copulaEstimation {intamap} | R Documentation |
ML-estimation of the spatial copula model parameters
Description
Estimates parameters of the spatial copula model using maximum likelihood.
Usage
copulaEstimation(obj,margin,trend,correlation,anisotropy,copula,tol=0.001,...)
Arguments
obj |
Intamap object, see description in |
margin |
list with the following elements:
|
trend |
list with the following elements:
|
correlation |
list with the following elements:
|
anisotropy |
list with the following elements:
|
copula |
list with the following elements:
|
tol |
Tolerance level for the optimization process. |
... |
Arguments to be passed to |
Details
copulaEstimation
performs maximum likelihood estimation of all possible parameters included in the Gaussian and
chi-squared spatial copula model: parameters of the predefined family of marginal distributions (including spatial trend
or external drift), correlation function parameters, parameters for geometric anisotropy and parameters for the copula
(only used for the chi-squared copula model). Due to the large number of variables that need to be optimized, a
profile-likelihood approach is used. Although convergence to a global optimum is not assured, the profile-likelihood method
makes it less likely that the optimization routine, optim
, gets stuck in a local optimum. The result of
copulaEstimation
is a list containing all parameter point estimates that are needed for plug-in spatial
prediction. It is advisable to check the output of the algorithm by trying different starting values for the optimization.
Value
A list with the following elements:
margin |
Same as the input except that the list element "params" now consists of the optimized parameters of the marginal distribution function. |
trend |
Same as the input except that the list element "params" now consists of the optimized parameters of the trend model. |
correlation |
Same as the input except that the list element "params" now consists of the optimized parameters of the correlation function model. |
anisotropy |
Same as the input except that the list element "params" now consists of the optimized parameters of geometric anisotropy. |
copula |
Same as the input except that the list element "params" now consists of the optimized copula parameters. |
Author(s)
Hannes Kazianka
References
[1] Kazianka, H. and Pilz, J. (2009), Spatial Interpolation Using Copula-Based Geostatistical Models. GeoENV2008 - Geostatistics for Environmental Application (P. Atkinson, C. Lloyd, eds.), Springer, New York
[2] 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
bayesCopula
, spatialPredict
, estimateParameters
Examples
data(intamapExampleObject)
## estimate parameters for the copula model
## Not run: copula<-list(method="norm")
anisotropy <- list(lower = c(0, 1), upper = c(pi, Inf), params = c(pi/3, 2))
correlation <- list(model = "Ste", lower = c(0.01, 0.01, 0.01), upper = c(0.99, Inf, 20),
params = c(0.05, 4, 3))
margin <- list(name = "gev", lower = c(0.01, -Inf), upper = c(Inf, Inf), params = c(30, 0.5))
trend <- list(F = as.matrix(rep(1, 196)), lower = -Inf, upper = Inf, params = 40)
estimates <- copulaEstimation(intamapExampleObject, margin, trend, correlation, anisotropy, copula)
## make predictions at unobserved locations
predictions <- bayescopula(intamapExampleObject, estimates, search = 25,
calc = list(mean = TRUE, variance = TRUE, excprob = 40, quantile = 0.95))
## End(Not run)