# RankControlWeightedKendall Class

### 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

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