Pca

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

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val SVD: DenseSVD

Breeze SVD object for the centred data matrix
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val colNames: Seq[String]

Sequence of column names of mat
lazy val cumuvar: DenseVector[Double]

Cumulative variance of the principal components
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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.
val mat: DenseMatrix[Double]

Data matrix with rows corresponding to observations and columns corresponding to variables
val n: Int

Number of observations
val names: List[String]

Column names (as a List)
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val p: Int

Number of variables
def plots: Figure

Diagnostic plots for the PCA
lazy val propvar: DenseVector[Double]

Proportion of variance explained by each principal component
lazy val scores: DenseMatrix[Double]

n x p matrix of scores - the rotated data
val sdev: DenseVector[Double]

Standard deviations of the principal components
def summary: Unit

Prints a summary of the PCA to console
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
lazy val totVar: Double

The total variance of the principal components
lazy val variance: DenseVector[Double]

Variances of the principal components
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val x: DenseMatrix[Double]

Centred data matrix
val xBar: DenseVector[Double]

Column means (for centring)

Related Docs: object Pca | package scalaglm

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

Instance Constructors

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val SVD: DenseSVD

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val colNames: Seq[String]

lazy val cumuvar: DenseVector[Double]

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val loadings: DenseMatrix[Double]

val mat: DenseMatrix[Double]

val n: Int

val names: List[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val p: Int

def plots: Figure

lazy val propvar: DenseVector[Double]

lazy val scores: DenseMatrix[Double]

val sdev: DenseVector[Double]

def summary: Unit

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

lazy val totVar: Double

lazy val variance: DenseVector[Double]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val x: DenseMatrix[Double]

val xBar: DenseVector[Double]

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped