ssw: Compute the sum of dissimilarity

In spdep: Spatial Dependence: Weighting Schemes, Statistics

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"), 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.

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)

spdep documentation built on Sept. 13, 2024, 5:07 p.m.