Packages

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, (@see classifier package) PoissonRegression - Poisson regression, ExpRegression - Exponential regression,

Linear Supertypes
GLM, AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. val add_1: Boolean
    Attributes
    protected
    Definition Classes
    GLM
  5. def apply(x: MatriD, y: VectoD, fname: Strings, nonneg: Boolean): ExpRegression

    Build an Exponential Regression model.

    Build an Exponential Regression model.

    x

    the input/design m-by-n matrix

    y

    the response vector

    fname

    the feature/variable name

    nonneg

    whether to check that responses are nonnegative

  6. def apply(x: MatriD, y: VectoI, fname: Strings, poisson: Boolean): 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, ... }

    fname

    the feature/variable name

    poisson

    whether it is PoissonRegression

  7. def apply(x_: MatriD, y: VectoD, t: VectoI): ANCOVA1

    Build an ANalysis of COVAriance (ANCOVA1) model.

    Build an ANalysis of COVAriance (ANCOVA1) model.

    x_

    the data/design matrix of continuous variables

    y

    the response vector

    t

    the treatment/categorical variable vector

    Definition Classes
    GLM
  8. def apply(t: VectoD, levels: Int, y: VectoD): ANOVA1

    Build an ANalysis Of VAriance (ANOVA) model.

    Build an ANalysis Of VAriance (ANOVA) model.

    t

    the treatment/categorical variable vector

    levels

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

    y

    the response vector

    Definition Classes
    GLM
  9. def apply(x_: MatriD, y: VectoD, 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
  10. def apply(ty: MatriD, k: Int, p: Int): TrigRegression

    Build a Trigonometric Regression model.

    Build a Trigonometric Regression model.

    ty

    the combined input vector and response vector

    k

    the maximum multiplier in the trig function 'kwt'

    p

    extra parameter to make apply methods unique (pass in 0)

    Definition Classes
    GLM
  11. def apply(t: VectoD, y: VectoD, k: Int, p: Int): 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'

    p

    extra parameter to make apply methods unique (pass in 0)

    Definition Classes
    GLM
  12. def apply(ty: MatriD, 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
  13. def apply(t: VectoD, y: VectoD, 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
  14. def apply(xy: MatriD, transform: FunctionS2S, tranInv: 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 (e.g., log)

    Definition Classes
    GLM
  15. def apply(x: MatriD, y: VectoD, transform: FunctionS2S, tranInv: 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
  16. def apply(xy: MatriD, 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
  17. def apply(x: MatriD, y: VectoD, 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 hyper-parameter (0 => OLS) in the penalty term 'lambda * b dot b'

    Definition Classes
    GLM
  18. def apply(x: MatriD, y: VectoD, w: VectoD): 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
  19. def apply(xy: MatriD): 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
  20. def apply(x: MatriD, y: VectoD): 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
  21. def apply(x: VectoD, y: VectoD): SimpleRegression

    Build a Simple Linear Regression model, automatically prepending the column of ones (form matrix from two column vectors [ 1 x ]).

    Build a Simple Linear Regression model, automatically prepending the column of ones (form matrix from two column vectors [ 1 x ]).

    x

    the input/design m-by-1 vector

    y

    the response m-vector

    Definition Classes
    GLM
  22. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  23. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
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    @native() @throws( ... )
  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  27. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  28. def hashCode(): Int
    Definition Classes
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    Annotations
    @native()
  29. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
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    @native()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. val technique: RegTechnique.Value
    Attributes
    protected
    Definition Classes
    GLM
  35. def toString(): String
    Definition Classes
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  36. final def wait(): Unit
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    @throws( ... )
  37. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  38. final def wait(arg0: Long): Unit
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Inherited from GLM

Inherited from AnyRef

Inherited from Any

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