View source: R/PLMIXfunctions.R
summary.gsPLMIX | R Documentation |
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.
## 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,
...
)
object |
Object of class |
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). |
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.
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 |
HPDintervals |
Numeric matrix with 100 |
Modal_orderings |
Numeric |
call |
The matched call. |
Cristina Mollica and Luca Tardella
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.
summary.mcmc
and HPDinterval
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)
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