Packages

class NLS_ODE extends Predictor with Error

Given an Ordinary Differential Equation 'ODE' parameterized using the vector 'b' with Initial Value 'IV' 'y0', estimate the parameter values 'b' for the ODE using weighted Non-linear Least Squares 'NLS'.

ODE: dy/dt = f(t, y) IV: y(t0) = y0

Times series data: z(t0), z(t1), ... z(tn)

Linear Supertypes
Error, Predictor, AnyRef, Any
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Inherited
  1. NLS_ODE
  2. Error
  3. Predictor
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Instance Constructors

  1. new NLS_ODE(z: VectorD, ts: VectorD, b_init: VectorD, w: VectorD = null)

    z

    the observed values

    ts

    the time points of the observations

    b_init

    the initial guess for the parameter values 'b'

    w

    the optional weights

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 predictor.

    Compute diagostics for the predictor. Override to add more diagostics. Note, for 'mse' and 'rmse', 'sse' is divided by the number of instances 'm' rather than the degrees of freedom.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    Predictor
    See also

    en.wikipedia.org/wiki/Mean_squared_error

  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): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics. FIX

    yy

    the response vector

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

    Return the quality of fit.

    Return the quality of fit.

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

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    NLS_ODEPredictor
  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. def init(_objectiveF: FunctionV_2S, _y0: Double): Unit

    Initialize NLS-ODE with the objective function and initial value/condition.

    Initialize NLS-ODE with the objective function and initial value/condition.

    _objectiveF

    the objective function indicating departure from observation

  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  24. 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
  25. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. def predict(zz: VectoD): Double

    Predict the value of 'y = f(zz)'.

    Predict the value of 'y = f(zz)'.

    zz

    the new vector to predict

    Definition Classes
    NLS_ODEPredictor
  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. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  32. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  33. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  34. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  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(): NLS_ODE

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

  40. def train(yy: VectoD): NLS_ODE

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

    yy

    the response vector

    Definition Classes
    NLS_ODEPredictor
  41. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  44. def wsseF(dy_dt: Derivative): Double

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

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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