# Compute Pairwise Competition Indices

### Description

This function computes competition indices based on pairs of plants, ignoring higher-order interactions.

### Usage

1 2 |

### Arguments

`plants` |
A spatstat point pattern object (class |

`maxN` |
Maximum number of nearest neighbors to include as potential competitors. Default is NULL (no restriction). |

`maxR` |
Maximum radius to search for potential competitors. Default is NULL (no restriction). |

`select` |
Optional user-supplied selection function for choosing competitors. Must have arguments |

`selpar` |
Parameter(s) for |

`kernel` |
Competition kernel function for computing the effect of competitor |

`kerpar` |
Parameter(s) for |

### Details

Traditionally, a competition index for a subject plant *i* is obtained in two stages: (1) Choose a set of competitors of *i* by some selection rule. (2) Compute a measure of the effect of each competitor *j* on plant *i*, and add over *j*. This effect of *j* on *i* is normally a function of the sizes of both plants and of the distance between them, which we call a competition kernel. The kernel may depend on other plant attributes, like species, and in some rare instances on the distance ranks or on the number of competitors. Conceptually, the first stage is not strictly necessary, it could be replaced by specifying zero kernel values (the effect of the selection is usually to truncate the kernel function beyond some distance). However, a separate selection rule may be more transparent, and may reduce the computational effort of searching for neighbors.

Some simple selection rules can be implemented by giving a value to `maxN`

and/or `maxR`

. In any case, reasonable limits on these variables may be advisable for reducing computation. If both arguments `maxN`

and `maxR`

are given, the neighbourhood is defined as the intersection of the neighbourhoods specified by these arguments.

More complex rules can be specified by the `select`

function, with parameters in `selpar`

. See `select`

for examples.

Kernel computation is specified by the `kernel`

function and the parameters in `kerpar`

. See `kernel`

for examples.

### Value

Returns the point pattern `plants`

, with the competition indices added to `marks(plants)`

as a data frame column `cindex`

.

### Note

Requires the spatstat package.

### Author(s)

Oscar García.

### References

http://forestgrowth.unbc.ca/siplab

García, O. “Siplab, a spatial individual-based plant modelling system”. Computational Ecology and Software 4(4), 215-222. 2014.

### See Also

`select`

, `kernel`

, `edges`

### Examples

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