tdpca: Tailors the choice of principal components for change...
In Tveten/tpca: A function for tailoring PCA to change detection

Description Usage Arguments Value

View source: R/tdpca.R

tdpca tailors the choice of principal components to keep when detection of changepoints in the mean vector or covariance matrix is the aim. It extends tpca by allowing the input to be a covariance matrix of a Hankel matrix (a data matrix with lagged variables stacked on top of eachother), and thus incorporate time dynamics. Note that the dimension for the change distribution is the dimension of the data without lagged variables. See the documentation for tpca for more information.

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tdpca(cov_mat, lag, change_distr = "full_uniform",
  divergence = "normal_hellinger", cutoff = 0.99,
  max_axes = ncol(cov_mat), n_sim = 10^3)

`cov_mat`	A covariance matrix of lagged variables. Must be positive definite.
`lag`	The number of lags used.
`change_distr`	A string or a change distribution object. A string can be used to choose among a set of already implemented distributions: 'full_uniform', 'mean_only', 'sd_only', 'cor_only'. Custom change distributions can be specified by using the `set_uniform_cd` function.
`divergence`	A string specifying which divergence metric to use. Available options: 'normal_hellinger', 'normal_KL' and 'normal_bhat'.
`cutoff`	A numeric between 0 and 1 governing how many principal axes to retain.
`max_axes`	An integer indicating the maximum number of axes that should be returned regardless of what the cutoff is.
`n_sim`	An integer specifying the number of simulation runs.

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

axes: A matrix with the chosen principal axes as rows, ordered in decreasing order of sensitivity.
which_axes: A vector indicating which principal axes that were chosen in decreasing order of sensitivity.
prop_axes_max: A vector with the proportion of simulations each axis was the most sensitive one.
divergence_sim: A matrix containing all the simulated draws from the divergence metric along each principal axis. It is of dimension data_dim x n_sim.
change_type: A character vector indicating the type of change for each iteration of the simulation.
change_sparsity: A numeric vector indicating the sparsity of the change for each iteration of the simulation.