ssw: Compute the sum of dissimilarity

Description Usage Arguments Value Author(s) See Also Examples

View source: R/prunecost.R

Description

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

Usage

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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

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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 May 23, 2021, 5:06 p.m.