Description Usage Arguments Details Value Author(s) References See Also Examples
Function for ordinary global spatio-temporal kriging on point support
1 2 3 |
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 |
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 (currently unused) |
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 |
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
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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)
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