CLM | R Documentation |
This function computes the Circular Local Minimization.
CLM(data, order0, ws=NULL, control.method=c("msce","cirktau"))
data |
matrix of circular data to be processed. |
order0 |
the initial order to be improved. |
ws |
the weights. |
control.method |
by default "msce", maximization of "cirktau". |
This function computes the Circular Local Minimization of the MSCE by default. It computes the Circular Local Maximization of the Circular Kendall Tau if control.method="cirktau"
.
This function returns a list with the next elements:
order0 |
the initial order introduced in the arguments. |
msce0 |
the mean sum of circular errors of the initial order with the data. |
or itemtau0the mean circular Kendall Tau of the initial order with the data.
final_order |
the final order after proccess the Circular Local Minimization. |
bestsce |
the msce of the final order with the data. |
or
bestau |
the mean circular Kendall Tau of the final order with the data. |
Author(s): Sandra Barragán. Maintainer: <sandra.barragan@gmail.com>
DWORK, C., KUMAR, R., NAOR, M. and SIVAKUMAR, D. (2001), Rank Aggregation Methods for the Web, Proceedings of the 10th International World Wide Web Conference, pp. 613–622.
sce
, cond.test
, mrl
, isocir
, plot.isocir
.
data(cirgenes)
aggre_order <- ACO(cirgenes[,c(1:5)], method="TSP", control.method="time")$TSP_order[1,]
CLM(cirgenes[,c(1:5)], order0 = c(1:5))
#datos<- rbind (c(0, 1/10, 1/9, 1, 11/10, 10/9)*pi,c(0, 1/2, 1/10, 1, 3/2, 11/10)*pi)
#CLM(datos, order0 = c(1:6))
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