Packages

c

scalation.analytics

NonLinRegression

class NonLinRegression extends Predictor with Error

The NonLinRegression class supports non-linear regression. In this case, 'x' can be multi-dimensional '[1, x1, ... xk]' and the function 'f' is non-linear in the parameters 'b'. Fit the parameter vector 'b' in the regression equation

y = f(x, b) + e

where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector 'b' by using Non-linear Programming to minimize Sum of Squares Error 'SSE'.

See also

www.bsos.umd.edu/socy/alan/stats/socy602_handouts/kut86916_ch13.pdf

Linear Supertypes
Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new NonLinRegression(x: MatriD, y: VectoD, f: (VectoD, VectoD) ⇒ Double, b_init: VectorD)

    x

    the input/design matrix augmented with a first column of ones

    y

    the response vector

    f

    the non-linear function f(x, b) to fit

    b_init

    the initial guess for the parameter vector b

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 build(x: MatriD, y: VectoD): Predictor
    Definition Classes
    Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    yy

    the response vector

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

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

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

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    NonLinRegressionPredictor
  16. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    NonLinRegressionPredictor
  17. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. val index_rSq: Int
    Definition Classes
    Predictor
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  23. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

    Build a map of selected quality of fit measures/metrics.

    Definition Classes
    Predictor
  24. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  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 predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.e.0, (b0, b1) dot (1.0, z1).

    Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.e.0, (b0, b1) dot (1.0, z1).

    z

    the new vector to predict

    Definition Classes
    NonLinRegressionPredictor
  29. 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
  30. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  31. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  32. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  33. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  34. def sseF(b: VectoD): Double

    Function to compute the Sum of Squares Error 'SSE' for given values for the parameter vector 'b'.

    Function to compute the Sum of Squares Error 'SSE' for given values for the parameter vector 'b'.

    b

    the parameter vector

  35. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  37. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  38. def toString(): String
    Definition Classes
    AnyRef → Any
  39. def train(yy: VectoD = y): NonLinRegression

    Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation for the response vector 'yy'.

    Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation for the response vector 'yy'.

    y = f(x, b)

    using the least squares method. Caveat: Optimizer may converge to an unsatisfactory local optima. If the regression can be linearized, use linear regression for starting solution.

    yy

    the response vector to work with

    Definition Classes
    NonLinRegressionPredictor
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. 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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