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)Results of running pmcmc()
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).
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.
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)