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)
```

## 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

Elias T. Krainski and Renato M. Assuncao