Packages

c

scalation.analytics

NonLinRegression

class NonLinRegression extends PredictorMat

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
PredictorMat, Error, Predictor, Fit, AnyRef, Any
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Inherited
  1. NonLinRegression
  2. PredictorMat
  3. Error
  4. Predictor
  5. Fit
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

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

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. def crossVal(k: Int = 10): Unit

    Perform 'k'-fold cross-validation.

    Perform 'k'-fold cross-validation.

    k

    the number of folds

    Definition Classes
    NonLinRegression → PredictorMat
  9. def crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10): Array[Statistic]
    Definition Classes
    PredictorMat
  10. val df: (Double, Double)
    Definition Classes
    Fit
  11. def diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null): Unit

    Given the error/residual vector, compute the quality of fit measures.

    Given the error/residual vector, compute the quality of fit measures.

    e

    the corresponding m-dimensional error vector (y - yp)

    w

    the weights on the instances

    yp

    the predicted response vector (x * b)

    Definition Classes
    Fit
  12. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def eval(): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    Definition Classes
    NonLinRegression → PredictorMat → Predictor
  16. 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

    Definition Classes
    PredictorMat → Predictor
  17. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

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

    Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.

    Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.

    Definition Classes
    Fit
  20. def fitLabel: Seq[String]

    Return the labels for the quality of fit measures.

    Return the labels for the quality of fit measures. Override to add more quality of fit measures.

    Definition Classes
    Fit
  21. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered). Override to add more quality of fit measures.

    Definition Classes
    Fit
  22. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. val index_rSq: Int
    Definition Classes
    Fit
  26. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  27. val k: Int
    Attributes
    protected
    Definition Classes
    PredictorMat
  28. val m: Int
    Attributes
    protected
    Definition Classes
    PredictorMat
  29. def mse_: Double

    Return the mean of squares for error (sse / df._2).

    Return the mean of squares for error (sse / df._2). Must call diagnose first.

    Definition Classes
    Fit
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. 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
    NonLinRegression → PredictorMat → Predictor
  34. def predict(z: MatriD): VectoD

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

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

    z

    the new matrix to predict

    Definition Classes
    PredictorMat
  35. 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
  36. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  37. 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

  38. def sumCoeff(b: VectoD, stdErr: VectoD = null): String

    Produce the summary report portion for the cofficients.

    Produce the summary report portion for the cofficients.

    b

    the parameters/coefficients for the model

    Definition Classes
    Fit
  39. def summary(): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    Definition Classes
    PredictorMat
  40. def summary(b: VectoD, stdErr: VectoD = null): String

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    b

    the parameters/coefficients for the model

    Definition Classes
    Fit
  41. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  42. def toString(): String
    Definition Classes
    AnyRef → Any
  43. 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
    NonLinRegression → PredictorMat → Predictor
  44. def train(): PredictorMat

    Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.

    Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.

    Definition Classes
    PredictorMat
  45. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  48. val x: MatriD
    Attributes
    protected
    Definition Classes
    PredictorMat
  49. val y: VectoD
    Attributes
    protected
    Definition Classes
    PredictorMat

Inherited from PredictorMat

Inherited from Error

Inherited from Predictor

Inherited from Fit

Inherited from AnyRef

Inherited from Any

Ungrouped