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

## 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

 ```1 2 3 4 5 6``` ```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 as `nbdists`, `nb2listw`