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)
A matrix with observations in the nodes.
Node index to compute the cost
Character or function to declare distance method.
method is character, method must be "mahalanobis" or
"euclidean", "maximum", "manhattan", "canberra", "binary"
method is one of "euclidean", "maximum",
"manhattan", "canberra", "binary" or "minkowisk", see
dist for details,
because this function as used to compute the distance.
method="mahalanobis", the mahalanobis distance
is computed between neighbour areas.
method is a
function, this function is
used to compute the distance.
The power of the Minkowski distance.
The covariance matrix used to compute the mahalanobis distance.
logical. If 'TRUE', 'cov' is supposed to contain the inverse of the covariance matrix.
A numeric, the sum of dissimilarity between the observations
data and the mean (scalar of vector) of
See Also as