# ssw

0th

Percentile

##### 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". 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.

See Also as nbcost

• ssw
##### 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)

Documentation reproduced from package spdep, version 0.6-9, License: GPL (>= 2)

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