consensus: Consensus Relations
In relations: Data Structures and Algorithms for Relations
consensus R Documentation
Consensus Relations
Description
Compute consensus relations of a relation ensemble.
Usage
relation_consensus(x, method = NULL, weights = 1,
control = list(), ...)
Arguments
x
an ensemble of relations (see
relation_ensemble()),
or something which can be coerced to such.
method
a character string specifying one of the built-in
methods for computing consensus relations, or a function to be
taken as a user-defined method, or NULL (default value). If
a character string, its lower-cased version is matched against the
lower-cased names of the available built-in methods using
pmatch(). See Details for available built-in
methods and defaults.
weights
a numeric vector with non-negative case weights.
Recycled to the number of elements in the ensemble given by x
if necessary.
control
a list of control parameters. See Details.
...
a list of control parameters (overruling those specified
in control).
Details
Consensus relations “synthesize” the information in the
elements of a relation ensemble into a single relation, often by
minimizing a criterion function measuring how dissimilar consensus
candidates are from the (elements of) the ensemble (the so-called
“optimization approach”), typically of the form
Φ(R) = ∑ w_b d(R_b, R) ^ e, where d is a suitable
dissimilarity measure (see relation_dissimilarity()),
w_b is the case weight given to element R_b of the
ensemble, and e ≥ 1. Such consensus relations are called
“central relations” in Régnier (1965). For e = 1, we
obtain (generalized) medians; e = 2 gives (generalized) means
(least squares consensus relations).
Available built-in methods are as follows. Apart from Condorcet's and
the unrestricted Manhattan and Euclidean consensus methods, these are
applicable to ensembles of endorelations only.
"Borda"the consensus method proposed by Borda (1781).
For each relation R_b and object x, one determines the
Borda/Kendall scores, i.e., the number of objects y such
that y R_b x. These are then aggregated across relations
by weighted averaging. Finally, objects are ordered according to
their aggregated scores. Note that this may result in a weak order
(i.e., with objects being tied).
One can enforce a linear order by setting the control parameter
L to TRUE, and obtain a relation ensemble with up to
n or all such solutions by additionally setting the control
parameter n to some positive integer or "all",
respectively.
"Copeland"the consensus method proposed by Copeland
(1951). For each relation R_b and object x, one
determines the Copeland scores, i.e., the number of objects
y such that y R_b x, minus the number of objects
y such that x R_b y. Like the Borda method, these are
then aggregated across relations by weighted averaging. Finally,
objects are ordered according to their aggregated scores.
Note that this may result in a weak order
(i.e., with objects being tied).
One can enforce a linear order by setting the control parameter
L to TRUE, and obtain a relation ensemble with up to
n or all such solutions by additionally setting the control
parameter n to some positive integer or "all",
respectively.
"Condorcet"the consensus method proposed by Condorcet
(1785). For a given ensemble of crisp relations, this minimizes
the criterion function Φ with d as symmetric
difference distance and e = 1 over all possible crisp
relations. In the case of endorelations, consensus is obtained by
weighting voting, such that x R y if the weighted number of
times that x R_b y is no less than the weighted number of
times that this is not the case. Even when aggregating linear
orders, this can lead to intransitive consensus solutions
(“effet Condorcet”).
One can obtain a relation ensemble with up to n or all such
solutions consensus relations by setting the control parameter
n to some positive integer or "all", respectively.
"CS"the consensus method of Cook and Seiford (1978)
which determines a linear order minimizing the criterion function
Φ with d as generalized Cook-Seiford (ranking)
distance and e = 1 via solving a linear sum assignment
problem.
One can obtain a relation ensemble with up to n or all such
consensus relations by setting the control parameter n to
some positive integer or "all", respectively.
"symdiff/F"an exact solver for determining the
consensus relation of an ensemble of crisp endorelations by
minimizing the criterion function Φ with d as
symmetric difference (“symdiff”) distance and e = 1
over a suitable class (“Family”) of crisp endorelations as
indicated by F, with values:
Ggeneral (crisp) endorelations.
Aantisymmetric relations.
Ccomplete relations.
Eequivalence relations: reflexive, symmetric, and
transitive.
