Packages

c

scalation.analytics.fda

Regression_F2S

class Regression_F2S extends Predictor

The Regression_F2S class performs functional linear regression with scaler response and functional covariates.

y = a + <b(t),x(t)> + ε

where <b, x> denotes the inner product of b and x.

Linear Supertypes
Predictor, AnyRef, Any
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Instance Constructors

  1. new Regression_F2S(x: MatriD, y: VectoD, t: VectoD, bf: DBasisFunction, technique: RegTechnique = Cholesky, lambda: Double = 1E-4)

    x

    the covariate matrix - treated as functional

    y

    the response vector

    t

    the time vector

    bf

    the basis function object with derivatives support

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. val _1: VectorD
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def crossValidate(k: Int): Double

    Test the accuracy of the predicted results by cross-validation, returning the sse.

    Test the accuracy of the predicted results by cross-validation, returning the sse.

    k

    number of crosses and cross-validations (defaults to 10x).

  9. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def eval(): Unit

    Compute the error and useful diagnostics for the entire dataset.

    Compute the error and useful diagnostics for the entire dataset.

    Definition Classes
    Regression_F2S → Predictor
  13. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector FIX - implement in classes

    Definition Classes
    Predictor
  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  21. def parameter: VectoD

    Return the vector of parameter/coefficient values.

    Return the vector of parameter/coefficient values.

    Definition Classes
    Predictor
  22. def predict(z: VectoD): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Regression_F2S → Predictor
  23. def predict(xf: FunctionS2S): Double

    Predict the y-value at time point 'tt'.

    Predict the y-value at time point 'tt'.

    xf

    the given function of time

  24. def predict(z: VectoI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Predictor
  25. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  26. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  27. val t0: Double
  28. def test(itest: VectoI): Unit

    Test the model on the testing set for cross-validation purposes

    Test the model on the testing set for cross-validation purposes

    itest

    indicies of the testing set

  29. val tn: Double
  30. def toString(): String
    Definition Classes
    AnyRef → Any
  31. def train(itest: IndexedSeq[Int]): Unit

    Train the model on the training set for cross-validation purposes

    Train the model on the training set for cross-validation purposes

    itest

    indicies of the testing set, to be excluded for training

  32. def train(): Regression_F2S

    Train the model using the smoothed data to find the regression coefficients 'b'.

  33. def train(yy: VectoD): Regression_F2S

    Train the model using the smoothed data to find the regression coefficients 'b'.

    Train the model using the smoothed data to find the regression coefficients 'b'.

    yy

    response vector

    Definition Classes
    Regression_F2S → Predictor
  34. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  37. def x_i(i: Int): VectorD

    The inner product of phi and a smoothed function

    The inner product of phi and a smoothed function

    i

    the i-th smoothed function function

  38. val xmoo: IndexedSeq[Smoothing_F]
  39. val z: MatrixD

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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