class PCA extends AnyRef
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.
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- PCA
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- new PCA(x: DenseMatrix[Double], covmat: DenseMatrix[Double])
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- lazy val center: DenseVector[Double]
The means of each column (axis) of the data.
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- val covmat: DenseMatrix[Double]
- lazy val cumuvar: DenseVector[Double]
The cumulative proportion of variance explained by the first n principal components.
- lazy val eigenvalues: DenseVector[Double]
Do SVD on the covariance matrix.
Do SVD on the covariance matrix.
eigenvalues: The vector of eigenvalues, from ranked from left to right with respect to how much of the variance is explained by the respective component.
loadings: the matrix of variable loadings (i.e., a matrix whose rows contain the eigenvectors (note: in R, the eigenvectors are the columns)
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- lazy val loadings: DenseMatrix[Double]
Do SVD on the covariance matrix.
Do SVD on the covariance matrix.
eigenvalues: The vector of eigenvalues, from ranked from left to right with respect to how much of the variance is explained by the respective component.
loadings: the matrix of variable loadings (i.e., a matrix whose rows contain the eigenvectors (note: in R, the eigenvectors are the columns)
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- lazy val nobs: Int
The number of observations.
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- lazy val propvar: DenseVector[Double]
The proportion of variance explained by each principal component.
- lazy val scores: DenseMatrix[Double]
Translate the original data points to the PC axes.
- lazy val sdev: DenseVector[Double]
The standard deviations of the principal components.
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- val x: DenseMatrix[Double]