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

## Examples

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