pat2dists | R Documentation |

Creates `n_sim`

null models by permutation of the original pattern and
calculates distances between all object of a pattern closer than `max_dist`

and determines the fractions of the perimeter of buffers with distance
`dist`

inside the study area (needed for edge correction).

```
pat2dists(
area,
pattern,
max_dist,
n_sim = 199,
max_tries = 1e+05,
save_pattern = FALSE,
verbose = FALSE
)
```

`area` , `pattern` |
Geodata (polygons) of study area and pattern in the
formats |

`max_dist` |
Maximum distance measured in the pattern. Usually smaller than half the diameter of the study area. |

`n_sim` |
Number of simulated patterns (randomizations) to be generated
for computing the envelope and correcting the biased empirical pcf.
Determines together with |

`max_tries` |
How often shall a relocation of an object be tried while randomizing the pattern. |

`save_pattern` |
Shall one simulated pattern be returned in the attributes
for debugging/later inspections. The pattern is provided as WKB (list of
raw vectors) in the attribute |

`verbose` |
Provide progress information in the console. Roman numerals (x: 10, C: 100, D: 500, M: 1000) indicate the progress of the simulation and 'e' denotes the empirical PCF. |

Null models are created by randomly rotating and randomly placing all objects within the study area without overlap. They are used for correcting the biased pcf and constructing a pointwise critical envelope (cf. Nuske et al. 2009).

Measuring distances between objects and permutation of the pattern is done using GEOS.

An object of class dists. If `save_pattern = TRUE`

an additional
attribute `randPattern`

is returned containing a WKB (list of raw vectors).

Nuske, R.S., Sprauer, S. and Saborowski, J. (2009): Adapting the pair-correlation function for analysing the spatial distribution of canopy gaps. Forest Ecology and Management, 259(1): 107–116. https://doi.org/10.1016/j.foreco.2009.09.050

`dists2pcf()`

, `plot.fv_pcf()`

```
# it's advised against setting n_sim < 199
ds <- pat2dists(area=sim_area, pattern=sim_pat_reg, max_dist=25, n_sim=3)
# verbose and returns one randomized pattern for debugging
ds_plus <- pat2dists(area=sim_area, pattern=sim_pat_reg, max_dist=5, n_sim=3,
verbose=TRUE, save_pattern=TRUE)
## Not run:
# wk's plot function needs additional package 'vctrs'
plot(attr(ds_plus, "randPattern"))
## End(Not run)
```

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