| CheckXT {RandomFields} | R Documentation |
CheckXT checks whether the coordinates of the data and related
parameters are specified correctly and transforms the coordinates into
a standard format
PrepareModel checks whether the parameters of the covariance
model and related parameters are specified correctly and transforms
the parameters into a standard format
convert.to.readable is the inverse function to
PrepareModel; see Details
plotWithCircles displays data values of marked point processes
by circles
GetDistributionNames returns the names of the currently
available marginal distributions of the random fields
paramextract extracts for some models some parameters
from an internal parameter list
CheckXT(x, y, z, T, grid, gridtriple)
PrepareModel(model, param, timespacedim, trend, method=NULL,
named=FALSE)
convert.to.readable(l, allowed=c("standard", "nested", "list"))
plotWithCircles(data, factor=1.0, xlim=range(data[,1])+c(-maxr,maxr),
ylim=range(data[,2])+c(-maxr,maxr),col=1, fill=0, ...)
GetDistributionNames()
paramextract(p, model=c("cutoff"))
x |
x coordinates |
y |
y coordinates |
z |
z coordinates |
T |
time instances |
grid |
see GaussRF |
gridtriple |
see GaussRF |
model |
see GaussRF |
param |
see GaussRF |
timespacedim |
dimension of the random field including the time dimension, if there is any |
trend |
mean or trend of the random field |
method |
simulation method |
named |
logical. If TRUE covnr and param
are returned with names |
l |
list as returned by PrepareModel |
allowed |
allowed output formats, see
CovarianceFct |
data |
matrix of 3 columns; first two columns give the coordinates, the third the data |
factor |
enlargement factor for data |
xlim |
see plot |
ylim |
see plot |
col |
border colour of circles |
fill |
filling colour of circles |
... |
further graphical parameters |
p |
internal parameter list; e.g. the columns of
CheckAndComplete(...)$param.
|
model |
the name of a covariance model. |
convert.to.readable is roughly speaking the inverse function to
PrepareModel. convert.to.readable also tries to
simplify the model definition, but cannot rediscover the given method for
the simulation of the nugget effect in all cases. Due to the
simplification in convert.to.readable and the special
definition of the nugget effect for nested models,
convert.to.readable may return a correct model definition in case
of incorrect input, namely if scale is set to 0 in a list
definition, see Examples.
Martin Schlather, martin.schlather@math.uni-goettingen.de http://www.stochastik.math.uni-goettingen.de/institute
x <- function(...) {
str(PrepareModel(...))
cat("--------------------------------\n")
str(convert.to.readable(PrepareModel(...)))
}
model <- list(list(model="whi", kappa=5, var=2, s=4), "+",
list(model="whi", kappa=1, var=3, s=0)) ## s=0 should not be used only in
## a model definition where the parameters are
## are given in a matrix, see the result
x(model=model, ti=1, me="ci")
## since convert.to.readable performs a one-step simplification,
## iterative calls may further simplify the model
xx <- convert.to.readable(PrepareModel(model=model, ti=1, me="ci"))
x(model=xx$mo, pa=xx$pa, ti=1, me=xx$me)
## back to the matrix definition of nested models
str(convert.to.readable(PrepareModel(xx, ti=1), allowed="nested"))
## back to the (correct) list definition
str(convert.to.readable(PrepareModel(xx, ti=1), allowed="list"))