MSmix (version 2.0.0)

confintMSmix: Asymptotic confidence intervals for the fitted mixture of Mallows models with Spearman distance

Description

Return the asymptotic confidence intervals of the continuous parameters (component-specific precisions and weights) of a mixture of Mallows models with Spearman distance fitted to full rankings.

print method for class "ciMSmix".

Usage

confintMSmix(object, conf_level = 0.95)
# S3 method for ciMSmix
print(x, ...)

Value

An object of class "ciMSmix", namely a list with the following named components:

ci_theta

Numeric \(G\)\(\times\)\(2\) matrix with the confidence intervals of the component-specific precision parameters in each row.

ci_weights

Numeric \(G\)\(\times\)\(2\) matrix with the confidence intervals of the mixture weights in each row (when \(G>1\)), otherwise NULL.

Arguments

object

An object of class "emMSmix" returned by fitMSmix.

conf_level

Numeric: value in the interval (0,1] indicating the desired confidence level of the interval estimates. Defaults to 0.95.

x

An object of class "ciMSmix" returned by confintMSmix.

...

Further arguments passed to or from other methods (not used).

Details

The current implementation of the asymptotic confidence intervals assumes that the observed rankings are complete.

References

Crispino M, Mollica C and Modugno L (2025+). MSmix: An R Package for clustering partial rankings via mixtures of Mallows Models with Spearman distance. (submitted)

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

McLachlan G and Peel D (2000). Finite Mixture Models. Wiley Series in Probability and Statistics, John Wiley & Sons.

Examples

## Example 1. Simulate rankings from a 2-component mixture of Mallows models
## with Spearman distance.
set.seed(123)
d_sim <- rMSmix(sample_size = 75, n_items = 8, n_clust = 2)
rankings <- d_sim$samples
# Fit the basic Mallows model with Spearman distance.
set.seed(123)
fit1 <- fitMSmix(rankings = rankings, n_clust = 1, n_start = 10)
# Compute the asymptotic confidence intervals for the MLEs of the precision.
ci95_fit1 <- confintMSmix(object = fit1)
print(ci95_fit1)
# Fit the true model.
set.seed(123)
fit2 <- fitMSmix(rankings = rankings, n_clust = 2, n_start = 10)
# Compute the asymptotic confidence intervals for the MLEs of the weights and precisions.
ci95_fit2 <- confintMSmix(object = fit2)
print(ci95_fit2)