Value Members

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

   AnyRef → Any

4.  val X: DenseMatrix[Double]

   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]
6.  val addIntercept: Boolean
7.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

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   Annotations

   @throws( ... ) @native()

9.  val coefficients: DenseVector[Double]

   Fitted regression coefficients

   Fitted regression coefficients

   Definition Classes

   Glm → Model

10.  val colNames: Seq[String]
11.  lazy val df: Int

    Degrees of freedom

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

    Definition Classes

    AnyRef

13.  val fam: GlmFamily
14.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

15.  lazy val fitted: DenseVector[Double]
16.  final def getClass(): Class[_]

    Definition Classes

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    Annotations

    @native()

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

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

18.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

19.  val its: Int
20.  lazy val n: Int

    Number of observations

21.  val names: Seq[String]

    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

    Definition Classes

    AnyRef

23.  final def notify(): Unit

    Definition Classes

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    Annotations

    @native()

24.  final def notifyAll(): Unit

    Definition Classes

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    Annotations

    @native()

25.  lazy val p: DenseVector[Double]

    p-values for the regression coefficients

26.  def plots: Figure
27.  lazy val pp: Int

    Number of variables (including the intercept)

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

    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]

    Final Q-matrix from the IRLS algorithm

30.  val r: DenseMatrix[Double]

    Final R-matrix from the IRLS algorithm

31.  lazy val ri: DenseMatrix[Double]

    Inverse of the final R-matrix

32.  lazy val se: DenseVector[Double]

    Standard errors for the regression coefficients

33.  def summary: Unit

    Prints a human-readable regression summary to the console

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

    Definition Classes

    AnyRef

35.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

38.  val y: DenseVector[Double]
39.  lazy val z: DenseVector[Double]

    z-statistics for the regression coefficients