Packages

c

scalation.analytics

ExpRegression

class ExpRegression extends Predictor with Error

The ExpRegression class supports exponential regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the exponential regression equation

log (mu (x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k

See also

www.stat.uni-muenchen.de/~leiten/Lehre/Material/GLM_0708/chapterGLM.pdf

Linear Supertypes
Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new ExpRegression(x: MatriD, nonneg: Boolean, y: VectoD)

    x

    the data/design matrix

    nonneg

    whether to check that responses are nonnegative

    y

    the response vector

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
    ExpRegressionPredictor
  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
    ExpRegressionPredictor
  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
    ExpRegressionPredictor
  16. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    ExpRegressionPredictor
  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. def ll(b: VectoD): Double

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL).

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL). '-2LL' is the standard measure that follows a Chi-Square distribution.

    b

    the parameters to fit

    See also

    www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf

    www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf

  23. def ll_null(b: VectoD): Double

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).

    b

    the parameters to fit

  24. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  25. def metrics: Map[String, Any]

    Build a map of diagnostics metrics for the overall quality of fit.

    Build a map of diagnostics metrics for the overall quality of fit.

    Definition Classes
    ExpRegressionPredictor
  26. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  30. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new vector to predict

    Definition Classes
    ExpRegressionPredictor
  31. 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
  32. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  33. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

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

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

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

    yy

    the response vector

    Definition Classes
    ExpRegressionPredictor
  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( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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