Kriging(krige.method, x, y=NULL, z=NULL, T=NULL, grid,
gridtriple=FALSE, model, param, given, data, trend=NULL, pch=".",
return.variance=FALSE, allowdistanceZero = FALSE, cholesky=FALSE)x
coordinates; coordinates of $n$ points to be krigedy coordinates.z coordinates.x,
y, and z should be
interpreted as a grid definition, see Details.grid=TRUE.
If gridtriple=TRUE
then x, y, and z are of the
form c(start,end,step); if
gridtriple=FALSE then x() to get all options.param=c(mean, variance, nugget, scale,...);
the parameters must be given
in this order. Further parameters are to be added in case of a
parametrised class of covariance functions, see
given; it might be a
vector or a matrix. If a matrix is given multivariate data are
assumed which are kriged separately.trend is a non-negative integer (monomials
up to order k as trend functions), a list of functions or a formula (the
summands are the trend functions); you have pch is printed after roughly
each 80th part of calculation.FALSE the kriged field is
returned. If TRUE a list of two elements, estim and
var, i.e. the kriged field and the kriging variances,
is returned.TRUE then
identical locations are slightly scatteredTRUE cholesky decomposition is used instead of LU.variance.return=FALSE Kriging returns a vector or matrix
of kriged values corresponding to the
specification of x, y, z, and
grid, and data.
data: a vector or matrix with one column
* grid=FALSE. A vector of simulated values is
returned (independent of the dimension of the random field)
* grid=TRUE. An array of the dimension of the
random field is returned (according to the specification
of x, y, and z).
data: a matrix with at least two columns
* grid=FALSE. A matrix with the ncol(data) columns
is returned.
* grid=TRUE. An array of dimension
$d+1$, where $d$ is the dimension of
the random field, is returned (according to the specification
of x, y, and z). The last
dimension contains the realisations. If variance.return=TRUE a list of two elements, estim and
var, i.e. the kriged field and the kriging variances,
is returned. The format of estim is the same as described
above.
The format of var is accordingly.
grid=FALSE: the vectorsx,y,
andzare interpreted as vectors of coordinates(grid=TRUE) && (gridtriple=FALSE): the vectorsx,y, andzare increasing sequences with identical lags for each sequence.
A corresponding
grid is created (as given byexpand.grid).(grid=TRUE) && (gridtriple=TRUE): the vectorsx,y, andzare triples of the form (start,end,step) defining a grid
(as given byexpand.grid(seq(x$start,x$end,x$step),
seq(y$start,y$end,y$step),
seq(z$start,z$end,z$step)))Cressie, N.A.C. (1993) Statistics for Spatial Data. New York: Wiley. Goovaerts, P. (1997) Geostatistics for Natural Resources Evaluation. New York: Oxford University Press. Wackernagel, H. (1998) Multivariate Geostatistics. Berlin: Springer, 2nd edition.
RandomFields,###Example 1: Ordinary Kriging
## creating random variables first
## here, a grid is chosen, but does not matter
step <- 0.25
x <- seq(0,7,step)
param <- c(0,1,0,1)
model <- "exponential"
RFparameters(PracticalRange=FALSE)
p <- 1:7
points <- as.matrix(expand.grid(p,p))
data <- GaussRF(points, grid=FALSE, model=model, param=param)
## visualise generated spatial data
zlim <- c(-2.6,2.6)
colour <- rainbow(100)
image(p, p, xlim=range(x), ylim=range(x),
matrix(data,ncol=length(p)),
col=colour,zlim=zlim)
## now: kriging
krige.method <- "O" ## ordinary kriging
z <- Kriging(krige.method=krige.method,
x=x, y=x, grid=TRUE,
model=model, param=param,
given=points, data=data)
image(x,x,z,col=colour,zlim=zlim)Run the code above in your browser using DataLab