RankControlWeightedKendall-class: RankControlWeightedKendall Class

In rankdist: Distance Based Ranking Models

Description

A S4 class to store control parameters for Weighted Kendall distance model fitting. It is derived from class RankControl-class.

Details

RankControlWeightedKendall is derived from virtual class RankControl. All slots in RankControl are still valid. This control class tells the solver to fit a model based on Weighted Kendall distance. The control parameters that start with prefix EM_ are intended for the EM iteration. The ones with prefix SeachPi0 control the behaviour of searching model ranking.

Slots

EM_limit

maximum number of EM iteration

EM_epsilon

convergence error for weights and cluster probabilities in EM iteration

SearchPi0_limit

maximum number of iterations in the local search of pi0.

SearchPi0_FUN

a function object that gives a goodness of fit criterion. The default is log likelihood.

SearchPi0_fast_traversal

a logical value. If TRUE (by default), immediately traverse to the neighbour if it is better than the current pi0. Otherwise, check all neighbours and traverse to the best one.

SearchPi0_show_message

a logical value. If TRUE, the location of the current pi0 is shown.

SearchPi0_neighbour

a character string specifying which type of neighbour to use in the local search. Supported values are: "Cayley" to use neighbours in terms of Cayley distance or "Kendall" to use neighbours in terms of Kendall distance. Note that Kendall neighbours are a subset of Cayley neighbours

optimx_control

a list to be passed to optimx. The list must not contain a component maximize=TRUE since internally the negation of the likelihood function is minimized.

assumption

A character string specifying which assumption to use when handling top-q rankings. Supported choices are "equal-probability" and "tied-rank".

See Also

RankData, RankInit, RankControl

Examples

# enabling  warnings
testctrl = new("RankControlWeightedKendall",optimx_control=list(dowarn=TRUE))

rankdist documentation built on July 28, 2019, 1:02 a.m.