gmSpatialModel-class {gmGeostats} | 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
bbox
proj4string
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")