cocoSim {cocons} | R Documentation |
draw realizations of nonstationary Gaussian processes with covariate-based covariance functions.
cocoSim(coco.object, pars, n, seed, standardize,
type = 'classic', sim.type = NULL, cond.info = NULL)
coco.object |
( |
pars |
( |
n |
( |
seed |
( |
standardize |
( |
type |
( |
sim.type |
( |
cond.info |
( |
'cond'
sim.type requires specifying in 'cond.info'
a list with 'newdataset'
a data.frame containing covariates present in model.list at simulation locations,
and 'newlocs'
a matrix with locations related to the simulation locations, matching indexing of 'newdataset'
.
type = 'classic'
assumes a simpler parameterization for the covariance function, assuming log-parameterizations for 'std.dev'
, 'scale'
, and 'smooth'
.
(matrix
) a matrix n x dim(data)[1].
Federico Blasi
## Not run:
model.list <- list('mean' = 0,
'std.dev' = formula( ~ 1 + cov_x + cov_y),
'scale' = formula( ~ 1 + cov_x + cov_y),
'aniso' = 0,
'tilt' = 0,
'smooth' = 0.5,
'nugget' = -Inf)
coco_object <- coco(type = 'dense',
data = holes[[1]][1:1000,],
locs = as.matrix(holes[[1]][1:1000,1:2]),
z = holes[[1]][1:1000,]$z,
model.list = model.list)
coco_sim <- cocoSim(coco.object = coco_object,
pars = c(0,0.25,0.25, # pars related to std.dev
log(0.25),1,-1), # pars related to scale
n = 1,
standardize = TRUE)
fields::quilt.plot(coco_object@locs,coco_sim)
## End(Not run)