Model structure behind a set of draws

nobs(), formula(), family(), getCall() and ngrps() on a frmtmb_draws report the model the sampler ran, by delegating to the fit stored inside it. They read structure only, so they work on draws from the formula route, which has no maximum-likelihood estimate.

Usage

# S3 method for class 'frmtmb_draws'
fixef(
  object,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  pars = NULL,
  ...
)

# S3 method for class 'frmtmb_draws'
VarCorr(
  x,
  sigma = 1,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

# S3 method for class 'frmtmb_draws'
ranef(
  object,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  pars = NULL,
  groups = NULL,
  ...
)

# S3 method for class 'frmtmb_draws'
nobs(object, ...)

# S3 method for class 'frmtmb_draws'
formula(x, ...)

# S3 method for class 'frmtmb_draws'
family(object, ...)

# S3 method for class 'frmtmb_draws'
getCall(x, ...)

# S3 method for class 'frmtmb_draws'
coef(object, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)

Arguments

object, x

A frmtmb_draws from frm_sample().

summary

If TRUE (brms's default), summaries; otherwise the draws, as above.

robust

If TRUE, median and MAD instead of mean and SD.

probs

The quantiles to report.

pars

For fixef() and ranef(), the coefficients to keep, by name without the b_ prefix, as in brms.

...

Refused: an argument the method does not have is an error naming it, rather than silently changing nothing.

sigma

Ignored, as in brms.

groups

For ranef(), the grouping factors to keep.

Value

As for the corresponding frmtmb_fit method.

Details

coef(), fixef(), ranef() and VarCorr() are posterior quantities, not structural ones, and they are brms's methods on these draws, compared identical() against brms's own installed methods in dev/brmsnames-findings.md. fixef() is a coefficients x statistics matrix; ranef() and coef() are a list keyed by grouping factor of levels x statistics x coefficients arrays, and coef() broadcasts every population-level coefficient over the levels, as brms's does; VarCorr() is a list keyed by grouping factor with sd, and cor and cov when the group has correlations, then residual__. With summary = FALSE each returns brms's raw draws instead: a draws x coefficients matrix, a draws x levels x coefficients array, or a draws x coefficients x coefficients array.

VarCorr()'s standard deviations and correlations are computed per draw from the sampled covariance parameters, because the sampler stores theta and not brms's sd_ and cor_ draws.

Examples

# \donttest{
if (requireNamespace("tmbstan", quietly = TRUE) &&
    requireNamespace("rstan", quietly = TRUE) &&
    !frmtmb.sample:::tmbstan_build_broken()) {
  set.seed(9)
  dd <- data.frame(x = rnorm(60), g = factor(rep(1:6, 10)))
  dd$y <- rnorm(60, 1 + 0.5 * dd$x + rnorm(6, 0, 0.5)[dd$g], 1)
  ds <- frm_sample(bf(y ~ x + (1 | g)), family = gaussian(),
                   data = dd, chains = 1, iter = 500, refresh = 0)
  nobs(ds)
  ngrps(ds)
  coef(ds)$g[1:3, , "Intercept"]
  dim(ranef(ds, summary = FALSE)$g)
  VarCorr(ds)$g$sd
}
#> frm_sample(): default priors (brms 2.23 defaults; prior = "flat" puts brms's (flat) in every slot)
#>   Intercept          student_t(3, 0.8, 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 1.08, 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
#>            Estimate Est.Error       Q2.5   Q97.5
#> Intercept 0.3791911 0.2658016 0.01011916 1.05593
# }