mcmc_thin.RdThin results of running [mcmc()].`mcmc_thin` takes every `thin`'th sample, while `mcmc_sample` randomly selects a total of `n_sample` samples.
mcmc_thin(object, burnin = NULL, thin = NULL) mcmc_sample(object, n_sample, burnin = NULL)
| object | Results of running [mcmc()] |
|---|---|
| burnin | Optional integer number of iterations to discard as "burn-in". If given then samples `1:burnin` will be excluded from your results. Remember that the first sample represents the starting point of the chain. It is an error if this is not a positive integer or is greater than or equal to the number of samples (i.e., there must be at least one sample remaining after discarding burnin). |
| thin | Optional integer thinning factor. If given, then every `thin`'th sample is retained (e.g., if `thin` is 10 then we keep samples 1, 11, 21, ...). |
| n_sample | The number of samples to draw from `object` *with replacement*. This means that `n_sample` can be larger than the total number of samples taken (though it probably should not) |