rank_summaries: Descriptive summaries for a partial ordering/ranking dataset

In PLMIX: Bayesian Analysis of Finite Mixture of Plackett-Luce Models

Descriptive summaries for a partial ordering/ranking dataset

Description

Compute rank summaries and censoring patterns for a partial ordering/ranking dataset.

Usage

rank_summaries(
  data,
  format_input,
  mean_rank = TRUE,
  marginals = TRUE,
  pc = TRUE
)

Arguments

data	Numeric N\timesK data matrix of partial sequences.
format_input	Character string indicating the format of the data input, namely "ordering" or "ranking".
mean_rank	Logical: whether the mean rank vector has to be computed. Default is TRUE.
marginals	Logical: whether the marginal rank distributions have to be computed. Default is TRUE.
pc	Logical: whether the paired comparison matrix has to be computed. Default is TRUE.

Value

A list of named objects:

nranked	Numeric vector of length N with the number of items ranked by each sample unit.
nranked_distr	Frequency distribution of the nranked vector.
na_or_not	Numeric 3\timesK matrix with the counts of sample units that ranked or not each item. The last row contains the total by column, corresponding to the sample size N.
mean_rank	Numeric vector of length K with the mean rank of each item.
marginals	Numeric K\timesK matrix of the marginal rank distributions: the (i,j)-th entry indicates the number of units that ranked item i in the j-th position.
pc	Numeric K\timesK paired comparison matrix: the (i,i')-th entry indicates the number of sample units that preferred item i to item i'.

Author(s)

Cristina Mollica and Luca Tardella

References

Marden, J. I. (1995). Analyzing and modeling rank data. Monographs on Statistics and Applied Probability (64). Chapman & Hall, ISSN: 0-412-99521-2. London.

Examples

data(d_carconf)
rank_summaries(data=d_carconf, format_input="ordering")

PLMIX documentation built on July 1, 2025, 1:12 a.m.