swh:1:snp:33a53053e50f7abe7d281cc0c803be827debf4a3
Tip revision: 7bcd055577ffe9bf93dad4805d1a800dce21127a authored by Edzer Pebesma on 19 June 2015, 17:44:14 UTC
version 1.0-25
version 1.0-25
Tip revision: 7bcd055
krigeST.Rd
% $Id: krige.Rd,v 1.25 2010-01-12 12:15:48 edzer Exp $
\name{krigeST}
\docType{methods}
\alias{krigeST}
\alias{krige,formula,ST-method} % calling krigeST
% \alias{idw,formula,ST-method}
\title{ Ordinary global Spatio-Temporal Kriging}
\description{
Function for ordinary global spatio-temporal kriging on point support
}
\usage{
krigeST(formula, data, newdata, modelList, y, nmax = Inf, stAni = NULL,
computeVar = FALSE, fullCovariance = FALSE,
bufferNmax=2, progress=TRUE)
}
\arguments{
\item{formula}{ formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name
\code{z}, for ordinary and simple kriging use the formula \code{z~1};
for simple kriging also define \code{beta} (see below); for universal
kriging, suppose \code{z} is linearly dependent on \code{x} and \code{y},
use the formula \code{z~x+y}}
\item{data}{ ST object: should contain the dependent variable and independent
variables.}
\item{newdata}{ ST object with prediction/simulation locations in
space and time; should contain attribute columns with the independent variables
(if present).}
\item{modelList}{ list with named elements: space, time and/or joint depending
on the spatio-temporal covariance family and an entry stModel. Currently
implemented families that may be used for stModel are \code{separable},
\code{productSum}, \code{metric}, \code{sumMetric} and \code{simpleSumMetric}.
See the examples section in \code{\link{fit.StVariogram}} or
\code{\link{variogramSurface}} for details on how to define spatio-temporal
covariance models. \code{krigeST} will look for a "temporal unit" attribute
in the provided modelList in order to adjust the temporal scales.}
\item{y}{matrix; to krige multiple fields in a single step, pass data
as columns of matrix \code{y}. This will ignore the value of the
response in \code{formula}.}
\item{nmax}{The maximum number of neighbouring locations for a spatio-temporal local neighbourhood}
\item{stAni}{a spatio-temporal anisotropy scaling assuming a metric spatio-temporal space. Used only for the selection of the closest neighbours. This scaling needs only to be provided in case the model does not have a stAni parameter, or if a different one should be used for the neighbourhood selection. Mind the correct spatial unit. Currently, no coordinate conversion is made for the neighbourhood selection (i.e. Lat and Lon require a spatio-temporal anisotropy scaling in degrees per second).}
\item{\dots}{ further arguments (currently unused)}
\item{computeVar}{logical; if TRUE, prediction variances will be returned}
\item{fullCovariance}{logical; if FALSE a vector with prediction
variances will be returned, if TRUE the full covariance matrix of
all predictions will be returned}
\item{bufferNmax}{factor with which nmax is multiplied for an extended search radius (default=2). Set to 1 for no extension of the search radius.}
\item{progress}{whether a progress bar shall be printed for local spatio-temporal kriging; default=TRUE}
}
\details{
Function \code{krigeST} is a R implementation of the kriging function from
\link{gstat} using spatio-temporal covariance models following the
implementation of \code{\link{krige0}}. Function \code{krigeST} offers some
particular methods for ordinary spatio-temporal (ST) kriging. In particular,
it does not support block kriging or kriging in a distance-based
neighbourhood, and does not provide simulation.
}
\value{
An object of the same class as \code{newdata} (deriving from
\code{\linkS4class{ST}}). Attributes columns contain prediction and prediction
variance.
}
\references{ N.A.C. Cressie, 1993, Statistics for Spatial Data,
Wiley.
\url{http://www.gstat.org/}
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package.
Computers \& Geosciences, 30: 683-691.
}
\author{ Edzer Pebesma, Benedikt Graeler }
\seealso{ \code{\link{krige0}}, \code{\link{gstat}}, \code{\link[gstat]{predict}} }
\examples{
library(sp)
library(spacetime)
sumMetricVgm <- vgmST("sumMetric",
space=vgm( 4.4, "Lin", 196.6, 3),
time =vgm( 2.2, "Lin", 1.1, 2),
joint=vgm(34.6, "Exp", 136.6, 12),
stAni=51.7)
data(air)
if (!exists("rural"))
rural = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
rr <- rural[,"2005-06-01/2005-06-03"]
rr <- as(rr,"STSDF")
x1 <- seq(from=6,to=15,by=1)
x2 <- seq(from=48,to=55,by=1)
DE_gridded <- SpatialPoints(cbind(rep(x1,length(x2)), rep(x2,each=length(x1))),
proj4string=CRS(proj4string(rr@sp)))
gridded(DE_gridded) <- TRUE
DE_pred <- STF(sp=as(DE_gridded,"SpatialPoints"), time=rr@time)
DE_kriged <- krigeST(PM10~1, data=rr, newdata=DE_pred,
modelList=sumMetricVgm)
gridded(DE_kriged@sp) <- TRUE
stplot(DE_kriged)
}
\keyword{ models }