summary.gsPLMIX {PLMIX}

Summary of the Gibbs sampling procedure for a Bayesian mixture of Plackett-Luce models

Description

summary method for class gsPLMIX. It provides summary statistics and credible intervals for the Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce models.

Usage

## S3 method for class 'gsPLMIX'
summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75,
  0.975), hpd_prob = 0.95, digits = 2, ...)

Arguments

object

Object of class gsPLMIX returned by the gibbsPLMIX function.

quantiles

Numeric vector of quantile probabilities.

hpd_prob

Numeric scalar in the grid of values spanning the interval (0,1) by 0.05, giving the posterior probability content of the HPD intervals. Supplied values outside the grid are rounded.

digits

Number of decimal places for rounding the posterior summaries.

...

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

Details

Posterior summaries include means, standard deviations, naive standard errors of the means (ignoring autocorrelation of the chain) and time-series standard errors based on an estimate of the spectral density at 0. They correspond to the statistics element of the output returned by the summary.mcmc function of the coda package. Highest posterior density (HPD) intervals are obtained by recalling the HPDinterval function of the coda package.

Value

A list of summary statistics for the gsPLMIX class object:

statistics

Numeric matrix with posterior summaries in each row (see 'Details').

quantiles

Numeric matrix with posterior quantiles at the given quantiles probabilities in each row.

HPDintervals

Numeric matrix with 100*hpd_prob% HPD intervals in each row.

Modal_orderings

Numeric G\timesK matrix with the estimated posterior modal orderings of each mixture component.

call

The matched call.

Author(s)

Cristina Mollica and Luca Tardella

References

Plummer, M., Best, N., Cowles, K. and Vines, K. (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, 6, pages 7–11, ISSN: 1609-3631.

See Also

summary.mcmc and HPDinterval

Examples

data(d_carconf)
GIBBS <- gibbsPLMIX(pi_inv=d_carconf, K=ncol(d_carconf), G=3, n_iter=30, n_burn=10)

## Summary of the Gibbs sampling procedure
summary(GIBBS)