object NonLinearSemivariogram
- Alphabetic
- By Inheritance
- NonLinearSemivariogram
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Value Members
-
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
- def apply(range: Double, sill: Double, model: ModelType): Semivariogram
-
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
- def apply(svParam: Array[Double], model: ModelType): Semivariogram
-
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
-
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
- def explicitNuggetModel(range: Double, sill: Double, model: ModelType): (Double) ⇒ Double
- def explicitNuggetModel(svParam: Array[Double], model: ModelType): (Double) ⇒ Double
-
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