gmSpatialModel-class | R Documentation |
This class is devised to contain a conditional spatial model, with: some conditioning data
(a sp::SpatialPointsDataFrame()
), an unconditional geospatial model (a structure with e.g.
a training image; or the information defining a Gaussian random field); and eventually some
extra method parameters. The class extends sp::SpatialPointsDataFrame()
and has therefore its slots,
plus model
(for the unconditional model) and parameters
(for the extra method information)
## S4 method for signature 'gmSpatialModel'
variogram(object, methodPars = NULL, ...)
## S4 method for signature 'gmSpatialModel'
logratioVariogram(data, ..., azimuth = 0, azimuth.tol = 180/length(azimuth))
## S4 method for signature 'gmSpatialModel'
as.gstat(object, ...)
object |
a gmSpatialModel object containing spatial data. |
methodPars |
(currently ignored) |
... |
further parameters to |
data |
the data container (see gmSpatialModel for details) |
azimuth |
which direction, or directions, are desired (in case of directional variogram) |
azimuth.tol |
which tolerance sould be used for directional variograms? |
You will seldom create the spatial model directly. Use instead the creators make.gm*
linked below
variogram
: Compute a variogram, see variogram_gmSpatialModel()
for details
logratioVariogram
: S4 wrapper method around logratioVariogram()
for gmSpatialModel
objects
as.gstat
: convert from gmSpatialModel to gstat; see as.gstat()
for details
data
a data.frame (or class extending it) containing the conditional data
coords
a matrix or dataframe of 2-3 columns containing the sampling locations of the conditional data
coords.nrs
see sp::SpatialPointsDataFrame()
bbox
see sp::SpatialPointsDataFrame()
proj4string
see sp::SpatialPointsDataFrame()
model
gmUnconditionalSpatialModel. Some unconditional geospatial model. It can be NULL.
parameters
gmSpatialMethodParameters. Some method parameters. It can be NULL
Other gmSpatialModel:
Predict()
,
as.gmSpatialModel()
,
make.gmCompositionalGaussianSpatialModel()
,
make.gmCompositionalMPSSpatialModel()
,
make.gmMultivariateGaussianSpatialModel()
data("jura", package="gstat")
library(sp)
X = jura.pred[,1:2]
Zc = jura.pred[,7:13]
spdf = sp::SpatialPointsDataFrame(coords=X, data=Zc)
new("gmSpatialModel", spdf)
make.gmCompositionalGaussianSpatialModel(data=Zc, coords=X, V="alr")
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