Description Usage Arguments Value

Performs efficient eigendecomposition of an input covariance matrix based on
which principal axes that are wanted. If all axes are wanted, `svd`

is used. `RSpectra::eigs_sym`

is used if only the
highest or lowest eigenvalues with corresponding eigenvectors are requested.

1 | ```
pca(cov_mat, axes = 1:data_dim)
``` |

`cov_mat` |
A covariance matrix. |

`axes` |
A vector indicating which principal axes are wanted. |

`pca`

returns an S3 object of class "pca". This is a list with
the following components:

`vectors`

A matrix with the chosen principal axes/eigenvectors as rows.

`values`

A vector of the corresponding eigenvalues

Tveten/tpca documentation built on June 10, 2021, 8:43 p.m.

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