spatialPredict {intamap} | R Documentation |
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.
## 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, ...)
object |
a list object. Most arguments necessary for interpolation
are passed through this object. See intamap-package for further
description of the necessary content of this variable |
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. |
The function spatialPredict
is a wrapper around different
spatial interpolation methods found within the link{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
automap
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
bayesCopula
idw
estimateParameters.idw
linearVariogram
transGaussian
krigeTg
yamamoto
yamamotoKrige
It is also possible to add to the above methods with functionality from other packages, if wanted. See description on http://www.intamap.org/newMethods.php You can also check which methods are available from other packages by calling
>methods(spatialPredict)
a list object similar to object
, but extended with predictions at
a the set of locations defined object
.
Jon Olav Skoien
gstat
,autoKrige
,
createIntamapObject
, estimateParameters
,
intamap-package
# 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