GZLM

Value Members

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

   

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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_i^{2, ... t_i}k]

    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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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[_]

    

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29. def hashCode(): Int

    

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30. final def isInstanceOf[T0]: Boolean

    

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

    

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32. final def notify(): Unit

    

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33. final def notifyAll(): Unit

    

    Definition Classes

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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

    

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

    

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

    

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    @throws( ... )