RankDistanceModel: Fit A Mixture of Distance-based Models

In rankdist: Distance Based Ranking Models

Description

RankDistanceModel fits ranking models based on inputs

Usage

RankDistanceModel(dat, init, ctrl)

Arguments

dat	A RankData object.
init	A RankInit object.
ctrl	A RankControl object.

Details

The procedure will estimate central rankings, the probability of each cluster and weights.

Value

A list containing the following components:

modal_ranking.est

the estimated modal ranking for each cluster.

p

the marginal probability of each cluster.

w.est

the estimated weights of each cluster.

param.est

the phi parametrisation of weights of each cluster (for Weighted Kendall model only).

SSR

the sum of squares of Pearson residuals

log_likelihood

the fitted log_likelihood

BIC

the fitted Bayesian Information Criterion value

free_params

the number of free parameters in the model

expectation

the expected value of each observation given by the model

iteration

the number of EM iteration

model.call

the function call

See Also

RankData, RankInit, RankControl

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