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

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Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new NonLinRegression(x: MatrixD, y: VectorD, f: (VectorD, VectorD) ⇒ 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: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. def fit: (VectorD, Double)

    Return the fit (parameter vector b, quality of fit rSquared)

  12. 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
  13. final def getClass(): Class[_]

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    AnyRef → Any
  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def predict(z: MatrixD): VectorD

    Predict the value of y = f(z) by evaluating the formula y = f(z_i, b) for each row of matrix z.

    Predict the value of y = f(z) by evaluating the formula y = f(z_i, b) for each row of matrix z.

    z

    the new matrix to predict

    Definition Classes
    NonLinRegressionPredictor
  20. def predict(z: VectorD): Double

    Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.

    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
  21. 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
  22. def sseF(b: VectorD): 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

  23. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  24. def toString(): String

    Definition Classes
    AnyRef → Any
  25. def train(): Unit

    Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation y = f(x, b) using the least squares method.

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

    Definition Classes
    NonLinRegressionPredictor
  26. final def wait(): Unit

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  27. final def wait(arg0: Long, arg1: Int): Unit

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  28. final def wait(arg0: Long): Unit

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