make_partial: Censoring of complete rankings/orderings

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

Censoring of complete rankings/orderings

Description

Return partial top rankings/orderings from complete sequences obtained either with user-specified censoring patterns or with a random truncation.

Usage

make_partial(
  data,
  format_input,
  nranked = NULL,
  probcens = rep(1, ncol(data) - 1)
)

Arguments

data	Numeric N\timesK data matrix of complete sequences to be censored.
format_input	Character string indicating the format of the data input, namely "ordering" or "ranking".
nranked	Numeric vector of length N with the desired number of items ranked by each sample unit after censoring. If not supplied (NULL), the censoring patterns are randomly generated according to the probabilities in the probcens argument.
probcens	Numeric vector of length (K-1) with the probability of each censoring pattern to be employed for the random truncation of the complete sequences (normalization is not necessary). It works only if nranked argument is NULL (see 'Details'). Default is equal probabilities.

Details

The censoring of the complete sequences can be performed in: (i) a deterministic way, by specifying the number of top positions to be retained for each sample unit in the nranked argument; (ii) a random way, by sequentially specifying the probabilities of the top-1, top-2, ..., top-(K-1) censoring patterns in the probcens argument. Recall that a top-(K-1) sequence corresponds to a complete ordering/ranking.

Value

A list of two named objects:

partialdata	Numeric N\timesK data matrix of partial (censored) sequences with the same format of the input data and missing positions/items denoted with zero entries.
nranked	Numeric vector of length N with the number of items ranked by each sample unit after censoring.

Author(s)

Cristina Mollica and Luca Tardella

Examples

data(d_german)
head(d_german)
d_german_cens <- make_partial(data=d_german, format_input="ordering",
                              probcens=c(0.3, 0.3, 0.4))
head(d_german_cens$partialdata)

## Check consistency with the nominal censoring probabilities
round(prop.table(table(d_german_cens$nranked)), 2)

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