best_models: Precision for the k Best-Performing Models
In MCMCprecision: Precision of Discrete Parameters in Transdimensional MCMC

Description Usage Arguments Examples

Assesses the precision in estimating the ranking of the k best-performing models.

1	best_models(samples, k, ties.method = "min")

`samples`	a matrix with posterior samples (one per row) for the model posterior probabilities (one model per column). Can be estimated using `stationary` with the argument `summary = FALSE`.
`k`	number of best-performing models to be considered
`ties.method`	a character string specifying how ties are treated, see `rank`

# a sequence of uncorrelated model indices
mult <- rmultinom(1000, 1, c(.05, .6, .15, .12, .08))
idx <- apply(mult, 2, which.max)
z <- letters[idx]
stat <- stationary(z, summary = FALSE)
best_models(stat, 3)