bamlss: neighbormatrix – R documentation

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neighbormatrix

Compute a Neighborhood Matrix from Spatial Polygons

Description

The function takes a SpatialPolygonsDataFrame and computes the neighbor penalty matrix that can be used to fit a Markov random field, e.g., using the smooth constructor smooth.construct.mrf.smooth.spec.

Usage

## Compute the neighborhood matrix.
neighbormatrix(x, type = c("boundary", "dist", "delaunay", "knear"),
  k = 1, id = NULL, nb = FALSE, names = NULL, ...)

## Plot neighborhood structure.
plotneighbors(x, add = FALSE, ...)

Arguments

`x`	An object of class `"SpatialPolygons"` or `"SpatialPolygonsDataFrame"`.
`type`	Which type of neighborhood structure should be used, `"boundary"` uses `poly2nb`, `"dist"` uses function `dnearneigh`, `"delaunay"` uses function `tri2nb` and option `"knear"` applies function `knn2nb`.
`k`	For `type = "knear"`, specifies number of nearest neighbors.
`id`	An identifier variable for which the penalty matrix should be computed.
`nb`	Should only the neighborhood object be returned.
`names`	Specifies the column where the regions names are provided in the data slot in the `"SpatialPolygonsDataFrame"`
`add`	Should the neighborhood structure be added to an existing plot?
`...`	Arguments to be passed to function `poly2nb`, `dnearneigh`, `tri2nb` or `knn2nb`.

Examples

data("LondonFire")

## Compute polygon boundary based
## neighborhood matrix.
nm <- neighbormatrix(LondonBoroughs)
print(nm)

## Plot neighborhood structures.
plotneighbors(LondonBoroughs)
plotneighbors(LondonBoroughs, type = "delaunay")
plotneighbors(LondonBoroughs, type = "dist", d1 = 0, d2 = 0.15)

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25