View source: R/bandwidth_selection_cv_tnkde_sf.R

bw_tnkde_corr_factor | R Documentation |

Calculating the border correction factor for both time and network bandwidths

```
bw_tnkde_corr_factor(
net_bws,
time_bws,
diggle_correction,
study_area,
events,
events_loc,
lines,
method,
kernel_name,
tol,
digits,
max_depth,
sparse
)
```

`net_bws` |
A vector of network bandwidths |

`time_bws` |
A vector of time bandwidths |

`diggle_correction` |
A Boolean indicating if the correction factor for edge effect must be used. |

`study_area` |
A feature collection of polygons representing the limits of the study area. |

`events` |
A feature collection of points representing the events |

`events_loc` |
A feature collection of points representing the unique location of events |

`lines` |
A feature collection of linestrings representing the underlying lines of the network |

`method` |
The name of the NKDE to use |

`kernel_name` |
The name of the kernel to use |

`tol` |
float indicating the minimum distance between the events and the lines' extremities when adding the point to the network. When points are closer, they are added at the extremity of the lines. |

`digits` |
An integer, the number of digits to keep for the spatial coordinates |

`max_depth` |
The maximal depth for continuous or discontinuous NKDE |

`sparse` |
A Boolean indicating if sparse or regular matrices should be used by the Rcpp functions. These matrices are used to store edge indices between two nodes in a graph. Regular matrices are faster, but require more memory, in particular with multiprocessing. Sparse matrices are slower (a bit), but require much less memory. |

A list of two elements, first the network correction factors, then the time correction factors.

```
# no example provided, this is an internal function
```

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