spatialPredict {intamap} | R Documentation |
Spatial prediction
Description
spatialPredict
is a generic method for spatial predictions
within the intamap-package
.
A series of methods have been implemented,
partly based on other R-packages (as krige
),
other methods have been developed particularly for the INTAMAP project. The object
has to include a range of variables, further described in
intamap-package
. The prediction method is
chosen based on the class of the object.
Usage
## S3 method for class 'automap'
spatialPredict(object, nsim = 0, ...)
## S3 method for class 'copula'
spatialPredict(object, ...)
## Default S3 method:
spatialPredict(object, ...)
## S3 method for class 'idw'
spatialPredict(object, ...)
## S3 method for class 'linearVariogram'
spatialPredict(object, nsim = 0, ...)
## S3 method for class 'transGaussian'
spatialPredict(object, nsim = 0, ...)
## S3 method for class 'yamamoto'
spatialPredict(object, nsim = 0, ...)
Arguments
object |
a list object. Most arguments necessary for interpolation
are passed through this object. See |
nsim |
number of simulations to return, for methods able to return simulations |
... |
other arguments that will be passed to the requested interpolation method. See the individual interpolation methods for more information. |
Details
The function spatialPredict
is a wrapper around different
spatial interpolation methods found within the intamap-package
or within other packages
in R
. It is for most of the
methods necessary to have parameters of the correlation structure
included in object
to be able to carry out the spatial prediction.
Below are some details
about particular interpolation methods
default
a default method is not really implemented, this function is only created to give a sensible error message if the function is called with an object for which no method exist
automap
If the object already has an element
variogramModel
with variogram parameters,krige
is called. If the this is not a part of the object,estimateParameters
is called to create this element.copula
spatial prediction using
bayesCopula
idw
applies inverse distance modelling with the idp-power found by
estimateParameters.idw
linearVariogram
this function estimates the process using an unfitted linear variogram; although variance is returned it can not be relied upon
transGaussian
spatial prediction using
krigeTg
yamamoto
spatial prediction using
yamamotoKrige
It is also possible to add to the above methods with functionality from other packages, if wanted. You can also check which methods are available from other packages by calling
>methods(spatialPredict)
Value
a list object similar to object
, but extended with predictions at
a the set of locations defined object
.
Author(s)
Jon Olav Skoien
References
Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B.M., Hristopulos, D., Pilz, J., Stohlker, U., Morin, G., Skoien, J.O. INTAMAP: The design and implementation f an interoperable automated interpolation Web Service. Computers and Geosciences 37 (3), 2011.
See Also
gstat
,autoKrige
,
createIntamapObject
, estimateParameters
,
intamap-package
Examples
# This example skips some steps that might be necessary for more complicated
# tasks, such as estimateParameters and pre- and postProcessing of the data
data(meuse)
coordinates(meuse) = ~x+y
meuse$value = log(meuse$zinc)
data(meuse.grid)
gridded(meuse.grid) = ~x+y
proj4string(meuse) = CRS("+init=epsg:28992")
proj4string(meuse.grid) = CRS("+init=epsg:28992")
# set up intamap object:
obj = createIntamapObject(
observations = meuse,
predictionLocations = meuse.grid,
targetCRS = "+init=epsg:3035",
params = getIntamapParams(),
class = "linearVariogram"
)
# do interpolation step:
obj = spatialPredict(obj) # spatialPredict.linearVariogram