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

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

## Arguments

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.

## Value

A numeric, the sum of dissimilarity between the observations

id of data and the mean (scalar of vector) of this observations.

## Author

Elias T. Krainski and Renato M. Assuncao

See Also as nbcost

## Examples

data(USArrests)
n <- nrow(USArrests)
ssw(USArrests, 1:n)
#>  3701.394
ssw(USArrests, 1:(n/2))
#>  1910.214
ssw(USArrests, (n/2+1):n)
#>  1625.882
ssw(USArrests, 1:(n/2)) + ssw(USArrests, (n/2+1):n)
#>  3536.096