Llinear orders: complete, reflexive,
antisymmetric, and transitive.
Mmatches: complete and reflexive.
Opartial orders: reflexive, antisymmetric and
transitive.
Ssymmetric relations.
Ttournaments: complete, irreflexive and
antisymmetric (i.e., complete and asymmetric).
Wweak orders (complete preorders, preferences,
“orderings”): complete, reflexive and transitive.
preorderpreorders: reflexive and transitive.
transitivetransitive relations.
Can also be referred to as "SD/F".
Consensus relations are determined by reformulating the consensus
problem as a binary program (for the relation incidences), see
Hornik and Meyer (2007) for details. The solver employed can be
specified via the control argument solver, with currently
possible values "glpk", "lpsolve", "symphony"
or "cplex" or a unique abbreviation thereof, specifying to
use the solvers from packages Rglpk (default),
lpSolve, Rsymphony, or Rcplex, respectively.
Unless control option sparse is false, a sparse formulation
of the binary program is used, which is typically more efficient.
For fitting equivalences and weak orders (cases E and
W) it is possible to specify the number of classes k
using the control parameter k. For fitting weak orders,
one can also specify the number of elements in the classes via
control parameter l.
Additional constraints on the incidences of the consensus solution
can be given via the control parameter constraints, in the
form of a 3-column matrix whose rows give row and column indices
i and j and the corresponding incidence I_{ij}.
(I.e., incidences can be constrained to be zero or one on an
object by object basis.)
One can obtain a relation ensemble with up to n or all such
consensus relations by setting the control parameter n to
some positive integer or "all", respectively.
(See the examples.)
"manhattan"the (unrestricted) median of the
ensemble, minimizing Φ with d as Manhattan (symmetric
difference) distance and e = 1 over all (possibly fuzzy)
relations.
"euclidean"the (unrestricted) mean of the ensemble,
minimizing Φ with d as Euclidean distance and
e = 2 over all (possibly fuzzy) relations.
"euclidean/F"an exact solver for determining
the restricted least squares Euclidean consensus relation of an
ensemble of endorelations by minimizing the criterion function
Φ with d as Euclidean difference distance and
e = 2 over a suitable family of crisp endorelations as
indicated by F, with available families and control
parameters as for methods "symdiff/F".
"majority"a generalized majority method for which the
consensus relation contains of all tuples occurring with a
relative frequency of more than 100 p percent (of 100
percent if p = 1). The fraction p can be specified
via the control parameter p. By default, p = 1/2 is
used.
"CKS/F"an exact solver for determining the
consensus relation of an ensemble of crisp endorelations by
minimizing the criterion function Φ with d as
Cook-Kress-Seiford (“CKS”) distance and e = 1 over a
suitable class (“Family”) of crisp endorelations as
indicated by F, with available families and control
parameters as for methods "symdiff/F".
For fitting equivalences and weak orders (cases E and
W) it is possible to specify the number of classes k
using the control parameter k.
One can obtain a relation ensemble with up to n or all such
consensus relations by setting the control parameter n to
some positive integer or "all", respectively.
"PC/F"an exact solver for determining the
consensus relation of an ensemble of crisp endorelations by
minimizing the criterion function Φ with d as
(generalized) paired comparison (“PC”) distance and
e = 1 over a suitable class (“Family”) of crisp
endorelations as indicated by F, with available families and
control parameters as for methods "symdiff/F", and
control option delta for specifying the paired comparison
discrepancies.
For fitting equivalences and weak orders (cases E and
W) it is possible to specify the number of classes k
using the control parameter k.
One can obtain a relation ensemble with up to n or all such
consensus relations by setting the control parameter n to
some positive integer or "all", respectively.
Value
The consensus relation(s).
References
J. C. Borda (1781),
Mémoire sur les élections au scrutin.
Histoire de l'Académie Royale des Sciences.
W. D. Cook and M. Kress (1992),
Ordinal information and preference structures: decision models and
applications.
Prentice-Hall: New York.
ISBN: 0-13-630120-7.
W. D. Cook and L. M. Seiford (1978),
Priority ranking and consensus formation.
Management Science, 24/16, 1721–1732.
doi: 10.1287/mnsc.24.16.1721.
