siplab (version 1.0)

pairwise: Compute Pairwise Competition Indices

Description

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

Usage

pairwise(plants, maxN = NULL, maxR = NULL, select = NULL, selpar = NULL,
         kernel, kerpar = NULL)

Arguments

plants

A spatstat point pattern object (class ppp), containing the plants coordinates and marks with the plant size and possibly other attributes.

maxN

Maximum number of nearest neighbors to include as potential competitors. Only one non-null maxN or maxR can be given. Default is NULL (no restriction).

maxR

Maximum radius to search for potential competitors. Only one non-null maxN or maxR can be given. Default is NULL (no restriction).

select

Optional user-supplied selection function for choosing competitors. Must have arguments (imarks, jmarks, dists, dranks, par), where imarks are the marks for the subject plant (a 1-row data frame), jmarks is a data fr

selpar

Parameter(s) for select, usually a list or vector. Default: NULL.

kernel

User-supplied competition kernel function for computing the effect of competitor $j$ on the subject plant $i$. Must have arguments (imarks, jmarks, dists, dranks, par), where imarks are the marks for the subject plant (a 1-row da

kerpar

Parameter(s) for kernel, usually a list or vector. Default: NULL.

Value

• Returns the point pattern plants, with the competition indices added to marks(plants) as a data frame column cindex.

encoding

UTF-8

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). A separate selection rule may reduce the computational effort of searching for neighbors.

Some simple selection rules can be implemented by giving a value to maxN or maxR. In any case, reasonable limits in one of these variables (they are mutually exclusive) may be advisable for reducing computation.

More complex rules can be specified by the select function, and parameters in selpar. See select for examples.

Kernel computation is specified by the kernel function, and parameters in kerpar. See kernel for examples.

References

http://forestgrowth.unbc.ca/siplab

See Also

select, kernel, edges

Examples

# Hegyi (1974) index
summary(pairwise(finpines, maxR = 6, kernel=powers.ker, kerpar =
  list(pi=1, pj=1, pr=1, r0=0.3048, smark="diameter")))