estimateParameters {intamap} | R Documentation |
Function to estimate correlation structure parameters. The actual parameters depend on the method used.
## S3 method for class 'automap': estimateParameters(object, ... ) ## S3 method for class 'copula': estimateParameters(object, ... ) ## Default S3 method: estimateParameters(object, ...) ## S3 method for class 'idw': estimateParameters(object, ... , idpRange = seq(0.1, 2.9, 0.1), nfolds = 5) ## S3 method for class 'linearVariogram': estimateParameters(object, ...) ## S3 method for class 'transGaussian': estimateParameters(object, lambda, significant = TRUE, ... ) ## S3 method for class 'yamamoto': estimateParameters(object, ... )
object |
an intamap object of the type described in intamap-package |
... |
other arguments that will be passed to the requested interpolation method. See the individual methods for more information |
idpRange |
range of idp (inverse distance weighting power) values over which to optimize mse |
nfolds |
number of folds in n-fold cross validation |
lambda |
lambda parameter for boxcox -transformation |
significant |
logical; if TRUE only transform if any of the four tests
described under interpolate are TRUE. |
The function estimateParameters
is a wrapper around different
methods for estimating correlation parameters to be used for the spatial
prediction method spatialPredict
.
Below are some details about and/or links to the different methods currently implemented
in the intamap-package
.
automap
estimateParameters
is called with an object of class automap,
autofitVariogram
will be called.
If object
already includes a variogram model when
spatialPredict
is called,
krige
in the gstat
-package will be called directly.copula
copulaEstimation
default
idw
idpRange
linearVariogram
transGaussian
krigeTg
). This means finding the best lambda
for
the boxcox
-transformation and the fitted variogram
parameters for the transformed variable. If significant = TRUE
will lambda
only be estimated
if the data show some deviation from normality, i.e., that at least one
of the tests described under interpolate
is TRUE.
yamamoto
estimateParameters.automap
, only to assure that there is a method
also for this class, difference to automap
is more important in spatialPredict
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(estimateParameters)
a list object similar to object
, but extended with correlation parameters.
Jon Olav Skoien
createIntamapObject
, spatialPredict
, intamap-package
library(intamap) set.seed(13131) # set up 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: idwObject = createIntamapObject( observations = meuse, formulaString=as.formula(zinc~1), predictionLocations = meuse.grid, class = "idw" ) # run test: checkSetup(idwObject) # do interpolation steps: idwObject = estimateParameters(idwObject, idpRange = seq(0.25,2.75,.25), nfold=3) # faster idwObject$inverseDistancePower