M. J. A. de Condorcet (1785),
Essai sur l'application de l'analyse à la probabilité des décisions
rendues à la pluralité des voix.
Paris.
A. H. Copeland (1951),
A Reasonable Social Welfare Function.
mimeo, University of Michigan.
E. J. Emond and D. W. Mason (2000),
A new technique for high level decision support.
Technical Report ORD Project Report PR2000/13, Operational
Research Division, Department of National Defence, Canada.
K. Hornik and D. Meyer (2007),
Deriving consensus rankings from benchmarking experiments.
In R. Decker and H.-J. Lenz,
Advances in Data Analysis.
Studies in Classification, Data Analysis, and Knowledge Organization.
Springer-Verlag: Heidelberg, 163–170.
F. Marcotorchino and P. Michaud (1982).
Agrégation de similarités en classification automatique.
Revue de Statistique Appliquée, 30/2, 21–44.
https://eudml.org/doc/106132.
S. Régnier (1965),
Sur quelques aspects mathématiques des problèmes de classification
automatique.
ICC Bulletin, 4, 175–191.
Examples
## Consensus equivalence.
## (I.e., in fact, consensus partition.)
## Classification of 30 felines, see Marcotorchino and Michaud (1982).
data("Felines")
## Consider each variable an equivalence relation on the objects.
relations <- as.relation_ensemble(Felines)
## This gives a relation ensemble of length 14 (number of variables in
## the data set).
## Now fit an equivalence relation to this:
E <- relation_consensus(relations, "symdiff/E")
## And look at the equivalence classes:
ids <- relation_class_ids(E)
## Or, more nicely:
split(rownames(Felines), ids)
## Which is the same as in the paper ...
## Consensus linear order.
## Example from Cook and Kress, pages 48ff.
## Relation from paired comparisons.
pm <- matrix(c(0, 1, 0, 1, 1,
0, 0, 0, 1, 1,
1, 1, 0, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 1, 0),
nrow = 5,
byrow = TRUE,
dimnames = list(letters[1:5], letters[1:5]))
## Note that this is a Cook and Kress "preference matrix" where entry
## (i,j) is one iff object i is preferred to object j (i > j).
## Set up the corresponding '<' relation:
R <- as.relation(t(pm))
relation_incidence(R)
relation_is(R, "tournament")
## Closest linear order:
L <- relation_consensus(R, "symdiff/L")
relation_incidence(L)
## Visualize provided that Rgraphviz is available.
if(require("Rgraphviz")) plot(L)
## But note that this linear order is not unique.
L <- relation_consensus(R, "symdiff/L", control = list(n = "all"))
print(L)
if(require("Rgraphviz")) plot(L)
## (Oh no: c is once first and once last.)
## Closest weak order relation with at most 3 indifference classes:
W3 <- relation_consensus(R, "symdiff/W", control = list(k = 3))
relation_incidence(W3)
