# nbcosts

0th

Percentile

##### Compute cost of edges

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.

Keywords
cluster, spatial
##### 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.

See Also as nbdists, nb2listw