Convert draws to a posterior draws object — sample-as

brms's converters, with brms's arguments and brms's output. The as_draws_*() family takes variable (exact names unless regex) and returns a posterior draws object; as_draws() is brms's as_draws_list(). as.matrix(), as.array() and as.data.frame() return brms's unclassed objects, with brms's deprecated pars and subset accepted under brms's own warning.

Usage

# S3 method for class 'frmtmb_draws'
as_draws(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

# S3 method for class 'frmtmb_draws'
as.data.frame(
  x,
  row.names = NULL,
  optional = TRUE,
  pars = NA,
  variable = NULL,
  draw = NULL,
  subset = NULL,
  ...
)

# S3 method for class 'frmtmb_draws'
as.array(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)

# S3 method for class 'frmtmb_draws'
as_draws_matrix(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

# S3 method for class 'frmtmb_draws'
as_draws_array(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

# S3 method for class 'frmtmb_draws'
as_draws_df(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

# S3 method for class 'frmtmb_draws'
as_draws_list(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

# S3 method for class 'frmtmb_draws'
as_draws_rvars(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)

as.mcmc(x, ...)

# S3 method for class 'frmtmb_draws'
as.mcmc(
  x,
  pars = NA,
  fixed = FALSE,
  combine_chains = FALSE,
  inc_warmup = FALSE,
  ...
)

# S3 method for class 'frmtmb_draws'
as.matrix(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)

Arguments

x: A frmtmb_draws object.
variable: Variables to keep, by exact name unless regex.
regex: If TRUE, variable is a regular expression.
inc_warmup: If TRUE, each chain starts with its warmup iterations, which frm_sample() keeps when rstan saved them (save_warmup = TRUE, rstan's default and brms's). Without saved warmup the draws come alone, as in brms. As in brms, it is refused with combine_chains = TRUE and on thinned draws.
...: For as.matrix(), as.array() and as.data.frame(), the regex, fixed and inc_warmup brms passes on, and the iteration and chain it hands to posterior::subset_draws() (as.array(x, chain = 1) is the first chain); anything else is refused by name, rather than silently changing nothing.
row.names, optional: Accepted for the generic and unused, as in brms.
pars: Variables to keep, in brms's spelling: NA (the default) for all of them, otherwise a character vector matched as a regular expression unless fixed = TRUE. The argument sits in brms's own second position, so as.mcmc(x, TRUE) is refused here exactly as brms refuses it.
draw: Draws to keep, by index.
subset: brms's deprecated alias of draw; it warns.
fixed: If TRUE, pars is matched by exact name.
combine_chains: If TRUE, one mcmc object over the pooled draws; otherwise an mcmc.list with one component per chain, which is what coda's diagnostics (gelman.diag()) need.

Value

A posterior::draws_matrix: one column per sampled variable and one row per draw.

Examples

# \donttest{
if (requireNamespace("posterior", quietly = TRUE) &&
    requireNamespace("tmbstan", quietly = TRUE) &&
    requireNamespace("rstan", quietly = TRUE) &&
    !frmtmb.sample:::tmbstan_build_broken()) {
  set.seed(9)
  dd <- data.frame(x = rnorm(80), g = factor(rep(1:8, 10)))
  dd$y <- rnorm(80, 1 + 0.5 * dd$x + rnorm(8, 0, 0.5)[dd$g], 1)
  fit <- frm(bf(y ~ x + (1 | g)) + gaussian(), data = dd)
  ds <- frm_sample(fit, chains = 1, iter = 500, refresh = 0)

  # hands the draws to the posterior package, keeping the frmtmb
  # parameter names
  dm <- as_draws(ds)
  posterior::summarise_draws(dm)
  # which is what variables() lists
  head(variables(ds))
}
#> frm_sample(): default priors (brms 2.23 defaults; prior = "flat" puts brms's (flat) in every slot)
#>   Intercept          student_t(3, 1, 2.5)
#>   sigma              student_t(3, 0, 2.5)  [natural scale]
#>   sd                 student_t(3, 0, 2.5)  [natural sd scale]
#>   b                  (flat), as brms leaves slopes
#> Warning: The largest R-hat is 1.1, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> [1] "b_Intercept"      "b_x"              "sigma"            "r_g[1,Intercept]"
#> [5] "r_g[2,Intercept]" "r_g[3,Intercept]"
# }