ssw: Compute the sum of dissimilarity
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description Usage Arguments Value Author(s) See Also Examples

This function computes the sum of dissimilarity between each observation and the mean (scalar of vector) of the observations.

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ssw(data, id, method = c("euclidean", "maximum", 
    "manhattan", "canberra", "binary", "minkowski",
    "mahalanobis"), p = 2, cov, inverted = FALSE)

`data`	A matrix with observations in the nodes.
`id`	Node index to compute the cost
`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.

A numeric, the sum of dissimilarity between the observations id of data and the mean (scalar of vector) of this observations.

Elias T. Krainski and Renato M. Assuncao

See Also as nbcost

data(USArrests)
n <- nrow(USArrests)
ssw(USArrests, 1:n)
ssw(USArrests, 1:(n/2))
ssw(USArrests, (n/2+1):n)
ssw(USArrests, 1:(n/2)) + ssw(USArrests, (n/2+1):n)