Pca

scalaglm.Pca

See thePca companion object

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

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

Attributes

colNames: Sequence of column names of mat
mat: Data matrix with rows corresponding to observations and columns corresponding to variables
Returns:: An object of type Pca with methods such as .loadings, .scores, .sdev and .summary
Companion:: object
Graph
Supertypes: trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any

Members list

Concise view

Value members

Concrete methods

Diagnostic plots for the PCA

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Prints a summary of the PCA to console

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Inherited methods

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Inherited from:: Product

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Inherited from:: Product

Concrete fields

Breeze SVD object for the centred data matrix

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Cumulative variance of the principal components

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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.

Attributes

Number of observations

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Column names (as a List)

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Number of variables

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Proportion of variance explained by each principal component

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n x p matrix of scores - the rotated data

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Standard deviations of the principal components

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The total variance of the principal components

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Variances of the principal components

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Centred data matrix

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Column means (for centring)

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