# NonLinearSemivariogram

### Related Doc: package interpolation

#### object NonLinearSemivariogram

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4. #### def apply(pts: Array[PointFeature[Double]], maxDistanceBandwidth: Double, binMaxCount: Int, model: ModelType): Semivariogram

pts

Points to be modelled and fitted

maxDistanceBandwidth

the maximum inter-point distance to be captured into the empirical semivariogram used for fitting

binMaxCount

the maximum number of bins in the empirical variogram

model

The ModelType being fitted into

returns

Semivariogram

6. #### def apply(range: Double, sill: Double, nugget: Double, model: ModelType): Semivariogram

range

Range (Flattening point) of Semivariogram

sill

Sill (flattening value) of Semivariogram

nugget

Nugget (intercept value) of Semivariogram

model

NonLinearModelType model to be returned

returns

Semivariogram

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12. #### def explicitModel(range: Double, sill: Double, nugget: Double, model: ModelType): (Double) ⇒ Double

range

Range of Semivariogram

sill

Sill (flattening value) of Semivariogram

nugget

Nugget (intercept value) of Semivariogram

model

ModelType input

returns

Semivariogram function

13. #### def explicitModel(svParam: Array[Double], model: ModelType): (Double) ⇒ Double

svParam

Semivariogram parameters in Array format (range, sill, nugget) or (range, sill)

model

ModelType input

returns

Semivariogram function

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19. #### final def isInstanceOf[T0]: Boolean

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20. #### def jacobianModel(variables: Array[Double], model: ModelType): (Double) ⇒ Array[Double]

variables

The (range, sill, nugget) variable's current value being used while optimizing the function parameters

model

The ModelType being fitted into

returns

https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant

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