CLM: Circular Local Minimization

In isocir: Isotonic Inference for Circular Data

Circular Local Minimization

Description

This function computes the Circular Local Minimization.

Usage

CLM(data, order0, ws=NULL, control.method=c("msce","cirktau"))

Arguments

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".

Details

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".

Value

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)

Author(s): Sandra Barragán. Maintainer: <[e-mail]>

References

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.

See Also

sce, cond.test, mrl, isocir, plot.isocir.

Examples

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))

isocir documentation built on Aug. 17, 2023, 9:07 a.m.