locations = CHOICE, ## or number
spacedimension=1:3,
multivariate = UNIVARIATE,
time=c(FALSE, TRUE),
holdout_points,
covariates,
trend,
n = 1,
seed = 0,
marginal= "Gaussian"
) {
res <- array(dim=c(length(nonstationarity),
length(trendnonstationarity),
length(anisotropy),
length(differentiability),
length(tail),
length(grid),
n
))
# Bsplines
}
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