## Consensus weak orders.
## Example from Emond and Mason, pages 28f.
## The reference provides 21 partial rankings of 15 objects,
## in 3 groups of 7 rankings (corresponding to three different
## ranking criteria) with respective weights 4, 5, and 7.
wei <- rep.int(c(4, 5, 7), rep(7, 3))
## The rankings are written by listing the object labels from the
## best to the worst, with a leading minus indicating a tie with
## the previous object:
EM_inputs <-
c("6 1 -7 -9 10 3 8 11 5 -12 2 -4 -13",
"6 10 9 3 4 -8 7 1 -5 -11 2 12 13 14 15",
"6 10 3 7 8 11 5 14 15 12 1 -4 -13 2 -9",
"6 9 -11 10 3 14 12 7 4 5 2 1 8 13 15",
"10 6 7 1 11 -13 4 2 3 9 12 14 -15 8 5",
"6 9 8 -10 11 4 1 5 7 15 2 12 14 13 3",
"1 -6 -10 7 -12 9 3 4 -11 -14 -15 2 -13 8",
"4 -10 1 -7 6 -9 -13 5 -14 3 12 8 11 -15 2",
"4 -9 5 1 14 11 8 3 6 2 -13 10 12 7 15",
"4 2 -5 8 15 7 11 -14 1 -12 -13 10 9 6",
"2 -11 -12 -14 -15 6 -13 3 -4 9 8 -10 1 -5 -7",
"4 14 10 2 5 3 1 13 12 7 15 8 11 6 9",
"4 2 5 1 15 7 13 14 3 -12 8 11 6 9 10",
"12 1 3 -4 2 11 -13 -15 9 14 6 8 7 -10 5",
"5 4 9 2 -7 14 8 -11 3 1 15 12 6 10 13",
"11 9 -14 15 12 3 4 13 8 6 7 10 5",
"12 11 2 1 3 9 8 10 13 -14 6 4 -15 5 7",
"4 -5 10 -12 3 8 -11 6 -7 -9 13 14 15",
"12 5 -13 14 3 8 15 4 9 -10 11 6 7",
"4 -5 -8 11 6 14 7 1 -2 -15 10 3 13 9 -12",
"10 8 5 -11 6 -14 9 4 -13 -15 3 -12 2 1")
## Using the Emond-Mason paired comparison dissimilarity, there
## are three consensus rankings when using the above weights:
EM_solutions <-
c("4 10 5-11 1 -2-14 3-12 9 8 6 7 13-15",
"4 10 5-11 1 -2 9 14 3-12 8 6 7 13-15",
"4 10 5-11 2-14 1 3-12 9 8 6 7 13-15")
## We can reproduce this as follows.
## We first provide a reader for the rankings, and a maker for
## creating the (possibly partial) ranking with the appropriate
## domain:
reader <- function(s) {
strsplit(unlist(strsplit(gsub(" *-", "-", s),
" +")),
"-",
fixed = TRUE)
}
maker <- function(s) {
ranking(lapply(reader(s), as.numeric),
domain = as.numeric(1 : 15))
}
EM_inputs <- lapply(EM_inputs, maker)
EM_solutions <- lapply(EM_solutions, maker)
## Package 'relations' uses NA for non-diagonal incidences
## featuring unranked objects.
## Following the reference, we impute these by zeroes:
ens <- relation_impute(relation_ensemble(list = EM_inputs), "omit")
## We can now obtain all consensus weak orders (corresponding to
## complete rankings) as follows:
con <- relation_consensus(ens, "PC/W", wei, delta = "EM", all = TRUE)
## To verify that these agree with the solutions given in the
## reference:
sets::set_outer(con, relation_ensemble(list = EM_solutions), `==`)
relations documentation built on March 7, 2023, 8:01 p.m.
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| consensus | R Documentation |
Consensus Relations
Description
Compute consensus relations of a relation ensemble.
Usage
relation_consensus(x, method = NULL, weights = 1,
control = list(), ...)
Arguments
x |
an ensemble of relations (see
|
method |
a character string specifying one of the built-in
methods for computing consensus relations, or a function to be
taken as a user-defined method, or |
weights |
a numeric vector with non-negative case weights.
Recycled to the number of elements in the ensemble given by |
control |
a list of control parameters. See Details. |
... |
a list of control parameters (overruling those specified
in |
Details
Consensus relations “synthesize” the information in the
elements of a relation ensemble into a single relation, often by
minimizing a criterion function measuring how dissimilar consensus
candidates are from the (elements of) the ensemble (the so-called
“optimization approach”), typically of the form
Φ(R) = ∑ w_b d(R_b, R) ^ e, where d is a suitable
dissimilarity measure (see relation_dissimilarity()),
w_b is the case weight given to element R_b of the
ensemble, and e ≥ 1. Such consensus relations are called
“central relations” in Régnier (1965). For e = 1, we
obtain (generalized) medians; e = 2 gives (generalized) means
(least squares consensus relations).
Available built-in methods are as follows. Apart from Condorcet's and the unrestricted Manhattan and Euclidean consensus methods, these are applicable to ensembles of endorelations only.
"Borda"the consensus method proposed by Borda (1781). For each relation R_b and object x, one determines the Borda/Kendall scores, i.e., the number of objects y such that y R_b x. These are then aggregated across relations by weighted averaging. Finally, objects are ordered according to their aggregated scores. Note that this may result in a weak order (i.e., with objects being tied).
