RankControl-class: RankControl Class
In simonfqy/rankdistext:
Description
Details
Slots
User-defined Criterion
See Also
Description
A virtual S4 class to store control parameters for model fitting.
Details
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.
Slots
EM_limitmaximum number of EM iteration
EM_epsilonconvergence error for weights and cluster probabilities in EM iteration
SearchPi0_limitmaximum number of iterations in the local search of pi0.
SearchPi0_FUNa function object that gives a goodness of fit criterion. The default is log likelihood.
SearchPi0_fast_traversala 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_messagea logical value. If TRUE, the location of the current pi0 is shown.
SearchPi0_neighboura 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_controla 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.
User-defined Criterion
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.
See Also
simonfqy/rankdistext documentation built on May 29, 2019, 8:19 p.m.
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Description Details Slots User-defined Criterion See Also
Description
A virtual S4 class to store control parameters for model fitting.
Details
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.
Slots
EM_limitmaximum number of EM iteration
EM_epsilonconvergence error for weights and cluster probabilities in EM iteration
SearchPi0_limitmaximum number of iterations in the local search of pi0.
SearchPi0_FUNa function object that gives a goodness of fit criterion. The default is log likelihood.
SearchPi0_fast_traversala 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_messagea logical value. If TRUE, the location of the current pi0 is shown.
SearchPi0_neighboura 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_controla list to be passed to
optimx. The list must not contain a componentmaximize=TRUEsince internally the negation of the likelihood function is minimized.
User-defined Criterion
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.
See Also
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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