| Kriging {RandomFields} | R Documentation |
The function allows for different methods of kriging.
Kriging(krige.method, x, y=NULL, z=NULL, grid, gridtriple=FALSE,
model, param, given, data, pch=".")
krige.method |
kriging method; currently only "S" (simple
kriging) and "O" (ordinary kriging) implemented. |
x |
(n x d) matrix or vector of x
coordinates; coordinates of n points to be kriged |
y |
vector of y coordinates. |
z |
vector of z coordinates. |
grid |
logical; determines whether the vectors x,
y, and z should be
interpreted as a grid definition, see Details. |
gridtriple |
logical. Only relevant if grid==TRUE.
If gridtriple==TRUE
then x, y, and z are of the
form c(start,end,step); if
gridtriple==FALSE then x, y, and z
must be vectors of ascending values.
|
model |
string; covariance model, see CovarianceFct, or
type PrintModelList() to get all options. |
param |
parameter vector:
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
CovarianceFct.
The value of mean must be finite
in the case of simple kriging, and is ignored otherwise. |
given |
matrix or vector of points where data are available. |
data |
the data values given at given; it might be a
vector or a matrix. If a matrix is given multivariate data are
assumed which are kriged separately. |
pch |
Kriging procedures are quite time consuming in general.
The character pch is printed after roughly
each 80th part of calculation. |
grid==FALSE : the vectors x, y,
and z are interpreted as vectors of coordinates
(grid==TRUE) && (gridtriple==FALSE) : the vectors
x, y, and z
are increasing sequences with identical lags for each sequence.
A corresponding
grid is created (as given by expand.grid).
(grid==TRUE) && (gridtriple==FALSE) : the vectors
x, y, and z
are triples of the form (start,end,step) defining a grid
(as given by expand.grid(seq(x$start,x$end,x$step),
seq(y$start,y$end,y$step),
seq(z$start,z$end,z$step)))
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 repetitions.
Martin Schlather, Martin.Schlather@uni-bayreuth.de http://www.geo.uni-bayreuth.de/~martin
Chiles, J.-P. and Delfiner, P. (1999) Geostatistics. Modeling Spatial Uncertainty. New York: Wiley.
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.
CondSimu,
CovarianceFct,
EmpiricalVariogram,
RandomFields,
## 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)