confintMSmix {MSmix} | R Documentation |
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 class 'ciMSmix'
print(x, ...)
Arguments
object |
An object of class |
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 |
... |
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.
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 (whenG>1
), otherwiseNULL
.
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)