# 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

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 |

`data` |
A matrix with observations in the nodes. |

`id` |
Node index to compute the cost |

`id.neigh` |
Idex of neighbours nodes of node |

`method` |
Character or function to declare distance method.
If |

`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`