nbcosts: Compute cost of edges

In spdep: Spatial Dependence: Weighting Schemes, Statistics

Compute cost of edges

Description

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.

Usage

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)

Arguments

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.

Value

A object of nbdist class. See nbdists for details.

Note

The neighbours must be a connected graph.

Author(s)

Elias T. Krainski and Renato M. Assuncao

See Also

See Also as nbdists, nb2listw

spdep documentation built on Sept. 1, 2025, 1:09 a.m.