Class/Object

scalaglm

Glm

Related Docs: object Glm | package scalaglm

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case class Glm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], fam: GlmFamily, addIntercept: Boolean = true, its: Int = 50) extends Model with Product with Serializable

Generalised linear regression modelling

y

Vector of responses

Xmat

Covariate matrix

colNames

List of covariate names

fam

Observation model. eg. LogisticGlm or PoissonGlm

addIntercept

Add an intercept term to the covariate matrix?

its

Max iterations for the IRLS algorithm (default 50)

returns

An object of type Glm with many useful methods providing information about the regression fit, including .coefficients, .p and .summary

Linear Supertypes
Serializable, Serializable, Product, Equals, Model, AnyRef, Any
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Instance Constructors

  1. new Glm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], fam: GlmFamily, addIntercept: Boolean = true, its: Int = 50)

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    y

    Vector of responses

    Xmat

    Covariate matrix

    colNames

    List of covariate names

    fam

    Observation model. eg. LogisticGlm or PoissonGlm

    addIntercept

    Add an intercept term to the covariate matrix?

    its

    Max iterations for the IRLS algorithm (default 50)

Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. val X: DenseMatrix[Double]

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    Design matrix (including the intercept column, if required)

    Design matrix (including the intercept column, if required)

    Definition Classes
    Glm → Model

  5. val Xmat: DenseMatrix[Double]

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    Covariate matrix

  6. val addIntercept: Boolean

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    Add an intercept term to the covariate matrix?

  7. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. val coefficients: DenseVector[Double]

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    Fitted regression coefficients

    Fitted regression coefficients

    Definition Classes
    Glm → Model

  10. val colNames: Seq[String]

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    List of covariate names

  11. lazy val df: Int

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    Degrees of freedom

  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  13. val fam: GlmFamily

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    Observation model.

    Observation model. eg. LogisticGlm or PoissonGlm

  14. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  15. lazy val fitted: DenseVector[Double]

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  16. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  17. val irls: (DenseVector[Double], DenseMatrix[Double], DenseMatrix[Double])

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    Tuple containing results of running the IRLS algorithm - not for general use

  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  19. val its: Int

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    Max iterations for the IRLS algorithm (default 50)

  20. lazy val n: Int

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    Number of observations

  21. val names: List[String]

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    Sequence of variable names (including the intercept)

    Sequence of variable names (including the intercept)

    Definition Classes
    Glm → Model

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

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    Definition Classes
    AnyRef

  23. final def notify(): Unit

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    Definition Classes
    AnyRef

  24. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  25. lazy val p: DenseVector[Double]

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    p-values for the regression coefficients

  26. def plots: Figure

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  27. lazy val pp: Int

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    Number of variables (including the intercept)

  28. def predict(newX: DenseMatrix[Double] = Xmat, response: Boolean = false): PredictGlm

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    Predictions for a new matrix of covariates

    Predictions for a new matrix of covariates

    newX

    New matrix of covariates

    response

    Fitted values on the scale of the response?

    returns

    Prediction object

  29. val q: DenseMatrix[Double]

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    Final Q-matrix from the IRLS algorithm

  30. val r: DenseMatrix[Double]

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    Final R-matrix from the IRLS algorithm

  31. lazy val ri: DenseMatrix[Double]

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    Inverse of the final R-matrix

  32. lazy val se: DenseVector[Double]

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    Standard errors for the regression coefficients

  33. def summary: Unit

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    Prints a human-readable regression summary to the console

  34. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  35. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
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    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  38. val y: DenseVector[Double]

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    Vector of responses

  39. lazy val z: DenseVector[Double]

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    z-statistics for the regression coefficients

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Model

Inherited from AnyRef

Inherited from Any

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