Packages

case class Pca(mat: DenseMatrix[Double], colNames: Seq[String]) extends Product with Serializable

Principal components analysis

Computed using SVD of the centred data matrix rather than from the spectral decomposition of the covariance matrix. eg. More like the R function "prcomp" than the R function "princomp".

NOTE: .loadings are transposed relative to the PCA function in Breeze

mat

Data matrix with rows corresponding to observations and columns corresponding to variables

colNames

Sequence of column names of mat

returns

An object of type Pca with methods such as .loadings, .scores, .sdev and .summary

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Instance Constructors

  1. new Pca(mat: DenseMatrix[Double], colNames: Seq[String])

    mat

    Data matrix with rows corresponding to observations and columns corresponding to variables

    colNames

    Sequence of column names of mat

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val SVD: DenseSVD

    Breeze SVD object for the centred data matrix

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  7. val colNames: Seq[String]
  8. lazy val cumuvar: DenseVector[Double]

    Cumulative variance of the principal components

  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. val loadings: DenseMatrix[Double]

    Loadings/rotation matrix.

    Loadings/rotation matrix. Note that this is the TRANSPOSE of the corresponding Breeze method. But this is the usual way the rotations are reported. See how the .summary method labels the rows and columns if you are confused.

  14. val mat: DenseMatrix[Double]
  15. val n: Int

    Number of observations

  16. val names: List[String]

    Column names (as a List)

  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. final def notify(): Unit
    Definition Classes
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    Annotations
    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  20. val p: Int

    Number of variables

  21. def plots: Figure

    Diagnostic plots for the PCA

  22. lazy val propvar: DenseVector[Double]

    Proportion of variance explained by each principal component

  23. lazy val scores: DenseMatrix[Double]

    n x p matrix of scores - the rotated data

  24. val sdev: DenseVector[Double]

    Standard deviations of the principal components

  25. def summary: Unit

    Prints a summary of the PCA to console

  26. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  27. lazy val totVar: Double

    The total variance of the principal components

  28. lazy val variance: DenseVector[Double]

    Variances of the principal components

  29. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws( ... )
  31. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  32. val x: DenseMatrix[Double]

    Centred data matrix

  33. val xBar: DenseVector[Double]

    Column means (for centring)

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