StatMethRank (version 1.3)

mwdbm: Fit a mixture weighted distance-based model

Description

This function computes fitting of mixture weighted distance-based model for the given data set of complete rankings.

Usage

mwdbm(dset, G, dset.agg = TRUE, dtype = "Kendall", noise = FALSE, iter = 100)

Arguments

dset

data set of complete rankings

G

number of clusters

dset.agg

whether the data set is in the aggregated form (default as FALSE)

dtype

type of the weighted distance measure Kendall or K(default) : "Weighted Kendall's tau", SqrtSpearman or SS : "Square root of weighted Spearman", Spearman or S : "Weighted Spearman", Footrule or F : "Weighted Spearman's footrule"

noise

whether a noise cluster is contained (default as FALSE)

iter

number of iterations of the EM algorithm (default as 100)

Value

a list of the fitting result, containing the following objects: $clusterNum number of clusters (excluding the noise) $dtype type of the distance measure $noise whether a noise cluster is contained $iterNum actual number of iterations of the EM algorithm $convergence whether the complete-data loglikelihood converges $clusterProb probability of each cluster $modalRank modal rankings $weight weight vectors for clusters $trueLoglik the true loglikelihood by the fitted model $squaredPearsonStat the sum of squares of Pearson residuals

Examples

data(Croon)
# Time comu
# mwdbm(Croon, 3)