spdep (version 0.5-3)

prunecost: Compute cost of prune each edge

Description

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", "other"), 
    p = 2, cov, inverted = FALSE, otherfun)

Arguments

edges

A matrix with 2 colums with each row is one edge

data

A data.frame with observations in the nodes.

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 dista

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 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)