Scale model for a few areas of different scales and/or differentiabilities
Let Z=(Z_1, … Z_k) be an k-variate random field and A_1,…, A_k a partition of the space. Then
Y(x) = ∑_{i=1}^k Z_i * 1(x \in A_i)
i.e. the model blends the components of Z to a new, univariate model Y.
RMblend(multi, blend, thresholds, var, scale, Aniso, proj)
multi |
a multivariate covariance function |
blend,thresholds |
The Default: |
var,scale,Aniso,proj |
optional arguments; same meaning for any
|
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
Bonat, W.H. , Ribeiro, P. Jr. and Schlather, M. (2019) Modelling non-stationarity in scale. In preparation.
Genton, Apanovich Biometrika.
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
x <- seq(0,1, if (interactive()) 0.01 else 0.5)
len <- length(x)
m <- matrix(1:len, nc=len, nr=len)
m <- m > t(m)
image(m) # two areas separated by the first bisector
biwm <- RMbiwm(nudiag=c(0.3, 1), nured=1, rhored=1, cdiag=c(1, 1),
s=c(1, 1, 0.5))
model <- RMblend(multi=biwm, blend=RMcovariate(data = as.double(m), raw=TRUE))
plot(z <- RFsimulate(model, x, x)) ## takes a while ...Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.