One can enforce a linear order by setting the control parameter
LtoTRUE, and obtain a relation ensemble with up to n or all such solutions by additionally setting the control parameternto some positive integer or"all", respectively."Copeland"the consensus method proposed by Copeland (1951). For each relation R_b and object x, one determines the Copeland scores, i.e., the number of objects y such that y R_b x, minus the number of objects y such that x R_b y. Like the Borda method, these are then aggregated across relations by weighted averaging. Finally, objects are ordered according to their aggregated scores. Note that this may result in a weak order (i.e., with objects being tied).
One can enforce a linear order by setting the control parameter
LtoTRUE, and obtain a relation ensemble with up to n or all such solutions by additionally setting the control parameternto some positive integer or"all", respectively."Condorcet"the consensus method proposed by Condorcet (1785). For a given ensemble of crisp relations, this minimizes the criterion function Φ with d as symmetric difference distance and e = 1 over all possible crisp relations. In the case of endorelations, consensus is obtained by weighting voting, such that x R y if the weighted number of times that x R_b y is no less than the weighted number of times that this is not the case. Even when aggregating linear orders, this can lead to intransitive consensus solutions (“effet Condorcet”).
One can obtain a relation ensemble with up to n or all such solutions consensus relations by setting the control parameter
nto some positive integer or"all", respectively."CS"the consensus method of Cook and Seiford (1978) which determines a linear order minimizing the criterion function Φ with d as generalized Cook-Seiford (ranking) distance and e = 1 via solving a linear sum assignment problem.
One can obtain a relation ensemble with up to n or all such consensus relations by setting the control parameter
nto some positive integer or"all", respectively."symdiff/F"an exact solver for determining the consensus relation of an ensemble of crisp endorelations by minimizing the criterion function Φ with d as symmetric difference (“symdiff”) distance and e = 1 over a suitable class (“Family”) of crisp endorelations as indicated by F, with values:
Ggeneral (crisp) endorelations.
Aantisymmetric relations.
Ccomplete relations.
Eequivalence relations: reflexive, symmetric, and transitive.
Llinear orders: complete, reflexive, antisymmetric, and transitive.
Mmatches: complete and reflexive.
Opartial orders: reflexive, antisymmetric and transitive.
Ssymmetric relations.
Ttournaments: complete, irreflexive and antisymmetric (i.e., complete and asymmetric).
Wweak orders (complete preorders, preferences, “orderings”): complete, reflexive and transitive.
preorderpreorders: reflexive and transitive.
transitivetransitive relations.
Can also be referred to as
"SD/F".Consensus relations are determined by reformulating the consensus problem as a binary program (for the relation incidences), see Hornik and Meyer (2007) for details. The solver employed can be specified via the control argument
solver, with currently possible values"glpk","lpsolve","symphony"or"cplex"or a unique abbreviation thereof, specifying to use the solvers from packages Rglpk (default), lpSolve, Rsymphony, or Rcplex, respectively. Unless control optionsparseis false, a sparse formulation of the binary program is used, which is typically more efficient.For fitting equivalences and weak orders (cases
EandW) it is possible to specify the number of classes k using the control parameterk. For fitting weak orders, one can also specify the number of elements in the classes via control parameterl.Additional constraints on the incidences of the consensus solution can be given via the control parameter
constraints, in the form of a 3-column matrix whose rows give row and column indices i and j and the corresponding incidence I_{ij}. (I.e., incidences can be constrained to be zero or one on an object by object basis.)One can obtain a relation ensemble with up to n or all such consensus relations by setting the control parameter
nto some positive integer or"all", respectively. (See the examples.)"manhattan"the (unrestricted) median of the ensemble, minimizing Φ with d as Manhattan (symmetric difference) distance and e = 1 over all (possibly fuzzy) relations.
"euclidean"the (unrestricted) mean of the ensemble, minimizing Φ with d as Euclidean distance and e = 2 over all (possibly fuzzy) relations.
