TranRegression

scalation.modeling.TranRegression
See theTranRegression companion class

The TranRegression companion object provides transformation and inverse transformation function based on the parameter 'lambda'. It support the family of Box-Cox transformations.

Attributes

Companion
class
Graph
Supertypes
class Object
trait Matchable
class Any
Self type

Members list

Value members

Concrete methods

def apply(x: MatrixD, y: VectorD, fname: Array[String], hparam: HyperParameter): TranRegression

Create a TranRegression object that uses a Box-Cox transformation. To change 'lambda' from its default value, call 'set_lambda' first.

Create a TranRegression object that uses a Box-Cox transformation. To change 'lambda' from its default value, call 'set_lambda' first.

Value parameters

fname

the feature/variable names (defaults to null)

hparam

the hyper-parameters (defualts to Regression.hp)

x

the data/input matrix

y

the response/output vector

Attributes

def apply(xy: MatrixD, fname: Array[String], hparam: HyperParameter, tran: FunctionS2S, itran: FunctionS2S, bounds: (Double, Double)): TranRegression

Create a TranRegression with automatic rescaling from a combined data matrix.

Create a TranRegression with automatic rescaling from a combined data matrix.

Value parameters

bounds

the bounds for rescaling

fname

the feature/variable names

hparam

the hyper-parameters

itran

the inverse transformation function to rescale predictions to original y scale

tran

the transformation function (defaults to log)

xy

the combined data/input and response/output matrix

Attributes

def apply(x: MatrixD, y: VectorD, fname: Array[String], hparam: HyperParameter, tran: FunctionS2S, itran: FunctionS2S, bounds: (Double, Double)): TranRegression

Create a TranRegression with automatic rescaling from a data matrix and response vector.

Create a TranRegression with automatic rescaling from a data matrix and response vector.

Value parameters

bounds

the bounds for rescaling

fname

the feature/variable names

hparam

the hyper-parameters

itran

the inverse transformation function to rescale predictions to original y scale

tran

the transformation function (defaults to log)

x

the data/input matrix

y

the response/output vector

Attributes

def box_cox(y: Double): Double

Transform y using the Box-Cox transformation.

Transform y using the Box-Cox transformation.

Value parameters

y

the value to be transformed

Attributes

def cox_box(z: Double): Double

Inverse transform z using the Box-Cox transformation.

Inverse transform z using the Box-Cox transformation.

Value parameters

z

the value to be inverse transformed

Attributes

def setLambda(lambda_: Double): Unit

Set the value for the 'lambda' parameter. Must be called before Box-Cox 'apply' method.

Set the value for the 'lambda' parameter. Must be called before Box-Cox 'apply' method.

Value parameters

lambda_

the new value for the 'lambda' parameter

Attributes