Predict {gmGeostats} | R Documentation |
Predict method for objects of class 'gmSpatialModel'
Description
This is a one-entry function for several spatial prediction and simulation methods, for model objects
of class gmSpatialModel. The several methods are chosen by means of pars
objects of the
appropriate class.
Usage
Predict(object, newdata, pars, ...)
predict(object, ...)
## S3 method for class 'gmSpatialModel'
predict(object, newdata, pars = NULL, ...)
## S4 method for signature 'gmSpatialModel'
predict(object, newdata, pars = NULL, ...)
## S4 method for signature 'gmSpatialModel,ANY,ANY'
Predict(object, newdata, pars, ...)
## S4 method for signature 'gmSpatialModel,ANY,gmNeighbourhoodSpecification'
Predict(object, newdata, pars, ...)
## S4 method for signature 'gmSpatialModel,ANY,gmTurningBands'
Predict(object, newdata, pars, ...)
## S4 method for signature 'gmSpatialModel,ANY,gmCholeskyDecomposition'
Predict(object, newdata, pars, ...)
## S4 method for signature 'gmSpatialModel,ANY,gmSequentialSimulation'
Predict(object, newdata, pars, ...)
## S4 method for signature 'gmSpatialModel,ANY,gmDirectSamplingParameters'
Predict(object, newdata, pars, ...)
Arguments
object |
a complete "gmSpatialModel", containing conditioning data and unconditional model |
newdata |
a collection of locations where a prediction/simulation is desired; this is typically
a |
pars |
parameters describing the method to use, enclosed in an object of appropriate class (according to the method; see below) |
... |
further parameters for generic functionality, currently ignored |
Details
Package "gmGeostats" aims at providing a broad series of algorithms for geostatistical prediction
and simulation. All can be accesses through this interface, provided that arguments object
and pars
are of the
appropriate kind. In object
, the most important criterion is the nature of its slot model
. In pars
its class counts: for the creation of informative parameters in the appropriate format and class, a series
of accessory functions are provided as well.
Classical (gaussian-based two-point) geostatistics are obtained if object@model
contains a covariance function,
or a variogram model. Argument pars
can be created with functions such as KrigingNeighbourhood()
,
SequentialSimulation()
, TurningBands()
or CholeskyDecomposition()
to respectively trigger a cokriging, as
sequential Gaussian simulation, a turning bands simulation, or a simulation via Cholesky decomposition.
The kriging neighbourhood can as well be incorporated in the "gmSpatialModel" object
directly, or even be
nested in a "SequentialSimulation" parameter object.
Conversely, to run a multipoint geostatistics algorithm, the first condition is that object@model
contains a
training image. Additionally, pars
must describe the characteristics of the algorithm to use. Currently, only
direct sampling is available: it can be obtained by providing some parameter object created with a call to
DirectSamplingParameters()
. This method requires newdata
to be on a gridded set of locations (acceptable
object classes are sp::gridTopology
, sp::SpatialGrid
, sp::SpatialPixels
, sp::SpatialPoints
or data.frame
,
for the last two a forced conversion to a grid will be attempted).
Value
Depending on the nature of newdata
, the result will be a data container of the same kind,
extended with the predictions or simulations. For instance, if we want to obtain predictions on the
locations of a "SpatialPoints", the result will be a sp::SpatialPointsDataFrame()
; if we want to obtain
simulations on the coordinates provided by a "data.frame", the result will be a DataFrameStack()
with
the spatial coordinates stored as an extra attribute; or if the input for a simulation is a masked grid of class
sp::SpatialPixels()
, the result will be of class sp::SpatialPixelsDataFrame()
which data
slot will be
a DataFrameStack.
See Also
Other gmSpatialModel:
as.gmSpatialModel()
,
gmSpatialModel-class
,
make.gmCompositionalGaussianSpatialModel()
,
make.gmCompositionalMPSSpatialModel()
,
make.gmMultivariateGaussianSpatialModel()