nbcosts: Compute cost of edges
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description Usage Arguments Value Note Author(s) See Also

The cost of each edge is the distance between it nodes. This function compute this distance using a data.frame with observations vector in each node.

nbcost(data, id, id.neigh,  method = c("euclidean", "maximum", 
    "manhattan", "canberra", "binary", "minkowski", "mahalanobis"),
    p = 2, cov, inverted = FALSE)
nbcosts(nb, data,  method = c("euclidean", "maximum", 
    "manhattan", "canberra", "binary", "minkowski", "mahalanobis"),
    p = 2, cov, inverted = FALSE)

`nb`	An object of `nb` class. See `poly2nb` for details.
`data`	A matrix with observations in the nodes.
`id`	Node index to compute the cost
`id.neigh`	Idex of neighbours nodes of node `id`
`method`	Character or function to declare distance method. If `method` is character, method must be "mahalanobis" or "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowisk". If `method` is one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowisk", see `dist` for details, because this function as used to compute the distance. If `method="mahalanobis"`, the mahalanobis distance is computed between neighbour areas. If `method` is a `function`, this function is used to compute the distance.
`p`	The power of the Minkowski distance.
`cov`	The covariance matrix used to compute the mahalanobis distance.
`inverted`	logical. If 'TRUE', 'cov' is supposed to contain the inverse of the covariance matrix.