Packages

object Regression_WLS

The Regression_WLS companion object provides methods for setting weights and testing.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Regression_WLS
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  15. def reweightX(x: MatriD, rW: VectoD): MatriD

    Reweight the data matrix 'x' by multiplying by the root weight 'rtW'.

    Reweight the data matrix 'x' by multiplying by the root weight 'rtW'.

    x

    the input/data m-by-n matrix

    rW

    the root weight vector (rtW: either rootW or rW)

  16. def reweightY(y: VectoD, rW: VectoD): VectoD

    Reweight the response vector matrix 'y' by multiplying by the root weight 'rtW'.

    Reweight the response vector matrix 'y' by multiplying by the root weight 'rtW'.

    y

    the response vector

    rW

    the root weight vector (rtW: either rootW or rW)

  17. def setWeights[MatT <: MatriD, VecT <: VectoD](x: MatT, y: VecT, technique: RegTechnique = QR, w0: VectoD = null): Unit

    Estimate weights for the variables according to the reciprocal predicted rad's.

    Estimate weights for the variables according to the reciprocal predicted rad's. Save the weight vector 'w' and root weight vector 'rootW' for the current model in companion object variables.

    x

    the input/data m-by-n matrix

    y

    the response vector

    technique

    the technique used to solve for b in x.t*w*x*b = x.t*w*y

  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  19. def test(x: MatriD, y: VectoD, z: VectoD, w: VectoD = null): Unit

    Test various regression techniques.

    Test various regression techniques.

    x

    the data matrix

    y

    the response vector

    z

    a vector to predict

    w

    the root weights

  20. def toString(): String
    Definition Classes
    AnyRef → Any
  21. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  23. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  24. def weights: VectoD

    Return the weight vector for the current model.

Inherited from AnyRef

Inherited from Any

Ungrouped