https://github.com/cran/RandomFields
Revision 84ac7774b2385ab164ebf06b957ce2e61978c2e3 authored by Martin Schlather on 02 February 2015, 00:00:00 UTC, committed by Gabor Csardi on 02 February 2015, 00:00:00 UTC
1 parent bc905e7
Tip revision: 84ac7774b2385ab164ebf06b957ce2e61978c2e3 authored by Martin Schlather on 02 February 2015, 00:00:00 UTC
version 3.0.62
version 3.0.62
Tip revision: 84ac777
datasets.R
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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