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)

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Error, Predictor, AnyRef, Any
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  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

    Coefficient/parameter vector [b_0, b_1, ...

    Coefficient/parameter vector [b_0, b_1, ... b_k]

    Attributes
    protected
    Definition Classes
    Predictor
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. val e: VectoD

    Residual/error vector [e_0, e_1, ...

    Residual/error vector [e_0, e_1, ... e_m-1]

    Attributes
    protected
    Definition Classes
    Predictor
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit: VectorD

    Return the quality of fit including 'rSquared'.

    Return the quality of fit including 'rSquared'.

    Definition Classes
    NLS_ODEPredictor
  13. def fitLabels: Array[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Predictor
  14. final def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  17. 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

  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  22. def predict(zz: VectoD): Double

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

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

    Definition Classes
    NLS_ODEPredictor
  23. def predict(z: VectorI): 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
  24. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. def train(): Unit

    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.

    Definition Classes
    NLS_ODEPredictor
  28. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. 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

Ungrouped