The means of each column (axis) of the data.
The cumulative proportion of variance explained by the first n principal components.
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)
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)
The number of observations.
The proportion of variance explained by each principal component.
Translate the original data points to the PC axes.
The standard deviations of the principal components.
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.