RandomFields (version 2.0.71)

CheckXT: Internal functions -- do not use them directly

Description

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

plotWithCircles displays data values of marked point processes by circles

GetDistributionNames returns the names of the currently available marginal distributions of the random fields GetrfParameters returns some internal constants

Usage

CheckXT(x, y, z, T, grid, gridtriple)
PrepareModel(model, param, trend, method=NULL, nugget.remove=TRUE)
plotWithCircles(data, factor=1.0, xlim=range(data[,1])+c(-maxr,maxr),
                ylim=range(data[,2])+c(-maxr,maxr),col=1, fill=0, ...)
GetDistributionNames()
GetrfParameters(initcov)

Arguments

x

x coordinates

y

y coordinates

z

z coordinates

T

time instances

grid

logical; simulation on a grid; see GaussRF

gridtriple

logical; grid is given in abbreviated form; see GaussRF

model

name of the covariance model; see GaussRF

param

parameter for the covariance model; see GaussRF

trend

mean or trend of the random field

method

simulation method

nugget.remove

if nugget is 0, the nugget part be removed in the returned model?

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 in plotWithCircles and any variables to be printed in Print

initcov

boolean, whether to call the C function InitModelList

See Also

CovarianceFct

Examples

model <- list(model="whittle", param=c(1,2,3,4,5))
Print(PrepareModel(model=model, me="ci"))

model <- list("+",
              list("$", var=2, s=4, list(model="whi", kappa=5)),
              list("$", var=3, s=0, list(model="whi", kappa=1)) )
Print(PrepareModel(model=model, me="ci"))