Removes iterations from each chain of a
object at quasi-evenly spaced intervals. Post-MCMC thinning is useful for
developing long-running post-processing code with a smaller but otherwise identical
post_thin(post, keep_percent, keep_iters)
Proportion (between 0 and 1) of samples to keep from each chain.
Number of samples to keep from each chain.
The samples will be removed at as evenly spaced intervals as possible, however, this is not perfect. It is therefore recommended to use the full posterior for final post-processing calculations, but this should be fine for most development of long-running code.
keep_iters are supplied, an error will be returned requesting that only
one be used.
mcmc.list object, identical to
post, but with fewer samples of each node.
Iteration numbers are reset after thinning the samples. So if running
on output passed through
post_thin(), you cannot trust the burn-in or thinning counts.
Again, this is not an issue for developing post-processing code.
# load example mcmc.list data(cjs) # take note of original dimensions post_dim(cjs) # keep ~20% of the samples cjs_thin1 = post_thin(cjs, keep_percent = 0.2) # note burn-in and thin intervals no longer correct! # but desired outcome achieved - identical object but smaller post_dim(cjs_thin1) # keep 30 samples per chain cjs_thin2 = post_thin(cjs, keep_iters = 30) post_dim(cjs_thin2)
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