Description Usage Arguments Details Value

View source: R/tdpca_change_handlers.R View source: R/tdpca_change_handlers.R

Changes an input correlation matrix that is based on lagged samples.
This function assumes that the correlation matrix is based on
observations *X_t = vec(x_{t - l}, x_{t - l + 1}, …, x_t)*.

1 2 | ```
change_cor_mat_tdpca(cor_mat, lag, affected_dims, do_nearPD = TRUE,
draw_cor = NULL, draw_sd = NULL)
``` |

`cor_mat` |
A correlation matrix of lagged observations to be changed. |

`lag` |
The lag used to obtain the lagged observations. |

`affected_dims` |
A vector specifying which unlagged dimensions
(dimensions of |

`do_nearPD` |
A logical indicating whether the Matrix::nearPD function should be run on the changed correlation matrix to find the closest positve definite matrix to it. Highly recommended, as the changes in correlation are not guaranteed to result in a valid correlation matrix. |

`draw_cor` |
A function to draw n (any natural number) changes in correlation from. |

`draw_sd` |
A function to draw n (any natural number) changes in standard deviation from. |

The lagged observations *X_t* imposes a block structure on its
correlation matrix, where the l + 1 diagonal blocks are all estimates of the
correlation matrix of *x_t*. `change_cor_mat_tdpca`

takes a set of
indices indicating affected dimensions of *x_t*, and translates this into
corresponding changes in the input correlation matrix of *X_t*.
A change in one dimension of *x_t* (the unlagged, original data)
corresponds to changes in (lag + 1) dimensions of *X_t*.
See `change_cor_mat`

for additional information.

A changed correlation matrix, guaranteed to be positive definite if do_nearPD = TRUE.

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