"euclidean/F"an exact solver for determining the restricted least squares Euclidean consensus relation of an ensemble of endorelations by minimizing the criterion function Φ with d as Euclidean difference distance and e = 2 over a suitable family of crisp endorelations as indicated by F, with available families and control parameters as for methods
"symdiff/F"."majority"a generalized majority method for which the consensus relation contains of all tuples occurring with a relative frequency of more than 100 p percent (of 100 percent if p = 1). The fraction p can be specified via the control parameter
p. By default, p = 1/2 is used."CKS/F"an exact solver for determining the consensus relation of an ensemble of crisp endorelations by minimizing the criterion function Φ with d as Cook-Kress-Seiford (“CKS”) distance and e = 1 over a suitable class (“Family”) of crisp endorelations as indicated by F, with available families and control parameters as for methods
"symdiff/F".For fitting equivalences and weak orders (cases
EandW) it is possible to specify the number of classes k using the control parameterk.One can obtain a relation ensemble with up to n or all such consensus relations by setting the control parameter
nto some positive integer or"all", respectively."PC/F"an exact solver for determining the consensus relation of an ensemble of crisp endorelations by minimizing the criterion function Φ with d as (generalized) paired comparison (“PC”) distance and e = 1 over a suitable class (“Family”) of crisp endorelations as indicated by F, with available families and control parameters as for methods
"symdiff/F", and control optiondeltafor specifying the paired comparison discrepancies.For fitting equivalences and weak orders (cases
EandW) it is possible to specify the number of classes k using the control parameterk.One can obtain a relation ensemble with up to n or all such consensus relations by setting the control parameter
nto some positive integer or"all", respectively.
Value
The consensus relation(s).
References
J. C. Borda (1781), Mémoire sur les élections au scrutin. Histoire de l'Académie Royale des Sciences.
W. D. Cook and M. Kress (1992), Ordinal information and preference structures: decision models and applications. Prentice-Hall: New York. ISBN: 0-13-630120-7.
W. D. Cook and L. M. Seiford (1978), Priority ranking and consensus formation. Management Science, 24/16, 1721–1732. doi: 10.1287/mnsc.24.16.1721.
M. J. A. de Condorcet (1785), Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix. Paris.
A. H. Copeland (1951), A Reasonable Social Welfare Function. mimeo, University of Michigan.
E. J. Emond and D. W. Mason (2000), A new technique for high level decision support. Technical Report ORD Project Report PR2000/13, Operational Research Division, Department of National Defence, Canada.
K. Hornik and D. Meyer (2007), Deriving consensus rankings from benchmarking experiments. In R. Decker and H.-J. Lenz, Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag: Heidelberg, 163–170.
F. Marcotorchino and P. Michaud (1982). Agrégation de similarités en classification automatique. Revue de Statistique Appliquée, 30/2, 21–44. https://eudml.org/doc/106132.
S. Régnier (1965), Sur quelques aspects mathématiques des problèmes de classification automatique. ICC Bulletin, 4, 175–191.
Examples
## Consensus equivalence.
## (I.e., in fact, consensus partition.)
## Classification of 30 felines, see Marcotorchino and Michaud (1982).
data("Felines")
## Consider each variable an equivalence relation on the objects.
relations <- as.relation_ensemble(Felines)
## This gives a relation ensemble of length 14 (number of variables in
## the data set).
## Now fit an equivalence relation to this:
E <- relation_consensus(relations, "symdiff/E")
## And look at the equivalence classes:
ids <- relation_class_ids(E)
## Or, more nicely:
split(rownames(Felines), ids)
## Which is the same as in the paper ...
## Consensus linear order.
## Example from Cook and Kress, pages 48ff.
## Relation from paired comparisons.
pm <- matrix(c(0, 1, 0, 1, 1,
0, 0, 0, 1, 1,
1, 1, 0, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 1, 0),
nrow = 5,
byrow = TRUE,
dimnames = list(letters[1:5], letters[1:5]))
## Note that this is a Cook and Kress "preference matrix" where entry
## (i,j) is one iff object i is preferred to object j (i > j).
## Set up the corresponding '<' relation:
R <- as.relation(t(pm))
relation_incidence(R)
relation_is(R, "tournament")
## Closest linear order:
L <- relation_consensus(R, "symdiff/L")
relation_incidence(L)
## Visualize provided that Rgraphviz is available.
if(require("Rgraphviz")) plot(L)
