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Summarize a fitted choice model

Source: R/posterior.R
summary.RprobitB_fit.Rd

Summarizes the marginal posterior distributions of the fitted model parameters with selectable statistics.

Usage

# S3 method for class 'RprobitB_fit'
summary(
  object,
  variables = NULL,
  statistics = c("mean", "mode", "sd", "rhat", "ess_bulk"),
  probs = NULL,
  ...
)

Arguments

object

[RprobitB_fit]
Fitted choice model.

variables

[character() | NULL]
Posterior variables to summarize.

statistics

[character()]
Posterior statistics to report, in this order. Available are "mean", "median", "mode", "sd", "mcse_mean", "mcse_median", "mcse_sd", "rhat", "ess_bulk", and "ess_tail", see the details.

probs

[numeric() | NULL]
Optional unique probabilities for posterior quantiles.

...

Currently not used.

Value

A summary.RprobitB_fit object.

Details

Every statistic describes the marginal posterior of one variable and is computed from the retained draws of all chains:

  • mean: the average of the draws, the usual point estimate.

  • median: the median value of the draws. If it differs clearly from the mean, the posterior is skewed.

  • mode: the most probable value. For continuous draws, it is the peak of a kernel density estimate; for integer-valued draws, such as the active class count, it is the most frequent value.

  • sd: the standard deviation of the draws, the posterior uncertainty of the parameter.

  • q<100 * p>: the quantile of probability p. With probs = c(0.025, 0.975), the two columns are the limits of the 95% credible interval.

  • mcse_mean, mcse_median, mcse_sd: the Monte Carlo standard errors of the mean, median, and standard deviation, the sampling error of these estimates that more iterations would reduce. Good values are below a tenth of sd; larger values mean that the reported digits are not yet reliable and the sampler should run longer.

  • rhat: the rank-normalized, folded split-R-hat of Vehtari et al. (2021). It compares the variance between the halves of all chains with the variance within them. With a single chain, it compares the two halves of that chain. Good values are at most 1.01; larger values mean that the chains have not mixed and the sampler should run longer, see plot(type = "trace").

  • ess_bulk: the effective sample size for the center of the posterior, the number of independent draws that carry the same information as the correlated draws. Good values are at least 100 per chain; smaller values mean that the sampler should run longer.

  • ess_tail: the smaller of the effective sample sizes of the 5% and 95% quantiles. It governs the precision of quantiles, credible intervals, and the standard deviation. The same rule applies: at least 100 per chain is good.

References

Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P (2021). “Rank-Normalization, Folding, and Localization: An Improved \(\widehat{R}\) for Assessing Convergence of MCMC.” Bayesian Analysis, 16(2), 667–718. doi:10.1214/20-BA1221 .

Examples

set.seed(1)
model <- fit(
  choice ~ x | 0, dgp_parameters = list(beta = c(x = 1)), chains = 1
)
summary(model)
#> Bayesian probit choice model
#> Formula: choice ~ x | 0 | 0 
#> Samples: 500 retained per chain, 1 chain
#>  variable dgp mean mode    sd rhat ess_bulk
#>   beta[x]   1  1.4  1.5 0.243 1.11     21.2