scalation.analytics

GZLM

Related Doc: package analytics

object GZLM extends GLM

A Generalized Linear Model (GZLM) can be developed using the GZLM object. It provides factory methods for General Linear Models (GLM) via inheritance and for proper Generalized Linear Models: LogisticRegression - logistic regression, PoissonRegression - Poisson regression, ExpRegression - Exponential regression,

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GLM, AnyRef, Any
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  1. final def !=(arg0: Any): Boolean

    Definition Classes
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  2. final def ##(): Int

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

    Definition Classes
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  4. var add_1: Boolean

    Attributes
    protected
    Definition Classes
    GLM
  5. def apply(x: MatrixD, nonneg: Boolean, y: VectorD): ExpRegression

    Build an Exponential Regression model.

    Build an Exponential Regression model.

    x

    the input/design m-by-n matrix

    nonneg

    whether to check that responses are nonnegative

    y

    the response vector

  6. def apply(x: MatrixD, y: VectorI, cn: Array[String]): PoissonRegression

    Build a Poisson Regression model.

    Build a Poisson Regression model.

    x

    the input/design m-by-n matrix

    y

    the integer response vector, y_i in {0, 1, ... }

    cn

    the names for all categories/classes

  7. def apply(x: MatrixD, y: VectorI, cn: (String, String)): LogisticRegression

    Build a Logistic Regression model.

    Build a Logistic Regression model.

    x

    the input/design m-by-n matrix

    y

    the categorical response vector, y_i in {0, 1}

    cn

    the names for both categories/classes

  8. def apply(x_: MatrixD, t: VectorI, y: VectorD, levels: Int): ANCOVA

    Build an ANalysis of COVAriance (ANCOVA) model.

    Build an ANalysis of COVAriance (ANCOVA) model.

    x_

    the data/design matrix of continuous variables

    t

    the treatment/categorical variable vector

    y

    the response vector

    levels

    the number of treatment levels (1, ... levels)

    Definition Classes
    GLM
  9. def apply(t: VectorI, y: VectorD, levels: Int): ANOVA

    Build an ANalysis Of VAriance (ANOVA) model.

    Build an ANalysis Of VAriance (ANOVA) model.

    t

    the treatment/categorical variable vector

    y

    the response vector

    levels

    the number of treatment levels (1, ... levels)

    Definition Classes
    GLM
  10. def apply(x_: MatrixD, y: VectorD, cubic: Boolean): ResponseSurface

    Build a Response Surface model.

    Build a Response Surface model.

    x_

    the input vectors/points

    y

    the response vector

    cubic

    the order of the surface (false for quadratic, true for cubic)

    Definition Classes
    GLM
  11. def apply(ty: MatrixD, k: Int, w: Double): TrigRegression

    Build a Trigonomtetric Regression model.

    Build a Trigonomtetric Regression model.

    ty

    the combined input vector and response vector

    k

    the maximum multiplier in the trig function (kwt)

    w

    the base displacement angle in radians

    Definition Classes
    GLM
  12. def apply(t: VectorD, y: VectorD, k: Int, w: Double): TrigRegression

    Build a Trigonometric Regression model.

    Build a Trigonometric Regression model.

    t

    the input vector: t_i expands to x_i

    y

    the response vector

    k

    the maximum multiplier in the trig function (kwt)

    w

    the base displacement angle in radians

    Definition Classes
    GLM
  13. def apply(ty: MatrixD, k: Int): PolyRegression

    Build a Polynomial Regression model.

    Build a Polynomial Regression model.

    ty

    the combined input vector and response vector

    k

    the order of the polynomial

    Definition Classes
    GLM
  14. def apply(t: VectorD, y: VectorD, k: Int): PolyRegression

    Build a Polynomial Regression model.

    Build a Polynomial Regression model.

    t

    the input vector: t_i expands to x_i = [1, t_i, t_i2, ... t_ik]

    y

    the response vector

    k

    the order of the polynomial

    Definition Classes
    GLM
  15. def apply(xy: MatrixD, transform: FunctionS2S): TranRegression

    Build a Transformed Multiple Linear Regression model.

    Build a Transformed Multiple Linear Regression model.

    xy

    the combined input/design m-by-n matrix and response m-vector

    transform

    the transformation function

    Definition Classes
    GLM
  16. def apply(x: MatrixD, y: VectorD, transform: FunctionS2S): TranRegression

    Build a Transformed Multiple Linear Regression model.

    Build a Transformed Multiple Linear Regression model.

    x

    the input/design m-by-n matrix

    y

    the response m-vector

    transform

    the transformation function (e.g., log)

    Definition Classes
    GLM
  17. def apply(xy: MatrixD, lambda: Double): RidgeRegression

    Build a Multiple Linear Robust Regression model.

    Build a Multiple Linear Robust Regression model.

    lambda

    the shrinkage parameter (0 => OLS) in the penalty term 'lambda * b dot b'

    Definition Classes
    GLM
  18. def apply(x: MatrixD, y: VectorD, lambda: Double): RidgeRegression

    Build a Multiple Linear Robust Regression model.

    Build a Multiple Linear Robust Regression model.

    x

    the centered input/design m-by-n matrix NOT augmented with a first column of ones

    y

    the centered response vector

    lambda

    the shrinkage parameter (0 => OLS) in the penalty term 'lambda * b dot b'

    Definition Classes
    GLM
  19. def apply(x: MatrixD, y: VectorD, w: VectorD): Regression_WLS

    Build a Multiple Linear Regression model using Weighted Least Squares (WLS).

    Build a Multiple Linear Regression model using Weighted Least Squares (WLS).

    x

    the input/design m-by-n matrix

    y

    the response m-vector

    Definition Classes
    GLM
  20. def apply(xy: MatrixD): Regression

    Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).

    Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).

    xy

    the combined input/design m-by-n matrix and response m-vector

    Definition Classes
    GLM
  21. def apply(x: MatrixD, y: VectorD): Regression

    Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).

    Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).

    x

    the input/design m-by-n matrix

    y

    the response m-vector

    Definition Classes
    GLM
  22. def apply(x: VectorD, y: VectorD): SimpleRegression

    Build a Simple Linear Regression model, automatically prepending the column of ones.

    Build a Simple Linear Regression model, automatically prepending the column of ones.

    x

    the input/design m-by-1 vector

    y

    the response m-vector

    Definition Classes
    GLM
  23. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  24. def clone(): AnyRef

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

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  26. def equals(arg0: Any): Boolean

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  27. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  28. final def getClass(): Class[_]

    Definition Classes
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  29. def hashCode(): Int

    Definition Classes
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  30. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  31. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  32. final def notify(): Unit

    Definition Classes
    AnyRef
  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  34. def setAdd_1(_add_1: Boolean = true): Unit

    Explicitly set the add_1 flag (column of all ones corresponding to b_0).

    Explicitly set the add_1 flag (column of all ones corresponding to b_0).

    _add_1

    the value to set the add_1 flag to

    Definition Classes
    GLM
  35. def setTechnique(_technique: RegTechnique = Fac_QR): Unit

    Explicitly set the regression technique to use.

    Explicitly set the regression technique to use.

    _technique

    the value to set technique to

    Definition Classes
    GLM
  36. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  37. var technique: RegTechnique.Value

    Attributes
    protected
    Definition Classes
    GLM
  38. def toString(): String

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  39. final def wait(): Unit

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    @throws( ... )
  40. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
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    @throws( ... )
  41. final def wait(arg0: Long): Unit

    Definition Classes
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    @throws( ... )

Inherited from GLM

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