object EmpiricalVariogram extends Serializable
This creates an empirical variogram from the dataset, which is then used to fit into one of the semivariogram ModelType for use in Kriging Interpolation
Linear Supertypes
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- def apply(length: Int): EmpiricalVariogram
-
def
linear(pts: Array[PointFeature[Double]], radius: Option[Double] = None, lag: Double = 0.0): Array[(Double, Double)]
Computes empirical semivariogram for Linear model
-
def
nonlinear(pts: Array[PointFeature[Double]], maxdist: Double, binmax: Int): EmpiricalVariogram
Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models
Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models
- pts
PointFeature array for creating the variogram
- maxdist
The bandwidth of variations that the empirical variogram is supposed to capture
- binmax
The maximum number of bins in the empirical variogram to be created