## But note that this linear order is not unique.
L <- relation_consensus(R, "symdiff/L", control = list(n = "all"))
print(L)
if(require("Rgraphviz")) plot(L)
## (Oh no: c is once first and once last.)
## Closest weak order relation with at most 3 indifference classes:
W3 <- relation_consensus(R, "symdiff/W", control = list(k = 3))
relation_incidence(W3)
## Consensus weak orders.
## Example from Emond and Mason, pages 28f.
## The reference provides 21 partial rankings of 15 objects,
## in 3 groups of 7 rankings (corresponding to three different
## ranking criteria) with respective weights 4, 5, and 7.
wei <- rep.int(c(4, 5, 7), rep(7, 3))
## The rankings are written by listing the object labels from the
## best to the worst, with a leading minus indicating a tie with
## the previous object:
EM_inputs <-
c("6 1 -7 -9 10 3 8 11 5 -12 2 -4 -13",
"6 10 9 3 4 -8 7 1 -5 -11 2 12 13 14 15",
"6 10 3 7 8 11 5 14 15 12 1 -4 -13 2 -9",
"6 9 -11 10 3 14 12 7 4 5 2 1 8 13 15",
"10 6 7 1 11 -13 4 2 3 9 12 14 -15 8 5",
"6 9 8 -10 11 4 1 5 7 15 2 12 14 13 3",
"1 -6 -10 7 -12 9 3 4 -11 -14 -15 2 -13 8",
"4 -10 1 -7 6 -9 -13 5 -14 3 12 8 11 -15 2",
"4 -9 5 1 14 11 8 3 6 2 -13 10 12 7 15",
"4 2 -5 8 15 7 11 -14 1 -12 -13 10 9 6",
"2 -11 -12 -14 -15 6 -13 3 -4 9 8 -10 1 -5 -7",
"4 14 10 2 5 3 1 13 12 7 15 8 11 6 9",
"4 2 5 1 15 7 13 14 3 -12 8 11 6 9 10",
"12 1 3 -4 2 11 -13 -15 9 14 6 8 7 -10 5",
"5 4 9 2 -7 14 8 -11 3 1 15 12 6 10 13",
"11 9 -14 15 12 3 4 13 8 6 7 10 5",
"12 11 2 1 3 9 8 10 13 -14 6 4 -15 5 7",
"4 -5 10 -12 3 8 -11 6 -7 -9 13 14 15",
"12 5 -13 14 3 8 15 4 9 -10 11 6 7",
"4 -5 -8 11 6 14 7 1 -2 -15 10 3 13 9 -12",
"10 8 5 -11 6 -14 9 4 -13 -15 3 -12 2 1")
## Using the Emond-Mason paired comparison dissimilarity, there
## are three consensus rankings when using the above weights:
EM_solutions <-
c("4 10 5-11 1 -2-14 3-12 9 8 6 7 13-15",
"4 10 5-11 1 -2 9 14 3-12 8 6 7 13-15",
"4 10 5-11 2-14 1 3-12 9 8 6 7 13-15")
## We can reproduce this as follows.
## We first provide a reader for the rankings, and a maker for
## creating the (possibly partial) ranking with the appropriate
## domain:
reader <- function(s) {
strsplit(unlist(strsplit(gsub(" *-", "-", s),
" +")),
"-",
fixed = TRUE)
}
maker <- function(s) {
ranking(lapply(reader(s), as.numeric),
domain = as.numeric(1 : 15))
}
EM_inputs <- lapply(EM_inputs, maker)
EM_solutions <- lapply(EM_solutions, maker)
## Package 'relations' uses NA for non-diagonal incidences
## featuring unranked objects.
## Following the reference, we impute these by zeroes:
ens <- relation_impute(relation_ensemble(list = EM_inputs), "omit")
## We can now obtain all consensus weak orders (corresponding to
## complete rankings) as follows:
con <- relation_consensus(ens, "PC/W", wei, delta = "EM", all = TRUE)
## To verify that these agree with the solutions given in the
## reference:
sets::set_outer(con, relation_ensemble(list = EM_solutions), `==`)
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