ror (version 1.2)

utagms.strong.necessary: UTA^GMS MCDA solver strong necessary relation

Description

Implements UTA^GMS robust ordinal regression: computes the strong necessary relation. Assumes ascending preferences, i.e. higher criterion evaluation means higher preferability (=better).

Usage

utagms.strong.necessary(performances, strongPrefs=NULL,
weakPrefs=NULL, indifPrefs=NULL, strictVF=FALSE)

Arguments

performances

m x n performance matrix with m alternatives and n criteria.

strongPrefs

k x 2 matrix of strong preferences statements (row indices of alternatives in the performance matrix). Each row r is a preference statements meaning that preferences[k,1] is strongly preferred to preferences[k,2].

weakPrefs

k x 2 matrix of weak preferences statements (row indices of alternatives in the performance matrix). Each row r is a preference statements meaning that preferences[k,1] is weakly preferred to preferences[k,2].

indifPrefs

k x 2 matrix of indifference preferences statements (row indices of alternatives in the performance matrix). Each row r is a preference statements meaning that preferences[k,1] is equally preferred to preferences[k,2].

strictVF

Whether to use sctrictly increasing (TRUE) or monotonously increasing (FALSE) value functions.

See Also

rorsmaa,ror-package

Examples

library(ror)

## Example with 3 alternatives and 3 criteria
performances <- matrix(c(1.0, 1.0, 1.0, 2.0, 1.0, 1.1, 2.0, 0.5, 3.0), ncol=3, byrow=TRUE)
## a3 > a2 (strongly preferred)
strongPrefs <- matrix(c(3, 2), ncol=2, byrow=TRUE)

## Strong necessary relation
strongnec <- utagms.strong.necessary(performances, strongPrefs)

## Sanity check, the relation have a3 >^N a2
stopifnot(strongnec[3,2] == TRUE)

## Not run: 
# ## Plot the relation
# plot(strongnec)
# ## End(Not run)