ssw
From spdep v0.69
by Roger Bivand
Compute the sum of dissimilarity
This function computes the sum of dissimilarity between each observation and the mean (scalar of vector) of the observations.
 Keywords
 multivariate, cluster
Usage
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". Ifmethod
is one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowisk", seedist
for details, because this function as used to compute the distance. Ifmethod="mahalanobis"
, the mahalanobis distance is computed between neighbour areas. Ifmethod
is afunction
, 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.
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)
Community examples
Looks like there are no examples yet.