polydeclust2d: Calculate Decluster Weights for 2D Samples Using...
In truemoid/declustr: Analysis of Spatial Sample Clustering and Declustering

Description Usage Arguments Value Examples

View source: R/polygonal-2d.R

polydeclust2d calcuates decluster weights for 2D point sample data using Voronoi tesselations of the point data. Weights are calculated as the area of influence of each sample divided by the total area of the domain and then normalized so that the sum of all weights is 1. This normalization is the default but can be turned off with the normalize argument.

1 2	polydeclust2d(x, y, mask, expand_mask = 0, normalize = TRUE, estdomain = TRUE)

`x, y`	Numeric vectors. x and y axis coordinates with equal length. Duplicate samples are assigned a zero weight.
`mask`	Numeric dataframe. x and y coordinates of grid mask. This limits the area of tessellation and hence the magnitude of edge weights. The dataframe must have only two columns with x and y coordinates in that order (the name of the columns is not important).
`expand_mask`	Scalar number (default 0). Units to exapand the mask. Sometimes when samples are on the mask boundary it is necessary to slightly expand the mask so that edge samples are included in the weighting process. Usually only a small value (e.g., 1 unit) is needed.
`normalize`	Boolean (default `TRUE`). Normalize the weights so that they sum to 1. Otherwise weights sum to the area of the mask.
`estdomain`	Boolean (default `TRUE`). Estimate the spatial domain using a Ripley-Rasson estimate (`ripras`) and intersect with the input mask to create a window for tesselation. This approach may reduce edge weights where the input mask is extrapolated well beyond the limits of the data.

A labelled list with a named vector of positive weights optionally normalized such that the sum of weights is 1. The list also contains the point pattern object used for the tessellation and a point pattern object with only the mask (no ripras).