View source: R/anisotropyChoice.r
anisotropyChoice | R Documentation |
This function combines segmentation of scattered 2D data and estimation of anisotropy parameters using the CTI method.
anisotropyChoice(object)
object |
An Intamap type object containing one
|
The function AnisotropyChoice
function employs the
doSegmentation
function to
automatically separate the original dataset into clusters based on the sampling density and the spatial
locations of the data (see doSegmentation
for
details). The results of the segmentation procedure and the
anisotropy analysis per cluster are returned in a matrix of
dimension [cl]x5, where [cl] is the number of clusters . Each row of
the matrix contains the cluster index, the anisotropy ratio, the
anisotropy direction, the number of cluster points and the area
inside the convex hull of the cluster. In addition, a single set of
anisotropy parameters is returned in the element anisPar
.
These parameters are calculated using weighted averages of the
covariance Hessian matrix estimates in each cluster. The weights are
based on the area enclosed by the convex hull of each cluster.
object
: A modified Intamap type object is returned,
which contains the results of the anisotropy parameter estimation.
The anisotropy parameters are returned in the element anisPar
as described below.
anisPar |
List element in
|
clusters |
list element added to the original object containing the segmentation results.
|
This function uses the akima
package to perform
"bilinear" and "bicubic" interpolation for the estimation of spatial
derivatives
D.T. Hristopulos, G.Spiliopoulos, A.Chorti
[1] http://www.intamap.org
[2] A. Chorti and D. T. Hristopulos (2008). Non-parametric Identification of Anisotropic (Elliptic) Correlations in Spatially Distributed Data Sets, IEEE Transactions on Signal Processing, 56(10), 4738-4751 (2008).
[3] D. T. Hristopulos, M. P. Petrakis, G. Spiliopoulos, A. Chorti (2009). Non-parametric estimation of geometric anisotropy from environmental sensor network measurements, StatGIS 2009: Geoinformatics for Environmental Surveillance Proceedings (ed. G. Dubois).
library(gstat)
data(walker)
object=createIntamapObject(observations=walker)
object=anisotropyChoice(object)
print(summary(object$clusters$index))
print(object$anisPar)
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