PCA

class PCA(val x: DenseMatrix[Double], val covmat: DenseMatrix[Double])

Perform Principal Components Analysis on input data. Handles scaling of the when computing the covariance matrix. Lazily produces the scores (the translation of the data to their new coordinates on the PC axes.

Input is a matrix that has data points as rows. Variable naming and documentation inspired and used directy from the 'princomp' function in R.

class Object
trait Matchable
class Any

Value members

Concrete fields

The means of each column (axis) of the data.

The means of each column (axis) of the data.

The cumulative proportion of variance explained by the first n principal components.

The cumulative proportion of variance explained by the first n principal components.

lazy val nobs: Int

The number of observations.

The number of observations.

The proportion of variance explained by each principal component.

The proportion of variance explained by each principal component.

Translate the original data points to the PC axes.

Translate the original data points to the PC axes.

The standard deviations of the principal components.

The standard deviations of the principal components.