ssw {spdep} | R Documentation

## Compute the sum of dissimilarity

### Description

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

### Usage

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

### Arguments

`data` | A matrix with observations in the nodes.
`id` | Node index to compute the cost
`method` | Character for declare the distance method. For "euclidean", "maximum", "manhattan", "canberra", "binary" and "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="other"`, any function must be informed in `otherfun` argument.
`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.
`otherfun` | A user defined function to compute the distance

### Value

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

### Author(s)

Elias T. Krainski and Renato M. Assuncao

### See Also

See Also as `nbcost`

### Examples

    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)

[Package spdep version 0.4-56 Index]