View source: R/reconstruct_pattern_marks.R

reconstruct_pattern_marks | R Documentation |

Pattern reconstruction of marked pattern

```
reconstruct_pattern_marks(
pattern,
marked_pattern,
n_random = 1,
e_threshold = 0.01,
max_runs = 10000,
no_change = Inf,
annealing = 0.01,
r_length = 250,
r_max = NULL,
return_input = TRUE,
simplify = FALSE,
verbose = TRUE,
plot = FALSE
)
```

`pattern` |
ppp object with pattern. |

`marked_pattern` |
ppp object with marked pattern. See Details section for more information. |

`n_random` |
Integer with number of randomizations. |

`e_threshold` |
Double with minimum energy to stop reconstruction. |

`max_runs` |
Integer with maximum number of iterations if |

`no_change` |
Integer with number of iterations at which the reconstruction will stop if the energy does not decrease. |

`annealing` |
Double with probability to keep relocated point even if energy did not decrease. |

`r_length` |
Integer with number of intervals from |

`r_max` |
Double with maximum distance used during calculation of summary functions. If |

`return_input` |
Logical if the original input data is returned. |

`simplify` |
Logical if only pattern will be returned if |

`verbose` |
Logical if progress report is printed. |

`plot` |
Logical if pcf(r) function is plotted and updated during optimization. |

The function randomizes the numeric marks of a point pattern using pattern reconstruction
as described in Tscheschel & Stoyan (2006) and Wiegand & Moloney (2014). Therefore,
an unmarked as well as a marked pattern must be provided. The unmarked pattern must have
the spatial characteristics and the same observation window and number of points
as the marked one (see `reconstruct_pattern_*`

or `fit_point_process`

).
Marks must be numeric because the mark-correlation function is used as summary function.
Two randomly chosen marks are switch each iterations and changes only kept if the
deviation between the observed and the reconstructed pattern decreases.

`spatstat`

sets `r_length`

to 513 by default. However, a lower value decreases
the computational time while increasing the "bumpiness" of the summary function.

rd_mar

Kirkpatrick, S., Gelatt, C.D.Jr., Vecchi, M.P., 1983. Optimization by simulated annealing. Science 220, 671–680. <https://doi.org/10.1126/science.220.4598.671>

Tscheschel, A., Stoyan, D., 2006. Statistical reconstruction of random point patterns. Computational Statistics and Data Analysis 51, 859–871. <https://doi.org/10.1016/j.csda.2005.09.007>

Wiegand, T., Moloney, K.A., 2014. Handbook of spatial point-pattern analysis in ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8

`fit_point_process`

`reconstruct_pattern`

```
## Not run:
pattern_recon <- reconstruct_pattern(species_a, n_random = 1, max_runs = 1000,
simplify = TRUE, return_input = FALSE)
marks_sub <- spatstat.geom::subset.ppp(species_a, select = dbh)
marks_recon <- reconstruct_pattern_marks(pattern_recon, marks_sub,
n_random = 19, max_runs = 1000)
## End(Not run)
```

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