Thin results of running `pmcmc()`

. This function may be useful
before using `pmcmc_predict()`

, or before saving pmcmc output to
disk. `pmcmc_thin`

takes every `thin`

'th sample, while
`pmcmc_sample`

randomly selects a total of `n_sample`

samples.

```
pmcmc_thin(object, burnin = NULL, thin = NULL)
pmcmc_sample(object, n_sample, burnin = NULL)
```

- object
Results of running

`pmcmc()`

- burnin
Optional integer number of iterations to discard as "burn-in". If given then samples

`1:burnin`

will be excluded from your results. 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, ...). Note that this can produce surprising results as it will always select the first sample but not necessarily always the last.- 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)