ssw {spdep}R Documentation

Compute the sum of dissimilarity

Description

This function computes the sum of dissimilarity between each observation and the mean (scalar of vector) of the observations.

Usage

ssw(data, id, method = c("euclidean", "maximum", 
    "manhattan", "canberra", "binary", "minkowski", "mahalanobis", 
    "other"), p = 2, cov, inverted = FALSE, otherfun)

Arguments

data A matrix with observations in the nodes.
id Node index to compute the cost
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 distance. If method="mahalanobis", the mahalanobis distance is computed between neighbour areas. If method="other", any function must be informed in otherfun argument.
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 numeric, the sum of dissimilarity between the observations id of data and the mean (scalar of vector) of this observations.

Author(s)

Elias T. Krainski and Renato M. Assuncao

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)

[Package spdep version 0.4-34 Index]