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 QR: DenseQR

   Breeze QR object for the design matrix

5.  val X: DenseMatrix[Double]

   Design matrix (including intercept, if required)

   Design matrix (including intercept, if required)

   Definition Classes

   Lm → Model

6.  val Xmat: DenseMatrix[Double]
7.  val addIntercept: Boolean
8.  lazy val adjRs: Double

   The adjusted R^2 value for the regression

9.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

10.  def clone(): AnyRef

    Attributes

    protected[lang]

    Definition Classes

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    Annotations

    @throws( ... ) @native()

11.  val coefficients: DenseVector[Double]

    Fitted regression coefficients

    Fitted regression coefficients

    Definition Classes

    Lm → Model

12.  val colNames: Seq[String]
13.  lazy val df: Int

    Degrees of freedom

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

    Definition Classes

    AnyRef

15.  lazy val f: Double

    The f-statistic for the regression analysis

16.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

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    Annotations

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

17.  lazy val fitted: DenseVector[Double]

    Fitted values

18.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

19.  lazy val h: Vector[Double]

    Vector containing the leverages (diagonal of the hat matrix)

20.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

21.  lazy val k: Int

    Degrees of freedom for the F-statistic

22.  lazy val n: Int

    Number of observations

23.  val names: Seq[String]

    Column names (including intercept)

    Column names (including intercept)

    Definition Classes

    Lm → Model

24.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

25.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

26.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

27.  lazy val p: DenseVector[Double]

    p-values for the regression coefficients

28.  lazy val pf: Double

    The p-value associated with the f-statistic

29.  def plots: Figure
30.  lazy val pp: Int

    Number of variables (including any intercept)

31.  def predict(newX: DenseMatrix[Double] = Xmat): PredictLm

    Predictions for a new matrix of covariates

    Predictions for a new matrix of covariates

    newX

    New matrix of covariates

    returns

    Prediction object

32.  val q: DenseMatrix[Double]

    n x p Q-matrix

33.  val qty: DenseVector[Double]

    Q'y

34.  val r: DenseMatrix[Double]

    p x p upper-triangular R-matrix

35.  lazy val rSquared: Double

    The R^2 value for the regression analysis

36.  lazy val residuals: DenseVector[Double]

    Residuals

37.  lazy val ri: DenseMatrix[Double]

    The inverse of the R-matrix

38.  lazy val rse: Double

    Residual squared error

39.  lazy val rss: Double

    Residual sum of squares

40.  lazy val se: DenseVector[Double]

    Standard errors for the regression coefficients

41.  lazy val sh: DenseVector[Double]

    Square root of the leverage vector

42.  lazy val ssy: Double

    The sum-of-squares of the centred observations

43.  lazy val studentised: DenseVector[Double]
44.  def summary: Unit

    Prints a human-readable regression summary to the console

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

    Definition Classes

    AnyRef

46.  lazy val t: DenseVector[Double]

    t-statistics for the regression coefficients

47.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

50.  val y: DenseVector[Double]
51.  lazy val ybar: Double

    The mean of the observations

52.  lazy val ymyb: DenseVector[Double]

    The centred observations