Description Details Slots User-defined Criterion See Also

A virtual S4 class to store control parameters for model fitting.

RankControl class must be extended to reflect what distance metric should be used. Possibles extensions are `RankControlWeightedKendall`

. 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.

`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.

You can specify user-defined criterion to choose modal rankings. The function object SearchPi0_FUN takes a list as argument. The components in the list include the following. `obs`

: the number of observations.
`w.est`

: the estimated weights. `log_likelihood`

: the estimated log_likelihood. With this information, most of the popular information criterion can be supported and customized criterion can also be defined.
A larger returned value indicates a better fit. Note that if you are fitting a mixture model the EM algorithm always tries to maximized the log likelihood. Thus the default value should be used in this case.

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