Packages

c

scalation.analytics

PredictorVec

abstract class PredictorVec extends Predictor with Error

The PredictorVec class supports term expanded regression. Fit the parameter vector 'b' in the regression equation. Use Least-Squares (minimizing the residuals) to solve for the parameter vector 'b' using the Normal Equations:

x.t * x * b = x.t * y b = fac.solve (.)

Linear Supertypes
Error, Predictor, AnyRef, Any
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  1. PredictorVec
  2. Error
  3. Predictor
  4. AnyRef
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  1. Public
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Instance Constructors

  1. new PredictorVec(t: VectoD, y: VectoD, ord: Int)

    t

    the input vector: t_i expands to x_i = vector

    y

    the response vector

    ord

    the order of the expansion

Abstract Value Members

  1. abstract def crossVal(k: Int = 10, ord: Int = 10, rando: Boolean = true): Unit

    The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.

    The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method. The 'algor' parameter may be specified as a lambda function to create the prediction algorithm.

    k

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

    ord

    the given order

    rando

    whether to use randomized cross-validation

  2. abstract def expand(t: Double): VectoD

    Expand the scalar 't' into a vector of terms/columns.

    Expand the scalar 't' into a vector of terms/columns.

    t

    the scalar to expand into the vector

  3. abstract def predict(z: Double): Double

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.

    z

    the new scalar to predict

Concrete 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. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  6. def backwardElim(cols: Set[Int]): (Int, VectoD, VectoD)

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit.

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def crossValidate(algor: (VectoD, VectoD, Int) ⇒ PredictorVec, k: Int = 10, rando: Boolean = true): Array[Statistic]
  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(tt: VectoD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    yy

    the test response vector

  13. 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
    PredictorVecPredictor
  14. 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
  15. def expand(t: VectoD): MatriD

    Expand the vector 't' into a matrix.

    Expand the vector 't' into a matrix.

    t

    the vector to expand into the matrix

  16. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. def fit: VectoD

    Return the quality of fit measures including 'rSq'.

  18. def fitLabel: Seq[String]

    Return the labels for the fit.

  19. def fitMap: Map[String, String]

    Build a map of quality of fit measures.

  20. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  21. def forwardSel(cols: Set[Int]): (Int, VectoD, VectoD)

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit.

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  23. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  25. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. def parameter: VectoD

    Return the vector of parameters/coefficients.

    Return the vector of parameters/coefficients.

    Definition Classes
    PredictorVecPredictor
  29. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new expanded/orhogonalized vector to predict

    Definition Classes
    PredictorVecPredictor
  30. 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
  31. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    PredictorVecPredictor
  32. var rg: Regression
    Attributes
    protected
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def toString(): String
    Definition Classes
    AnyRef → Any
  35. def train(yy: VectoD = y): Regression

    Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.

    Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.

    yy

    the response vector

    Definition Classes
    PredictorVecPredictor
  36. def vif: VectoD

    Compute the Variance Inflation Factor (VIF) for each variable to test for multi-collinearity by regressing 'xj' against the rest of the variables.

    Compute the Variance Inflation Factor (VIF) for each variable to test for multi-collinearity by regressing 'xj' against the rest of the variables. A VIF over 10 indicates that over 90% of the variance of 'xj' can be predicted from the other variables, so 'xj' is a candidate for removal from the model.

  37. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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