Ordinary global Spatio-Temporal Kriging
Function for ordinary global and local and trans Gaussian spatio-temporal kriging on point support
krigeST(formula, data, newdata, modelList, beta, y, ...,
nmax = Inf, stAni = NULL,
computeVar = FALSE, fullCovariance = FALSE,
bufferNmax=2, progress=TRUE)
krigeSTTg(formula, data, newdata, modelList, y, nmax=Inf, stAni=NULL,
bufferNmax=2, progress=TRUE, lambda = 0)formula |
formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name
|
data |
ST object: should contain the dependent variable and independent variables. |
newdata |
ST object with prediction/simulation locations in space and time; should contain attribute columns with the independent variables (if present). |
modelList |
object of class |
y |
matrix; to krige multiple fields in a single step, pass data
as columns of matrix |
beta |
The (known) mean for simple kriging. |
nmax |
The maximum number of neighbouring locations for a spatio-temporal local neighbourhood |
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). |
... |
further arguments used for instance to pass the model into vgmAreaST for area-to-point kriging |
computeVar |
logical; if TRUE, prediction variances will be returned |
fullCovariance |
logical; if FALSE a vector with prediction variances will be returned, if TRUE the full covariance matrix of all predictions will be returned |
bufferNmax |
factor with which nmax is multiplied for an extended search radius (default=2). Set to 1 for no extension of the search radius. |
progress |
whether a progress bar shall be printed for local spatio-temporal kriging; default=TRUE |
lambda |
The value of lambda used in the box-cox transformation. |
Function krigeST is a R implementation of the kriging function from
gstat using spatio-temporal covariance models following the
implementation of krige0. Function 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.
An object of the same class as newdata (deriving from
ST). Attributes columns contain prediction and prediction
variance.
Edzer Pebesma, Benedikt Graeler
Spatio-Temporal Geostatistics using gstat. Benedikt Graeler, Edzer Pebesma, Gerard Heuvelink. The R Journal, accepted.
N.A.C. Cressie, 1993, Statistics for Spatial Data, Wiley.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers \& Geosciences, 30: 683-691.
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)
suppressWarnings(proj4string(stations) <- CRS(proj4string(stations)))
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.