prunecost
From spdep v0.69
by Roger Bivand
Compute cost of prune each edge
If any edge are dropped, the MST are pruned. This generate a two subgraphs. So, it makes a tree graphs and tree dissimilarity values are computed, one for each graph. The dissimilarity is the sum over sqared differences between the observactions in the nodes and mean vector of observations in the graph. The dissimilarity of original graph and the sum of dissimilarity of subgraphs are returned.
Usage
prunecost(edges, data, method = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "mahalanobis"), p = 2, cov, inverted = FALSE)
Arguments
 edges
 A matrix with 2 colums with each row is one edge
 data
 A data.frame with observations in the nodes.
 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 vector with the differences between the dissimilarity of all nodes
and the dissimilarity sum of all subgraphs obtained by pruning one
edge each time.
See Also
See Also as prunemst
Examples
d < data.frame(a=2:2, b=runif(5))
e < matrix(c(1,2, 2,3, 3,4, 4,5), ncol=2, byrow=TRUE)
sum(sweep(d, 2, colMeans(d))^2)
prunecost(e, d)
Community examples
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