# EmpiricalVariogram

### Related Docs: class EmpiricalVariogram | package interpolation

#### object EmpiricalVariogram

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

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13. #### def linear(pts: Array[PointFeature[Double]], radius: Option[Double] = None, lag: Double = 0.0): Array[(Double, Double)]

Computes empirical semivariogram for Linear model

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15. #### 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

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