Working with samples

This vignette outlines some ways of working with samples generated from running an MCMC with monty_sample. We’ll flesh this out as we develop nicer ways of working with samples, and mostly exists so that we can point to it from other documentation, including from other packages (e.g., from odin2).

library(monty)

The structure of monty samples

All samplers and runners produce samples with the same basic structure. This structure is documented (here) so you are free to use this structure if you want to just dive in and manipulate things. Please treat output as read-only; extract data all you want, but make a copy and don’t change any value within the samples structure if you are going to pass it back into a monty function, as we assume that they have not been modified.

Below, samples is a poorly mixed result of samples with 2 parameters, 2000 samples, and 4 chains. It was the result of running monty_sample() on the banana model from vignette("samplers"), and has class monty_samples.

samples
#> 
#> ── <monty_samples: 2 parameters x 2000 samples x 4 chains> ─────────────────────
#> ℹ Parameters: 'alpha' and 'beta'
#> ℹ Conversion to other types is possible:
#> → ! posterior::as_draws_array() [package installed, but not loaded]
#> → ! posterior::as_draws_df() [package installed, but not loaded]
#> → ! coda::as.mcmc.list() [package installed, but not loaded]
#> ℹ See `?monty_sample()` and `vignette("samples")` for more information

Each monty_samples object contains an element pars which contains sampled parameters

dim(samples$pars)
#> [1]    2 2000    4

Conversion to posterior’s draw objects

We implement methods for posterior::as_draws_array and posterior::as_draws_df, which you can use to convert to formats that you might be familiar with from use with other statistical packages. This only preserves the parameters ($pars) from above.

samples_df <- posterior::as_draws_df(samples)

With this, you can access summary methods already implemented elsewhere:

posterior::summarise_draws(samples_df)
#> # A tibble: 2 × 10
#>   variable  mean median    sd   mad     q5   q95  rhat ess_bulk ess_tail
#>   <chr>    <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>    <dbl>
#> 1 alpha    2.27   0.645  3.06  1.39 -0.612  7.73  1.85     5.79     43.5
#> 2 beta     0.608  0.364  1.39  1.02 -2.05   2.78  2.10     5.32     11.7

With these objects you should be able to use any of the plotting functions in bayesplot, for example, MCMC visual diagnostics, without much trouble.

We also support conversion to draws_array; let us know if you need the other conversion functions (as_draws_list, as_draws_rvars, as_draws_matrix).

Conversion to coda’s mcmc.list objects

The coda package has utilities for working with MCMC, and many other packages are compatible with its mcmc.list object type (e.g., the ggmcmc package). We provide a method for coda’s as.mcmc.list, if that package is available:

samples_coda <- coda::as.mcmc.list(samples)
coda::effectiveSize(samples_coda)
#>    alpha     beta 
#> 39.92519 68.47306