Thin samples

Thin results of running monty_sample(), reducing autocorrelation between samples and saving space. This function may be useful before running onward simulations, or before saving output to disk.

Usage

monty_samples_thin(
  samples,
  thinning_factor = NULL,
  burnin = NULL,
  save_full_chains = FALSE
)

Arguments

samples

A monty_samples object, from running monty_sample()

thinning_factor

Optional integer thinning factor. If given, then we save every thinning_factor'th step. So if thinning_factor = 2 we save every second step, and if 10, we'd save every 10th. We will always include the last point in the chain, and exclude points counting backwards.

burnin

Number of steps to discard as burnin from the start of the chain.

save_full_chains

Logical, indicating whether or not the full chains of the parameters (without discarding the burnin and thinning) should additionally be saved. This is useful if you are thinning to reduce the size of observations but still want to retain the full chains of the parameters for evaluation purposes.

Value

A monty_samples object (as for monty_sample()), typically with fewer samples.

Limitations

Subsetting parameters ($pars) and density ($density) is easy enough, but the main use of this function is subsetting chains that have observations, otherwise you could simply cast to samples_df and use functions from the posterior package.

We can only subset observations where the observer was able to tidy them up into a nice array for us. This will typically be the case (for example when using odin/dust, trajectories will be in a nice array).

More specifically, an array is "nice" if the last two dimensions represent samples and chains; in that case we subset along the samples dimension and leave everything else alone. For each element of $observations that cannot be subsetted, we will issue a warning.

We cannot generally subset "details", and will pass that along unmodified.